AMAZONS WITHIN THE AMAZON: A MULTISCALE ASSESSMENT OF URBANIZATION By ROBERTA MENDONÃ‡A DE CARVALHO A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2019
Â© 2019 Roberta MendonÃ§a De Carvalho
To my father Roberto ( in memorian ), whom I lost during the PhD way
4 ACKNOWLEDGMENTS I had to step away to see it better . The wonders of Geography , with so many dimensions of space , and the dance of scales before and beyond our eyes has always amazed me . If I was born a geographer and my study proves that. I write these words still shaken by the final dot of my dissertation. My hands are trembling, my heart racing. It is done. I breath e y oga breath s . I s tretch y oga stretch es . And I look back. Yes, I did it , b ut not alone. I identify countless DNAs of help. No words will ever fully express my gratitude for a helping hand when I was on the verge of drowning. And oh boy, I have drowned countless times. Each time, I was rescue d by one or many of the people i n my supporting system. I am grateful for all the smile s I encountered on a rainy day , guidance unasked for, and models of scholar ly and professional behavior. I thank my fell ow PhD students. Alexandra Sabo was my favorite reviewer (to say the least) . Agh ane Antunes was my oracle . Aline Carrara was Aline. Gabriel C arrero was my neighbor in Gainesville and in Geography . Caroline Huguenin offered hugs, wise words of encouragement and map rescue magics . Angelica Nunes gave kind ears and great reviews. Isabel, Felipe , and CloÃª Friday visits gave me reasons to smile. Carmem Beatriz offered hugs and ears. Joe Lacey spent his valuable hours revising my work, and others more encouraging me to move on. Simone Athayde shared aca demic support and yoga ; David Kaplan was an inspiration in scholarship ; Jane Southworth wisely lead s the Geography Department. I give huge thank s t o my adviser in the last round , Timothy Fik ; and committee member Steven Perz. I appreciate all of my Geogra phy Department colleagues, students, faculty , and staff . I e specially thank fellow graduate student s Audrey Smith
5 and Mehedy Hussain; and Administrative Assistan t Alex Henao. Claudio S z lafz s tein was my f ormer adviser, co author, friend and now colleague . Ritaumaria Pereira spread the news that led me here . Evandro Moretto shared a great dataset that made possible most analysis in my study . I thank the University of Florida ; the Water Institute Fellow (WIGF) 2015 cohort and faculty ; and the Tropical Conser vation Development (TCD) Program, including professors, staff and students. Cynthia Simmons and Robert Walker offer ed support from early days, nearly to the end . I am also extremely grateful to those I love and have neglected . I thank NazarÃ© Imbiriba ( Chuc a ) for teaching me to see beyond what is in my eyes. I thank my family and friends for understanding of the long retreat, especially the ones in Brazil. In particular but not limited to my stepson Rafael, aunt Ana Claudia; baby sister Edra and baby brother Lucas. I thank my sister K edyma for not having me there but having me here. I thank my friend for life , Danielle Redig. Thank you to my brother , Ygor Carvalho, for his surprise visits and unconditional support . I thank brother s in law Justin Schweitzer and Arlindo Siqueira, family I did not chose, but love. For my daughter is the true reason I want to become a better person and a scholar . I thank her for spen ding her teenager years with a mostly unavailable PhD mom. I thank my m other, for being there more than ever . Sempre . She was a rock : a rock that kept me rolling, pushing me, guiding me , and sometimes , even dragging me to the right path. She has always defied barriers and set a high example . I could never have done it without the privilege of being her daughter.
6 I wish I had the chance to thank my father . We lost him unexpectedly in my first month of this doctoral program . Above all, I thank my beloved husband and life long accomplice . He said yes when most said no. He said y es to me, and to the adventure of our lives. For courageously stepp ed completely outside his comfort zone. He was my comfort zone w whether I had none I had plenty. He encourag ed me to have wings and fly , even if that meant cutting up his own arms wings . This is not an end . Instead, I see this as a point along my journey. I am eager to turn the page and move to the next chapter. Before I move on, I pause to look back fondly at what was accomplished.
7 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 10 LIST OF ABBREVIATIONS ................................ ................................ ........................... 11 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTER 1 AMAZONS WITHIN THE AMAZON: A MULTISCALE ASSESSMENT OF URBANIZATION ................................ ................................ ................................ ..... 14 2 URBANIZATION IN THE BRAZILIAN AMAZON: HOMOGENOUS OR HETEROGENOUS? ................................ ................................ ............................... 17 Study Area ................................ ................................ ................................ .............. 20 Process of Amazon Occupation ................................ ................................ .............. 20 Far from Pristine: Uncovering the Colonial Narrative ................................ ....... 20 Historical Background: State Development Programs ................................ ...... 22 Population: Reconfiguring the Amazon ................................ ............................ 25 Spatial Reconfigurat ion: Theoretical Background for Drivers of Urbanization and Municipal Division ................................ ................................ ................................ 27 Drivers of Urbanization in the Amazonian Context ................................ ........... 27 Cities of the Forest or Forest of the Cities ................................ ........................ 29 Heterogeneity in Unplanned Urban Occupation in the Amazon ....................... 30 Hydropower as a Driver of Urbanization ................................ ........................... 33 Methods and Data ................................ ................................ ................................ .. 36 Data ................................ ................................ ................................ .................. 36 Variables of the Model ................................ ................................ ...................... 37 Outcome variable ................................ ................................ ....................... 37 Dummy variable ................................ ................................ ......................... 37 Independent variables ................................ ................................ ................ 38 Latitude and longitude ................................ ................................ ................ 38 Quadrants ................................ ................................ ................................ .. 38 Results and Discussion ................................ ................................ ........................... 39 Model Fitness ................................ ................................ ................................ ... 39 Concluding Rem arks ................................ ................................ ............................... 46 3 HYDROPOWER INFRASTRUCTURE AND STATE SCALE URBANIZATION ...... 55 Theoretical Framework ................................ ................................ ........................... 58
8 Study Area ................................ ................................ ................................ .............. 60 ParÃ¡ in Amazon Context ................................ ................................ ......................... 63 Historic Overview of Municipality Formation ................................ ..................... 66 Attempts to Understand Urbanization in ParÃ¡ ................................ .................. 73 Urbanization and Hydropower Systems ................................ ................................ .. 74 Hydropower in ParÃ¡ ................................ ................................ .......................... 74 TucuruÃ Hydropower Plant: Some Historical Background ................................ . 76 A Broader Horizon for a Dam's Impacts: Connection to Urbanization .............. 77 Methods ................................ ................................ ................................ .................. 79 Data Analysis ................................ ................................ ................................ ... 79 Data Set ................................ ................................ ................................ ........... 80 Outcome variable urban population percentage ................................ ..... 80 ................................ ......... 81 Categorical variable across time ................................ ................................ 82 Models ................................ ................................ ................................ .............. 82 Results and Discussion ................................ ................................ ........................... 83 New Municipalities, Urbanization and Hydropower Grid ................................ ... 85 Conc luding Remarks ................................ ................................ ............................... 86 4 URBAN EXPANSION IN A RESOURCE FRONTIER: A MUNICIPAL LEVEL APPROACH ................................ ................................ ................................ ............ 94 Study Area ................................ ................................ ................................ .............. 96 Theoretical Ground for Urbanization, Urban Morphology, and Urban Land Change ................................ ................................ ................................ ................ 97 Historical and Population Context ................................ ................................ ......... 101 Urban Versus Rural Population: A Dichotomy ................................ ...................... 102 Urban Land Expansion ................................ ................................ ......................... 105 Data and Methods ................................ ................................ ................................ 106 Data ................................ ................................ ................................ ................ 106 Land Cover Classification ................................ ................................ ............... 107 Results and Discussion ................................ ................................ ......................... 108 Barcarena Land Cover Classes ................................ ................................ ...... 108 Forest ................................ ................................ ................................ ....... 108 Agriculture ................................ ................................ ................................ 108 Bare Soil ................................ ................................ ................................ .. 109 Urban ................................ ................................ ................................ ....... 110 Water ................................ ................................ ................................ ....... 110 Accuracy Assessment ................................ ................................ .................... 110 Land Cover Change and Morphology ................................ ............................. 111 Co ncluding Remarks ................................ ................................ ............................. 113 CONCLUSION ................................ ................................ ................................ ............ 119 LIST OF REFERENCES ................................ ................................ ............................. 122 BIOGRAPHIC AL SKETCH ................................ ................................ .......................... 144
9 L IST OF TABLES Table page 2 1 Variables in the models ................................ ................................ ...................... 51 2 2 Model se lection sample equations ................................ ................................ ...... 52 2 3 Spatial model of urban population 2010 as a function of hydropower, quadrants, urban population in 2000 and total population in 2000 ..................... 53 2 4 Marginal prediction of interaction term hydropower and quadrant ...................... 54 2 5 Margins coefficient, municipalities and quadrants ................................ .............. 54 3 1 Variables used in the models ................................ ................................ .............. 89 3 2 Margins for urban population regression for 1980, 1991, 2000, 2010 for proposed and conventional hydropower impact classification on 83 municipalities affected (yes) and not affected (no) by hydropower ..................... 90 3 3 Urban population regression for 1991, 2000, 2010 for proposed and conventional hydropower impact classification on 143 municipalities affected (yes) and not affected (no) by hydropower (confidence interval 95%) ................ 90 3 4 Spatial model of urban population 2010 as a function of hydropower, quadrants, urban population in 2000 and total population in 2000 ..................... 93 4 1 Land cover classification ................................ ................................ .................. 117 4 2 (KAPPA) ................................ ................................ ................................ ........... 118
10 LIST OF FIGURES Figure page 1 1 Multiscale approach and hydropower as common thread s ................................ . 16 2 1 Brazilian Legal Amazon and Western and Eastern Amazon .............................. 48 2 2 New municipalities created in the Brazilian Amazon per Decade 1950 1990 ..... 4 9 2 3 Total of Municipalities in Brazilian Amazon per decade 1980 2010 .................... 49 2 4 Brazilian Amaz on by quadrants ................................ ................................ .......... 50 2 5 Urbanization and hydropower impact by quadrant in BLA ................................ .. 51 3 1 Study area: State of ParÃ¡ ................................ ................................ ................... 88 3 2 Municipalities considered impacted by hydropower: conventional and proposed classification ................................ ................................ ....................... 89 3 3 Difference between impact on urban population percentage in municipalities affected and not affected by hydropower with proposed and conventional set of categorical variables for model 1991 2010 ................................ ..................... 90 3 4 Difference between impact on urban population percentage in municipalities affected and not affected by hydropower with proposed and conventional set of categorical variables for model 1980 2010 ................................ ..................... 91 3 5 Urbanization change in ParÃ¡ and hydropower infrastructure 1980 2010 ............ 91 3 6 Foundation timeline of municipalities in ParÃ¡ and hydropower system. Source: IBGE 1980, 1991, 2010 ................................ ................................ ......... 92 4 1 Study area: Barcarena and State of ParÃ¡ and the Brazilian Amazon ............... 115 4 2 Total population increase percentage by decade in Brazil, Amazon, ParÃ¡ and Barcarena (Source: IBGE, 1980, 1991, 2010) ................................ .................. 116 4 3 Barcarena urban, rural and total population 1980 2018 (Source: IBGE 1980, 1991, 2010) ................................ ................................ ................................ ...... 116 4 4 Barcarena land cover change from 1984 to 2017 per class ............................. 117 4 5 Prediction of urban and agriculture land cover change ................................ ..... 118
11 LIST OF ABBREVIATIONS ADA Agency of Development of the Amazon ALBRAS AlumÃnio Brasileiro (Brazilian Aluminum) BLA Brazilian Legal Amazon BMR BelÃ©m Metropolitan Region COSIPLAN Committee of South American Infrastructure and Planning of UNASUL EIA Environmental Impact Assessment ENIDS National Integration and Development Axis GIS Geographical Information System IBGE Brazilian Institute of Geography and Statistics IIRSA Initiative to Integrate the Regional Infrastructure of South America INCRA National Institute for Colonization and Agrarian Reform INPE National Institute for Space Research IS Index of Services IS Index of Services LULC Land Use and Land Change PCA Principal Component Analysis SPVEA Superintendenc e of Economic Recovery Plan of the Amazon SUDAM Superintendence of Development of the Amazon UNASUL Union of South American Nations USIPAR ParÃ¡ Steel Mill Company
12 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy AMAZONS WITHIN THE AMAZON: A MULTISCALE ASSESSMENT OF URBANIZATION By Roberta MendonÃ§a De Carvalho December 2019 Chair: Timothy Fik Major: Geography Urbanization in the Brazilian Amazon has intensified in recent decades, with urban population rates surpassing 70% and over 400 new municipalities created in the past forty years. While acknowledging that urbanization has numerous drivers, this study analyze d the impact of hydropower on population growth in Amazonian municipalities as a multiscale process, considering regional, state and municipal frames . generation potenti al, along with public policies oriented towards regional integration and energy generation, are bringing about significant change as hundreds of new dams are planned and constructed. This study seeks to understand the diversity and spatial organization of urbanization drivers across the region, with a particular focus on the role of hydropower development in spurring urban growth relative to other drivers. To do so, we used spatial socioeconomic and hydropower development data from 1980 to 2010 to assess si milarities and differences in the urbanization process across the Amazon, using quantitative analysis and r emote s ensing . Results showed that , overall , urbanization was higher in regions impacted by hydropower development in regional and state scales . Land use and land cover change points at urban land expansion , and loss of forest as the main changes, indicating the
13 undercounted impact of the hydropower system. Although urbanization is a widespread process across the Amazon, it is happening with different intensities in different regions . Urbanization is manifesting as a function of multiple drivers, including investments in hydropower systems , altering the spatial configuration to debunk different outcomes.
14 CHAPTER 1 AMAZONS WITHIN THE AMAZON: A MULTISCALE ASSESSMENT OF URBANIZATION Urbanization in the Brazilian Amazon has intensified in recent decades . U rban population has exceeded 70% and more than 400 municipalities were created in the past 40 years. The Amazon intense spatial reconfiguration in the past few decades involves but is not limited to the loss of forest cover. Shifts in national geopolitics led to development initiatives that resulted in a significant population increase. In the process, governments s ought to reo rganiz e how space and thus natural resources in the Amazon were to be exploited and used. U rbanization was the dominant factor in reshap spatial configuration. Urbanization as a means of reconfiguring space in the Amazon challenges treat ment of the region as a single homogene ous space. The rainforest is often viewed as a h omogen ous space , but this obscures the various local outcomes of urbanization. Recent spatial and social changes belie the assumption of ho mogeneity. T he existence of a dynamic network of urban centers in the Amazon is closely related to the dynamics of deforestation and corresponding economic changes. This research aimed to assess urbanization in the Amazon o n three different scales . D iffer ent scales allow us to observe spatial differences from local to regional in the context of the Brazilian portion of the Amazon basin. This allow ed us to examine different manifestation s of urban expansion and other dynamics in the broader context of the r egion . In this work, urbanization is used as a synonym for increase d urban population . While urbanization reflects multiple driving forces , this study examines the urbanization process through the role of investment in hydropower . This allow us t o
15 evaluate the role of hydropower as a driver of urbanization. A nested cross scale approach allowed investigat ion of the role of hydropower projects at different scale s ( Figure 1 1 ) The analysis is divided across scales by investigation of a specific scale in chapters 2, 3 and 4 . In each study , I pursue d an historical approach . I related the main historical facts to increase d urbanization . In Chapter 2 discussed urbanization in the Brazilian Amazon at the regional level , focusing on historical increases in urban population since 1980 . This study contributes to our understanding of the spatially heterogenous urbanization process that is reshaping the Amazon and pro vides an assessment of how infrastructure investments affect urban dynamics . In Chapter 3, this research assessed the urbanization process at the state scale , considering a more refined reconfiguration of municipalities affected by dams, urbanization and the creation of new municipalities. State territorial configuration and hydropower system development are strongly connected. Urbanization rates are higher in areas with more elements of hydroelectric system. Also, new territorial divisions are spatially related to the elements of the system. The state of ParÃ¡, particularly important as a frontier state, with high deforestation and urbanization rate s , was affec ted early by enlargement of the hydropower energy system . In Chapter 4, this study focus ed on the single municipality as a unit of analysis, showing the process of land cover change and u r ban land expansion. S patial changes within a single municipality sho w the expansion of urban areas and related covers, with a slower increase of agricultur e , and significant loss of natural cover. L oss of forest is the biggest conversion to other uses.
16 In Chapter 5, provides provide the concluding remarks for all studies, as well as the next steps of research. Figure 1 1 . Multiscale a pproach and h ydropower as c ommon t hread s
17 CHAPTER 2 URBANIZATION IN THE BRAZILIAN AMAZON : HOMOGENOUS OR HETEROGENOUS? At first glance, it would seem obvious to label the Amazon 1 as a rain forest biome . However, this gigantic region , spanning 5,016,136 km 2 , is actually a heterogeneous space in several respects . T he Amazon has undergone intensive urbanization since the mid 20 th century , result ing in a net work of towns and cities . Calling the Amazon a forest biome is mislead ing , as the basin is now home to more than 22 million people . More than 74% of that population lives in urban areas (IBGE, 2012) . Since the 1950s, populations of some of these cities and states have increased by as much as 500% (da Trindade Jr, 2005; IBGE, 1970, 2012) . in contrast, grew by approximately 254% (Sawyer, 2015) . Despite high urbanization, some localities remain largely rural . Thu s, the region is heterogeneous in terms of its human occupation and spatial processes . T he increase of Amazonians living in urban are as from 36 to 74% highli ghts a trend toward overall urbanization (Barbieri, Monte mÃ³r, & Barbieri, 2007) . Such percentage include urbanization in the forest centered configuration confronts paradigms and leads to the question of h ow urban is the forest and how forest is the urban among . Usually, urban expansion proceeds alongside forest decline. Once the urbanization de forestation dynamic of the Amazon is recognized , the next logical step is to understand how and where these processes are taking place and what forces are driving them. 1 Amazon hereof refers to Brazilian Amazon.
18 Amazon by multiple drivers (Costa & Pires, 2015; da Trindade Jr, 2005; Parry et al., 2018; Sawyer, 2015) . Urbanization is among the outcomes of these drivers . I n most cases, urbanization changes rural land uses and land cover. As such, the developing urban network constitutes a key change dynamic responsible for re cent spatial reconfiguration (Becker, 2005, p. 31) . 2 2 in the Forest , highlighted by several authors (Browder & Godfrey, 1997; Garcia, Soares Filho, & Sa wyer, 2007; JÃºnior, 2013a; M Santos & Silveira, 2001) , urban reconfiguration of the Amazon, is still an oft ignored reality in relevant literature. The new scenario points at an inverse relationship between forests and urban population in the Amazon . T he triggers leading the forest into an urbanization path must be identified and comprehended . Infrastructure investments are among the main drivers of changes. L inks between infrastructure and urbanization have been long established (Guedes, Costa, & BrondÃzio, 2009; Jones, 2016; TaubenbÃ¶ck, Wegmann, Roth, Mehl, & Dech, 2009; United Nations, 2014) . P hysical infrastructure is a well established driver of urbanization worldwide (L. A. Brown, Sierra, Digiacinto, & Smith, 1994; Parry, Day, Amaral, & Peres, 2010; Thypin Bermeo & Godfrey, 2012) . Hydropower infrastructure are among the drivers of change and urbanizatio n in the Amazon . Past hydropower investments have stimulated urban growth by attracting population to construction sites. Dam construction creates jobs. The infrastructure required for construction camps stimulates urban buildup. The result is migration, a nd 2 In some cases, this expression is translated from Portuguese as towns. I choose to use cities as a broader term to identify urban settings, without distinction in size of hierarchical levels.
19 concentrated urban population. Hydropower thus constitutes a source of urban spatial reconfiguration in the Amazon , given past investments and governmental siting of new dam projects. The region has broadly distributed but locally specific hydropower po tential . F ederal policies underscore hydropower as the national energy strategy (Fearnside & Millikan, 2015; Pinto, 2012; Silva et al., 2012; Tundisi, Goldemberg, Matsumura Tundisi, & Saraiva, 2014; World Commission on Dams, 2000) . Given the hydropower potential of the Amazon Basin, the largest freshwater reserve in the world, this study focuse d hydropower system . This includes dams, transmission lines, and substations. More than 100 large dams are in operation in the Ama zon : 137 planned, and 78 inventoried (Fearnside & Millikan, 2015; Simmons, 2018) . T he number of hydropower dams in the Amazon is thus likely to inc rease in the future . The government stated goal is to integrate the Amazon region and northern Latin America via a network of energy infrastructure projects (Eduardo & Costa, 2000; IIRSA, 2011; J. C. Tavares, 2016) . T his study examine d the impacts of existing hydropower projects on nearby urban growth as compared to urban areas withou t hydropower projects . This will thus help to fill a gap in scientific research by considering an infrastructural driver of urbanization beyond previous scientific stud ies on the road infrastructure (Barber, Cochrane, Souza, & Laurance, 2014; Southworth, Munroe, & Nagendra, 2004; SuÃ¡rez, Zapata RÃos, Utreras, Strindberg, & Vargas, 2013; Walker et al., 2017) . T his study address e d urbanization at a regional scale in the Amazon with municipal units of observation . This allow us to evaluate local variation and thus heterogeneity within the basin. C onsidering the municipal division as unit provides
20 ground to build a profile of local urbanization clusters across the Amazon . That allow us to ask where urbanization happened in the Amazon, and whether hydropower projects led to greater urbanizat ion in nearby municipalities . In addition, this study provides a historical context for occupation , concentrating on development policies of the Amazon and an overview of its population growth. Next, I offer theorical background for spatial reconfiguration, including discussions on urbanization , territorial and heterogeneity and hydropower . I then proceed with quantitative analysis, performing a spatial model to investigate the correlations of urbanization and hydropower infrastructur e along the region . Last ly , I provide concluding remarks . Study Area The Brazilian Legal Amazon accounts for nine states . The scale in this study asses the municipal units of all states . Currently, the BLA consists of 807 municipalities, encompassing more than 5 million km 2 , or 60% . Another spatial scale assessed is the division of the Amazon into Easter and Western (Figure 3 1). Process of Amazon Occupation Far from Pristine: Uncovering the Colonial Narrative Ana Roosevelt did n ot discover the Monte Alegre cave paintings, n or did the botanist who saw the painting s before her, as described in the book The Last Frontier (London & Kelly, 2007) . Local people had long known about the paintings and were the ones who guided the foreign expedit i on to uncover the secrets of the Amazon. W ha t the scientific community mistakenly calls discover y relies on inhabit ants of the ancient Amazon. Denevan ( 1992) showed that the Amazon was far from pristine , and Roosevelt showed that old settlements in the Amazon were much more ancient than previously
21 thought . People have settled in the region for m ore than 10 ,000 years and with advanced culture of land use. If we disregard the 11 milli on indigenous peoples from 2,000 tribes in the Amazon at the time of European contact , we ignore most of the region (Heckenberger, 2003) . Notions of the Amazon as an empty space derive from the colonial history of rapid population decline, caused by the genocide of indigenous peoples of that period (Heck, Loebens, & Carvalho, 2005) . F or centuries, the Amazon was portraited as exotic and distant from Brazil and the world. A rchaeological findings show that the Amazon was not a pris tine region (Denevan, 1992; Heckenberger, 2003) . With exceptions such as the Rubber Boom, the region and its scattered communities were portrayed as secluded and characterized by the slowness of economic activity and longstanding traditions (Browder & Godfrey, 1990) . Despite the archaeological work that demystified the Amazon , t e r m s such as , , , region throughout the 20th century (Becker, Costa, & Costa, 2009; Bertha Becker, 1990) . This served the political purposes of th e Brazilian state as it sought to integrate the Amazon . In the process, the state portrayed the Amazon as a single unified whole. In so doing, the Brazilian State failed to consider the various heterogeneities among localities and peoples (Becker, 1989, p.31) . A few state capitals that emerged from the coloni al period concentrated most of the urban population . Rural people s , whether comm unities of indigenous people , ribeirinhos, or quilombolas, were scattered inside the forest walls and thus invisible (Glielmo, 2010) . N ational efforts for Brazil to modernize created a neo colonial narrative that treated the Amazon as an empty space encourag ing immigration from Europe . This
22 was paired with the goal of creat ing a whiter phenotype of desc e n d ants. Such early policies foreshadowed later efforts to colonize the Amazon by recruiting migrants from other re gions of Brazil. Historical Background: State Development Programs As early as the 1930s, the Brazilian state encourage d migration to frontier areas , to promote national integration and modernization (Backhouse, 2013; Bertha Becker, 1989; Denevan, 1992) . The state saw an abundance of land and other natural res ources in the Brazilian interior and viewed colonization as a mechanism to promote exploitation of those resources , to impel economic growth . Thus, the Brazilian state promoted various initiatives to impel land occupation , based on a development model of intensive and exponential natural resource exploitation (B ecker, 2001, 2005; MagalhÃ£es, 2007; Schmink & Wood, 1992; Tavares, 2001) . Development was perceived in its economic dimension , and mainly stood for industrialization and commodity based activities at the considerable expense of environmental degradation . By pursuing that path , development was highly extractive and thus environmentally un sustainable (Sawyer, 2015) . The extractive model resulted in abrupt socioeconomic, de mographic , and environment changes, generating negative outcomes for both natural ecosystems and local livelihoods. Although national development plans date from the 1930s, Military Regime , initiated with a coup in 1964 , that aggressive development policies were established . The military initiated a fast paced development trajectory followed by aggravated socioeconomic inequalities and environmental degradation (D. Santos, Celentano, Garcia, Aranibar, & VerÃssi mo, 2014) . M assive changes during the second half of the last century include significant population increase and rapid urbanization.
23 In t he late 1960s , the military creat ed the Brazilian Legal Amazon (BLA) as a state planning region. The BLA w as a symb ol of the national occupation policies devoted to integrating the Amazon in to the national economy . Geographically, the BLA encompasses a geopolitical and administrative area that includes all seven states in the North region (Acre, AmapÃ¡, Amazonas, ParÃ¡, Roraima, RondÃ´nia, and Tocantins) ; and part s of states from the Northeast region (MaranhÃ£o) and the Central West region ( Mato Grosso ) . I n the 1960s and early 1970s, the Superintendency for Amazonian Development (SUDAM) emerged as an executive agency to advance national policies in the BLA. Under S UDAM , the state offered fiscal incentives to attract capital investments for regional development. The SUDAM agency p romoted c attle ranching and large scale agriculture as prominent economic activities. M assive subsidies such as tax exemption s and access to low interest credit were used to incentivize agroindustry ventures, making investments very lucrative . Large scale agriculture and cattle ranching were soon responsible for much of the economic growth in the B LA (Gomes & Vergolino, 1997) . Large scale agriculture and cattle ranching also became large ly responsible for (Fonseca et al., 2017; Godar, Tizado, & Pokorny, 2012; P. D. Richards, 2012) . On a global scale, the tropical rainforest was the site of accelerated losses of natural cover . Eighty percent of deforestation in the transnational Amaz on occurs within its Brazilian territory. So far, BLA has lost around 800 ,000 km 2 of forest (INPE, 2018) . If the deforested areas were merged to form a country, it would be the 33 rd largest countr y in the world : larger than most countries in Latin America, Asia, Africa, and Europe. Since deforestation
24 monitoring begun in the 1970s , annual deforestation rates have oscillated to record high s of almost 30 ,000 km 2 in 1995 and 2003 and to record low s of 4 , 6 00 km 2 in 2012 (INPE, 2018) . After a tendency to decrease until 2012, numbers are on the rise again. The SUDAM agency and other agencies also pursued other development policies in t he 1970s. In addition to capital investment, the state promoted colonization. This was driven by geopolitical concern about other countries occupying the Amazon and also driven by economic development. Slogans such as "integrar para nÃ£o entregar" (integrate in order to not give it away) and "terra sem homens para homens sem terras" (land for men wi thout land) were popularized to promote colonization. This was a nationalist campaign to connect land to people without land. At the time, people in the Northeast faced harsh socio economic conditions . To someone living with dry land and desertification, scarce access to water , and indications of severe social misery, a piece of land in the forest must have seemed like a promise of eternal paradise (Paviani, 2007; Rego, 2016; Tavares, 2011) . Energy infrastructure also became a priority. T he national power company EletrobrÃ¡s, created in the 1950s, became an essential instrument for research and development of the hydropower system via new projects (Bertha Becker , 1989, 2005; Schmink & Wood, 1992; M. G. da C. Tavares, 2011) . hydropower resources became a target for new energy projects. Such projects were politically and economically supported . E nterprises in the region demanded infrastruct ure to support the growth of agricultural and industrial activities. Investments and colonization also stimulated demand for new roads and improved highways .
25 Transport infrastructure in turn facilitated spontaneous migration into the Amazon, resulting in r apid population growth. Population: R econfiguring the Amazon According to the 1960 Brazilian census, the total population for the North Region was around 2.9 million, of which 35% w as urban , versus the national urbanization rate of 44% . By the 1970 census, the North region had grown by 1.3 million, reaching 4.2 million people, and the urban population increased to 43% versus national urbanization rate of 55% (da Trindade Jr, 2005) . Int er regional migrants were attracted to the Amazon by the vision of jobs. L abor would be in demand by major infrastructure ventures and opportunities for land settlement in colonization projects . Megaprojects included both roads and hydropower projects. C onstruction of the Tucu ru Ã Hydropower Plant began in the late 1970s and transpired alongside the construction and extension of feder al highways (BRs). Both contributed to rapid in migration, which triggered the expansion of urban centers old and new. In many cases, urban growth proceeded spontaneously along the roads, as seen frequently in settlements in northeast ern ParÃ¡. S evere econo mic crises of the 1980s led to decreased industrial production and access to credit for agricultural projects. legitimacy and led to the withdrawal of fiscal incentives and support for directed colonization. Howeve r, the economic recession affected populations in many parts of Brazil and attracted a second wave of migrants to the Amazon, drawn by prospects of improving their quality of life . In many instances, labor opportunities proved limited in rural areas of the Amazon, pushing people to make localized moves to urban areas in the region . Guedes, Costa, and BrondÃzio (Guedes et al., 2009) sa id "urbanization in the
26 region has been interpreted as a [ deliberated ] strategy to stimulate regional economic development and alleviate demographic pressures in other part By 1991, t he population of the BLA surpassed 17 million, with over 50% concentrated in urban areas (IBGE, 1991) . ex peri enced large population increases . This included state capitals such as SÃ£o LuÃs and BelÃ©m , to small settlements and villages along the CarajÃ¡s mining corridor in the MarabÃ¡ region (Amaral, 2004; Cabral, 1995; Miranda Rocha, 2011; Thypin Bermeo & Godf rey, 2012) . A gricultural activities that attracted people to the region, should have resulted in increase d rural population . Instead, the urban population grew faster during the 1980s. For instance, Parauapebas, in the CarajÃ¡s corridor, grew an average of 19% annually. MarabÃ¡ grew 8% annually ; while Imperatriz, in MaranhÃ£o, nearly doubled from 294,000 in 1980 to 456,000 in 1991. In 2000 , the BLA population reached 20 million, with 57% urban. In 2010, the census indicated that the population had surpassed 25 million people. The latest estimate indicated 27 million people in 2013 (SUDAM, 2016) . While the population rose, especially in urban areas, deforestation proceeded. Forest cover disappeared around urban areas. This suggests that land economy is a big influence on forest loss . D eforestation is more likely to occur on high value land near urban areas (Browder, 2002; Lambin, Geist, & Lepers, 2003; Parry, Day, et al., 2010; Parry, Peres, Day, & Amaral, 2010; K. C. Seto, Gu neralp, & Hutyra, 2012; Karen C Seto & Shepherd, 2009) . This calls for more attention to spatial distribution of the urban population over time .
27 Spatial Reconfiguration: Theoretical Background for Drivers of Urbanization and Municipal Division Urbanization is a historical process with many drivers (Kotkin, 2006; Wranghan, 2009) . One indicat ion of the rise of new cities is a shift from nomadism to sedentary settlement. The social and physical reconfiguration of space is impelled by the acceleration of population growth and stemmed from the cumulative concentration of people in certain areas. Agglomerations led to a concentration of economic activities and services that characterize cities. In Brazil, a core administrative element of the urbanization process that reflects and reinforces the reorganization of population in space is the creation of new municipalities. The creation of new municipalities in the Amazon has surged since 1950 . In Brazil, a municipality by definition has one urban center, the administrative seat. If new urban centers emerge, local populations may petition the federal g overnment to be independently recognized as a new municipality. This is how the emergence of new urban centers in the Amazon led to the creation of new municipalities. Since 1950, nearly 5 00 new municipal ities were recognized in the Amazon . In the 1980 s al one , 143 new municipal ities were created . The urbanization process continue d in the 1990s, with 243 additional municipalities recognized . The urban reconfiguration process slowed in the 2000s : o nly three new municipalities were created in the first decade of the millennium . Overall, in the three decades encompassing 1980 2010, the total number of municipal ities increased from 338 to 807 ( Figure 2 2; Figure 2 3). . Drivers of Urbanization in the Amazon ian Context Bertha (2013, p35.) said if cities are dynamic and also represent the core of economic expansion in the region, they have failed to promote development in the
28 Amazon . For Becker, the dynamic role of cities implies that different economic activities generate positive externalities due to agglomeration and spatial efficiencies, improving the economic and social as well as environmental aspects of the system. However, urban areas in the Amazon are often islands of economic activities that, instead, foster a distorted spatial model of regional development that increases socioeconomic inequalities and reflects unsustainable extraction of natural resources (Becker, 2013) . The detachment from the concept of dynamism by Becker stems from the fact that development in the Amazon is based on an extractiv e rather than industrial or service economy. Another explanation , as in dicated in the previous section, is that many urban centers in the Amazon are young and small and still developing . Wh at ever the reasons for new urban centers , their sustainability is directly linked to their capacity to generat e job s and income s . Industri alization, as a key element of the urbanization process, fulfill s those demands in the assumptions of many development theories . Nevertheless , urbanization in the Amazon deviates from the urban industrial model since the urbanization process is not connect ed to industrial investments . Even when industrial investment s are made, it does not necessarily translate into local jobs . Recent urbanization in the Amazon is often framed in terms of boomtown models (Dufour & Piperata, 2004; Isserman, Merrifield, Geography, & Jan, 2016; Miranda Rocha, 2011; Perz, 2002; Simmons, Perz, Pedlowsky, & Silva, 2002; Thypin Bermeo & Godfrey, 2012; Walke r & Homma, 1996) . Boomtowns arise in areas with new extractive activities, which permit the rapid emergence of new urban population s . The economic
29 possibilities of exploiting natural resources attract population to less occupied or unoccupied areas. The overall increase in the percentage of urban population escalated from 37% in 1970 to 74% in 2010 (IBGE, 2012) , reflect ing spontaneo us urbanization rather than plann ed urban growth . R apid and frequently unplanned urbanization often spawns environmental and socio economic imbalances . S hort term economic prosperity attract s waves of migra nt s , transform s areas in to urban boomtowns , often generating an environment of contradictions . Mitschein, Miranda, & Paraense (19 89) called this Spontaneous urbanization has produced unexpected physical and socioeconomic outcomes. Prior studies have considered the matter and analyzed it mostly as an aggregation phenomenon in a regional context. Such analyse s assess either isolated cases of population increase in individual cities or regional approaches assuming a homogen eity (Bertha Becker, 2005, 2013; Browder, 2002; Browder & Godfrey, 1997; Eloy & Brondizio, 2015b; Godfrey & Browder, 1996; Guedes et al., 2009; Thypin Bermeo & Godfrey, 2012) . Such approaches often fail to account for heterogeneities across the r egion caused by distinct local drivers. Cities of the Forest or Forest of the Cities Because of u rbanization , the Amazon can no longer be characterized entirely as forest ecosystems. The Amazon is much more than forest . Now, cities have become a key elemen t of the Amazonian landscape . The forest is now accompanied by urban landscapes , resulting in a more complex system of elements and interactions . U rbanization necessarily implies a spatial reconfiguration that includes the loss of natural habitats . In the Amazon, natural areas mean forest : deforestation is its antithesis. With the current political shifts toward a predatory far right government
30 retreating from environmental policies , recent deforestation rates have risen (INPE, 2018) . P hysic al changes in the Amazon threaten the future of the Amazon forest (Lovejoy & Nobre, 2018) . Deforestation is as much a global as a local concern . Cities in the Amazon are not isolat ed areas under a dome : they are part of the w hole, part of the regional system. Because urban areas reflect extractive economies that damage local ecosystems, urbanization is an environmental concern in the Amazon. L ivelihood s of people in the Amazon affec t the forest and are cause for global concern . This makes the future of Amazonian cities locally and globally relevant (Robertson, 2005) . Heterogeneity in U nplanned U rban O ccupation in the Amazon I nadequa te planning and accelerated urbanization affect much of the Amazon. Becker (Bertha Becker, 2013) call ed it unhealthy urbanization. Such processes are often presented as a uniform or homogeneous . C reation of new urban areas and rapid urban growth , though common, are not ubiquitous. Heterogeneity is present in the urbaniz ation of the Amazon because of different intensities of triggers . Location, infrastructure investments and migration are among the key drivers that produce differences in the urbanization process and how the space is occupied. Perceiving such different out comes allows for comprehending urbanization as reconfiguring the Amazon at different intensities. This a lso allows us to trace a more accurate profile of triggers and consequences of urbanization . S hifting from the assumption that the Amazon is homogenous social ly and spatial ly processes and patterns to a heterogenous approach, allows to identifying degrees of interactions and interdependence among cities under sub regional geopolitical divisions . W hen Godfrey and Browder (Browder & Godfrey, 1997; Godfrey,
31 1990; Godfrey & Browder, 1996) focused on heterogeneity as one of the most important characteristics of urban Amazon regional disaggregation, their work still focused on processes at an aggregated level without considering the interactions among cities in networks. B ertha Becker also pursues analysis at an aggregated scale when regard ing the Amazon as an urban forest (Bertha Becker, 1990, 2005, 2013) . Furthermore, in most cases, when a system of Amazon cities is considered, they are often framed as isolated spatial entities rather than interdependent elements (Bertha Becker, 1990, 2013; Browder & Godfrey, 1997; Mitschein et al., 1989) . Urbanization at a local scale in an individual city involves the in migration of people in response to urban based job growth. The classic pattern of rural urban migration has long affected countries in the global South . This pattern reflects the desire of individuals to leave lower payi ng jobs in agriculture to pursue higher paying jobs in manufacturing . This also reflect ed in the lower availability of rural jobs with the increase in mechanization in the agriculture chain (Perz, 2000; Vining Jr., 1986) . In the case of Brazil, from 1980 to 2010 , the rural population decreased from 32 to 15% of the total. The rural exodus amounted to some 47% of the rural population , despite a 70 million person i ncrease in the country during the same period ( Censo DemogrÃ¡fico 2010 , 2012) . Present global dynamics show the same spatial pattern : increasing urbanization , with urban population s of between 75 to 90 % in coming decades (DESA, 2018) . Les s developed regions will undergo more urbanization than developed nations (United Nations, 2014) . The rural exodus from agriculture is well documented for the case of Brazil (Barbieri et al., 2007; Carr, 2009; Perz, 2000; Simmons et al., 2002) .
32 Historically, governments considered relationships among cities in planning for occup ation of the Amazon . The development plan s prioritized the opening of road to foster the creation of networks of towns along those routes . Such plans only transpired in the States of ParÃ¡ and RondÃ´nia . But the plan s w ere based on (Christaller, 1966) . His theory viewed system s of cities connected in a network by roads and organized in an urban hierarchy . On that interdependen t along roads like the Transamazon Highway. , the system of cities comprised a three level hierarchy . Cities and towns were organized in a uniform spatia l pattern described as hexagon al (Rego, 2016 ) . he rurÃ³polis was to be the top of the pyramid and the domina n t development pole . This major urban center had satellite settlements and a greater concentrat ion of public services and permanent housing for app roximately 1000 families. The next level was the agrÃ³polis , smaller towns of around 300 families , allowing for agricultural production, markets, and storage. T the satellites , agrovilas , were the smallest units of the system, each wit h roughly 50 families. Both agrovilas and agrÃ³polis were entirely dependent on their rurÃ³polis (Brondizio, Moran, Mausel, & Wu, 1994; Moran, 1981; Rego, 2015; Smith, 1982; Walker, Perz, Arima, & Simmons, 2011) . Although incompletely executed, the plan helped define urban cen ters along the Transamazon Highway . Those towns ultimately transformed the spatial configuration of the Transamazon corridor and thus a portion of the Amazon region. The Amazon
33 experience d notable population and landscape changes related to implementation of these Christaller style settlements (Walker et al., 2011) . The static Christaller concept, as applied in the Amazon , must be made dynamic to address the gro wth of urban towns in networks. Despite the creation of new urban clusters, military planning failed part ly because it did not address the dynamics of urban network evolution . U rban centers grew at different rates and resulted in a different network of tow ns than originally planned . To help understand the Amazon urban system, this study examines whether there is a correlation between the hydropower network and urbanization rates among nearby urban centers in the Amazon . The focus is to overcome the limitations of previous research that tended to consider urban centers in the Amazon as independent and static entities . Hydropower as a Driver of Urbanization The d on neoliberal policies using infrast ructure projects and market forces driving development . The rise in large scale energy production from hydropower dams was based on the abundant hydrological capacity of the region . I n conjunction , the government framework linked the notions of development and exploration of the Amazon to increase d economic activity based on exploitation of natural resources. Despite a recent decrease i energy use ( to 62 % from over 80 %) in past decades, the energy matrix is still heavily supported by hy dropower. T he ris ing of hydropower infrastructure to support this matrix, has rarely been studied as a complete system as analyses are usually confined to direct ly impacted areas, and rarely broaden ed to include indirect impacts of the energy system such a s the consequences of transmission lines and distribut i o n node s (Little,
34 2014) . Indirect impacts also extend to the influence of hydropower infrastructure on urban growth . I therefore raise the question of the impact of hydropower infrastructure on urban growth among localities across the Amazon as a region . I thus consider hydropower infrastructure as a driver of urbanization in the form of increase d urban population in existing urban centers. Scholars have long pointed to infrastructure investment as essential to regional development, with implications for urb anization (Spit 1999; Geist and Lambin 2002; WCD 2000; Becker 1989; Barber et al. 2014; Walker et al. 2017; Southworth, Munroe, and Nagendra 2004; Richards and VanWey 2015; Brondizio and Moran 2012; Browder 2002 ) . However, for the Amazon , infrastructure has been addressed primarily in the form of roads as a driver of land cover change and linked to deforestation (Aldrich, Walker, Simmons, Caldas, & Perz, 2012; Barber et al., 2014) . R oads make the forest accessible to migrant fa rmers, who follow the roads to occupy land and clear the forest (Barber et al., 2014; Brondizio & Moran, 2012; Garcia et al., 2007; Geist & Lambin, 2002; Godar et al., 2012; Rudel, Defries, Asner, & Laurance, 2009; Walker, 2004; Walker et al., 2017; Wood & Porro, 2002) . The industrial revolution could have happened without an increase in the energy supply (Jones, 2016; Wrigley, 2010) . Urbanization, development , and energy access are thus a triad in development planning . Access to electricity allows the process of industrialization to accelerate. Developed countri es were successful in promoting industrialization as the base for their economic growth. There is an on going debate about the mix of energy sources , and whether less developed nations should follow the current model in order to achieve economic and social progress (Almeida Prado et al.,
35 2016; Moore, Dore, & Gyawali, 2010; Pinto, 2012) . That i mplies a vital link between electricity and the capacity to expand industry as development. In the Amazon context, energy for development implies the expansion of hydropower infrastructure. Meeting energy demand serves as a starting point for massive infra structure projects across the Amazon. Finding alternatives to fossil fuel energy after the petroleum cris i developing the Amazon. The military and governments since have generally viewed the Amazon as a vast fo u nt of raw materials, including the giant hydrological potential of its abundant rivers (WCD, 2000) . Infrastructure projects have promoted deforestation and changes in land use (Walker, Perz, Caldas, & Silva, 2002) . In the Amazon, roads, hydropower dams, and other infrastructure projects have attracted people from rural areas into urban areas. Linking public investm ent to demographic impact is increasingly important with the emergence of the Initiative to Integrate the Regional Infrastructure of South America (IIRSA) in the early 2000s . The I IRSA gave rise to COSIPLAN (the South American Infrastructure and Planning C ouncil ) , part of the intergovernmental continental union of twelve South American Nations (UNASUL) (Castelar, 2015; Timoth y J. Killeen, 2007; Verdum & Carvalho, 2006) an integrate the regional transportation system by integrating waterways, railroads, roads, ports, and hydropower plants. For the entirety of South America, there are 177 dams planned , 241 sited , and 210 inventoried (IIRSA, 2011) . In the Amazon, major basins have already be en dammed in the past decade as a result of totals (Fearnside &
36 Millikan, 2015; Verdum & Carvalho, 2006; Wanderley et al., 2007) . I nvestment s in the infrastructure system under IIRSA imply a significant impact on urban growth . This Chapter 2 aims at investigat ing answering the questions of whether impacts of hydropower on urbanization are homogenous or heterogenous. Methods and Data Data This study utilized a regional databa s e for the BLA with socioeconomic (Moretto, 2016) and spatial data . All socioeconomic data w ere drawn from the dataset of the hydropower plants) (M oretto, 2016) . The dataset used in the statistical regression contains total and urban population s for 771 municipalities in BLA for the years 1991, 2000 , and 2010. This data set also provided the categorical variable used in the model to differentiate municipalities affected by dams. This classification from the Aneel (ANEEL, 2001) guides environmental compensation for the municipalities , in the form of royalties . Aneel sets physical c onnection to the hydropower reservoir as the condition for a municipality to be considered affected by dams. Spatial data consists of shapefiles for Amazon divisions: municipalities shapefiles ; Easter n and Western Amazon ; latitude and longitude for munici pal headquarters ; and layers for the hydropower system with dams, transmission lines and substations (IBGE, 2019b) . Data for latitude and longitude for each municipality was grouped regional quadrants. The distinction permitted a comparative analysis of urban growth rates among distinct regions .
37 A map with the statistical results and geographical spatial data provided for the visual interpre tation of results. Variables of the M odel Variables used in the analysis are listed in Table 2 1. Outcome v ariable Based on the adopted definition of urbanization as the percentage of urban population, I selected it as the dependent variable. Models were t ested with urban population percentage for 1991, 2000, and 2010. I explored spatial models considering as dependent variable the increase of urban percentage, the absolute urban percentage, the percentage of urban versus total population , and the urban pop ulation growth rate from previous years . The o utcome v ariable in the final model corresponds to urban population by total population in 2010 , divided by 1,000 . Dummy v ariable The dummy variable is static for all periods because of the original dataset . It indicates connection to the hydropower system through the reservoir area and it is . However, I acknowledge that there were changes across time, as new infrastructure such as substation and transmission lines were built since 1991 . S mall and large scale hydropower dams were also built, such as the Belo Monte, in the state of ParÃ¡. The constant value of the hydro variable presents a limitation to designing an accurate spatiotemporal profile of changes. Conversely, because of limited s patial data , I opted for considering the dummy variable as a static component along with the analysis.
38 Independent v ariables The independent variables used in the final model consist of total and urban population in 2000 , and the spatial interaction betwee n quadrants and categorical . The final model stands for Urban Population 2010 = Hydro*Quadrant + Total Population 2000 + Urban Population 2000. Latitude and l ongitude Latitude and longitude for each municipality correspond s to the location of the municipal urban center, according to the Brazilian Institute of Geography and Statistics (IBGE, 2012) . Despite the large range of municipalities areas, coordi nates for urban headquarters tends to concentrate urban population, serving as a reasonable proxy for its location . Latitude and longitude were used as the spatial component for the explanatories and final models. spatia l . However, possibly because of the refined of the scale and large number of municipalities, results were not satisfactory. Segmenting municipalities in based on latitude and longitude quadrants improved the model. Quadrants I divided the BLA into fo ur quadrants considering Latitude 5.354386 and Longitude 56.092721. Such quadrants were e stablished based on official division of Amazon Oriental and Occidental , or Eastern and Western (Brasil, 1967, 1968) . West includes the states of Ama zonas, Acre , RondÃ´nia , and Roraima . East includes the states of ParÃ¡, MaranhÃ£o, AmapÃ¡, Tocantins , and Mato Grosso. Given the physical impossibility of defining quadrants by state boundaries, I aimed at a more refined scale to identify impacts in different subregions . I visually delimited the longitude from north to
39 central west point (Figure 3 4) . Results and Discussion Model Fitness The dummy variable hydro is exogenous , not changed by other variables . The t otal population and urban populations for the previous year are also endogenous. However, because the units of observation are spatial units, spatial autocorrelation is likely among values of the observations. Because of that, OLS is not efficient to provide consistent parameter estimators (Wooldridge, 2015) . Assuming spatial correlation, I started by exploring simple and spatial regression in two different sets , considering all 771 units of analysis. Exploratory models were divided into two sets of dependent variables: urban population increase and urban population percentage . Models were tested using three time periods, for 1991 2000, 2000 2010, and 1991 2010 . Both sets of models showed better results for the overall period 1991 to 2010 and using u rban population percentage as the outcome variable. During the exploratory process, I also performed a time series analysis considering the years as 1, 2, 3. T he model using the panel data could not be improved because of inflated autocorrelati on in the er ror terms. . The second stage consisted of taking a step back to reassess the available data. Because there were missing data for some municipalities (mostly in 1991), the data set was rec onfigured to eliminate 189 municipalities. Data tested positive for autocorrelation models, I divided the area into quadrants: Northeast, Southeast, Southwest, and Nort hwest. Urban and total population data for all years were divided by 1,000 to
40 facilitate the reading of impact. Throughout the model, an increase or decrease in population data correspond to 1,000 thousand people as a baseline. I checked for extreme outlie rs and used a histogram test with the outcome variable to confirm a normal data distribution, with no need for additional data normalization. T he characteristic of data with three distinct periods implied autocorrelation among population variables . Given t he spatial component of municipal headquarters and the categorical variable for the hydropower system , I chose to run a spatial autoregressive model ( SAR ). SAR provides methods for fitting models using spatial data . It fits linear models with autoregressiv e errors and spatial lags of the dependent and independent variables. Thus, SAR makes it possible to determine effects of the values of neighboring spatial units on outcomes in a given spatial unit (StataCorp, 2017) . The goal is to test for th e spillover effects of hydropower across municipalities in the Amazon and how they behave spatially, through a criterion of proximity (Ãlvarez, Barbero, & ZofÃo, 2016) . The SAR models use autoregressive panel data analysis to include spatially lagged dependent and independent variables to capture spatial interdependence, following the Le Sage approach (LeSage & Pace, 2009) . The model was derive d from SAR (LeSage & Pace, 2009) . (2 1) P arameters for estimatin g effects of the independent variables in the model are measures possible increase in urban population from time t 1 to t, whereas Wi1 and i2 measure possible increase in u rban population from area i2 to i1 (Stata, 2017). In the mathematics of SAR models, Wy is the lag of the dependent variable, whereas Wxj is
41 the lag of the independent variable (Stata, 2017, pg.). In this study, Wy was added to d how mu ch the outcomes are affected by nearby outcomes (Stata, 2017). According to in spatial statistics, data should be normally distributed, so the assumption of normality was tested by measuring kurtosis and skewness. K urtosis and skewness for "latitude," "longitude," exhibit low values, and are thus normally distributed. Eliminating units with zero values for urban population caused distribution of urban populations to also show as normal. Model fitness was evaluated using indicators of maximum likelihood (ML). Results for Akaike information Criterion to estimate the predictor error and improve the model, and Bayesian information criterion (BIC) helped to move towards the best models as AIC and BIC gradually improving. The model worked a t a 95% confidence interval. A sample of interactions and the improvements in outcomes are shown in (Table 2 2). Final model consisted of the geographically weighted two stage least square of urban percentage population in 2010 as a function of the interac tion between hydropower and quadrant, total population in 2000 and urban population in 2000 (Table 2 3). In exploratory analysis considering all years, I had to drop units with empty values. This resulted in a sample of 582 municipalities. However, the bes t model pointed to the timeframe of data from 2000 and 2010, when most municipalities had values for. I then rerun the model, excluding only municipalities with empty values for these years. The best model sample totals 762 municipal units.
42 Spatial regres sion model allows for the assessment of direct and indirect effects. Indirect affects stand for spillovers. Northeast was set up the base category, as the comparison analysis requires a starting point. The analysis presented a limitation of in significant p value s the explanatory variables for direct and total impact in Southwest and Northwest . All variables were non significant for the spillover effect. This means that for the municipalities with hydropower, proximity to other municipalities does not affect the urbanization percentage. D irect impacts of hydro power projects point to a 4. 8 % increase in urban population percentage for the year 2010 for every 1,000 people . This means that the urban population percentages in municipalities considered affected by dams were higher when compared to municipalities considered to be un affected. Considering the direct impacts by quadrant, and Northeast as the reference category , South east shows 9% increase. The insignificant p value s for Southwest and Northwest overall show th ese variables cannot be explained by the current model . A n in significant result might be because this region presents the small er number of municipalities Northwest corresponds to 8% of the sample and Southwest to 19% . In the case of Northwest, the hydropower elements are visually less than the other regions, and since spillover is not registered, it is not possible to measure the effect of proximity to other municipalities affected by hydropower. In Northwest, only nine municipalities are considered affected. by a dam. Direct impact shows that there is a 9 % increase in urban population percentage in the Southwest if compared to the Northeast. In terms of population units, it means 90 more people for each 1,000 people. The impact of hydropower over t otal population
43 shows a decrease of 3% per each 1,000 people . Meaning the higher total population in 2000, the smaller the increase in urban population in 2010. With the general high percentage of urbanization in the Amazon, one could consider the urban po pulation percentage tend to present smaller increase rates in areas with a higher urban population. And municipalities with a lower total population tend to present higher increase in 2010. Urban population in 2010 is 0.4% higher as a function of urban pop ulation in 2000. As previously stated, the indirect impacts were non significant . Therefore, there is no substantial change in the total impact. Total impact reproduces the direct overall impact of 4,8% ; and urban population in Southeast is also the same as direct impact ( 9 % ). Similar results are seen for impacts of urban and total population in 2000. The most significant direct impact occurred in the Southwest region, with the largest percentage of municipalities considered as affected by a dam (52%). Fin ally , the model indicates a negative impact of the total population , which exhibits a small coefficient for the urban population in 2000. I ndirect effect s are the spillover . O verall, they were not significant, so the model suggests no spatial spillover eff ects. The model indicates that the presence of hydropower projects results in a 5.63% higher urban population percentage . Again, p value for Northwest was nonsignificant. The Southeast presented the highest total impact. Again, northwest had nonsignificant p values. There was a negative impact of population in 2000 (Table 2 3). This explains a possible decrease in the total population versus an increase in the urban perc entage.
44 The marginal coefficients provide the margins of response for specified values of covariate . M arginal coefficients for quadrants without considering the hydropower variable placed the Southeast as the higher value (6 3 %) , followed by Southwest ( 56 %) , Northeast (54%) , and Northwest (5 1 %). Again, the Southeast presents the most significant effect on urban population in 2010. T his region concentrates large mining venture s , agricultural and cattle industry, and, the highest deforestation areas. Based on the marginal prediction, the unit of analysis is 1,000 people. For every 1,000 people increase , t he average impact on urban population is 56% on municipalities not affected by hydropower, and 6 0 % on municipalities affected. A difference of less than 1 perc ent. Predictions for the interaction between hydropower and quadrants show that Northeast has the highest difference. In the Northeast , hydropower impact on urban population for municipalities affected by hydropower is 59 %, while 51% in those not affected . model, regarding the overall impact of hydropower by the quadrant, hydropower had a 64% impact on the urban population in 2010 on municipalities considered affected ; and 58.8% in municipalit ies not affected by the hydropower system. The presence of hydrop ower infrastructure, placing a municipality under the category of affected, and, therefore, exhibiting a positive value of 1 as a binary categorical variable, explains the 5.2% increase of urban population in 2010 if C ombi ning the interaction of categorical variable and quadrant, when comparing the marginal coefficients of municipalities not affected versus affected the Northeast presents the larger difference ( 8 %) , although not the higher percentage of impact on urban population . South eas t presents the larger impacts, but the lower difference
45 between them (3,5%) . Difference in impact on urban population decreases from 8 to 6% in the Southeast. Southwest presents the second high difference in marginal coefficients (6%), meaning there is less difference on urban population impact between municipalities considered affected or not. This still means the effect of hydropower infrastructure in this region is felt strong er on areas considered affec ted by the dams . T he Northeast presented a stronger significance in hydropower impacts . Conversely, i t also presents the following smaller percentage (the same as the least significant region of Northwest) of municipalities considered affected by the hydro power system : 15% . One explanation could be the influence of the state capital of ParÃ¡, BelÃ©m, as a node for Amazon activities . T he main destin ation of energy produced in other regions. The Southeast region that , with some of the most relevant results in p revious models, was interesting because of municipalities affect ed or not affected by hydropower. T he difference of impact on the urban population was less than 1% . Nonetheless, 40% of municipalities (84 of 210) are considered impacted by the dams. The Sou thwest region showed the highest impact of hydropower on urban populations (68%), with 5.4% difference i n the coefficient of impact. It also has the highest percentage of municipalities considered impacted (59 of 113). The large scale dams of Jirau and Santo Antonio started to operate in 2013 in the city of Porto Velho, in the State of RondÃ´nia in 2014 . A few miles away, however, construction began in 2008 and 2009, which might explain the highest impact on urban population in 2010. Th is area also has a higher percentage of municipalities affected by the hydropower system (52%).
46 The Northwest is still the most isolated and preserved are a in the BLA. It also has the lowest number of municipalities (61) and the smallest percentage of impa cted municipalities. The Northwest presented the only negative impact of hydro if considered municipalities not affected by the system. This means that urban population tend to decrease in municipalities that are not affected by dams. Urbanization impact i s higher i n municipalities not affected (56%) versus affected (53%). The proximity of the two mega dams probably attracted people to other urban centers , serving as a driver of intra and intermigration. Concluding Remarks Overall, there is broad agreement on the importance of increas ing in urbanization in the Amazon. That said , we do not yet fully understand the drivers of urbanization in the Amazon . We tend to view it as a homogeneous process occurring the same way throughout the region (Guedes et al., 2009; Thypin Bermeo & Godfrey, 2012; Trindade, 2013) . Alternatively, previous work examined urbanization in single towns, which fails to scale up or consider interactions with drivers or among urban centers (Cardoso, NegÃ£o, & Pereira, 2012; Carmo & Costa, 2016; Mitschein et al., 1989; Monte MÃ³r, 2015) . This study presente d a regional analysis considering the municipal level and the impacts of a key driver as a stimul us of the urbanization process. This approach permits observation of local heterogeneity within the regional context while examining the effect of hydropower s ystems as a driver of urbanization . The foregoing analysis show ed a strong relationship between the hydropower system and urbanization . In municipalities where a hydropower project is near the urban center, urban population percentages are higher. Further, the analysis confirmed sub regional spatial variations in urbanization, such that urbanization is more accentuated i n
47 some quadrants than others . These findings expand on understanding of urbanization in the Amazon in terms of the impact of hydropower pro duction across the region (JÃºnior, 2013a) . These findings expand on the call for more discussion of impacts of the hydropower system on urbanization , including areas that are not directly affected by the reservoir. T his study indicates an overall direct impact of hydropower on urban population percentages. Municipalities with nearby hydropower facilities have an urban population percentage 5. 2 % higher than those without a nearby hydropower facility . In this sense, infrastructure development impels urbanization. Most notably, this is the first study to investigate how urbanization is taking place in the Amazon, at the municipal level, and identifying cross scale urbanization as a possible outcome of the hydropower system. Such a scenario is especially worrisome in light of setbacks in environmental policies and loosening of licensing process recently put in practice by Brazil extreme right wing government. At the same time, Brazil seems to be on th e verge of another economic crisis. The past shows that the last severe economic recession , which occurred in the 1980 s, caused a steep population influx into urban areas . If th at pattern persists under the possible future scenario of another economic cris is , urbanization in the Amazon will continue to increase . This raises the question of whether hydropower projects will continue to go forward. If they do, they could assume a yet stronger role as a driver of where urbanization occurs in the Amazon . Under t his scenario, urbanization in the Amazon tends to be the outcome of some key drivers like infrastructure, and the urbanization forestation symbiosis will most likely continue to diminish.
48 However, some limitations are worth noting T he sample size had to be reduced due to the lack of data on urban population for municipalities in 1991. In addition, the static treatment of the hydropower variable may create bias in the observed effects, since hydropower projects are implemented over time . Future work should a im for a more comprehensive time series analysis, also considering the year of municipal creation, the year of new hydropower infrastructure and other important spatial variables such as the municipalit ies Figure 2 1 . Brazilian Legal Amazon and W estern and Eastern Amazon
49 Figure 2 2 . New m unicipalities created in the Brazilian Amazon per Decade 1950 1990 Figure 2 3 . Total of Municipalities in Brazilian Amazon per decade 1980 2010
50 Figure 2 4. B razilian Amazon by q uadrants
51 Figure 2 5 . Urbanization and hydropower impact by quadrant in BLA Table 2 1 . Variables in the models Variables Descriptive Independent Quadrant Latitude 5.354386, Longitude 56.092721 Hydro Categorical variable to indicate the hydropower system Tot al population 1991 th T otal population by thousand 1991 Tot al population 2000 th T otal population by thousand 2000 Tot al population 2010 th T otal population by thousand 2010 Urban population 1991 th Urban population by thousand 1991 Urban population 2000 th Ur ban population by thousand 2000 Urban population 2010 th Urban population by thousand 2010 Dependent Urban Population 2010 Percentage of urban population in 2010
52 Table 2 2. Model selection sample equation s Model Independent variable Independent spillover variable Dependent spillover variable AIC BIC Impact of Hydro % 1 Hydro 279.1606 266.0612 9.43 2 Hydro Hydro Yes 308.4768 286.6444 3.23 3 Hydro*Quadrant Hydro Quadrant Yes 330.8325 278.4348 6.20 4 Hydro*Quadrant Urban population 1991 th Yes 392.9729 344.9417 5.87 5 Hydro*Quadrant Total Population 1991 th Urban population 1991 th Urban Population 2000 th Yes 400.7731 352.009 5.17 6 Hydro*Quadrant Total Population 199 1 th Total Population 2000 th Urban population 1991 th Urban population 2000 th Yes 410.0561 348.9256 5.29 7 Hydro*Quadrant Total Population 199 1 th Total Population 2000 th Urban population 2000 th Yes 411.8314 355.0673 5.37 8 Hydro*Quadrant Total Population 2000 th Urban population 2000 th Yes 411.6904 359.2928 5.64 9 Hydro*Quadrant Total Population 2000 th *Quadrant Urban po pulation 2000 th Urban population 2000 th *Quadrant Yes 459.3711 380.7746 5.21
53 Table 2 3 . Spatial model of u rban p opulation 2010 as a function of hydropower, quadrants, urban population in 2000 and total population in 2000 Variables Coef. Std. Err. z P>|z| [95% Conf Interval ] Urban Population 2010 Hydro 1 . 0 477288 .0 15 3119 3 . 12 0.00 2 * . 0 17718 . 0777395 Quadrant Southeast . 093591 . 0170557 5.49 0.000 * . 06 01625 . 1270195 Southwest . 0257534 . 023 3308 1.10 0. 27 0 .0 199742 . 071481 Northwest .0244451 . 0308642 0.79 0. 428 . 0849378 . 0360475 Totpop_2000_th .0034745 . 0007841 4.43 0.000 * .00 5 0113 .001 9376 Urbpop_2000_th .00 42423 .0008 00 1 5 . 30 0.000 * .002 6741 .005 8104 Indirect Hydro 1 .00 05233 .00 24966 0 .2 1 0. 834 . 00 43699 . 0 054165 Quadrant Southeast . 0009241 . 00 43774 0 . 21 0. 833 . 00 76555 . 0 095037 South west . 000256 . 00 13 437 0 . 1 9 0. 833 .00 23776 .0 028895 Northwest .000 2156 .00 09085 0. 24 0. 812 . 00 19962 . 00 1565 Totpop_2000_th .00 0034 . 0001 599 0 . 21 0. 832 . 000 3474 . 000 2794 Urbpop_2000_th .000 0415 . 000 1954 0 . 21 0. 832 . 000 3414 . 000 4244 Total Hydro 1 .0 482521 .01 57963 3.0 5 0. 002 * . 0 172919 . 0 792122 Quadrant Southeast . 0945151 . 0 178287 5 . 30 0. 000 * .0 595715 .1 294587 South west . 0260094 .02 42013 1 .0 7 0. 283 . 0 214243 . 073443 Northwest .0 246607 . 03 06825 0 . 80 0. 422 . 08 4 7974 . 0 354759 Total Population 2000 th .003 5085 . 000 7 862 4 . 46 0. 000 * . 00 50494 . 001 9675 Urban Population 2000 th .004 2838 . 0008 052 5 . 32 0. 000 * . 002 7055 . 005 862 Notes . * p <0.05.
54 Table 2 4 . Marginal prediction of interaction t erm h ydro power and q uadrant Margin Std. Err. z P>|z| [95% Conf Interval ] Hydro 0 .5584817 .0079011 70.68 0.000* . 5429957 .5739676 1 .6067337 .0136306 44.51 0.000 * . 5800184 .6334491 Quadrant Northeast .5355096 .0133355 40.16 0.000 * .5093725 .5616467 Southeast .6300247 .0109683 57.44 0.000* .6085272 .6515223 Southwest .561519 .0179661 31.25 0.000 * .5263062 .5967318 Northwest .5108489 .0261645 19.52 0. 000* .4595674 .5621303 Hydro #quadrant 0#Northeast .5093633 .0147536 34.52 0. 000* .4804467 .5382798 0#Southeast .6192508 .0136978 45.21 0. 000* .5924036 .6460981 0#Southwest .5427162 .0229726 23.62 0. 000* .4976908 .5877415 0#Northwest .5280425 .0252604 20.90 0. 000* .4785329 . 577552 1#Northeast .592641 .0287839 20.59 0. 000* .5362256 .6490565 1#Southeast .6535664 .0169134 38.64 0. 000* .6204168 .6867161 1#Southwest .6026043 .021337 28.24 0. 000* .5607845 .6444242 1#Northwest .4732797 .0601274 7.87 0. 000* .3554322 .5911272 Notes . * p <0.05. Table 2 5 . Margins c oefficient, m unicipalities and q uadrants Hydro and Quadrant Hydro (1) Hydro (0) Difference (%) Total Positive Hydro / Total Municipalities Municipal i ti e s Affected (%) Northeast .592641 .5093633 8 39 / 263 1 5 Southeast .6535664 .6192508 3.5 113 / 285 40 Southwest .6026043 .5427162 6 7 9/1 48 5 3 Northwest .4732797 .5280425 5.5 9/ 59 15
55 CHAPTER 3 HYDROPOWER INFRASTRUCTURE AND STATE SCALE URBANIZATION There is a growing consensus that understanding the urban phenomena in the Amazon is vital to tracing its physical and social changing (Bertha Becker, 2013; Brond zio, 2016; Eloy & Brondizio, 2015a; Trindade, 2013) . Within a state boundary, the creation of new municipalities poses as a significant indicator of the urbanization process in practice and acts as driver and echo of such changes. A lack of planning and an accelerated urbanization process combine to drive the complex expansion of the urbanization of the Brazilian Amazon. Accentuated urbanization that includes over 70 percent of the population and the creation of new , largely unplanned, and in most cases , spontaneous urban areas , have propelled the emergence of an intricate system of cities that defies typical hierarchical patterns (Sathler, Monte MÃ³r, Carvalho, & Costa, 2010) , establishing additional layers of int eraction within sub state divisions. In addition, this urban network is deeply related to physical and environmental changes at scales from the local to global . In the state of ParÃ¡, the rise of urban clusters has contributed to reshaping the geography of the Amazon region, expanding urban areas while webbing their complex interaction, sh rinking the forest frontier, where 60 new municipalities were created since 1980. Not coincidently, the rise in new municipalities happens in parallel with major infrastruc ture projects aiming to bring economic development that include , but are not limited to , dams and their network elements. The elements of the energy grid system, in particular hydropower stations, substations and transmission lines have been shown to be ma jor sources of environmental, socio and economic changes (de Sousa JÃºnior & Reid, 2010; Fearnside, 1999; Latrubesse et al., 2017) . However, the impact s of
56 hydropower production and transmission on urbanization seem to be limited by the ir direct proximity to reservoirs , as in Tucu ru Ã Hydropower Plant and surrounding areas (Castro, 2009; JatobÃ¡ Caramelo & Faria Cidade, 2012; Miranda Rocha, 2011) . That direct connection between urbanization and the proximity of reservoirs and hydropower plants provides a stronger and well established impacts makes direct impact a fact . Yet, as a state co mprehends a system of interactions among its elements, municipalities in this particular case, chances are that impacts of dams infrastructure are probably perceived as spillovers on the entire system , rather than restricted to direct connection. This C hapter 3 thus aims to extend the conceptualization of impacts to investigate the links between the hydropower system and increa sed urbanization increase across the municipalities of ParÃ¡ by considering all elements of the hydropower system as drivers of ch ange, from 1980 to 2010. The main premise of this argument is that if a state can be considered as a system of municipalities, the n the elements of the hydropower network will have an impact beyond direct spatial connections. R oads are linked to deforestation and urbanization (Barber et al., 2014; Browder & Godfrey, 1990; Heitor Pellegrina, 2014 ; Kaimowit, 2002; Perz, 2002) . There is a consensus that hydropower system s create environmental disturbance s that contribute to the loss and degradation of natural ecosystems (Finer & Jenkins, 2012; Latrubesse et al., 2017; Nowak et al., 2000) . However, the impact of t hese hydropowe r systems is neither defined nor quantified (Almeida Prado et al., 2016; de Sousa JÃºnior & Reid, 2 010; Hyde, Bohlman, & Valle, 2018; Lees, Peres, Fearnside, Schneider, & Zuanon, 2016) . Many of the se impacts are undercounted even in official reports such as the
57 Environmental Impact Assessment (EIA) (Almeida Prado et al., 2016; Fearnside, 2005; Laurance, 2018; Little, 2014; Moore et al., 2010) . Among the impacts of dams, urbanization is recognized, but linked almost exclusively to direct impact s in the areas surrounding the reservoir (UNPD, 2012) . More recently, teleconnections between dams and urbanization were identified , showing distal connections and relationships are not limited by spatial contiguity (Karen C Seto et al., 2012) . Despite these findings, much of the urbanization impact of dams is limited to areas surro unding the hydropower plant, as in the TucuruÃ and Belo Monte dams in ParÃ¡ (Costa & Pires, 2015; Danilo, Montoya, Maria, Lima, & Adami, 2018; Miranda Rocha, 2011; Richards & VanWey, 2015; Santos, 2017) . Recent studies on TucuruÃ in Barcarena, a municipality 300 kilometers from the hydro plant, suggest revising the (da Trindade Jr, 2005; Fearnside, 2001, 2016; Monteiro & Monteiro, 2007) . Notably, TucuruÃ Dam is the main source of energy for the aluminum production industry in Barcarena . Focusing on hydropower system impacts on urbanization in the area directly affected by the reservoir prevents a holistic assessment of regional effects. Despite increase d interest in urban phenomena and their drivers , in particular the social and physical impacts linking dams to the urbanization (Caravaggio, Costantini, Iorio, Monni, & Paglialunga, 2017; Machado, 1999; Roscoche & Vallerius, 2014) , no study has examined urbanization and hydropower systems at a subnational scale in ParÃ¡, or any other Brazilian Amazon state . Thus, the purpose of this research is to : 1) provide an account for the main factors that led the overall urbanization process in the state of ParÃ¡, accounting for the main social , political , and historical trajector ies that led to the
58 present urban setting; and 2 ) investigate the relation of increase d urbanization from 1980 to 2010 through the lens of the hydropower system. I begin by providing the theoretical ground for addressing urbanization in the Amazon. Next , I introduce the study area of ParÃ¡ and provide d a n historic al account of the urbanization process there . Afterward , I introduce d the context of the TucuruÃ hydropower plant , and provide a scientific literature review to support a broader understanding of the impacts of hydropower o n urbanization , across municipal units. These steps contribute the main establish a profile background to address urbanization and hydropower impacts in ParÃ¡, tha t are analyzed with statistical and spatial analysis relating the hydropower infrastructure and urbanization in a spatial temporal assessment. Theoretical F ramework Urbanization at the city level is generally assumed to result from an influx of population and an increase in economic activity (Alonso, 1977; Ana Claudia Duarte Cardoso & Miranda, 2018; Lefebvre, Bononno, & Smith, 2003) . The recent Amazon urbanization is commonly described as the boomtown phenomenon (Browder & Godfrey, 1997; Thypin Bermeo & Godfrey, 2012) ; a steep increase in economic activity , usually linked to access to natural resources. E conomic possibilities of explo iting these resources attract s an intense influx of population to less occupie d or unoccupied areas. Considering the population increase overall, with an even more significant increase across urban populations and the creation of new urban area s , urban expansion in the Amazon has proceeded at a rapid pace, produc ing unexpected outco mes and often resulting in social and environmental vulnerability (Mansur et al., 2016; Mitschein et al., 1989; Parry, Day, et al., 2010; Trindade, 2013) . Previous studies aiming to explain
59 urbanization in the Amazon , usually from a regional approach, ofte n analyze a group of towns with in and across state s , rather than assessing urbanization in individual cities (Bertha Becker, 2005, 2013; Browder & Godfrey, 1997; Thypin Bermeo & Godfrey, 2012) . Recent theories, however, have focused on cumulative causation leading to urban agglomerates. Urbanization can be a product of economic r elations hips such as cumulative causations and their self reinforcing collective performance and linkages from agglomeration and labor migration (Godfrey, 1990; Krugman, 1990) . Assuming the territory is an open system, receiving and sending inputs, I consider the state division to be the system and the municipalities in it, t he elements. Infrastructure investment generally has been linked to increased urbanization (Spit, 1999) . It has also been linked to changes in Amazon land cover, primarily deforestation (Aldrich et al., 2012; Barber et al., 2014) . Planners and other scholars have identified this role of infrastructure investments a s key driver s of urbanization across aggregated and disaggregated scales (Barber et al., 2014; Eloy & Brondizio, 2015a; Godfrey & Browder, 1996) . Nonetheless, there are still gaps in assessing urbanization as a function of hydropo wer inputs in an aggregated form . Using an aggregated scale , I aim to help fill the gap in aggregated urbanization assessment, adding to the investigation the inductor role of the hydropower networks physical components , including transmission lines and substations in the state of ParÃ¡. I then aim to expand the notion of hydropower network impacts on urbanization to the entire state system of cities . The current model defines municipalities as affected by dams based on physical proximity to the hydropower plant and reservoir . By moving
60 beyond this conceptualization, I argue that these impacts can be discerned assessing the impact s not necessarily linked to the system by a physical thread of elements. Conceptualizing a cumulative causation framework, I assume that a new element in the system will affect all other elements . This is not to limit the urbanization to a single driver. Urbanization is therefore stimulated by different elements , including natural population in crease. This study framed hydropower network infrastructure as the element triggering changes that lead to increase in urbanization . Seeing space as socially produced (Lefebvre, 1991) the Amazon is reconfigured as urban : a product of the c omplex interaction of public policies designed for economic development . In this scenari o , the State is the main driver of this social reconfiguration , push ing change s in spatial practices, perceptions, and values. The global phenomena must, therefore, be translated into a local context to be understood. As t he Amazon space is reconfigured as urban, space is socially produced . As a social product, space assumes the comp lexities of interactions that shape and categorize its form . The Government acts as the main inductor of spatial reconfiguration (Lefebvre, 1991) . Through public policies, the Government invests in reconfiguring the space as a social produc t or a s a complex social construction which affects spatial practices and perceptions , and, in the Amazon case, drivers toward urbanization. Study Area ParÃ¡ is one of the 27 federative units (states) of Brazil . A long with eight other states, it constitute s the Brazilian Legal Amazon (BLA). ParÃ¡ shares physical borders with six other Amazon states to the north, west, south, and east ; and with the countries of Surinam and Guyana to the far north (Figure 3 1) . This high number of geopolitical borders further increas e s the state geographic importance in the region and country .
61 T he state of ParÃ¡ accounts for an area of 1,247 , 954 kmÂ², larger than most countries in Latin America . Par Ã¡ is the second largest subnational division in the Amazon, and 13th largest subnational entity in the world. The territory is divided into 144 diverse municipalities. Within the territorial divisions are Altamira, the fifth largest municipality in the wor ld with 160 ,000 kmÂ², and Marituba, with 103 kmÂ². ParÃ¡ is the most populous Amazonian state at 8,513 , 497 million people (IBGE, 2018) , o populati on . etween 1970 and 2000, the state of ParÃ¡ more than tripled (Azzoni et al., 2016) . significant geographical importance to Brazil comprises abundant natural resources , such as min eral and freshwater deposits. T wo of the six large st Brazilian mining projects are in ParÃ¡ (Little, 2014) . Large scale minin g reshap es the region and requires high amounts of energy. Because rich hydrological potential hydropower serves as the main source of energy for highly energy demanding minin g extraction and industrial activities. ParÃ¡ has two large scale dams in operation : TucuruÃ and Belo Monte (Figure 3 1) . The state of ParÃ¡ has attr acted massive investment in infrastructure . The resulting influx in population exploits natural resources and causes loss of forest cover . The east and south ParÃ¡ form much of the area known as the A rc of D eforestation, comprising 500 km 2 with the highest deforestation rates in the Amazon (Marques, 2016) . D eforestation was first measured in the Amazon in 1980 ( INP E , 2018) . Si nce then, ParÃ¡ ( along with the state of Mato Grosso) has led the annual deforestation rates . At present date, around 48% of its area is deforested (Fonseca et
62 al., 2017) . With new waves of deforestation , cleared forest areas increased 278% more than in 2018 ( G1, 2019; Phillips, 2019) . The state made headlines as 893 km 2 was deforested in July 2019 (Maisonnave, 2019; Phillips, 2019) . S hifts in spatial, social , political , and economic spheres often place ParÃ¡ as the protagonist of change in the Amazon. As a process, the accentuated urbanization is driven by all above cited spheres and beyond . U rbanization can be perceived by the increase s in urban population and the cre at ion of new municipalities . This is either planned or spontaneous geopolitical reconfiguration of space . Thus , ParÃ¡ is a challenging c ase study to understand Amazon urbanization. Over 60 new municipalities have been created since the 1980s, refle cti ng the s influence in reshaping space (Lefebvre, 1991) . N ew subna tional divisions imply a rearrangement of spatial configuration as a response to external drivers and lie among the unfolds of the Amazon development 1 and colonization plans embodied in the 1960s. N ational policies aimed to coloniz e 2 the Amazon, integrating the region to the rest of the country, while stimulating the extraction of natural resources, turn the land productive in terms of agricultur e and cattle ranching, and serve as a settlement site for destination population surplus a nd unemployed from the rest of the country (Bertha Becker, 1990; Bunker, 1988; Smith, 1982) . 1 The term development is herein applied mimics the then govern ment approach to attach it to mere economic processes, not reflecting my personal view of the further dimensions the process of development should be contemplated with, that includes, however not limited to environmental and socio sustainability. 2 The t occupied.
63 ParÃ¡ in Amazon Context Applying the classic land theory of Central Place (Alonso, 1977; King, 1984) to ParÃ¡ allows fo r identification of major hubs. Central Place theory hierarchy usually comes with age and political importance . T he state capital often sits atop the urbanization pyramid. Nonetheless, urban cluster s in ParÃ¡ have been reconfigure d by adding new municipalit ies (Pinheiro, Pena, Amaral, & Amin, 2011) . The rise of new municipalities was mostly unplanned . Public and private investment in strategic sectors such as hydropower infrastructure drive urbanization of space, but also of the territory and of the society, inducting a desmetropolization (Milton Santos, 1996, 20 08) . T his unbalanced outcome in ParÃ¡ and the Amazon brings the need to understan d the nature of these new territorialities . The region rivers and waterways historically play a major role in shaping the locations of towns and cities and the connections among them. According to Garcia, Soares Filho, and Sawyer (2 007) , as well as to city primacy law (Jefferson, 2017) , such connections show that Bel Ã©m and Manaus are both major hubs or macro poles in the Brazilian Legal Amazon . C lassified by the index of services (IS), this is the ratio between the service economy domestic product and the gross domestic product (Garcia et al., 2007) . The index of services regional center, influencing surroundin g municipalities pressure (Garcia et a l., 2007 , p. 721 ) . Garcia et al. (2007) applied an urban hierarchy framewor k to the Amazon, classifying its municipalities into nine macro poles, 29 meso poles, and 48 micro poles using the index of services (IS). Macro poles for IS are above 20%, mostly corresponding to state capitals. Meso poles IS are between 3.2 and 20%; and micro -
64 pole s , are between 0.5 and 3.2%. As urbanization is a spatial process, there is a linear connection between instances of hierarchies . E ach instance of hierarchy contains smaller layers. For example, macro poles contain meso poles, meso poles contain micro poles, and so on. In this case , macro poles correspond to Brazilian Legal Amazon state capitals. Using the index of services, more interaction also translates to more anthropic pressure . O f the eleven cities in ParÃ¡ that conform to the 48 micro poles the highest IS is in the capital , BelÃ©m . S ome of the lowest are Breves and Almeirin, with municipalities at both ends of the spectrum. This means a high heterogeneity of socioeconomic indexes even among the most prominent urban center in the state. ParÃ¡ shows a reality of contradictions. Some municipalities are larger than the intra state subdivision, called microregions or integration regions , equivalent to counties . A study by the Brazilian Ministry of Planning (Brasil, 2008a) identifies six major territorial areas based on overlapping socioeconomic indexes, population density, physical biomes, and occupational profiles. In the Amazon, ParÃ¡ is the only state that contains three levels of subdivisions . Despite its attempt to homogenize processes and regions, this state stands out for diversity. S ubdividi ng the Amazon into eastern and western regions creates two poles represented by the capitals BelÃ©m and Manaus . T he se two cities exhibit commonalities and differences. Both are old settlements in privileged locations on main waterways of the Amazon River. Since the opening of the Amazon frontier in the 1960s, ParÃ¡ has witnessed urban sprawl well beyond i ts capital BelÃ©m : spreading across its territory and concentrated west in Manaus, as is
65 is also of vital importan massive population migration, and changes in land use and cover , with the upsurge of planned and spontaneous urban settlements . L argescale construction of main and back roads along with agricultural e xpansion, logging, cattle ranching, and large scale mining projects contributed to the spatial and social reshaping of the Eastern Amazon (Simmons et al. 2002; Guedes, Costa, and BrondÃzio 2009). From a geopolitical perspective, the state is strongly conn ected to global commodity chains. M ajor development projects in the Amazon concentrate on extracti ng natural resources to supply the growing global market for minerals. ParÃ¡ is the export leader among Amazon states . This translate s into infrastructure proj ects directly linked to the urbanization process. Although urbanization has numerous drivers, the main driver in ParÃ¡ are mining exports, energy production, and ranching (P. Richards & VanWey, 2015) . Parauapebas and OriximinÃ¡ are t w o exemplars of rapid urban growth i n the state of ParÃ¡, in this case largely resulting from mineral extraction and export . T hey show the importan ce of smaller cities as sites of rapid economic growth and development . ParÃ¡ s changing urban dynamics stem from infrastructure development such as dams , transmission lines, and substations , as well as roads and ports . These smaller bu t rapidly urbanizing nodes are impacted by the development of the aforementioned infrastructure, wh e ther directly touched by them or not . Amazonian boomtowns reflect the economic boom cycles that triggered the spatial and economic reconfiguration of ParÃ¡. C ities and towns were not to shelter such large population. The result is an unpredictably complex urban network, with the rise of
66 medium sized towns across the territory. In most cases, these towns are tied to major natural resource export ventures . Historic Overview o f Municipalit y Formation In the 1600s, the Dutch, French, and English converged to inva de Northeast Brazil. Pioneer military occupation w ere verified at the mouth of the Amazon River. The future state capital of ParÃ¡, BelÃ©m , was founded in 1616, by raising a fortress able to contain the foreign invasion. Portuguese geopolitical moves secure d th is entrance of the Amazonian territory. In 1621, Portugal created a political administrative congregation in and MaranhÃ£o, to continue the national strategy of securing access to the Amazon Region (M. G. da C. Tavares, 2008) . For Machado, cited by Tavares (1989: 28), the first system of territorial control of the Amazon occurred with the introduction of Religious Companies. Carmelites, Mercedarians, Franciscans, and Jesuits, receiv ed selec t territories as mission areas, and began to dominate the vast area of the region. Th us , indigenous populations were prevented from forming alliances with other European Courts. The spatial division was based on religious domain. This gave the Jesuits control over the entire southern part of the Amazon river, which involved the tributaries of rivers Tocantins, Xingu, TapajÃ³s, and Madeira. Mercedarians and Carmelites assumed the Urubu Valley, the Negro River, the rivers Branc o , and SolimÃµes. The Piedade and Santo AntÃ´nio Franciscans were able to assume control of the left bank of the lower Amazon , GurupÃ¡ ; as well as Cabo do Norte and MarajÃ³. By 1730, Portugal seemingly had no policy of valuing the land, the people, or the resources of the Amazon . In contr ast, the Jesuits handled their activities in harmony with the natural space (Reis, 1956: 53).
67 In the mid seventeenth century, Portugal relinquished religious control of the region and instead began controlling t he occupation and economic activities of the Jesuits. The 62 villages established by the missionaries corresponded to the 18 th century parishes founded by the Portuguese in the Amazon region. These parishes were soon elevated to the status of villages, named after cities in Portugal (Fernando & Rodrigues, 2011; B. M. dos Santos, 2013) . E xpulsion of the Jesuits from the Portuguese colonies, includin g Africa, first happened in ParÃ¡ . This made ParÃ¡ the protagonist for territorial and geopolitical changes in the Amazon from days. Thus, when new administrative units formed , the villages began to function with a n elected Municipal Council. Their task was to stimulate local development and to control indigenous labor. Before being expelled, the Jesuits converted indigenou s people to Catholicism and established domain beyond religious valves. Their global scope allowed the Jesuits to obtain tr ade advantages , including ex empt ion from taxation, and develop ed a monopoly on much of the wealth produced in the state. T hey ranched more than 100,000 head of cattle on MarajÃ³ island , and held sugar mills in MaranhÃ£o , and GrÃ£o ParÃ¡ (Glielmo, 2007) . What can be seen at this point are the first concrete attempts of the state to reshape the space (Lefebvre, 1991) . Marques de Pombal, who led the Jesuit expulsion, also catapult ed Spain to trade the Patra Basin for the entire Amazon Region. The Spanish were no longer a threat as foreign invaders anymo re, at least not in Brazi l .
68 What follows is the first explicit attempt to set boundaries in the Amazon territory. O n 6 July 1752 Pombal told brother Francisco Xavier de Mendonca Furtado, then Governor of the Grand ParÃ¡, his mission to demarcate the boarde rs of the Amazon . This le d to active immigration of citizens from Madeira and AÃ§oures islands, interracial marriage between Portuguese and indigenous, and the creation of an enslaved workforce. There was a lso a clear strategy to occupy remote areas in the extreme north (Glielmo, 2007) . , to exercise control , and to continue to exploit indigenous labor. This system of occupation and terr itorial control the first steps in creating the municipalities that form most of today Amazon. The riverine areas of ParÃ¡, in particular, were marked by nuclear settlement and large administrative units that shape d the geographical mapping of t he territory (Machado, op cit, p.104). During the nineteenth century, ancient villages disbanded and were reduced to hamlets. Some became extinct and those that survived became Municipal headquarters. R econfiguration of the territory was constant and unst able. The ParÃ¡ geopolitic al divisions recognized today were created in 1889. Nonetheless, boundaries within ParÃ¡ continue to blur. The geopolitical instability of ParÃ¡ territorial demarcation beg an a long time ago . Even before 1891, when the Imperial Constitution granted municipal autonomy, the governor of ParÃ¡ Justo Chermont spoke of the incompatibility of municipal autonomy and the absence of clear municipal boundaries. Three years later, Law n umber 226 of 6 July 1894, provide d regulation s to creat e new municipalities . T he territory would
69 continue to be divided into municipalities, then subdivided into districts (article 55). Law number 226 also guarantee d municipal autonomy and independence , barring violation of federal or state laws (article 56). However, public resources were lacking, and interest in land demarcation was limite d . Therefore , delimiting the area of municipalities was not a priority of the State Government (1913, p.253) Even the first rubber cycl e (1879 1912), which allowed colonial expansion and attracted economic, social , and cultural transformations, was not enough to widely establish clear municipal boundaries throughout the state. The exception was the municipalities of SÃ£o JoÃ£o do Araguaia ( 1908) , ConceiÃ§Ã£o do Araguaia (1909) , Altamira (1911) , and MarabÃ¡ (1913) in Southern ParÃ¡. Most of the economic revenues were then concentrated in the state capitals of BelÃ©m and Manaus (M. G. da C. Tavares, 2008) . However, the rubber boom significantly influenced migration . The rubber boom attract ed people and formed a new spatial cluster. Th us , the occupation of the Amazon focused economic interests mainly on natural resources. Understanding the process of municipal creation in the state of ParÃ¡ in the twentieth century requires attention to one essential element : the construction of terrestrial communication networks. Before roads and railroads, waterways were the main networks for connection, communication, circulation, and economic activity. A public police arrangement to settle 10,000 colonist families in BraganÃ§a in 1874 and the construction of the BelÃ©m BraganÃ§a railway in 1875 show an attempt at terrestrial occupation and colonization. In the next decades , 10 new villages and towns were established along the railroad. T hey later bec a me municipalities. By 1938, ParÃ¡ had 53 mu nicipalities.
70 With the advent of the 1943 Constitution, came a municipalization wave. For the first time , states created certain requirements for population, housing structure, and minimum income for the new municipalities . This was a clear attempt to prev ent further divisions of the territory into autonomous units (Tavares, 2008) . In 1948, L aw number 158 describe d the competencies and responsibilities of municipal layers. Tax collection was describ ed as municipal compete nce, and so was the organization of local public services. The first project aimi ng to integrate the Amazon w as in the mid 20 th century. In 1953, after creating the Superintendence for Economic Valorization of Amazonia (SPVEA), later organized as Superintendence of Development of the Amazon (SUDAM), a highway linked the capital BelÃ©m to the rest of Brazil through the national capital, BrasÃlia. A boom of cities fo llowed along BR 163, known as BelÃ©m Brasilia. The Military Regime in Brazil (1964 to 1985) then began an aggressive policy to further occupy the Amazon, on the assumption of wasted space. The policy aimed to exploit natural resources (Da Silva et al., 2015) . Part of the plan was to urbaniz e the region . That was abandoned early. The urbanization plan revolved around the new road Transa mazon : an iconic venture that continues to symbolize the exposure of the ( 1 966) ce ntral place theory, envisioned a network of small towns with different function s and weights, planned along the Transamazon portion of ParÃ¡ . This new urban environment that would allow agrarian reform and income equality . The network of small towns would receive guidance on group conduct, morality, community, and religious spirit, in an attempt to create a kind of utopic society that would inhibit segregation by any creed,
71 past ties, or tendency. This proposal by urbanist JosÃ© Geraldo da Cunha Camarg o, (Rego, 2016) . The idea was to create rural urbanism . D istinct hierarchical levels called agrovilas, agrÃ³polis, r urÃ³polis, and cities would form a network . These no des would secure rural populations. The city countryside link mimics the integration of the Garden City (Camargo apud Rego in the article: Planned Communities in the Amazon). In February 1974 , the first r urÃ³polis was inaugurated in ParÃ¡, named after Presid ent MÃ©dici. The plan was soon abandoned as unviable , but it l eft it fingerprints on the spatial configuration of that portion of ParÃ¡. Despite its u topi an patent , the Transamazon attracted a massive influx of people and allowed deforestation to spread (Kat zman, apud Rego , 2016). Transamazon, a controversial road that once showed the world how impassable and inaccessib le the Amazon is, remains unfinished and symbolizes danger instead of integration. C ommon sense suggest s that planned urbanization was destined to fail . However, what happened along part of the Transamazon highway had a successful precedent n orth of ParanÃ¡ . I n the late 1920s, the English company Parana Plantations assumed responsibility for colonization endeavors in north ParanÃ¡. Onc e more, the Amazon brought a reality of its own, defying labels, and requiring a local approach to adapt. Numerous records confirm that occupation of the Amazon was disordered and predatory. Under leadership of the military regime, the motto "integrate to not give it up " and "men without land to land without men" elevated the idea of developing and occupying the region to a national security strategy. A lack of adequate planning
72 unfold ed in the next decades reducing forest cover, los ing biodiversity, and altering ecosystem s . Amazon occupation was founded on the urge to exploit and populate the area at all costs. It collaterally granted protection from f uture internationalization efforts. The idea was to expan d agricultural frontiers, and to install industries with public and private capital (Cavalcante, 2012 ) . T o populate the Amazon, occupation plans were created to attract colonists . Becker (2001) said the occupation and de velopment policies for the region greatly increased immigration . Its origins is traced to remedy severe socio environmental disturbances, such as the uncontrolled use of land, the aggravation of social conflicts, the advent of epidemics, and impacts on local biodiversity in other parts of the country (Becker, 2001) . D evelopment of the Amazon unfolded in two distinct periods. In t he first period , starting in the 1960s, urban and rural areas were severely di stressed . The second period began with the Federal Constitution of 1988, when a new perspective began to incorporate public policies aimed at sustainable development. This new perspective also recognized the environmental jeopardy the initial occupation cr eated . Both waves of development greatly changed the spatial configuration in the state of ParÃ¡. I n 1970 , the National Integration Plan (PIN) and the National Integration and Development Axis (ENIDS) were developed (Serra & FernÃ¡ndez, 2004) . These programs were prompted by the "AvanÃ§a Brasil" Proj ect (Forward Brazil) (Becker, 2004 ; p.76). C onstruction, expansion, and opening of roads such as the CuiabÃ¡ SantarÃ©m Highway, the Perimetral Norte, and the Transamazon Highway (Br 230 ) were part of a
73 plan to occupy and interconnect the Amazon region to other econom ic centers in Brazil. The Transamazon Highway especially reshape d territoriality in ParÃ¡, contributing to deforestation and the rise of new urban cores. In previous centuries, Amazon cities faced the local rivers, the main conduit for people and goods. As new roads shift ed transportation networks, cities instead faced roadways . The fringes of such roads gave rise to cities. O utcomes of this occupation also surprised the government. Brazil create d the Agrarian Reform Institute (INCRA) to promote the regularization of land throughout the Amazonian territory. What unfolded in ParÃ¡ during the 1980s, 1990s, and 2000s, and beyond was a series of the most violent conflicts involving land access in the Amazon (Aldrich et al., 2012; Simmons et al., 2002) . MarabÃ¡ developed under a converging of new roads, mining and hydropower projects ; and bec a w cities were named after violent conflicts, explaining why there is a TailÃ¢ndia (Thailand), and a Palestina (Palestine) in ParÃ¡. Attempts to Understand Urbanization in ParÃ¡ Urbanization in the Brazilian Amazon fits into three major phases. The first phas e relied on the rubber boom export ing latex to external market s , from 1879 1912 , and short recovery time during World War II . The second phase started in the 1930s and proliferated in the 1960s . It aimed to occupy the land and make it productive through ex tractive economic activities focused on export natural resources , such as minerals and logging, and later agricultural commodities such as cattle and soy. It encompassed social, political and economic investment to attract migrants by promis ing land , or jobs . The third phase took hold in 1980 s (P. Richards &
74 VanWey, 2 015) . U rbanization of the Amazon continues to prevail . Almost 80% of current urbanization was not forecasted by in the occupation plans and development policies. U rbanization is reshaping the Amazon. The attempts to identify applicable framework share the search for incorporating local contexts and complexities to universal phenomen a . Trindade JÃºnior ( 2013) compile d example frameworks to urbanization . Browder & Godfrey ( 1997) said urbanization i s polymorphous and disarticulated . They cited different socio spatial interactions, with hybrid microsocial elements as strong points of regional urbanization . Machado ( 1999) said heterogeneity in the hierarchical levels of urban areas indicate s new urban clusters and municipalities are also created t end ed to have rural activities. De Oliveira ( 2000) highlights urban society dissipated not necessarily just over the urban landscape; Hurtienne (2001) evidence d an intricate urbanization and establishe d a population threshold to indi cate urbanization. Monte MÃ³r ( 2015) said extensive urbanization extend ed urban livelihood beyond city walls, promoting an urban lifestyle. Becker (1985) in 1985 co i ned 2004, she said the Amazon w as an urbanized jungle, directly linked to the expansion of economic activities, whereas the frontier is already born urbanized , preceding the rural momentum that usually unfolds into urban areas. Urbanization and Hydropower System s Hydropower in ParÃ¡ Demand for energy is growing worldwide (da Silva Veras, Mozer, da Costa Rubim Messeder dos Santos, & da Silva CÃ©sar, 2017) . T international scenario is a contradictory way . T is
75 also a global provider of mineral and food commodities . As a ccess to electricity is vital to accelerate industrialization and increas es local energy demand in a region with abundant hydraulic reso u rces . economic development and participation in the global commodity export boom since the 198 0s . I ncreasing energ y demand to supply industrial production , it also accounts for a large portion of the generated energy . Soy, cattle, minerals, and ethanol are among the most important economic activities in the state (Backhouse, 2013; Gibbs et al., 2015; Southworth, Munroe, & Nagendra, 2004; SUDAM, 2016) . ParÃ¡ has a strategic geographical location . In addition, to availab le land, fertile soil and abundant natural resources , o cean access offers transportation flow and connection to other markets . T he internal waterway connec t s the Amazon river and other tributaries. Those features make ParÃ¡ a natural destination for products leavin g the Amazon for the international market. Brazil feeds China wit h mineral and natural commodities and shows no signs of abandoning its extraction based economy . In this context, energy becomes even more important. Brazil relies on hydropower as its main energy source for electricity production (65%). Hydropower as a sy stem has also become increasingly important as a driver of changes, including land use and cover ; and urbanization of the surrounding areas . Brazil plans to increase the energy grid with 46% more transmission lines to add new substations, and to build six more large scale and 17 small dams in the Amazon (Brazil, 2 018) . Large dams and hydroelectric plants cause large scale socioeconomic and environmental impact s . The energy system in ParÃ¡ has relie d on the TucuruÃ dam since 1984 and represents much of the energy produced in the Amazon. In 2005, ParÃ¡
76 produced over 2 . 3 million metric tons of oil equivalent, wh ile the remaining Amazon states produced only approximately 1 . 1 tons (Brasil, 2008b) . In 2016, another large dam, Belo Monte, started to operat e and more are planned or inventoried. TucuruÃ was the only large dam in the state for ov er 3 decades start in 1984 and provides a good baseline for the complex outcomes in hydropower infrastructure. Over the years, es and substations have been added to make the system more robust the system . In parallel, ParÃ¡ configuration was also reshaped , in part on the account of impacts from TucuruÃ and the energy grid system that evolves from it. TucuruÃ Hydropower Plant : Some H istorical B ackground The first large capacity dam in the Amazon, TucuruÃ hydropower plant is currently the fourth largest dam in the world . It is located o n the Tocantins Araguaia , the largest 100% Brazilian Amazon river basin. The Tocantins r iver flows through four states in the B LA in to the Atlantic Ocean. A great portion of the energy produced in TucuruÃ travel s over 300 kilometers to reach its d estination in the aluminum industrial hub of Barcarena . Th e TucuruÃ hydroelectric power plant was proposed in 1974 to supply energy to Japanese and Brazilian joint ventures in AlbrÃ¡s and Alunorte (Pinto, 2012; Tavares, 2001; WCD, 2000) . T he Japanese government backed out after increase d construction costs. Construction costs were then covered by the Brazilian government to produce low priced energy . This subsidized the Japanese Brazilian company AlbrÃ¡s to produce and export aluminum to Jap an (Pinto, 2012) . TucuruÃ began operating in 1984, and aluminum production of AlbrÃ¡s/Alunorte beg an i n 1985 (Moura & Maia, 2012) .
77 The main pur pose of TucuruÃ was to supply energy to the mineral industr ial complex of Barcarena , which attract ed several other mineral companies in the next decades . The energy met the needs of recent industry in ParÃ¡ , MaranhÃ£o, and later the newly formed state of Toc antins. In 1986, 60% of the energy generated in TucuruÃ had large scale industrial consumers, reaching almost 70% in 1992. By 1999, more than 50% of its production was for industr ial use (Comissao Mundial de Barragens 2000) . A smaller share s erved the state capital and other resident ial and municipal uses. In ParÃ¡, hydropower energy supply went from 4% in 1980 to 92% in 1985 . A fter TucuruÃ begun operating, it increased and to 96% in 2005 (Dias, 2014) . TucuruÃ and its system are thus the main elements of A B roader Horizon for a Dam's Impacts : Connection to Urbanization Past studies of Amaz onian urbanization at the basin or state scale concentrated on aggregate urban outcomes or individual urban units. Many relate urbanization to multiple drivers and account for the impact of occupation policies and large infrastructure projec ts such as roads, mining ventures, and dams (Browder, 2002; Browder & Godfrey, 1997; El oy & Brondizio, 2015b; Godfrey & Browder, 1996; Guedes et al., 2009; Thypin Bermeo & Godfrey, 2012) . However, in most cases, urbanization and the hydropower infrastructure systems are related spatially , limiting the impact to the surrounding areas of th e reservoir of the major hydropower plant. The connection between the TucuruÃ dam and its direct impacts on urbanization have been widely explored by considering its surrounding areas, especially in the municipality of TucuruÃ and its neighbor Breu Branco (Miranda Rocha , 2011; T avares , 2001; WCD , 2000; Browder and Godfrey , 1997) . Lago de TucuruÃ subnation al regional division accounts for the seven municipalities The p opulation
78 in Lago de TucuruÃ before construction of the hydro plant was about 17 ,000 . In 1970, it was 56% rural. The population in 2 010 was about 360 ,000 : 68% urban. F rom 1970 to 2010 , the population of Lago de TucuruÃ increased by 1,955% , higher than the 879% increase in Tucur u Ã . The u rban population of TucuruÃ reached 95% in 2010. F our of the six municipalities forming the area were created in the 1990s, a reflex of space reconfiguration , probably stimulated by the population increase (Miranda Rocha, 2011) . P ublic infrastructure projects affect ed industrialization and urbanization outside the areas themselves. The framework for addressing urbanization as one outcome of the hydropower system in ParÃ¡ is drawn from two interconnected theor ies . The first is Central Place Theory, developed by Christaller and published in 1933 (C hristaller, 1966) , adapted by von ThÃ¼nen shortly after, and reframed by LÃ¶sch and Alonso. This theory explains the weight of centrality on different land uses driven by economic connections and the dominance of land rent (Alonso, 1977; Robert Sinclair & Sinclair, 1967; Sathler et al., 2010) (1996) theory of cumulative causation and industrialization. Its main presumptions rely on the fact that urban areas possess synergies, resulting primarily from trade relations that stimulate economic activit y . With increasing trade, and commercial activities propelled by the increase in energy supply, urban centers expand their capacity to develop commercial exchange and a higher degree of specialization. A given city supplies to others what they need and do not produce, making it rational for cities to trade. Therefore, cumulative causation happens within each city , and e ach city grows quickly than with interurban trade (Fujita, Krugman, & Venables, 1999 ; Krugman, 1990, 1996) . I apply these two theories to explain increase d urbanization in response to the hydropower system.
79 Central place theory helps explain population boosts near municipal headquarter s as the system of municipalities reacts to input from the hydropower system. T he hydropower system s influence on urbanization extend beyond physical connections and is not limited by proximity to power plant or reservoir. This work takes into account urbanization in ParÃ¡ receives different stimuli . It also accounts t he hydropower system will probably continue to expand and its impacts to unfold. Thus, is seeks to answer the question how has hydropower impacted urbanization across all municipalities in a single state. Methods Data Analysis This study use s an empirical analysis of spatial, temporal, and population data for municipalities in the state of ParÃ¡, Brazil. Spatial data corresponds to 14 3 municipalities in ParÃ¡ and the hydropower infrastructure network. Temporal data consist of population data fo r 1980, 1991, 2000 and 2010, and starting operation year of the transmission lines. Because municipalities did not all form at the same time, I generate d two major datasets to run distinct temporal models. One includ ed all municipalities with entries for a ll years (83 units) . The other included municipalities with data from 1991 onward (143 units). To better conceptualize impacts of the hydropower grid on urbanization in ParÃ¡, I divided the data analysis into two major parts . T he first part was GIS mapping by municipal foundation date and the infrastructure of the hydropower grid, with transmission lines, substation and hydro plant. The second part was a temporal statistical analysis, with a longitudinal model assessing increase d urban population percent as a function the presence or absence of hydropower (StataCorp, 2017) .
80 For the statistical analysis, I r a n two models for each of the time series set s : the first group fo r med by years 1980, 1991, 2000 and 2010 , and the second for 1991, 2000, 2010 . One model used the varia (2016) dataset , which I called conventional hydro . Th is is a categorical variable , representing municipalities that were impacted by dams and municipality considered not impacted . A binary code e stablishes values are one for affected and 0 for not affected . The second model assumed a , called proposed hydro . The proposed hydro was based on visual identification of municipalities physically linked to an y elements of the hydroelectric grid , by overlaying shapefiles of the municipalities downloaded from IBGE ( 2019b) with shapefiles of the hydropower system download from Aneel (2016) . A time matrix was created for the categorical variable based on the year of operation of transmission lines. Shapefiles corresponded to layer s for hydroelectric dams, transmission lines, and substations . Both models use the same total and urban population data from the years of 1980, 1991, 2000 and 2010, and the operation dates of the transmission lines. Data Set Outcome variable urban population percentage Urban population as a percentage of total population was used as the outcome for all analysis. The decision to use the percentage of urban population takes into account a worldwide tendency toward urbanization, not necessarily proportional incre ase in total population. Also, in absolute terms, the percentage of urban population allows for an urbanization assessment under a standardized scale. A 10,000 person increase would affect a 20,000 person town differently than it would affect a 200,000 per son town. I also considered the average urbanization percentage in the Amazon
81 (over 70%) show an increase tendency despite differences in rate across the municipalities and state. T he project Performances of Br azilian M unicipalities A ffected by H ydropower plants (Moretto, 2016) differentiate s whether a municipality is considered affected by a dam according criteria proposed by Aneel (ANEEL, 2001) . T his classification guide s financial compensation to the municipalities areas flooded by the reservoir . This dataset provides what I call as conventional classification of hydropower impact , or (Figure 3 2) . There were 39 municipalities affected by hydropower according to the conventional classification. classification as impacted municipalities with physical connection to all elements of the hydropower system . To investigate broader impacts of dams o n urbanization, I proposed different criteria to include physical connections with dams, transmission lines and substations in ParÃ¡. I called this new dataset proposed hydro . E xample s of proposed new classification for municipalities as affected by dams are the municipalities of BelÃ©m (the capital) and Barcarena, the destination of most of energy produced by the TucuruÃ Dam and consumed by the aluminum activities . Moretto's compilation (2 016) classify both municipalities as not impact ed by dams because they are located 300 kilometers away from the TucuruÃ reservoir. P roposed hydro sees these municipalities as physically connected to the hydropower system through transmission lines and s ubstations and directly impacted by it . They are direct recipients of energy inputs. There were 82 municipalities affected by hydropower according to the proposed classification.
82 Categorical variable across time The time related categorical variable was cr eated to indicate when the hydropower lines started to operate (ANEEL, 2016) . Hydropower lines begun operating in 1990 (28), 2003 (1) and 2004 (4). There were seven municipalities considered affected by conventional classification that were con sidered not affected in the proposed classification with no entry for the time variable. Assuming that municipalities were affected by the hydropower system in different periods across the time studied, I created a vector of dummy variable hydro, with a z ero value if the municipality was considered not affected by a dam, and 1 if considered affected. Next, I added to the data set the year the transmission lines beg a n opera ting. I n the TucuruÃ Dam, new transmission lines were added to the system after 1984 . To map the variability of hydropower , I assumed that if the year observed is less than the year of the starting For example, the transmission line crossing MarabÃ¡ begun operating in 1990. T herefore, this unit has 0 ; Models Models capable of analyzing panel (longitudinal) data were utilized, as the goal was to investigate the hydropower system population across multiple periods . As a random effect model, it assumes the variation across entities is random and uncorrelated with the predictor or independent variables included in the model (Bates, 2010; StataCorp, 2017) . Variables used as independent correspond to the percentage of urban population in 1980, 1991, 2000 , and 2010 . D ependent variables are the dummy variable s representing the presence of hydropower across the same
83 years (Table 3 1). Panel data considers the dimensions of space and time and is written as (StataCorp, 2017) . The final best models outcome from testing differe nt combinations of the interaction terms and accounts for urban population percentage as a function of regressed hydro, time, weight matrix of hydro and time, and error terms. (3 1) (3 2) w here Y nt is a n x 1 vector of observations for the dependent variable for time period t with n number of panels. W ith periods 1 to 4, according to the models starting from 1980 or 1991 , X nt is a matrix of the time varying regressors (Proposed_H ydro nt , Hydro nt and time nt ). Cn is a vector of panel level effects. The random effect model assumes that Cn is independent and identically distributed (i.i.d.) across panels with mean and variance . U nt is the spatially lagged error . V nt is a vector of disturbances and is i.i.d. across panels and time with variance is . W and M are spatial weighting matrices . Results and Discussion The first set of analyses correspond to timeframe 1980 2010. The margins coefficients show an overall higher impact for the conventional hydro. For conventional and proposed there is a consistent increase on impact (Table 3 2) . For municipalities affected, the difference between conventional and proposed hydro varies from 2 3%. And the differenc e between conventional and proposed hydro for municipalities affected varies from 1 3%. Nonetheless , the impact on urban population is higher on municipalities with hydropower elements. The difference points that a conventional classification indicated str onger correlation. For the proposed hydro, some
84 municipalities were considered affected because the transmission lines run through them. Results indicate this connection might not be sufficient to overcome the proximity to other elements such as substation and proximity to reservoir . Another possible explanation could be the age of municipalities. This model contemplates municipalities that existed in 1980 and continued to exist to the present. Seniority, I this case, made difference suggesting the conventi onal classification is more suited to asses hydropower impact on urbanization without spatial reconfiguration of additional state subdivision . As t he State of ParÃ¡ created 60 municipalities after 1980 . The second set of models considered 1991 2010 as the timeframe ( Table 3 3) . In this model, all municipalities in state of ParÃ¡ are included in the analyses. It also shows that impact on hydropower over urban population increases after time independently to whether a municipality is affected or not. However, c ontrary to the previous models, it shows significant differences between the proposed and conventional classification results. For municipalities not affected, the conventional hydro is higher, and results increase with about the same difference (17% in 1991, and 16 in 2000 and 2010). For municipalities affected , the proposed hydro has higher coefficients , and the difference from conventional hydro varies from 11 13% . These models indicate a higher impact on municipalities affected with the prosed hydro . Comparing the two models, the difference between the conventional hydro affected and not affected remains at 4% for 1991 2010 and varies between 5 6% for 1980 2010. The same difference for the proposed hydro models increases to 6 7% for 19 80 2010 . The highest difference is observed for 1991 2010 (32% in each year).
85 These results indicate the model 1991 2010 is more representative of the current stage of spatial division and population distribution in the state of ParÃ¡. For this model, there is a h igher difference in results between conventional and proposed impacts. to stable 32% for 1991 2010 (Figure 3 3 ) (Figure 3 4). This indicates the proposed hydro shows a significant higher impact on municipalities affected by the hydropower system. Accord ing to this result , hydropower system will continue to impact significantly more urban population in municipalities with the physical presence of elements of the hydropower . A higher impact on urban population might be correlated to th e attraction factor i nfrastructure projects might deliver , drawing population to concentrate where there is more investments. This is a trend overserved in the Amazon and other places that is backed up by the availability of resources in urban areas, or lack of investments and opportunities in rural, originating rural exodus. Largely, the impact on municipalities with the presence of hydropower infrastructure is higher in all years and considering the two time period sets. The difference is diminished or even reversed in the c ase of absence of hydropower for the conventional and proposed dataset. The results for 2010 on the model from 1991 2010, the most recent profile, points at a greater gap on the impact over urbanization on municipalities that present hydropower. New Munic ipalities , Urbanization and Hydropower Grid Corroborating the main argument of connection between infrastructure and urbanization, spatial data show increase d activity along with the substations of the hydropower grid in ParÃ¡. Within one single decade, fro m 1981 to 1991, 42 new municipalities were created . Most were created along transmission lines and adjacent
86 areas. From 1992 to the present , the state of ParÃ¡ has added 18 new seats to its territorial divisions. At total of 60 new municipalities were created . Currently, there are 144 municipalities composing ParÃ¡ . Just one was added since 2010 (Figure 3 5 ) . When using urban population increase between 1980 and 2010, the map of ParÃ¡ shows the western part with smaller rates (Figure 3 6 ) . While this map does not provide a clear connection between hydropower and urban population as Figure 3 3 , the map points at the higher outliers (re aching 9,000%) on the eastern part . Concluding Remarks U rbanization in the Amazon is becoming an important component of the spatial, soci al and environmental dynamics of the region. While considering multi drivers, scholars have to identify boomtowns (B. Becker, 2013; Godfrey, 1990; Thypin Bermeo & Godfrey, 2012) . In ParÃ¡, most studies aim at a specific municipality or region (Ana ClÃ¡udia Duarte Cardoso & Neto, 2013; da Trindade Jr, 2005; Miranda Rocha, 2011; V. M. dos Santos, 2017; Trindade JÃºnior & Barbosa, 2017) . Among these drivers are infrastructure invest i ments (Spit 1999; Geist and Lambin 20 02; WCD 2000; Becker 1989; Barber et al. 2014; Walker et al. 2017; Southworth, Munroe, and Nagendra 2004; Richards and VanWey 2015; Brondizio and Moran 2012; Browder 2002) . D measu re , and often undercounted (Almeida Prado et al., 2016; Fearnside, 2005; Laurance, 2018; Little, 2014; Moore et al., 2010) . When urbanization is considere d an impact it is usually tied to surrounding areas of reservoirs and large capacity hydro plants (UNPD, 2012) . This study addressed urbanization process in ParÃ¡ . It also examined broader impacts of the hydropower system on the municipal units . Based on physical connections with the elements of the hydropower grid. Results show the model with the
87 proposed hydropower variable, extending the concept of impacted municipalities based on physical connections to the elements of the system, rather than proximity to reservoir, sugges t a higher difference of impact between municipalities affected and not affected. Municipalities affected by hydropower present a higher impact on u rban population . Overall, this study points at an increase in hydropower impact on municipalities affected or not affected by the hydropower system , suggesting a stronger influence for a higher percentage of urban population considering the redivision of municipalities into new units, as seen in ParÃ¡ with the 60 new additions since 1980 . The findings complemen t the need for a broader understanding of urbanization and the impacts of hydropower infrastructure on the spatial reconfiguration of the Amazon (Costa & Pires, 2015; da Trindade Jr, 2005; Danilo et al., 2018; Fearnside, 2001, 2016; Miranda Rocha, 2011; P. Richards & VanWey, 2015; V. M. dos Santos, 2017) . U rbanization and the spatial reconfiguration of ParÃ¡ is increasing , and the hydropower system is strengthening. , with plans for the addition of new dams, transmission lines and substations. Most notably, this is the first study to investigate the the hydropower infrastructure upon the current urbanization process at a st ate scale considering all its municipal units across time and space in the Amazon . Future work should concentrate of refining impacts of the hydropower system on urbanization and account for it as the system continues to grow its tentacles. This refining i ncludes additional data for elements such as substations, energy consumption
88 and an updated set of spatial data with the inclusion of recently operating transmission lines. Figure 3 1. Study area: State of ParÃ¡
89 Figure 3 2. Municipalities considered impacted by hydropower: conventional and proposed classification Table 3 1. Variables u sed in the m odels Variables Descripti on Independent Urban population percentage Percentage of urban population in 1980, 1991, 2000, 2010 Dependent Hydro Temporal categorical variable in 1980, 1991, 2000, 2010 a positive value reflects the year of operation of transmission line Proposed hydro Temporal categorical variable in 1980, 1991, 2000, 2010 a positive value reflects the year of operation of transmission line after visual reassessment
90 Table 3 2. Margins for urban population regression for 1980, 1991, 2000, 2010 for proposed and conventional hydropower impact classification on 83 municipalities affected (yes) and not affected (no) by hydropower (confidence interval 95%) Year Conventional Hydro no Proposed Hydro no Conventional Hydro yes Proposed Hydro yes 1980 .2883774 (.23, .35) .2602142 (.51, .47) .3912118 (.26, .52) .35223 (.14, .56) 1991 .3956259 (.33, .46) .3675694 (.16, .58) .4984603 (.37, .63) .4595852 (.25, .67) 2000 .4601181 (.40, .52) .4320681 (.22, .64) .5629525 (.43, .69) .5240839 (.30, .73) 2010 .4857667 (.43, .54) .4577152 (.25, .67) .5886011 (.46, .72) .549731 (.34, .76) Table 3 3. Urban population regression for 1991, 2000, 2010 for proposed and conventional hydropower impact classification on 143 municipalities affected (yes) and not affected (no) by hydropower (confidence interval 95%) Year Conventional H ydro no Proposed Hydro no Conventional Hydro yes Proposed Hydro yes 1991 . 3577728 (. 31 ,. 40 ) . 1891624 (. 54 ,. 32 ) . 3966256 (. 3 1 , .4 0 ) . 5107299 (. 39 ,. 62 ) 2000 . 4560885 (. 40 ,. 50 ) . 2912086 (. 16 ,. 42 ) . 4949413 (. 42 ,. 56 ) . 6127761 (. 49 ,. 73 ) 2010 . 4972598 (. 45 ,.5 4 ) . 3317114 (. 20 ,. 46 ) . 5361125 (. 4 6, .60 ) .6 532788 (. 53 ,. 77 ) Figure 3 3 . Difference between impact on urban population percentage in municipalities affected and not affected by hydropower with proposed and conventional set of categorical variables for model 1991 2010 32 32 32 4 4 4 1991 2000 2010 Percentage Year Proposed hydro Conventional hydro
91 Figure 3 4 . Difference between impact on urban population percentage in municipalities affected and not affected by hydropower with proposed and conventional set of categorical variables fo r model 1980 2010 Figure 3 5 . Urbanization change in ParÃ¡ and hydropower infrastructure 1980 2010 6 7 7 6 5 6 6 5 1980 1991 2000 2010 Percentage Year Proposed hydro Conventional hydro
92 Figure 3 6 . Foundation timeline of municipalities in ParÃ¡ and hydropower system. Source: IBGE 1980, 1991, 2010
93 Table 3 4 . Spatial model of urban population 2010 as a function of hydropower, quadrants, urban population in 2000 and total population in 2000 Variables Coef. Std. Err. z P>|z| [95% Conf Interval ] Urban Population 2010 Hydro 0 Base Hydro 1 . 0 848297 .0 362134 2 . 35 0.0 19 * . 0 1 39528 . 1559066 Year 1991 Base 2010 . 1659696 . 054682 3 . 0 4 0. 002* . 0587948 . 27 3 1444 Urbpop_2000_th .0042423 .0008001 5.30 0.000 * .0026741 .0058104 Urban Population 2010 W Hydro 0 Base Hydro 1 . 4080889 . 3281122 1 . 24 0. 214 . 2349992 1 . 051177 Year 1991 Base 2000 . 0797963 .0777658 1. 03 0. 305 . 072622 . 2322145 2010 . 0042971 . 0 816567 0 .0 5 0. 958 . 1557471 . 1643413 Urbpop_2000_th .0042423 .0008001 5.30 0.000 * .0026741 .0058104 Indirect Hydro 1 .0005233 .0024966 0.21 0. 834 . 0043699 . 0054165 Quadrant Southeast .0009241 . 0043774 0 . 21 0. 833 . 0076555 . 0095037 South west .000256 . 0013437 0 . 19 0. 833 .0023776 .0028895 Northwest .0002156 .0009085 0. 24 0. 812 . 0019962 . 001565 Totpop_2000_th .000034 . 0001599 0 . 21 0. 832 . 0003474 . 0002794 Urbpop_2000_th .0000415 . 0001954 0 . 21 0.832 . 0003414 . 0004244 Total Hydro 1 .0482521 .0157963 3.05 0. 002 * . 0172919 . 0792122 Quadrant Southeast .0945151 . 0178287 5 . 30 0. 000 * .0595715 .1294587 South west .0260094 .0242013 1.07 0. 283 . 0214243 . 073443 Northwest .0246607 . 0306825 0 . 80 0. 422 . 0847974 . 0354759 Total Population 2000 th .0035085 . 0007862 4 . 46 0. 000 * . 0050494 . 0019675 Urban Population 2000 th .0042838 . 0008052 5 . 32 0. 000 * . 0027055 . 005862 Notes . * p <0.05.
94 CHAPTER 4 URBAN EXPANSION IN A RESOURCE FRONTIER : A MUNICIPAL LEVEL APPROACH S ignificant changes in the Brazilian Amazon started less than a century ago . T he r ainforest went from an inaccessible and unproductive mass of forest to a massive political, economic , 1 and develop the area. The development model based on natural resource exploitation has been producing unforeseen outcomes at considerable environmental expenses . E xpansion of urban areas , increase d urbanization, loss of forest areas, and increase d agricul tural activities. The expansion of urban areas and increase in urban population throughout the Amazon is reshaping boundaries, triggering constant land cover and land use transformation . It shows no signs of retreating (Ana ClÃ¡udia Duarte Cardoso et al., 2013; Gomes & Vergolino, 1997; Mitschein et al., 1989; Sathler et al., 2010; Trindade, 2013) . As a worldwide p rocess , (Lefebvre, Bononno, & Smith, 2003; UNDESA , 2018; Seto & Shepherd, 2009) , the ub iquitous characteristics of urbanization need not erase the heterogeneity and singularity of local processes if viewed carefully ; h owever , much research is needed on urbanization of the Amazon 2 . Much of the spatial reconfiguration research has been devoted to deforestation as the main representation of physical changes (Davidson et al., 2012; Lovejoy & Nobre, 2018; Soares Filho et al., 2006) . In most cases , urbanization was treated as a social phenomenon, rather than a physical one (Guedes et al., 2009; Monte MÃ³r, 2015; 1 The term occupy herein used does not reflect my personal view given that the Brazilian Rainforest occupation by traditional population dates to thousa nds of years (Denevan, 1992; London & Kelly, 2007) . Rather , I reproduce the term extensively used by the Brazilian Government in public policies for the Amazon mainly during the last half of the past century. 2 The term Amazon herein refers to the Brazilian Amazon
95 Perz, 1999; Thypin Bermeo & Godfrey, 2012; Trindade, 2013) . Nonetheless there seems to be a consensus that both physical and social processes are a result of the unique interaction between the forest and its context (Barbieri, Monte MÃ³r, & Bilsborrow, 2009; Bertha Becker, 2013; Monte MÃ³r, 2015; P. Richards & VanWey, 2015) . The spatial manifestation of urbanization in the Amazon is a reflection and a driver of changes in the use of space. Despite the importance of forest preservation and environmental issues . Given the intensive process of urban areas expansion reshaping the landcover and land uses in the Amazon, t he spatial expansion of urbanization should be accounted as an important element of the new forest reality. This research investigates the expansion of urbanization and the subsequent spatial reconfiguration of the c hanges in land use and land cover in Barcarena, m otivated by answering the broad question of how urbanization takes place in a forest frontier, in the form of urban land use expansion and land cover transition. The guiding premise relies on the fact that, due to the singular combination of elements and geography, nor the increase in population nor the expansion of urban areas in Barcarena are sufficiently fit to a traditional path of urbanization. This research developed land cover maps to identify landscap e changes through a period of 33 years, from 1984 to 2017. This spatiotemporal analysis focused on changes in land cover and use within the categories of forest, urban, bare soil, and agriculture to traces the extent of the transitions between these covers . Further , this research focused on the physical reconfiguration of space during urbanization to address urban morphology and verify whether it follows a pre established pattern
96 common to other places . The land cover maps also identify whether the Amazon u rban sprawl follows generic landscape patterns observed elsewhere. Study Area The municipality of Barcarena, in the state of ParÃ¡, is the site of notable economic forces and intensive urbanization (Figure 4 1) . The municipal geopolitical boundaries sum 1,310,588 km 2 . Barcarena near one of the two largest metropolitan regions in the Amazon , the BelÃ©m Metropolitan Region (BMR). There is an on going debate to formally incorporate Barcarena into BMR (Lima, Cardoso, & Holanda, 2005; Tourinho, Pinheiro, & Bello, 2018) . Linked to the energy grid from the TucuruÃ Dam, it houses one of the world's biggest aluminum industrial plant s. Located near the most significant metropolitan areas in eastern Amazo n, the municipality has seen its population increase by 5 00% since 1980 3 . L andscape has been reconfigured as a result of population influx. At the same time, Barcarena further catapults the urbanization begun in the Amazon in past decades as it contains many of the drivers impacting spatial and socioeconomic changes. Industrialization, exportation node, infrastructure, transportation, and hydropower grid are the primary development forces converging in Barcarena ( EstatÃstica Municipal de Barcarena , 2014; Monteiro & Monteiro, 2007; Moura & Maia, 2012) . L ocation also places Barcarena in global trade . T he Port of Vila do Conde is an import node in commodit ies export and global trading. Barcarena is a city of the forest connected to the world, with important intra regional and global performances. 3 Include population estimates for 2017 (Tourinho et al., 2018) .
97 A s a municipal unit, Barcarena is a heterogeneous layer. I ts large area includes undisturbed natural areas and allows a variety of social uses of space . It also evidences an example for blurred frontier between the urban and the rural landform and use in the Amazon. The diverse use of space in Barcarena includes major industrial zones , solid residues deposit , proce ssing facilit ies , a Company Town settlement, a large scale port, local small scale s , and the central area that concentrates the municipal headquarters . Barcarena is 300 km from the TucuruÃ dam, and is spread across five different municipalities. The munici pality is n ot located in the surrounding areas reservoir to be considered directly impacted (A neel , 2001; Moretto, 2016) . However, t his does not prevent establishing indirect hydropower impacts on urbanization. I propose to rethink and expand the impacts of dams beyond where the energy is produced or processed to include locations where it is also being received. Thus, establishing a connection between the hydropower s ystem and indirect urbanization impacts. Theoretical Ground for Urbanization, Urban Morphology, and Urban Land Change Global u rbanization has reached 54% in 2014 . World dominant economies show an even higher urban population : 73% in Europe, 81% in the US, 85% in Brazil , a nd more than 70% in the Brazilian Amazon. Following the urbanization trend, global rural population is expected to decrease from 3.4 billion to 3.2 billion people between 2014 and 2050 (United Nations, 2014) . Urbanization is a process of population migration from rural lands dominated by agricultural activities to areas where economic activities are dominated by manufacturing and service (Montgomery, Stren, Cohen, & Reed, 2013; United Nations, 2014) . Urbanization, therefore, is more than spatial . It involves aspatial aspect s as s ocial processes manifest ing in vari ous forms and scales.
98 In most cases, urbanization manifests in spatial forms. The city or urban space becomes central , as processes unfold, and is the unit base for different scales of measurement. M etropolitan area is a synonym for city or urban area . I ntrametropolitan scale add resses individual cities, while intermetropolitan scale can include a network of cities. This research c onsider e s the growth of urban space as equivalent to the morphology of urbanization, occurring at an intrametropolitan scale. A variety of theories are used to classify urban morphology, perhaps because of divers e outcomes relat ed to physical and social characteristics space. Using a broad scale of forms, morphology can usually take five shapes: sprawl, infilling, extension, linear development, and large scale projects. Urban sprawl is scattered suburbanization. Infilling is the creation of new developments in urban areas with the past use deactivated (railroad yard, waterfront, industrial brownfield). Urban expansion through large scale project s occurs wh en new zones are created to provide for airports, ports, and industrial sites. Extension is the standard form of urban expansion, wherein land adjacent to urban use becomes urban. Linear development is an extension driven by transportation infrastructure a nd shaped by its corridors (Camagni, Gibelli, & Rigamonti, 2002; JapiassÃº & Lins, 2014) . 4 4 Three of the five urban expansion forms are usually used interchangeably. Linear development, urban extension, and urban sprawl can be t reated as synonyms. Some analysts refer to sprawl as the generalized form of an urban extension. However, sprawl, especially in the English academic literature has capture d the headlines for being a phenomenon typical of the North American urbanization pat tern, most often linked to a negative connotation that connects vigorous urbanization unrelated to population pressures to unnecessary use of land. Urban sprawl usually produces less dense ly populated areas and is linked to the excessive spatial growth of cities (Brueckner, 2000; Brueckner & Fansler, 1983; Ciscel, 2001) . Given this negative connotation and avoiding discussing the economic merits of this urban expansion format, urban sprawl constitutes the classic model of the United States suburban based development . It can also be seen elsewhere, although not as a dominant phenomenon. The sprawl phenomenon in Europe, although restricted by land limitation among others, has been addressed as a concern of scholars and government s alike (European Environment Agency, 2006; Rics et al., 2007; United Nations, 2014) . In Brazil , on the other hand, not only the classic concept of sprawl is not easily multiplied as a form of urban expansion , but also other distinguishable formats are identified such as between intra and extra urban occupation and municipal boundaries (JapiassÃº & Lins, 2014) .
99 Classic theories of urban expansion assume that urban areas develop as a succession to agricultural use of land (Godfrey, 1990; Godfrey & Browder, 1996; Muth, 1961; Simmons et al., 2002; Watkins & Perry, 1977) . The classic theory of von ThÃ¼nen implies an evolution in the pattern of agriculture to a form that maximizes location rent (ThÃ¼nen & Hall, 1966) . This foundational concept was later adapted to urban areas by Alonso (1977) and by Walker (2001) to explain urban sprawl. Turner's (Turner, 1920; Turner & Bogue, 2010) classical yet recently revisited model of frontier expansion was used to explain the process in the Midwestern United States, where urbanization did involve succession to agricultural land use across a broad region. This model has also been applied around the globe, including other parts of Brazil (L. A. Brown et al., 1994; Bylund, 1960; Hudson, 2018; James, 2018) . Location theory helped to ground spatial analysis and geographical elements in econo mics (Fujita, 1996; Fujita et al., 1999; Walker & Richards, 2013) . To explain urban e xpansion and the role of agriculture in this transition requires understanding the connections among urban and agricultural land, population, and activities. F ew studies have addressed links between urbanization and land cover change. Urbanization in a re source frontier was analyzed aspatially . Studies claimed that urban land use models (Becker 1990; Godfrey 1990; Godfrey and Browder 1996; Guedes, Costa, and BrondÃzio 2009) . Becker said urbanization in the Amaz on is not a result of agriculture : instead, the "frontier is born already urbanized." Thus, cities emerge directly as cities and not successor s of agricultural lands (1990, p.44) . Although this concept accounts for the urbanization process, it fails to consider spatial expansion .
100 Thus , urbanization as a spatial process in the Amaz on lacks empirical analysis . If urbanization is a distinc t spatial process, failing to consider its morphology leaves a gap in understanding of physical components of the Amazon . Mapping urbanization in the Amazon region requires thinking outside box. R ainforest cities are still poorly defined . The Amazon is a trend y research topic, but few studies focus on the increasingly populated urban zones (Browder, 2002; Guedes et al., 2009 ; Perz, 1999; Simmons et al., 2002) . And fewer relate increased urbanization in Barcarena as an outcome of investments and infrastructure elsewhere. P ioneer urbanization studies of the Amazon include the book Rainforest Cities (Browder & Godfrey, 1997) . Its main theory assume s principles are independent and not originat ing from a single maste r principle. N ine independent principles compos e the conceptual framework of Rainforest Cities. As seen through the principles of urbanization, Barcarena (1) is a heterogeneous space; (2) has a configuration of settlement systems that is irregular and poly morphous, disarticulated from any single master principle of spatial organization; (3) its urban growth is fundamentally disarticulated from agricultural development; (4) its dynamics of rapid urbanization are disarticulated from the process of regional in dustrialization; (5) its urbanization is variously linked to economic forces operating at the global level, but is not subordinated to a world economic system; (6) is a technological crossroads link ing specific activities to global circuits of information and exchange; (7) is largely a geopolitical creation but remains politically disarticulated within the centralist state; (8) its established dichotomous categories of rural and urban become problematic because of complex and regionally heterogeneous local migration patterns; and (9) environmental change,
101 including tropical deforestation processes, are increasingly mediated by regional, urban based interests. Amazon municipalities, the municipalit y fits into the main profile described by Browder and Godfrey (1997) . Histor ical and Population C ontext Barcarena fate was shaped by the discover y of large bauxite deposits in OriximinÃ¡ , 600 km away. Bauxite is a mineral compound with high aluminum concentration. Refining bauxite into aluminum is highly water and energy de pendent. Mining extraction dictate d the vertex of development policies from the 1970s (Serra & FernÃ¡ndez, 2004; WCD 2000) . Since then, mining is responsible for socio, political, economic and spatial changes in the state of ParÃ¡. I n 1973, the first petroleum crisis increased the price of energy worldwide and jeopardized the technological and aluminum dependent Japanese industrial production. The aluminum producti on chain is one of the highest energy consuming industries. Along with th is international crisis , discover y of sizeable bauxite reserve s in the northeastern Amazon led to the opening of an industrial plan t to process bauxite into alumin a in Barcarena. Up to this point, Barcarena had no prospects of becoming an industrial hub (Pinto 2012; 2000). The choice for Barcarena relies on geographical advantages . D istance and access to the bauxite mines, and waterway convergence of Amazon rivers and the Atlantic Ocean allowed for a large capacity port . In addition, the site has abundant access to water for industrial use and it is near an urban center . Finally, Barcarena offered available and unoccupied land (Nahum, 2008; Pinto, 2012; M. G. da C. Tavares, 2011; WCD, 2000) .
102 grew 57%, and Amaz on population grew 1 34 % , above the national average (Figure 4 2). increased 116%, representative of the regional pattern. population grew by 399%. A suddenly industrializ ed economy with a significant population increase makes Barcarena a n interesting case of land use changes . Comparing population increase in different geographic temporal scales shows more intense urbanization in Barcarena (Figure 4 3) . Amazon opulation growth is higher than Brazil , indicating interregional migration . I n 1980, small town population was below national and regional levels , and mostly rural . In 1991, population increased by 129% , despite Brazil facing economic recession and decreas ed growth rates (IBGE, 1970, 2 012) . Currently, there are five large scale mining plants in the municipality. Urban V ersus Rural Population : A Dichotomy Boundaries between urban and rural areas in the Amazon are blurred. The dichotomy passes through a more in depth discussion of the meaning of rural and urban, according to local perspectives (Eloy & Brondizio, 2015a; Guedes et al., 2009; Rego, 2015; Schmink & Wood, 1992) . population defies urbanization trends as rural population continue s to increase. Given Amazon urbanization rates , i population surpassed rural as industrial activity is a stimuli for urban migration. Higher i ncrease in urban populations is observed even in municipalities with no industrial activities. Part ly , this can be explained because the outdated sectorial classification by t he Brazilian Institute of Geography and Statistics (IBGE) . In Barcarena, 90 municipal sectors are considered rural and 34 sectors are urban. The IBGE sectorial classification
103 has r emained static for the last three census periods (1991 to 2010 ) (Tourinho et al., 2018) . Some sectors are considered rural nonetheless the current spatial configuration points at urban land use and cover. Carmo ( 2015) points to the company town Vila dos Cabanos as an example of population accounted as rural when, in fact, it is an urban environment . Despite industrialization, Barcarena labor demand d oes not absorb the population influx . Intramigration leads people to occu py areas classified as rural, causing urbanization to spread. This sector of the population is caught up between the dichotomy of rural and urban lifestyle, nonetheless, driving urbanization to more remote areas . Although this study considers the links be tween the spatial configuration of urban as the main focus , it also acknowledges that spatial reconfiguration of urban areas is, in most cases, directly linked to the increase in urban population. Such dichotomy in s for a reasse ssment of urban and rural boundaries. Spatial R econfiguration , a B road A pproach for C onnections : 30 or 300 Mega infrastructure ventures commonly encompass transportation and energy generation. Such ventures also function as the primary sponsor for economic development (Little, 2014) . T his is true for the large scale dam of TucuruÃ, wh ose energy production made viable the development of an industrial hub in Barcarena. I mpacts of the dam extend beyond industrialization support and have been widely discussed (Campos, 2011; de Faria, Jaramillo, Sawakuchi, Richey, & Barros, 2015; Latrubes se et al., 2017; Moore et al., 2010; Soito & Freitas, 2011; Tundisi et al., 2014) .
104 Nonetheless, many of the impacts are unaccounted for (Laurance, 2019) . Undercounted socioecological impact s , including on indigenous and t raditional population livelihood has not been restricted to the Amazon (Athayde, 2014; Fearnside, 2016) . The l ack of social and environmental data (Hyde et al., 2018) implicates impact monitoring . In addition, a large number of dams started operating before collecting baseline data (Castello et al., 2013) . Frameworks to assess the require a multidisciplinary approach given the multiple layers of interactions, from biophysical to socioeconomic and geopolitical (P. H. Brown, Tullos, Tilt, Magee, & Wolf, 2009) . There is challenge in classifying and defin ing extent s of direct and indirect impacts of dams (Little, 2014) counted based on direct physical connection to the reservoir, downstream and upstream, failing to include interactions of spatial and socio economic networks outside the direct connection . F ew studies link Barcarena to possible impacts of the TucuruÃ Dam (Fe arnside, 2016; D. R. De Lima & Mota, 2009; Pinto, 2012) . State research agency Fapespa included Barcarena among the municipalities related (not impacted by) to the TucuruÃ Lake Region to proposed integrated policies (Costa & Pires, 2015) . Nonetheless many studies acknowledg e TucuruÃ Dam energy supply is vital to Barcarena s industrial activities (Moura & Maia, 2012; Nahum, 2006; M. G. da C. Tavares, 2001; Trindade JÃºnior & Barbosa, 2017) . In most cas es, Barcarena social and environmental impacts are restricted to triggers of industrial activity within the municipal boundaries (Monteiro, 2006; Monteiro & Monteiro, 2007) ; rarely are they related within the outreach of direct or indirect impacts by TucuruÃ .
105 Rethinking the horizon of the s even more relevant when consider ing that hydroelectricity accounts for industrial processe s consume nearly half of that energy . N ational energy and development policies continue to rely on building more dams in the Amazon ( Almeida Prado et al., 2016; Brazil, 2018; IIRSA, 2011; Timothy J. Killeen, 2007) . By reinforcing the already mentioned connection between Barcarena and the TucuruÃ dam, I propose to expand the notion of space and its configuration, adding dimensions bey ond topographic, and contesting the notions of far and near. Instead, the focus on socio spatial interactions and consider the relational space as an element that connects Euclidian distant elements (Harvey, 1994; Warf, 2010) . TucuruÃ Dam serves as the agent of the sending system, and Barcarena as an agent of the receiving system, with flows of energy, investments, and infrastructure that culminates in urbanization increase in a certain way, expanding the framework of teleconnections (Munroe, McSweeney, Olson, & Mansfield, 2014; Karen C Seto et al., 20 12) . T he 30 kilometers between Barcarena and BelÃ©m, and the 300 kilometers between Barcarena and TucuruÃ dissolve s in the face of other factors . E nergy, rather than mineral resources or labor, is the main component of the industrial process (Fearnside, 2016) . Urban Land Expansion According to location and urban theories (Alonso, 1977; Jacobs, 2016) , urban expansion usually happens through encroachment of agricultural land. Classic location theory considers land rent as a function of distance from the city center , and accounts for agricultural use preceding urban land expansion . Urban land expansion causes agricultural land to expand further from the city . Therefore, urban encroaches onto
106 agriculture that encroaches on the forest. It can be inferred that there is no direct transition from urban ont o the forest. Data and Method s This study used remote sensing and GIS to generate Land Cover classification and a Land Cover Change (LULC) to provide land cover information (Chen, Powers, de Carvalho, & Mora, 2015; De Carvalho & Szlafsztein, 2018; Herold, Roberts, Gardner, & Dennison, 2004; Jensen, 2015; Rindfuss, Walsh, Turner, Fox, & Mishra, 2004; Karen C. Seto & Christensen, 2013) . A supervised classification was performed t o produce the spatial temporal land cover change . Image r y w as selected f or the years 198 4 , 1999 and 2017 , a 15 and 18 year interval . In total , there were 33 years of land cover assessment. These dates cover the star t of the dam operations , covers a mid period after a steep population increase, and extend s to recent years . Data This study used cloud free satellite imagery from the Landsat P roject for 198 4 and 1999 (USGS, 2019) and Sentinel 2 for 2017 (ESA, 2019) to generate a thematic cover map of the area. A se arch for cloud free images was the reason for using two different satellite sources. For 1984 and 1999 it was used 2 images from Landsat 5, corresponding to Path 223 and 224 and Row 61 of the Landsat images with 30 meter resolution, mosaicked to match stud y area boundaries, obtained from the Landsat open access global land cover services ( www.glovis.usgs.gov ). For Sentinel 2, image correspond to tile 22M. Sentinel image s meter re solution, obtained from the Satellite Sentinel Project from http://www.satsentinel.org . Before image preprocessing, the images were subset to match the study area shapefile extracted from the set of municipal boun daries of the state of ParÃ¡ (IBGE,
107 2019) . Images from Google Earth (Google Earth, n.d.) for 1984, 1999, and 2017 were used for each land cover type for image validation. Land Cover Classification The aim was to examin e the expansion of urbanization , a ssuming urbanization i s the expansion of urban areas . Five main land covers were detected in Barcarena: urban, bare soil, agriculture, forest , and water (Table 4 1 ) . Images from Google Earth (Google Earth, n.d.) were used to better assess the land cover classes and to produce training sample data as part of the supervised classification (Pabi, 2007; Suribabu, Bhaskar, & Neela kantan, 2012; Wondrade, Dick, & Tveite, 2014) . Between 30 and 60 training samples (TS), corresponding to points and polygons, were collected for each thematic class and for each year of images, created in Google Earth images for 1984, 1999 and 2017 (Jensen, 2015) . The TS is data collection process also consider ed visual interpretation of the data based on image comparison and dates and previous knowledge of the area. A nalysis of TS allowed the ide ntification of statistical similarities between the spectr al signatures, contributing to more accurate thresholds of local land cover classification. S upervised classification with maximum likelihood algorithm was applied to each image (Dean & Smith, 2003; Lu, Mausel , BrondÃzio, & Moran, 2004; Yuan, Sawaya, Loeffelholz, & Bauer, 2005) . In addition to the raw image bands, additional layers were also created . A Soil Adjusted Vegetation Index (Huete, 1988) , Tasseled Cap transformation (Crist & Cicone, 1984; Healey, Cohen , Zhiqiang, & Krankina, 2005) and a Principal Component Analysis (PCA) (Deng, Wang, Deng, & Qi, 2008; Zabalza et al., 2014) were all created.
108 S upervised classification based on the maximum likelihood algorithm is approp riate given the purpose of this study to survey urban sprawl . T he focus was on s patial extent of built u p areas . M aximum likelihood classification has been widely used , as it reduces the data require d (Hassan, 2017; Jensen, 2015; Yuan et al., 2005) . N onetheless , it deliver s great potential to extract informat ion from the urban scenes. Erdas Engine was used to apply the maximum likelihood algorithm and thus created the five thematic classes . Results and Discussion Barcarena Land Cover Classes Forest The large st area land cover class was forest. F orest cover represents vegetation in any stage, other than agricultural use. The areas have low seasonality and low variation in canopy density. Barcarena has forest cover intertwined with other uses across its entire area. A dense forest area can be near urban areas and industrial sites ; or intertwined with agricultural land. Some stages of agricultural areas may be confused with forest. Forest cover was visually easily distinguished from agricultural use based on texture and intensity of green on Google E arth image ry for 2017, with clear resolution . Natural forest has irregular shapes, and agricultural activi ties have more defined boundaries. To distinguish agriculture areas with vegetation and forest for lower resolution images in 1984 and 1999, forest cover was divided into subcategories of dense and sparse forest . Next, they were stacked into one single category . Agriculture Herein, agricultural land use and cover encompasses agriculture, and pastureland. Land use for crops at all stages, pastur es and recently deforested areas
109 are classified under agriculture to provide training and test samples. An a dded challenge consist ed of distinguish ing the class of bare soil from agricultur e as it can be a step for the cultivation process, or to pasture us e. In Barcarena, there were many patches with some vegetation surrounded by forest , usually in isolated areas . Such areas usually account for deforestation. Deforestation may happen to extract logs and wood . In some cases , area s go through natural or manag ed secondary regrowth and reforestation or migrate to agricultural and pastureland . Using this guideline to provide training and test samples, agricultur al areas are considered as distinguishable from forest at all other stages, independently from the prec eding or proceeding stages. Bare Soil As an industrial site, Barcarena presents some open areas for industrial residues. These areas var ying land cover depending on the stage, from open plain soil to chemical deposit. Mapping bare soil becomes relevant as outdoor industrial deposits expand presence indicates changes in the land use and cover dynamics that include expansion of industrial activities and urban land use. T o map these sites expansion in previous years, the training points for bare soil classification were expanded to match the different cover stages, meaning it would eventually pick up soil as in unpaved roads. Bare soil also occurs as a stage for agricultur al use and urban land expansion. Bare soil training and testing samples represent open, non vegetated areas and also encompass industrial deposits. Over the years, the industrial deposit areas have been extended as the aluminum chain activity also increase d. Bare soil in industrial sites usually present high concentration of bauxite or kaolin clay, presenting distinct spectral signatures that
110 were combined into a single class that includes other types of open soil that are neither agriculture nor urban. Urb an Mapping urban land expansion is complicated in Barcarena because other land uses, including agricultural activities, are often intertwined with urban areas. The class that stands for urban cover encompasses built up areas in general. Conversely, the pro file of different spectral signatures and urban materials adds an extra challenge in isolating those areas. More specifically, the built structures comprise mostly of different roofing materials, some vegetation and/or gardens, unpaved roads (which provide soil like signatures), and paved roads, thus providing mixed spectral signatures when considering an overall urban area. The residuals and dirt particles from soil, industrial residues sites and unpaved roads accumulate on top of the roofs, made usually o f either ceramic or metal, causing additional mixture of signatures, mixing pixels that might correspond to bare soils instead of urban or buildup areas. Water Water covers a significant area in Barcarena. Its boundaries include a large portion of river i n the northern part. The area also presents some rivers and creeks running inside the territory. W ater is not included in trajectory maps , analysis, nor considered as part of the total land cover assessment , as it presents little variability. Accuracy As sessment Since image heterogeneity presents a major challenge in land cover classification, accuracy assessment was carried out in combination with manual interpretation of Landsat images, topographic maps, google earth high resolution images, and random s tratified sampling. For each image b etween 150 180 points were
111 created using the equalized stratified random function in ArcMap Pro (Hassan & Southworth, 2017; Petit et al., 2001) . The number and spatial distribution of verification point s were varied due to dominance of certain land cover classes . The testing points were cross checked and validated manually through overla ys with the true color original Landsat and Google Earth imagery corresponding to the year and approximately the month of collection. y, parametric Kappa coefficient ( Table 4 2 ) . The overall classification accuracy was 83 9 6 %, with kappa values ranging between 83% and 96 % . The overall accuracies for 2017 presented the highest value , indicating higher qual ity in imagery. The Kappa statistics for 2017 was also the higher, 95 %. The lowest overall and kappa accuracies were measured for 1999 . Water presented the highest user and producer accuracy in all three years. ies for u rban were over 9 5 %, an d accuracy var ied from 76 to 92 %. Agriculture presented a reduction in Bare soil presented the lowe st user accuracies in 1984 and 1999. Bare soil cover can often be confusing and perhaps the most challenging task in image classification, particularly in Barcarena, given the similar spectral reflectance as stages of agricultural land use , unpaved roads and open areas within the urban environment . Li quid content from th e industrial residue deposits may also be mistaken as water pixels . Forest presented consistent levels of user accuracy , and Land Cover Change and Morphology Barcarena is a dynamic area, with changes in land use a nd land cover throughout the area. U rban land use is mostly i n the northern portion of the territory,
112 around its headquarters and industrial plants . Paved and unpaved roads link the other sparse urban cores . Another area to the west has a higher concentration of urban land use . Forest is the main coverage throughout the territory. L and cover maps (Figure 4 4 ) show agricultural use as sparse . In 1984, without considering water bodies as a land cover, Barcarena was 87% forest cover (690 km 2 ), 2% urb an land cover (15 km 2 ) , 8% agricultural use (63 km 2 ), and 4% bare soil (28 km 2 ). In 1999, all coverages change slightly . Forest cover decreases slightly to 85%, while urban slightly increases to above 2%. Bare soil increases to 5.5% (38 km 2 ), and agricultu ral use increases to over 9% (65 km 2 ). In 2017, more significant changes are observed in all, but agriculture. Forest cover decreases to 78%, urban cover expands to 6%, and bare soil to 7%. Agricultural use, instead, presents a small decrease to just below 9%. From 1984 to 2017, t here was a loss of 75 km 2 of forest, and an increase in 32 km 2 in urban area, 30 km%, representing 75 km 2 of loss of green coverage. Urban land use expands to almost 9%, 32 km 2 o f added area. Similarly, bare soil expands 30 km 2 , while agriculture, even though presenting an overall increase from 63 to 70 km 2 , presents the smallest increase in percent of total area (7%). There was a slight variation in area across the years , most probably because of non significant errors and reso lution differences . Urban land expansion in Barcarena is polymorphous. Extension is observed i n the areas surrounding the municipal headquarters and company towns. New urban areas are adjacent to the occupation in 1984. Nonetheless, in 1999 and 2017, the focus of urban sprawl is i n the central and southern part of Barcarena. The spatial pattern also indicates these urban areas showing some linear development along roads.
113 Concluding Remarks Population increase has restructured Barcarena in the last four dec ades. T he dynamics , outcomes and drivers of urbanization and demographic concentration ought to be included in the priorities of topics that help understand the complex reality. The temporal frame provided a reliable baseline to understand some of the impacts of an increasing hydropower system. Barcarena serves as an example of the diversity of the Both population and land cover showed significant changes from the 1 980s to the current time. Th e main proposition was that urban land conversion into forest and agricultural area would surpass agricultural activities due to the accentuated urbanization process. Land cover trajectory maps showed that t he classic theory of urban land expansion (Alonso, 1977; ThÃ¼nen & Hall, 1966) still applies to the urbanization process in Barcarena. In fact, there has been an expansion of agricultural land cover. Yet, when using the temporal changes, the prediction pattern show that urban land area might surpass agriculture (Figure 4 5 ) . Linking the hydropower system with spatial and populational changes in Barcarena seems a logic connection due to the destination of energy, used in the industrial process es that takes place in the municipality. This study has demonstrated that development processes happening in Barcarena are complex, and spatial changes occurring as a consequence of the urbanization process and the overall population increase are still not easily assessed, even considering the five main land cover classes.
114 Aiming at Amazon s environmental preservation, it appears prudent to recognize the vital role of the local population and the urbanization tendency Barcarena presents . The impacts of hydropower system s can unfold in cascade effect onto urbanization processes in place s at d istant over space but , nonetheless connected by the conduction and destination of energy. These regions should be considered as part of the impacts new dams may have on distant places. Mostly, connection such as energy destination should be taken into acco unt when assessing hydropower impact on urbanization within This study thus strength s the connections and drivers of urbanization beyond what is most often taken into account. As the Amazon acquires a n urban profile, invest igating the impacts and triggers of these processes contribute s to a better assessment of local reality and spatial reconfiguration. Although Barcarena is an outlier, combining major industrial activities and transportation nodes, urbanization shows no sig n of retreating. Barcarena shows how those processes are reshaping this region.
115 Figure 4 1. Study a rea : Barcarena and State of ParÃ¡ and the Brazilian Amazon
116 Figure 4 2 . Total p opulation increase percentage by decade in Brazil, Amazon, ParÃ¡ and Barcarena (Source: IBGE , 1980, 1991, 2010 ) Figure 4 3 . Barcarena urban, rural and total population 1980 2018 (Source: IBGE 1980, 1991, 2010) 28% 62% 60% 12% 21% 52% 48% 129% 15% 26% 19% 38% 12% 23% 22% 58% Brazil Amazon ParÃ¡ Barcarena Population Increase Percentage 1980 1991 2000 2010 0 20000 40000 60000 80000 100000 120000 140000 1980 1991 2000 2010 2018 Population Years Rural Urban Total
117 Table 4 1 . Land c over c lassification Land Cover Description Urban Built up , residential and commercial, paved and unpaved roads, most metal and ceramic roof Agriculture Agriculture and activities in all stages and animal husbandry pastureland ; m ain crops are banana , cacao, coconut, orange, lemon, papaya, and black pepper Forest Forest cover in stand s for any other vegetation other than agricultural Bare Soil Soil without coverage, industrial soil and residues deposits of bauxite ore and kaolin clay Water Water bodies Figure 4 4 . Barcarena l and c over c hange from 1984 to 2017 per class
118 Table 4 2 . Accuracy a ssessment for 1984, 1999, and 2017 with users accuracy (UA), producers accuracy (PA) , overall accuracy (OA) and Kappa accuracy (KAPPA) 1984 1999 2017 UA/PA UA/PA UA/PA Urban 96/79 95/76 95 / 92 Agriculture 96/88 95/83 92 / 96 Bare Soil 46/ 100 40/89 98 / 98 Water 100/8 9 100/77 98 / 98 Forest 96/ 85 85 / 100 96 / 96 OA 87% 83% 96 % KAPPA 83% 79% 95 % Figure 4 5 . Prediction of u rban and a griculture l and c over c hange 0 2 4 6 8 10 12 1984 1999 2017 Percentage of Total Area Urban Agriculture
119 CHAPTER 5 CONCLUSION Urban expansion in Brazil is an intensive process based on rural out migration. However, empirical accounts of urban expansion in the Amazon appear to challenge existing labels for various reasons. As a relatively recent phenomenon, urbanization in the AmazÃ´nia faces the unique context of a forest frontier. The geographical, environmental, historical, and social characteristics that compose this frontier are still responding to unprecedented spatial changes. Urbanization in the Brazilian Amazon has intensified in recent de cades, with urban population rates surpassing 8 0% and over 400 new municipalities created in the past forty years. The Amazon has presented intense spatial reconfiguration the in the past decades that per passes but is not limited to the loss of forest cover. Under the reginal dynamic, there was a significant population increase and a shift in the geopolitics of the region, with actions that stated in the second half of the last century, led by governmental approach to reordination on how the space and r esources were to be used and explored. Urbanization in the Amazon became a dominant process to reshape spatial configurations. In this work, Chapter 2 shows that hydropower and urbanization rates present clear connections. At a regional scale, stronger imp acts on urbanization are felt in the southeastern Amazon, where there is the higher number of hydropower system elements, including the oldest large scale dam in operation, and its transmission lines and substations. This results from this chapter will all ow for future research to concentrate on adding other variables to the statistical analysis, as well as search for a model that better explains the relationship between variables at a unit of analysis level.
120 In Chapter 3 approached a state scale, and also presents an overall result of a greater impact on the percentage of urban population in municipalities presenting some form of physical infrastructure of the hydropower network is greater than on those without. The longer a municipality is directly connect ed to a hydropower system structure, the higher is the effect on urbanization. Furthermore, this work also shows a connection between the creation of new municipalities and the hydropower infrastructure network. The results from this Chapter will allow for the next step s of research considering: a thoroughly revisi on of the proposed and conventional categorical variable, with updated spatial layers incorporated; and the inclusion of a buffer zone to establish distance to roads . Chapter 4 presented spatial c hanges and urbanization expansion at a municipal scale not directly considered affected by dams. This research has shown significant losses of forest and changes in land use and land cover , pointing at expansion of urban areas and gradual reduction of agri cultural land . Land conversion from forest into urban and industrial use are significantly higher than agricultural conversion. As the municipality's population growth, chances are there will be the environmental disturbance of natural ecosystems at least along the areas the population is spread. This Chapter will allow for the next steps of research to be concentrated on building a trajectory model to predict land cover changes; and include additional data with imagery for five year interval . Overall, this study has evidenced a heterogeneity within the urbanization process in different scales in the Brazilian Amazon, in what is here been called Amazons within the Amazon. Above all, has also demonstrated the importance of urban expansion and
121 urbanization inc reasing rates for the spatial reconfiguration of the rainforest. While attempting to isolate the hydropower system as single driver of the urbanization process, it is acknowledge d that such a complex outcome deviates for a complex and diverse set of other Facing the energy demand and the Amazon hydroelectrical potential, investments in amplifying the hydropower system are most likely continue to increase. In most cases, as a direct and indirect source of environmental disturbance an d loss of natural coverage, while encouraging an unplanned and massive urbanization.
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144 BIOGRAPHIC AL SKETCH Roberta MendonÃ§a De Carvalho was proudly born and raised in the largest city in the Brazilian Amazon BelÃ©m, ParÃ¡. She received her PhD from the University of Florida on urbanization in the Amazon. Her current research interests embrac e urbanization as a worldwide process and how it unfolds at the local scale. Her interdisciplinary background gathers social, physical and environmental geography applied to urban studies in a multiscalar approach. She uses Remote Sensing to filter urban s patial reconfiguration as well as the loss of natural areas within city boundaries. Roberta has also worked in private, government and non governmental sectors in positions that advocate for local matters related to the environment and development. She hol ds a BA in Business Administration and an MA in Natural Resources Management and Local Development in the Amazon from the Federal University of ParÃ¡. She is a faculty in the Urban Studies Program at the University of Pittsburgh. Roberta is also a member of the Amazon Dams Network, has won the Ruth McQuown Scholarship Award 2019 and is a former Water Institute Graduate Fellow Cohort from 2015 .