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Modeling High-Resolution Gridded Population Surface in Alachua County, Florida

Permanent Link: http://ufdc.ufl.edu/UFE0044292/00001

Material Information

Title: Modeling High-Resolution Gridded Population Surface in Alachua County, Florida
Physical Description: 1 online resource (71 p.)
Language: english
Creator: Jia, Peng
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: alachua -- dasymetric -- florida -- gainesville -- gis -- population -- rts -- superfund
Geography -- Dissertations, Academic -- UF
Genre: Geography thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The demand for small-scale population data is increasing in a variety of fields. Spatial techniques including Geographic Information Systems (GIS) and Remote Sensing (RS) have been more often used for the population study in recent years, which makes it possible to conduct the dasymetric mapping, a vital technique transforming a traditional choropleth map into a continuous statistical surface with values at all locations rather than solely aggregated values over the existing units. In this study, I adopted the Heuristic Sampling Method and detailed property types of parcels from the Department Property Tax Data Files in 2010 as ancillary data, to disaggregate the population counts from U.S. Census 2010 onto a quadrilateral grid composed of 30m×30m cells covering the Alachua County, Florida, named High-resolution Gridded Population Surface (HGPS). At last, the HGPS has been applied to two case studies. The first one is to estimate the numbers of potential passengers within the service areas of bus routes in Gainesville, Alachua County. The second one is to estimate the numbers of population potentially exposed to the contamination at Cabot-Koppers Superfund Site in Gainesville. By comparing the results from HGPS and traditional population grids produced by areal weighting method (AWM), with the actual numbers from relevant records, the HGPS was proven to be more reasonable and valuable. HGPS is an important step forward towards solving the Modifiable Areal Unit Problem (MAUP) and expected to serve as a more accurate denominator in various research fields, such as public health, crime analysis and so on.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Peng Jia.
Thesis: Thesis (M.S.)--University of Florida, 2012.
Local: Adviser: Qiu, Youliang.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044292:00001

Permanent Link: http://ufdc.ufl.edu/UFE0044292/00001

Material Information

Title: Modeling High-Resolution Gridded Population Surface in Alachua County, Florida
Physical Description: 1 online resource (71 p.)
Language: english
Creator: Jia, Peng
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: alachua -- dasymetric -- florida -- gainesville -- gis -- population -- rts -- superfund
Geography -- Dissertations, Academic -- UF
Genre: Geography thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The demand for small-scale population data is increasing in a variety of fields. Spatial techniques including Geographic Information Systems (GIS) and Remote Sensing (RS) have been more often used for the population study in recent years, which makes it possible to conduct the dasymetric mapping, a vital technique transforming a traditional choropleth map into a continuous statistical surface with values at all locations rather than solely aggregated values over the existing units. In this study, I adopted the Heuristic Sampling Method and detailed property types of parcels from the Department Property Tax Data Files in 2010 as ancillary data, to disaggregate the population counts from U.S. Census 2010 onto a quadrilateral grid composed of 30m×30m cells covering the Alachua County, Florida, named High-resolution Gridded Population Surface (HGPS). At last, the HGPS has been applied to two case studies. The first one is to estimate the numbers of potential passengers within the service areas of bus routes in Gainesville, Alachua County. The second one is to estimate the numbers of population potentially exposed to the contamination at Cabot-Koppers Superfund Site in Gainesville. By comparing the results from HGPS and traditional population grids produced by areal weighting method (AWM), with the actual numbers from relevant records, the HGPS was proven to be more reasonable and valuable. HGPS is an important step forward towards solving the Modifiable Areal Unit Problem (MAUP) and expected to serve as a more accurate denominator in various research fields, such as public health, crime analysis and so on.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Peng Jia.
Thesis: Thesis (M.S.)--University of Florida, 2012.
Local: Adviser: Qiu, Youliang.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044292:00001


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1 MODEL ING HIGH RESOLUTION GRIDDED POPULATION SURFACE IN ALACHUA COUNTY, FLORIDA By PENG JIA A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DE GREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2012

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2 2012 Peng Jia

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3 To my mom and dad who always support me unconditionally

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4 ACKNOWLEDGMENTS It is with a great deal of gratitude that I would like to acknowledge a group of individuals in the Department of Geography for my impressive graduate education experience. First, I want to personally thank Dr. Youliang Qiu, my thesis advisor, for helping me in large and small ways. Through working as his teaching assistant and discussing with him about researc h, he gave me both criticism and encouragement and looking back, I can see that I gained a lot I am proud to say he is not only an excellent advisor, but also a reliable friend. I would also like to thank Dr. Peter R. Way len and Dr. Liang Mao for serving on my thesis committee. Dr. Waylen was understanding and gave me support in both my academic and personal path Dr. Mao's logic al thinking and challenging questions made me never stop expanding and refining my ideas and research. T his thesis would not have been p ossible without their help and support. Further more I would like to thank Dr. Richard Rheingans in the Department of Environmental and Global Health. I met him at my turning point and I found my way that was really fit to me through working and discussing with him. Not only did he provide me a n excellent working atmosphere to do my research and complete this thesis he provided me with a flexible working schedule and the freedom to pursue my wide range of interests as well I wish to extend my sincere appreciation to Dr. Corene J. Matyas who first encouraged me to come to the University of Florida. Although developing different interest s from her afterwards her kindness consideration and serious instructions real ly helped me become accustomed to American ways of thinking and communicating since I arrived at the U.S., which I will forever be grateful for

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5 Special thanks to Dr. Robert V. Rohli in the Department of Geography & Anthropology at LSU and Dr. Andrew Tatem at UF who gave me generous help and instructions during my tough time of greatest need. Thank you to Dr. Timothy Fik who introduced me to the statistical world in a unique way. I got a lot of knowledge from his classes and he is a professor I greatly admi re. Thank you also to Dr. Jane Southworth, Ms. Julia Williams, Ms. Desiree Price and Ms. Rhonda Black, who provided me much advice and ease during my graduate study. Many thanks to my friends for their help and company: to John D. Anderson, my close colleague in the Emerging Pathogens Institute who gave me much help with my work and also gave me a unique opportunity to celebrate a Thanksgiving party ; to Mia Jakubisin my good friend who gave me a great deal of help with my language; to Mario Mighty, who helped me a lot while working as a teaching assistant; to Yilun Sun in the Department of Biostatistics, my great collaborator whose expertise is complementary to mine ; and to some of my friends, Zhuoji e Huang, Jing Sun, Qiuyin Qi, Yang Yang and Ying Wang for a lot of fun when we gathered. L ast but not least I would like to show my biggest appreciation to my parents Xiu Lee and Hongwei Jia for their care and understanding throughout my life. Because of their support I could always dev elop my own interests and pursue my dreams since my childhood. T alk ing with them on the phone always encouraged me and calmed me when I was disappointed or frustrated as t hey always know just what to say I appreciate the life they gave me from the bottom of my heart. Although they could not read my thesis easily they always knew exactly what I was doing. It is absolutely an important element for a digital city." Dad said.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 7 LIST OF FIGURES .......................................................................................................... 8 LIST OF ABBREVIATIONS ............................................................................................. 9 ABSTRACT ................................................................................................................... 10 CHAPTER 1 INTRODUCTION .................................................................................................... 12 2 LITERATURE REVIEW .......................................................................................... 16 Demographic Products ........................................................................................... 16 Dasymetric Methods ............................................................................................... 17 3 STUDY AREA AND DATA ...................................................................................... 23 Study Area .............................................................................................................. 23 Census Data ........................................................................................................... 23 Tax Parcel Data ...................................................................................................... 24 4 METHODS .............................................................................................................. 27 5 RESULTS ............................................................................................................... 35 Population Density Fraction .................................................................................... 35 Dasymetric Mapping ............................................................................................... 37 Case Study 1: Estimation of Bus Passengers ......................................................... 38 Case Study 2: Estimation of Population at Risk to Environmental Pollution ........... 40 6 CONCLUSIONS ..................................................................................................... 57 7 DISCUSSIONS ....................................................................................................... 59 LIST OF REFERENCES ............................................................................................... 68 BIOGRAPHICAL SKETCH ............................................................................................ 71

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7 LIST OF TABLES Table page 3 1 Proportion of area and population of different property types in the urban area of Gainesville, Alachua County, Florida ...................................................... 25 5 1 Inhabitable property types .................................................................................. 44 5 2 Absolute aggregated density .............................................................................. 45 5 3 Relative density .................................................................................................. 46 5 4 Numbers of actual passengers and estimated population within the buffer zone of each route .............................................................................................. 46 5 5 Numbers of actual population and estimated population within the buffer zone with different radii ....................................................................................... 47

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8 LIST OF FIGURES Figure page 3 1 Urban area of Gainesville, Alachua County ........................................................ 25 3 2 Overview o f Florida Real Property (Source: Property Tax Oversight, Florida Department of Revenue) .................................................................................... 26 4 1 Flowchart of dasymetric mapping ....................................................................... 32 4 2 Mismatch of boundary between two sources of data .......................................... 33 4 3 Mismatch of boundary between two sources of data .......................................... 34 5 1 Choropleth map of population density in Alachua County .................................. 47 5 2 Dasymetric map of population density in Alachua County .................................. 48 5 3 Courses of 13 bus routes ................................................................................... 49 5 4 Routes 20 (blue) and 21 (orange) ....................................................................... 49 5 5 Route 8 (orange) and 29 (dark yellow) ............................................................... 50 5 6 Route 9 (green), 35 (blue) and 36 (purple) ......................................................... 50 5 7 Choropleth map of population density in the urban area of Gainesville .............. 51 5 8 Dasymetric map of population density in the urban area of Gainesville ............. 52 5 9 Overview of the Cabot Koppers Superfund Site on Google Earth (Source: http://aquaticpath.phhp.ufl.edu/waterbiology/handouts/CabotKoppers.pdf) ........ 53 5 10 Visual comparison of the choropleth (up) and dasymetric map (down) within the buffer with a 0.5 miles radius centered on the Cabot Koppers Site .............. 54 5 11 Visual comparison of the choropleth (up) and dasymetric map (down) within the buffer with a 2.5 miles radius centered on the Cabot Kopp ers Site .............. 55 5 12 Visual comparison of the choropleth (up) and dasymetric map (down) within the buffer with a 4 miles radius centered on the Cabot Koppers Site ................. 56 7 1 Overlay of bus routes and property types ........................................................... 67 7 2 The vertical (A) and side (B) view of a mixed use parcel .................................... 67

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9 LIST OF ABBREVIATION S AWM Areal W eighting Method AHR Asthma Hospitalization Rate CEDS Cadastral based Expert Dasymetric System EPA Environmental Protection Agency GIS Geographic Information Systems GPW Gridded Population o f the World GRUMP Global Rural U rban Mapping Project HGPS High resolution Gridded Population Surface LAH Limited Access Highway s MAUP Modifiable Areal Unit Problem NPL National Priority List RS Remote Sensing RTS Regional Transit System USCG U.S. Census Grids

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10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science MODEL ING HIGH RESOLUTION GRIDDED POPULATION SURFACE IN ALACHUA COUNTY, FLORIDA By Peng Jia May 2 012 Chair: Youliang Qiu Major: Geography The demand for small scale population data is increas ing in a variety of fields S patial techniques including Geographic Information Systems (GIS) and Remote Sensing (RS) have been more often used for the populati on study in recent years which makes it possible to conduct the dasymetric mapping a vital technique transforming a traditional choropleth map into a continuous statistical surface with values at all locations rather than solely aggregated values over the existing units In this study, I adopted the H euristic S ampling M ethod and detailed property types of parcels from the Department Property Tax Data Files in 2010 as ancillary data, to disaggregate the population counts from U.S. Census 2010 onto a quadrilateral grid composed of 30 m 30m cells covering the Alachua County, Florida, named Highresolution Gridded Population Surface (HGPS) At last the HGPS has been applied to two case studies. The first one is to estimate the numbers of potential passengers within the servi ce areas of bus routes in Gainesville, Alachua County. The second one is to estimate the numbers of population potentially exposed to the contamination at Cabot Koppers Superfund Site in Gainesville By comparing the results from HGPS and traditional population grids

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11 produced by areal weighting m ethod (AWM) with the actual numbers from relevant records, t he HGPS was proven to be more reasonable and valuable. HGPS is an important step forward towards solving the Modifiable Areal Unit Proble m (MAUP) and expected to serve as a more accurate denominator in various research fields, such as public health, crime analysis and so on.

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12 CHAPTER 1 INTRODUCTION As cities develop dramatically and their inner structures get more complex, the study of population dist ribution therein is of extreme importance because the location of people, compared to traditional statistical numbers, is more critical t o many aspects in our daily life, especially in response to natural and artificial disasters. For instance, e mergency management plans evacuation routes and allocates resources speedily based on where people live; environmental health assessment needs to s patially correlate environmental risk factors with the number of people living around and their health outcomes; infrastructure development has a demand for the structure and distribution of people in the target area in order to minimize the inequity caused by unfair inaccessibility; surveying and analyzing the distribution of demographic characteristics need to be done prior to any optimal site selection with the purpose of private profits or public service. Census data are still the primary data source for a variety of demographic researches, but they are usually released to the public about once in a decade and besides, in an aggregated form in order for confidentiality and administrative purposes (Wu 2006). We call this aggregated form the choropleth map that is composed of a series of vector polygons representing administrative units and values depicting the aggregated attributes over those units Martin (2011), in his up to date review paper, concluded four major limitations of the conventional choropleth representation from Census data. First of all, the Modifiable Areal Unit Problem (MAUP) is a matter of prime importance, which means that to modify areal boundaries or the level of data aggregation will substantially change the

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13 distribution of population values (Openshaw, 1984). Secondly, the time lag between data collection and publication makes data easily out of date for many contemporary applications. Thirdly, the periodic changes of administrative unit boundary hamper the comparison of data in different years. Fourthly, Census data represent the night time residential population distribution, instead of daytime. In sum, the continuous and dynamic system of the population has been poorly described by discrete aggregated data and static models; further more, the choropleth map not only removes the spatial heterogeneity within the given units by replacing a range of values with one aggregated value, but also subjects to abrupt changes at the boundary of artificially defined units that do not necessarily r elate to any natural or artificial phenomenon. Although the finest administrative unit at present, Census block, is usually enough for some largescale studies, such as estimating the number of people affected by hurricane or earthquake, the problems still remain in small scale issues, for example, visualizing the population distribution within Census blocks, or counting the people within the area that does not coincide with any Census unit (Wu 2006). In one word, accurate population estimations at small sc ales, as can be imagined, are being strongly desired to meet the growing demand for immediate and well informed decision making Dasymetric mapping is a particular cartographic process by which a traditional choropleth map can be transformed into a continuous statistical surface with values at all locations rather than aggregated values over the existing units (Mennis, 2009; Martin, 2011). In the case of this study where the population counts on Census block level have been disaggregated to a set of finer grids that did not exist before, the

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14 continuous statistical surface is a quadrilateral grid layer with cells representing the population counts within them. Although more accurate, the dasymetric mapping remains unpopular and highly subjective due to lack of goodquality ancillary data, as well as limited computer power. Additionally, the data transferring during the spatial disaggregation process is complex because of the mismatched boundaries between source and target areas and their heterogeneity in terms of density (Li, 2010). Therefore, the dominant spatial models representing demographic characteristics have been still choropleth maps until recently. Spatial techniques have been more often used for the population stu dy in recent years ( Saporito et al. 2007; Langford 2007; Lin et al. 2011), especially Geographic Information Systems (GIS) and Remote Sensing (RS), which make the dasymetric mapping possible and easy to operate. GIS provides a unique platform to perform al l necessary spatial operations, and RS provides a large pool of ancillary data to assist disaggregating data. Although Sharkova (2000) proved that GIS was optional in the datapoor research environment as the high cost was a natural concomitant of limited improvements brought by GIS, the results from various methods showed that the GIS method still outperformed others even under the situation of poor data, not to mention that more and more highresolution spatial data are becoming available. Most of present pop ulation products were produced under the assistance of satellite derived land cover/use data with a certain degree of misclassification, where the densit y of different types of hous ing failed to distinguish from each other, especially in hyper heterogeneous urban areas. This issue was unavoidable before because of inaccessibility to goodquality data. Based on author's current knowledge, there are few

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15 well modeled population product s which not only b ase themselves on more accurate data than remote sensing images, but also consider various types of residential and nonresidential property. It is fortunate that, over the past 20 years' efforts, the State of Florida has developed a statewide digital parcel map dataset that contains the boundary of parcels in all 67 count ies of Florida with associated tax information from the Florida Department of Revenue's tax database. Following the brief review of some of the present demographic products and most f requently used dasymetric approaches, I described the study area, data and methodology for producing the High resolution Gridded Population Surface (HGPS) in Alachua County Florida, and then applied it to two case studies in order for demonstrating the st rength s of the HGPS, as well as exploring the applications of the HGPS in the field of transportation and public health. One case study is to estimate the potential passengers served by Regional Transit System (RTS) in the city of Gainesville, Alachua County ; the other is to estimate the numbers of population potentially exposed to the contamination at Cabot Koppers Superfund Site in Gainesville

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16 CHAPTER 2 LITERATURE REVIEW Demographic Products The major demographic products open to the public at present include U.S. Census Grids (USCG), Gridded Population o f the World (GPW), Global Rural U rban Mapping Project (GRUMP), LandS can Global and LandScan USA model The USCG is a quadrilateral grid layer with a 250m resolution in 50 metropolitan statist ical areas with a population of one million or more and a 1km resolution in the remaining regions of the U.S., but this product is simply produced by proportionally allocating the population counts at the Census block level to the grids; for example, if a grid cell contains 40% area of one block and 60% of a second one, the population count for that grid cell would be the sum of 40% population of the first block and 60% of the second. Therefore, the USCG is just simple degradation of the Census block counts wit hout any aid of ancillary data. The GPW is also simple grid data based on the original Census units with a 5km resolution for 19902015 in five year increments. The GRUMP adds urbanrural specification to the GPW by reallocating the urban population on a 1km grid layer with the aid of the night time lights data. The LandS can Global model, with a 1km resolution is the earliest dasymetric product to reflect the diurnal change in population distribution, representing an average ambient population over 24 hours (Dobson et al. 2000). In 2000, the Oak Ridge National Laboratory extended the LandScan Global model and developed the LandScan USA model with a 90m resolution to capture the diurnal population dynamics. The LandScan USA, consisting of two different c omponents, nighttime and daytime population

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17 distribution, is produced based on layers representing land cover/use, transportation networks, various landmarks, elevation, slope, etc.; however, although considering various factors, each grid cell in both Lan dScan Global and USA model was weighed empirically, so the subjec tivity was unavoidable during the process of modeling (Bhaduri et al. 2007) There is an up to date online service named Kingston Automated Geoinformation Service (KAGIS) that allows end users to obtain the dasymetric population map of any area of interest in the United Kingdom based on a set of zone boundaries of a selected Census year by following a few instructive checkbox and button clicking steps (Shi and Walford 2010). It only takes two minutes to complete the entire process from data transmission to producing a map at the English county level. It is a good Internet service technology to integrate with the data and method in my study. Dasymetric Methods All the contemporary techniques for small scale population estimations can be classified into two categories, according to their goals. One group of methods inputs Census population counts and outputs a finer popul ation surface by interpolation and disaggregation technique, in order to reflect much more spatial details within Census units ( Bielecka 2005; Mennis and Hultgren 2006; Maantay et al. 2007; Aubrecht et al. 2009; Li and Corcoran 2010; Ural et al. 2011); in contrast, the other group of methods constructs the relationship between known population counts and other variables first, and then uses th at relationship to estimate unknown population, which is often used to estimate intercensal population because Census data are only available every ten years ( Wu and Murray 2007; Wu et al. 2008; Tapp 2010; Qiu et al. 2010; Silvn Crdenas et al. 2010; Dong et al. 2010). Actually, to construct the relationship between population

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18 and at least one additional variable from ancillary data is also necessary for the first group of methods, and the original population counts are all from Census data in both groups. The distinguished distinction between them is that the former redistributes known population counts from up to down in Census years and hence preserves the volume within original units, called downscaling; whereas the latter calculates unknown population counts from down to up in intercensal years, called upscaling. Wu (2005), Maantay (2007) and Mennis (2009), complementing each other, have reviewed nearly all the present approaches in details, so to review them all deviates from the goal of this paper; instead, I focused on how to redistribute known population counts to a finer level in a more accurate way and explored the application of results. Dasymetric mapping is a vital technique i n the first category of methods. T he Russian geographer, Semenov Tyan Shansky (18271914) has often been credited with inventing the dasymetric map (Bielecka 2005) and the American geographer, John Kirtland Wright (18911969), may perhaps be the first person to publish a paper on dasymetric mapping in an Englishlanguage journal A dasymetric map is typically described in contrast to a choropleth map where the boundar ies of regions are typically, f or convenience of enumeration, derived from administrative or jurisdictional divisions rather than the pattern s of features the map itself depicts In this part I g a ve a brief review of the most frequently used dasym etric methods that transferred data from one set of geographic zones, commonly C ensus tract block group or block to another set of noncoincident zones, such as school zones or grids. Areal weighting method works based on the simplest assumption that the population is evenly distributed in the source zone. If the target zone is completely

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19 located within a source zone, the population in the target zone would be the product of the population in the source zone and area ratio of the target zone to the source zone. If the target zone contains or intersects more than one source zone, each intersecting zone within the target zone would get a certain amount of population proportional to the area ratio of itself to the source zone it belongs to and the total popul ation in the target zone would be the sum in all intersecting zones. The U.S. Census Grids is produced based on this approach because of its simple performance. The major weakness of this method is its unrealistic assumption that the population is always evenly distributed across the source zone and population is still assigned to uninhabitable areas in the new target zone using this method, which fails to reflect the strengths of dasymetric mapping T herefore, to disaggregate the population counts to finer unit s need the aid of ancillary data. Filtered areal weighting method is an improvement of the areal weighting method, which employs ancillary data, such as classified remote sens ing data or land cover/use polygon, to mask nonresidential areas and redistribute the population solely to the remaining areas. For example, Maantay (2007) used the layers of landmark and water body to remove the uninhabitable areas from C ensus tract and block group maps, as one of three methods involv ed in his study This method assumes that all the people live in the classified residential zones and the population density is homogeneous among all residential areas but the truth is that nonresidential areas often have population as well, and the dens ity of diffe rent types of residential hous ing varies a lot. A further step towards accuracy is to use highresolution satellite imageries or aerial photos to differentiate various residential dwellings, such as single family, multifamily and

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20 apartment (Ur al et al. 2011). T he major drawback here is the expensive satellite data; moreover, the available pixel resolution is limited and the issue of mixed pixel decomposition in remote sensing images remains unsolved very well, especially in the urban areas (Maantay et al. 2007). Image texture method is to use Census data and highresolution satellite images, such as ALOS (2.5m for panchromatic and 10m for multi spectral), IKONOS (1m for panchromatic and 4m for multi spectral) and QuickBird (0.6m for panchromatic and 2.4m for multi spectral), to build the correlation between the population density and image texture by extracting Homogeneous Urban Patches (HUP) from images on a basis of the rule of maximizing betweenpatch differences while minimiz ing within patch differences, classifying and matching them with Census data ( Liu and Clarke 2002; Maantay et al. 2007). Even the images containing only three visible bands from Google Earth were used in a case study in the Lake Tai basin, eastern China (Y ang et al. 2011) due to the high cost of purchasing required remote sensing data. As the technique of object oriented classification progresses, this method is much easier to realize than before; however, the correlation between the population density and image texture was not high enough due to the misclassification and complexity of spectral texture. H euristic S ampling M ethod uses similar ancillary data involved in other methods, but predefines a series of classes and selects all Census units entirely occ upied by a certain class to calculate the density on each class of land type. Mennis (2003) predefined three classes, high density urban, low density urban and nonurban areas and used this approach to produce a population surface in five counties of Pennsylvania, where he calculated the density of each class county by county and the population

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21 counts within block groups were redistributed to each grid cell based on two factors: the relative density of each class and percentage of total area of each class within a block group. However, the ancillary data he used were too coarse, which led to the sparse involved classes and invisible improvements in highly urbanized areas after dasymetric mapping. In addition, this method fails to reveal the intraclass vari ations in population density. Regression method is a priori to set up a certain number of classes, classify the land cover/use data and overlay it with Census data in order to build a statistical relationship between the dependent variable, population counts, and independent variables, the area ratio of all predefined classes within C ensus units Yuan (1998) built a correlation between the population counts and land cover types in four counties of central Arkansas, where the correlation was assumed to vary county by county and the scaling techniques were applied to adjust the statistical results because the coefficients representing the density of the most sparsely populated areas were negative. The weakness of this method is that the automatic regression pr ocedure hides the intraclass variations in the population density and outliers that would influence the determination of the coefficients, which can be avoided in the manual H euristic S ampling M ethod. Another issue is that if using the coefficients from t he regression procedure to estimate the population from down to up, rather than redistribute the population from up to down, few aggregated values within Census units would be equal to the original counts. Cadastral based Expert Dasymetric System (CEDS), t he upto date population disaggregation product in New York City, used the highly detailed cadastral data and

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22 two types of proxies, residential area (RA) and number of residential units (RU), to redistribute population from Census units onto each parcel (M aantay et al. 2007). It was assumed that the population was proportional to the residential area; in other word, the population density on all residential types of hous ing was stable across the New York City where this assumption was applicable due to relatively high density and similar type and area of residential units in that small study area. H owever, it did not apply to large regions or the regions with a variety of property types, such as Florida. Most of the methods in earlier studies did not consider detailed property types due to the limitation of the ancillary data that were collected for various purposes, but the Tax Parcel data in my study was specifically collected for the purpose of property tax collection, which has a closer relationship to the population density. Highresolution Gridded Population Surface (HGPS) was produced using a similar disaggregating method as H euristic S ampl ing M ethod (Mennis 2003), but Tax Parcel data was more appropriate than the ancillary data used in his study. The property boundaries and types are clearly recorded in Tax Parcel data and can be directly used without extracting and classifying where the er rors often occur.

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23 CHAPTER 3 STUDY AREA AND DATA Study Area T he State of Florida is in the southeastern United States located on the nation's Atlantic and Gulf coasts. T he study area of dasymetric mapping, Alachua County is located in the north part of the Florida. Both case studies were conducted in the urban area of Gainesville (Figure 3 1) the county seat and largest city in Alachua County, with a population of 176,096, also home to the University of Florida Among all the residential areas in the urban area of Gainesville, the single family homes occupy the largest proportion (23.4%) of the total area, where 38.04% of the populations live (Table 31). The second largest percentage of the population (25.12%) live in the multi family homes with 10 units or more that only take up about 2.9% of the total area, where a large difference between the density of single family and multi family homes can be seen. There is also a large group of people (28%) living in the vacant residential hous ing and nonresidential areas, according to the Census 2010. Census Data U.S. Census data are collected every ten years, which consist of three levels including C ensus tract, block group and block, and count all the residents within administrative units on each level. In contrast to a de facto population, the census population is a de jure population. A de jure population reports all the residents on a basis of the ir home address regardless of they were physically present there at the reference date. A de facto population reports all the people physically present in the given area at the reference date, which is less stable and collected harder than a de

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24 jure population, but appropriate to some applications such as emergency response in the daytime (Wu 2006). Tax Parcel Data Tax Parcel data is a statewide digital parcel map dataset developed by the State of Florida, which contains the boundary of parcels in all 67 count ies of Florida with associated tax information from the Florida Department of Revenue's tax database. Figure 31 shows the percentage of several major property types across the Florida, a ccording to which we can see that single family has the largest proportion (70.3%) among all the property types including both residential and nonresidential followed by vacant residential (15.8%), others (5.4%), commercial/industrial (3.2%), agricultural (2.5%), multi family (1.8%) and nonagricultural acreage (1.1%) Ideally, people only live on residential parcels, but in reality it is often not the case (Wu 2006). Wu mentioned that, according to U.S. Census 2000, about 2.8% (7.8 million) of the total population in the U.S. lived on nonresidential types of parcels that is also defined as group quarters in which unrelated groups of people reside which was not a small enough number to be ignored. Therefore, I decided to consider the group quarters when matching C ensus with T ax P arcel data, instead of solely residenti al types.

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25 Table 31. Proportion of area and population of different property types in the urban area of Gainesville, Alachua County, Florida Property Type Area (%) Population (%) Vacant Residential 3.99 3.23 Single Family 23.4 38.04 Mobile Homes 0.72 0.49 Multi 2.9 25.12 Condominium 0.37 1 Retirement Homes 0.03 0.1 Miscellaneous 0.62 0.3 Multi family (<10 units) 0.61 4.02 Undefined 2.72 2.93 Non residential Space 64.64 24.77 Note: The total number of the population in this area is 176,096. Figure 31. Urban a rea of Gainesville, Alachua County

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26 Figure 32 Overview of Florida Real Property (Source: Property Tax Oversight, Florida Department of Revenue)

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27 CHAPTER 4 METHODS F or the purpose of preserving pycnophylactic property, meaning that the aggregated esti mated values within any unit on the dasymetric map should be equal to the original value of that unit on the choropleth map (Maantay et al. 2007), instead of estimat ing population from down to up, I intend ed to redistribute the Census population counts on the block level onto a quadrilateral grid composed of 30 m 0 m cells by the weight s I created The entire flowchart is shown in Figure 4 1 In the T ax P arcel data, there is an attribute called CENBLK10 recording which block each parcel belongs to; h owever, I use d the function of spatial intersection in ArcGIS 10.0 (ERSI Inc., Redland CA) to link Census and Tax P arcel data because few p arcels match ed their corresponding blocks well through my prel iminary examination T he parcels with the same type and close to each other were often m apped together as a large parcel instead of separated appropriately to fit themselves to corresponding blocks, so a large parc el often overlaid more than one block but only one of them wa s assigned to the parcel which le d to a loss of population on any block that had a nonzero population but was not assigned. A typical example wa s shown in Figure 4 2 The shaded community in Fi gure 4 2A wa s only divided into two parcels but several blocks where light red lines and dark black lines represent ed the boundar y of parcel s and block s, respectively. By comparing this vector polygon layer with its highresolution counterpart on Google Earth (Figure 4 2B and D ) the latter clearly show ed that the paths and open space within the community were recognized on the Census block map, but hidden on the T ax P arcel map. Wu (2006) also pointed that the T ax P arcel data in the city of Austin, T axes did

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28 not depict the city street surface areas. T wo blocks to which two parcels were separately assigned were highlighted in dark yellow in Figure 4 2B and the remaining shaded blocks d id not contain any parcel according to the T ax P arcel records. I n Figu re 4 2C all the blocks were colored by population counts and we can see that not all the remaining twelve blocks in Figure 4 2A had zero populati on; instead, six of them had more than 30 persons but all of them would be lost and hence the number of the population was underestimat ed if I use d the attribute CENBLK10 to link two sources of data. Another type of mismatch wa s shown in Figure 43 T he dark yellow polygon i n Figure 43A wa s a multi family parcel and black lines depicted block boundar ies ; green and red lines in Figure 43B separately depict ed the boundaries of parcels and blocks. By comparing two images we can see that only about one fourth of the parcel i s the real parcel and the remaining three fourths were vegetation and water body. I n contrast to the Tax Parcel data, Census block data depict ed the boundary of parcels more ac curately ; in other words, the property type wa s more homogeneous within a block than a parcel but there were no descriptions about the property type in Census blo ck data Therefore, I beg a n the dasymetric mapping us ing the function of spatial intersection in ArcGIS to overlay the block layer on t he parcel layer in order to take advantage of the geographical division of the block data and type information of the parcel data to cr e ate a new layer representing all of their intersect ing areas with the attribute of property type

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29 After intersecting, I sum med the parcels in each block by property type and calculate d the area proportion of each inhabitable type ove r each block. In addition, I also got the area proportion of each inhabitable type over the total inhabitable area in each block in order for the following weighting process Given the proportion of each inhabitable type within blocks, I select ed all eligible blocks in a strict way that only the blocks with the proportion of a certain type larger than 50% and the total proportion of the remaining types less than 0.1% were selected for sampling The little proportion (less than 0.1%) of remaining types in many blocks usually caused by the mismatch between two sets of boundar y would not influence the dominance of the monotype within blocks, so the threshold of 0.1% rather than zero, increased the number of qualified blocks and made the results much more r eliable and representative. Next I found the total population and area of each type and calculated thei r aggregated population density called the absolute pop ulation density which may be expressed as: abs,u = B b ubP / B b ubA (1) where abs,u = absolute population density of the property type u, Pub = population count in block b dominated by type u, Aub = total area of type u in block b, and B represent ed all the eligible monoblocks of type u across the Florida. The n, I merge d the types with the similar absolute population density into several classes, normalize d all merged absolute densities and g o t the relat ive density for each class, also called population density fraction. This step may be expressed as: rel,v abs,v / V x x abs (2)

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30 where rel,v = rel ative population density of class v, abs,v = abs olute population density of class v, abs,x = absolute population density of class x, and V represent ed all the classes after merging The population counts were transferred from block s to each inhabitable intersecting zone based on the weight of each parcel, which wa s calculated in the following w ay: rel,p rel,v pb) / ) (v rel, xb P x (3) where rel,p = weight of parcel p, rel,v = rel ative population density of class v that the parcel p belong ed to, pb = the area ratio of parcel p over the total inhabitable area in block b, x b = the area ratio of parcel x over the total inhabitable area in block b, and P represent ed all the inhabitable intersecting parcels in block b. The population count on each block wa s redistributed to all inhabitable intersecting zones within the block and each zone g ot a certain amount of population as following: Pp = Pb rel,p (4) where Pp = population count on parcel p, Pb = population count in block b in which parcel p wa s located, and rel,p = weight of parcel p. In order to transfer population counts from intersecting zones to each grid cell within or intersecting with them I created a grid of 30 m 30m cells covering the entire Florida and intersect ed it with the layer of intersecting zones. Under the assumption that the population wa s evenly distributed within each intersecting zone, the population count in each grid wa s calculated as following:

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31 Pg = ) (x gx I xP (5) where Pg = population count in grid g, Px = population count in parcel x, gx = the area ra tio of the overlaid area by grid g and parcel x over the area o f parcel x, and I represent ed all the inter secting zones overlaid with grid g. Till here, the HGPS in Alachua County, Florida, with 30 m 0 m quadrilateral cells has been created

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32 Fig ure 4 1. Flowchart of dasymetric mapping Census 2010 Relative Density Area % Inhabitable Intersecting Zone Tax Parcel 2010 Spatial Intersect & Select Inhabitable Property Types Calculate Area % of Each Inhabitable Type over Each Block Select Qualified M ono class blocks & Calculate Density Absolute Density of Each Inhabitable Type Merge Types with Similar Density & Normalization Relative Density of Each Merged Type Calculate Area % of Each Inhabitable Parcel over Total Inhabitable Area in Each Block Absolute Weight of Each Inhabitable Parcel Normalization Relative Weight of Each Inhabitable Parcel in Each Block Population Count in Each Block Relative Weight Population Count on Each Inhabitable Parcel Spatial Intersect with 30m30m Grids Population Counts on 30m30m Grids

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33 A B C D Figure 42 Mismatch of boundary between two sources of data

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34 A B Figure 43. Mismatch of boundary between two sources of data

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35 CHAPTER 5 RESULTS Population Density Fraction After examining the population density in all qualified monotype blocks and the definition s of property type s in Tax Parcel data, I select ed 33 types as the inhabitable parcels in this study (Table 5 1 ). The classification scheme in Tax Parcel data is very detailed, but has not been taken full advantage of in earlier studies to assist dasymetric mapping. Compared to the three classes high density urban, low density urban and nonurban areas extracted from Landsat TM imagery in the Heuristic Sampling Method (Mennis, 2003) and nine merged classes in Wu's study (2006), including single family, multifamily, commercial, office, industrial, civic, open space, tr ansportation and undeveloped, I involve d more independent property types in this study. Some of th e 33 property types have a small number of parcels in Alachua County, which led to insufficient eligible blocks for them. In addition to Alachua County, therefore, I examined the population density on each property type in other four counties with a big ci ty and complex types of parcels there, including Orange County (Orlando), Miami Dade County (Miami), Duval County (Jacksonville) and Hillsborough County (Tampa) Although the absolute population density of the same inhabitable type vari ed a little among five counties, th at difference was less significant than the difference of the densit y among various residential types in any county; moreover, I found that the relative differences among different types in each county were more stable in f ive counties C onsequently I applied a set of densities from the entire Florida to Alachua County, which is also easy for future use under the assumption that the accuracy

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36 brought by sampling county by county i s f u rther less comparable to the time and cos t spent on sampling in all 67 counties of Florida. I sampled the mono type blocks across the entire Florida and calculated the absolute aggregated density of each type (Table 5 2 ). The types with the similar absolute aggregated density were collapsed such that the initial Tax Parcel map wa s reclassified from 33 original inhabitable categories into 14 classes, the absolute density of which, then, were normalize d and transformed into the relative density for each class, called density fraction (Table 5 3 ). In Table 5 2 as I expected, the population density in the multi family (003 and 008) i s higher than other residential types, followed in order by the retirement homes (006), condominium (004), single family (001), cooperatives (005), vacant residential lots (000), mobile homes (002), miscellaneous residential lots (007) and undefined residential lots (009). Several nonresidential types also have a high density, among which the private schools and colleges (072) have the highest density than any other residential or nonresidential type, and the density of privately owned hospitals (073) is higher than condominium but lower than retirement homes; in addition, the homes for the aged (074), office buildings (018), mobile home parks (028) and und efined government lots (080) have the similar density as single family. Although there is only one eligible block for retirement homes, the density on it is reasonable as a residential type. Actually, there are some other types which have a small amount of mono type blocks, such as miscellaneous and undefined residential lots (007 and 009), stores (011), office buildings (018), private schools and colleges (072), sanitariums (078), undefined government lots (080), military lots (081), public

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37 schools and hos pitals (083 and 085). Due to few amount and small area, any of those types rarely occupy an entire block exclusively, unlike single family; however, I consider ed the Census as a reliable data source, so the density of any property type would be more than z ero only if at least one eligible bl ock with nonzero density exist s, and there is no better way to find out a more reliable density but accepting the one from few samples. Dasymetric Mapping The distribution of population density in the entire Alachua County is shown in both choropleth (Figure 51) and dasymetric way (Fig ure 52 ) for comparison, where a large difference in the spatial patterns of the population distribution can be seen particularly in two magnified areas As shown in Figure 51 the population wa s evenly distributed nearly everywhere across the entire county; however, in Figure 52, the clustering of inhabitable parcels emerged in the middle area of Alachua County and some were sparsely spread in the surrounding, which explains the dissimilar spatial patterns and might have misled some spatial analysis. It is worth noting that the significant cont rast of spatial patterns in the magnified areas between Fig ure 51 and 5 2 i s partly attributed to the fact that they are located in t he suburb rather than city. The improvement s of dasymetric maps are mainly embodied in two aspects, spatial patterns and intrablock heterogeneity of the population distribution. The changes in spatial patterns are more apparent in the country or suburb e specially visually, than in the city because most area s in the city are already highly populated; however, the geographical heterogeneity in the city would expect to be more obvious, as shown in the following case studies in the urban area of G ainesville.

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38 Case Study 1: Estimation of Bus Passengers Regional Transit System ( RTS ) provides high quality bus transportation services in the city of Gainesville, Florida. From the website of RTS ( http://ufl.transloc.com/) I can get the records of how many passengers served per month in 2010 by each route. In addition, the real time function of locating buses on RTS website allows me to get the course s of all the routes with the background of the city map, so I can exactly digiti ze all the bus routes. Although Gainesville RTS maintains 31 city routes and nine campus routes during the weekdays in 2010, 13 out of 31 city routes covering most of main roads in the city of Gainesville were examined in detail: Routes 2, 5, 8, 9, 10, 15, 20, 21, 23, 24, 29, 35 and 36 (Fig ure 53 ). The similar routes were considered as one route and the numbers of population served by each of them were summed. For example, in Fig ure 54 except the pure blue line curving towards southeast from the northwes tern terminal (Oak's Mall) to the first apparent intersection (between SW 62nd Blvd and SW 20th Ave) which i s exclusively served by Route 20, the major parts of the routes Route 20 and 21 run overlay from that intersection to the eastern terminal (Rawlings Hall at University of Florida) where the color of blue and orange alternate. Therefore, Route 20 and 21 were merged into one. One of another two integrated routes wa s merged by Route 8 and 29 (Fig ure 55 ), where most of the course Route 29 (dark yellow) runs belong to one part of the course Route 8 (orange) runs; the other integrated route consist ed of Route 9 (green), 35 (blue) and 36 (purple) in Fig ur e 56 where three routes run the similar course at the north of the west east major road (Archer Road) and serve a large residential area together at

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39 the south. The routes of Route 2, 5, 10, 15, 23, and 24 are relatively separate from each other, so each of them wa s taken as an independent route without merging. The actual numbers of passengers served by each route in February, March, September, October and November downloaded from RTS website were given in Table 5 4 University of Florida is a major part of Gainesville, where many students and employees take the bus to the campus for studying or working. Considering the fact that some local people may probably go out of the town for travelling during vacations, and most of the students in UF may go out for exchange study and internships during summer vacations and go home f or spending holidays during winter vacations, I only examined those five months when most of people and students were usually at work or study regularly, and 22 weekdays were counted for one month. Most of transit planners and researchers universally accept that the 400m (1/4 mile) is the m aximum walking distance that most potential passengers are willing to travel to reach their nearest bus stop (Biba et al. 2010). The bus stops are densely placed along the bus routes in Gainesville, so I can ea sily take the buffer zone of any course as the sum of the buffer zones of all bus stops along that course. For the sake of simplicity, it wa s assumed that only the people living in multi family housing and condominium ha d a demand for bus ride. Two methods were used to estimate the number s of p opulation within the 400m buffer zone: the first one was to overlay the buffer zone with the traditional population grids produced by the areal weighting method (AWM) and then sum the values of all the grid cells within the buffer zone, also called the U. S. Census Grids method; the second one wa s the dasymetric way, that is, to overlay the buffer zone with HGPS and

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40 sum the values of all the grid cells within the buffer zone. The same last step for both methods wa s to extract the population counts exclusively in the area of multifamily and co ndominium parcels that is, the number s of potential passengers counted in this study. The results before and after clip ping from both methods were summarized in Table 54 As expected the overestimation from USCG i s quite exaggerative for all routes; however, the results from HGPS show s the same scale of overestimation. After clipp ing, the estimated population numbers of each route lower dramatically as a whole regardless of the method; in particular, for the two merg ed routes, Route 20&21 and Route 9&35&36, the estimated numbers by the traditional method even decline below the actual numbers of passengers served in several months, but still fairly overestimated by the dasymetric method. For the other seven routes, whi le overestimated by both methods, the numbers estimated by the traditional method, rather than dasymetric method, are much closer to the actual numbers of passengers. For the sake of visual comparison, the choropleth and dasymetric map are shown in Fig ure 5 7 and 58 It can be see n that there were more zeropopulation areas spread amid blocks in the dasymetric map (white area) and more intrablock variations in population density, which have been completely hidden by constant intrablock values in the trad itional map. Case Study 2: Estimation of Population at Risk to Environmental Pollution The Superfund is an environmental program created by the Comprehensive Environmental Response, Compensation and Liability Act of 1980 to clean up abandoned hazardous was te sites. A Superfund site is a hazardous waste site placed on the U.S. Environmental Protection Agency's (USEPA) National Priority List (NPL)

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41 after a current or potential health impact assessment. In this study I selected one of the sites on the NPL withi n my study area to explore the application of the HGPS in environmental and public health. The Cabot Koppers Superfund Site is located at the northwestern corner of the intersection between N orth west 23rd Avenue and N orth Main St reet in the City of Gainesville consisting of two portions (Figure 5 9 ) T he Koppers Site, covering 82 acres on the western side, was polluted with wood treating chemicals in soils and groundwater; and the Cabot Si te on the eastern side cover s 49 acres and contains groundwater contamination from pi netar, pine oil and charcoal production. In a study for the benefits and costs of remediation at 150 Superfund sites (Hamilton and Viscusi 1999), it has been found that the cost of avert ing one cancer case is over $100 million due to the exceedingly small expected number of cancers prevented by remediation. Therefore, i t is of importance to estimate the number of population exposed to the major affected media prior to any remediation, which is also imperati ve to assess the cost effectiveness of the remediation. Given the numbers of surrounding population within the buffer with a 0.5, 2 and 4 miles radius centered on the Cabot Koppers Site from the EPA Region 4 Reuse Fact Sheets (Table 55) I calculated the total population and solely the population living in residential property within those buffers, se parately, by both USCG and HGPS for comparison, which were the same two methods used in the first case study The results were summarized in Table 55 From t he Figure 59 we can see that none of residential parcels are located within the Cabot Koppers Site; hence the population there should be few, which indicates that

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42 the HGPS result is more reasonable than USCG. For the estimated numbers of total population within the buffer with three radii, although faintly, the HGPS results are closer to the numbers in 2007 from the EPA Sheets except within the buffer with a 0.5 mile radius; however, both HGPS and USCG were created based on Census 2010 and the population may possibly increase slightly from 2007 to 2010, so it is quite possible that the slight overestimation of 1.57 % is even less than the increasing rate during those three years. Compared to USC G, the HGPS produced not only closer but als o more rational and stable results with an overestimation approximately ranging from 3% to 5%. The Floridan Aquifer under the Cabot Koppers Site, into which the soil contamination would leach, is the source of drinking water for over 175,000 people in Alac hua County. Assuming that only the people living in residential types of housing near the Cabot Koppers Site are regularly supplied by contaminated groundwater and steadily affected by the soil pollution, that portion of the people probably at risk was sep arated from the people living in nonresidential types of housing and their numbers were recalculated using both methods. The differences between two sets of results are more apparent and the results from HGPS are obviously more sensible than USCG that onl y depends on the area ratio of the housing over the entire block. In addition to the distinctions of number the spatial distribution of the population at risk for contamination within the buffer with a 0.5, 2 and 4 miles radius centered on the Cabot Koppers Site is separately shown in Figure 510, 511 and 5 12, where all the spatial units with nonzero values and the finest resolution on their own levels are drawn. It is worth noting that for the purpose of preserving the volume within original units all the break values in the legend representing population counts are shown to the

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43 nearest hundredth place because those values are the numbers calculated by multiplying the total population counts wi th the weight of each subunit (subblock for choroplet h maps; grid for dasymetric maps). The visual comparison between choropleth and dasymetric maps indicates not only somewhat different spatial patterns of the population, but also more detailed intrablock variations of the population on dasymetric maps, wh ich have been completely hidden on the traditional choropleth maps. In Figure 510 for example, due to a large scale, it can be seen that 1) there are lots of people on the choropleth map but few on the dasymetric one within the region of Cabot Koppers Si te; 2) more people on the choropleth map than the dasymetric one live in the northeastern part of the buffer regions; 3) more detailed intrablock variations are observed nearly everywhere on the dasymetric map than the choropleth one.

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44 Ta ble 5 1. Inhabitable property types Class Sub class Code Vacant Residential 000 Single Family 001 Mobile Homes 002 Multi family 10 units or more 003 Residential Property Multi family less than 10 units 008 Condominia 004 Cooperatives 005 Retirement Homes 006 Miscellaneous Residential 007 Undefined Reserved for Use by Department of Revenue 009 Vacant Commercial 010 Store 011 Commercial Property Mixed use store and residential or residential combination 012 Office buildings 018 Mobile home parks 028 Industrial Property Vacant Industrial 040 Agricultural Property Improved agricultural 050 Vacant 070 Churches 071 Private schools and colleges 072 Institutional Property Privately owned hospitals 073 Homes for the aged 074 Orphanages, other non profit or charitable services 075 Sanitariums, convalescent and rest homes 078 Undefined Reserved for future use 080 Military 081 Public county schools 083 Colleges 084 Government Property Public Hospitals 085 Counties 086 State 087 Federal 088 Municipal 089

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45 Table 5 2. Absolute aggregated density Code Type Block Pop Area (km2) Density (persons/100m2) Mean (persons/100m2) Std. (persons/100m2) 000 Vacant 98 3 902 1.94 0.2 0.54 0.79 001 Single family 11 130 581 343 193.28 0.3 0.33 0.2 002 Mobile home 308 6,469 3.65 0.18 0.24 0.18 003 Multi fml 457 65 369 7.81 0.84 1.06 1.3 008 Multi fml (<10) 69 4 119 0.6 0.69 0.73 0.61 004 Condominia 144 5 953 1.62 0.37 0.48 0.66 005 Cooperative 29 912 0.32 0.29 0.28 0.2 006 Retirement 1 293 0.06 0.49 --007 Miscellaneous 6 58 0.08 0.07 0.77 1.33 009 Undefined 7 24 0.04 0.06 0.44 0.42 010 Vacant 23 560 0.26 0.21 0.26 0.18 011 Store 2 7 0.04 0.02 0.03 0.02 012 Mixed use 11 200 0.12 0.16 0.2 0.16 018 Office building 4 170 0.07 0.25 0.21 0.35 028 Mobile home 839 25 986 9.5 0.27 0.33 0.38 040 Vacant 0 -----050 Agricultural 78 311 11.33 0.003 0.03 0.09 070 Vacant 0 -----071 Church 18 150 0.23 0.07 0.07 0.08 072 Private School 7 1 817 0.21 0.87 1.76 0.91 073 Private Hospital 15 2 574 0.6 0.43 0.81 0.72 074 Aged Home 22 1 648 0.56 0.29 0.52 0.64 075 Charitable 0 -----078 Sanitarium 1 115 0.06 0.18 --080 Undefined 8 599 0.26 0.23 0.46 0.37 081 Military 2 60 0.28 0.02 0.57 0.55 083 Public sc hool 7 162 0.24 0.07 0.16 0.21 084 College 11 3 171 3.35 0.09 0.77 1.19 085 Public Hospital 2 35 0.06 0.06 0.27 0.26 086 County 39 1 891 1.51 0.13 0.34 0.38 087 State 29 10 028 17.59 0.06 0.86 2.07 088 Federal 79 4 345 18.93 0.02 0.13 0.12 089 Municipal 63 1 989 1.17 0.17 0.35 0.33

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46 Table 5 3. Relative density Code Density (persons /100m 2 ) Density Fraction 072 0.87 0.18657 003 0.84 0.18014 008 0.69 0.14797 006 0.49 0.10508 073 0.43 0.09222 004 0.37 0.07935 001,005,074 0.3 0.06434 018,028,080 0.25 0.05361 000,002,010,012,078,089 0.2 0.04289 086 0.13 0.02788 007,009,071,083,084,085,087 0.07 0.01501 011,081,088 0.02 0.00429 050 0.003 0.00064 040,070,075 0.00001 0.000002 Table 54. Number s of actual passengers and estimated population within the buffer zone of each route Route 20&21 9&35&36 5 15 2 8&29 24 23 10 Actual p assengers (p ersons) February 6,471 6,869 1,638 882 322 1,273 303 -349 March 5,786 6,072 1,589 1,001 369 1,261 355 -353 September 8,498 8,682 1,824 1,179 394 1,673 395 246 492 October 7,780 7,840 1,724 1,126 390 1,474 392 229 456 November 6,705 6,845 1,498 1,008 322 1,318 364 201 402 Average 7,048 7,262 1,654 1,039 359 1,400 362 225 410 Estimated t otal p opu lation (p ersons) AWM 17,132 20,353 13,340 10,201 7,201 22,654 8,961 4,167 14,838 HGPS 17,530 20,060 13,180 11,173 7,335 22,485 9,122 3,955 14,701 Overestimation (%) AWM 143.1 180.3 706.5 881.8 1,906 1,518 2,375 1,752 3,519 HGPS 148.7 176.2 696.9 975.4 1,943 1,506 2,420 1,658 3,486 Estimated p opulation living in multi family and apartments (p ersons) AWM 4,576 7,148 2,958 1,295 933 3,605 690 1,369 2,843 HGPS 7,296 10,789 3,942 2,953 1,368 6,050 1,696 1,922 4,316 Overestimation (%) AWM 35.1 1.6 78.8 24.6 159.9 157.5 90.6 508.4 593.4 HGPS 3.5 48.6 138.3 184.2 281.1 332.1 368.5 754.2 952.7

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47 Table 55 Numbers of actual population and estimated population within the buffer zone with different radii Radius (mile s ) 0 (on site) 0.5 2.5 4 Actual population 4,274 55,595 97,670 Total population (p ersons ) AWM 202 4,341 58,071 101,342 HGPS 20 4,486 57,658 100,745 Difference (persons, HGPS USCG) 145 413 597 Overestimation (%) AWM 1.57% 4.45% 3.76% HGPS 4.96% 3.76% 3.15% Population living in residential property (p ersons ) AWM 2,526 28,625 51,993 HGPS 3,638 37,932 70,521 Difference (persons, HGPS USCG) 1,112 9,307 18,528 Fig ure 51. Choropleth map of population density in Alachua County

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48 Fig ure 52. Dasymetric map of population density in Alachua County

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49 Fig ure 53 Courses of 13 bus routes Fig ure 54 Routes 20 (blue) and 21 (orange)

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50 Fig ure 55 (left). Route 8 (orange) and 29 (dark yellow) Fig ure 56 (right). Route 9 (green), 35 (blue) and 36 (purple)

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51 Fig ure 57 Choropleth map of population density in the urban area of Gainesville

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52 Figure 58 Dasymetric map of population density in the urban area of Gainesville

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53 Figure 59 Overview of the Cabot Koppers Superfund Site on Google Earth (Source: http://aquaticpath.phhp.ufl.edu/waterbiology/handouts/CabotKoppers.pdf )

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54 Figure 51 0 Visual comparison of the c horopleth (up) and dasymetric map (down) within the buffer with a 0.5 miles radius centered on the Cabot Koppers Site

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55 Figure 51 1 Visual comparison of the c horopleth (up) and dasymetric map (down) within the buffer with a 2.5 miles radius centered on the Cabot Koppers Site

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56 Figure 51 2 Visual comparison of the c horopleth (up) and dasymetric map (down) within the buffer with a 4 miles radius centered on the Cabot Koppers Site

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57 CHAPTER 6 CONCLUSIONS The purpose of this study is to use the dasymetric mapping method to redistribute aggregated population counts on Census block level to 30m30m quadrilateral grids in Alachua County Florida, and apply the HGPS to two case studies in the field of transport ation and public health estimate, one of which is to estimate the numbers of potential passengers within the servi ce areas of bus routes and the other is to estimate the numbers of population potentially exposed to the contamination at a Superfund Site. B y compar ison of the results from the traditional and dasymetric method, as well as the actual numbers from relevant records, the advantages of the HGPS over the traditional population grids are demonstrated. The more detailed spatial patterns and intra blo ck spatial variations of the population distribution coinciding more with real situations but hidden by the traditional aggregated numbers in preference to the improvement in estimated numbers, are the most significant advantages brought by the HGPS Furthermore, it allows us to flexibly extract the population counts in any certain group on a basis of various conditions. The HGPS will definitely advance the GIS application in the field of transportation and public health; additionally, the novel integration of Census and T ax Parcel data also holds promise for improved research in many other areas, such as e mergency management and environmental assessment. This study is an important step forward towards solving the MAUP, where the resolution of 30m pro vided us with more freedom not only to delineate any types of zone, such as school attendance zone and affected area of pollution sources or modify present areal boundaries or the level of data aggregation without substantially changing

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58 the distribution of population values but also to combine with some major spatial data with the same resolution, such as Landsat TM data. Gridded population surfaces outperform traditionally choropleth representation in every aspect, which has been more and more realized by demographic researchers. It is a necessary step to build a digital city. However, the accuracy of gridded population surfaces needs to be more assessed wherever gridded data are available in the future research, according to which we could get to know w hich parameters and factors would significant ly decide the accuracy of HGPS. Anyways, we can foresee that a new era of HGPS is on the way.

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59 CHAPTER 7 DISCUSSIONS To the author's current literature review, this study is the first one to utilize the detailed property types in the Tax Parcel data as ancillary data to disaggregate Census data onto high resolution grids. Xie (2006) used Tax Parcel data in the City of Boca Raton, Florida, to ai d differentiating residential buildings extracted from remote sensing data from other buildings and delineating the housing units ; h owever, the accuracy of the classification was not assessed and there were also two weak assumptions in his study: 1) the number o f persons in each housing unit was the same within each block group, calculated by dividing the total population of the block group by the total number of housing units; 2) all the persons lived on residential parcels. I jump ed over the block group level and directly disaggregated population counts from blocks to grids for two reasons One is that I found some small and irregular blocks that were actually just medians, but ha d a certain amount of population on them I assume d th at th ose part s of population did not exist in reality, so to disaggregate population from block groups would count some nonexisting population and lead to overestimat ion. The other reason is that I would like to control the error produced during the disag gregation process within a finer unit; in other words, the error of the redistribution in one block if existing, would not influence the results in other blocks within the same block group. The weakness of doing this is that no finer data can be used to c ompare with the disaggregated results in the U.S. Comparing the gridded results with the existing gridded product only appeared once in earlier studies (Martin et al. 2011), where the population counts in Northern Ireland were disaggregated from the

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60 Census output areas on to 100m grid s and compared with the true 100m Northern Ireland Grid Square Product. In the first case study, as the adjacent parcels with the same type were mapped together without any boundary separating them and the information of building footprints were unavailable in Tax Parcel data, there i s no way to know the number of households and the position of building entrances on each parcel. Therefore, I d id not use the parcel network method presented by Biba (2 010) to calculate the distance of shortest path from the centroid of each parcel to its nearest bus stop. Unless that distance is accurate, otherwise it would be meaningless even though it can be done. I created a 400m buffer zone for each bus stop of each route, merged them all and f ound that it wa s the same as the buffer zone of the entire route because of densely and evenly placed stops, so I directly used the 400m buffer zone of each route to represent the service area. A large difference of estimated population within the 400m buffer zone of each route from two methods wa s not observed before clipping because, for the purpose of serving more residents, the bus routes have been built in the urban area where the heterogeneity within blocks i s less apparent than in the country; moreover, t he heterogeneity within blocks i s not as obvious as within Census tracts or block groups because blocks are already the finest administrative units. However, w hen I separate d the multi family and condominium parcels where the potential passengers in this study live d from other property types and solely examined the population on those two types, the differences between two groups of numbers appear ed. Compared to the dasymetric method, the estimated numbers from the tradit ional method are much closer to the

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61 actual numbers of passengers for all the routes except Route 20&21. However, for two routes out of nine, the underestimation occurs when using the traditional method, which means that the actual number of passengers over passes the total number of residents; especially for the Route 20&21, the actual passengers are more than the total residents in each month and compared to the average passengers in five months, the underestimation rate reaches 35%. It is rarely true that all the residents who may have a demand for bus ride truly take the bus but still 35% less than the actual passengers. It would be too early to say either method is better until I found out the possible reasons behind the contradictory results. From the ov erlaid layer of bus routes and p roperty types in the urban area of Gainesville (Fig ure 7 1 ), we can see that Route 20, 21, 9, 35 and 36 nearly run completely through the campus, multi family and condominium zone, where, through the field survey, most of the neighborhoods are rental for students at the University of Florida, so they five are appropriate routes used to estimate the potential passengers within their buffer zones because the people living on campus are usually served by campus routes and the ones living in rental neighborhoods may most probably have a demand for bus ride. Among those five routes, Route 20 and 21 run along nearly the same track and serve the same region, like one route, so to merge them is more suitable than merge Route 9, 35 a nd 36, the service regions of which are a little different at the south of Gainesville. Still merging them is because a large part of the tracks those three routes run overlay, that is, the buffer zone of each route also overlays a lot with each other. If counting the number of population in the buffer zone for each route separately, all the people living in the overlaying zone would be counted twice or three times but in RTS records, they are only

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62 counted once. To merge them is for the better match between the number of population and passengers. Not all the potential passengers in multifamily and condominium truly take the bus every day, some of which also have their own cars, so, compared to the actual passengers, moderate overestimation is more reasonable than a little underestimation. Therefore, the dasymetric method got more realistic results than the traditional method, which can also be explained by the theory of two methods. T he former estimates the population by getting a subset of the gridded population surface, on which population has been reasonably redistributed, and calculating the sum of values of all the grids on the subset; the latter just by calculating the area ratio of the subblock located within the buffer zone over the entire original block and multiplying that area ratio with the population counts on the original block to get the estimated population. Assuming that a sub block within the buffer zone occupies a certain proportion of the entire block, regardless of how crowded or scarce that subblock actually is, a constant proportion of the population on the original block the subblock belongs to would be assigned. As to why the estimations from the traditional method are closer to the actual passengers for the other eight routes let us go back to the Figure 7 1 and we can see that, i n contrast to five aforementioned routes, Route 5, 15, 2, 8, 29, 24, 23 and 10 are all running amid single family zones where most of people are assumed to rely on their own cars for going out. The s ingle family houses hold the largest part of the population (38.04%) in Gainesville (Table 3 1 ). There are also some communities of multi family and condominium there, but few students live there because of being far from the campus. T he order of the routes listed in Table 5 4, from the smallest overestimation

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63 rate of the dasymetric result after masking on the left to the largest on the right also reflects a general trend that the tracks of the routes listed on the left intersect more with the university z one ( the white ellipse ) than the tracks of the routes on the right. In sum, for Route 5, 15, 2, 8, 29, 24, 23 and 10, the population estimated by neither USCG nor HGPS is the actual number of people who may have the demand for the bus service. In addition to work or study locations and proximity to the nearest bus stop, the ridership patterns is often influenced by some other demographic characteristics, such as car ownership, age distribution and income level (Biba et al. 2010). T he experience s from this c ase study can also be used to evaluate the cost and efficiency of bus services and optimize them in the future. In the second case study, knowing the detailed spatial distribution of the population at risk rather than only an estimated number would be greatly helpful to calculate the individual risks, where the extent of individual exposure to contamination usually depends on the distance to the pollution sources, sometimes as well as the direction. For example, the affected media would be the air near a toxic waste dump rather than soil and groundwater at Cabot Koppers Site. In that case, we need to identify how the population is distributed within the affected area and how far the individuals reside from the dump, as well as which direction the dominant wind goes through that area, in order to assess the individual risks. In Hamilton's study (1999), the contaminated groundwater plume maps were overlaid with the Census block map to estimate the expected numbers of cancers. A similar procedure might be involved in the process of assessing the extent of exposure

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64 around the Cabot Koppers Site, so t he HGPS would be more a ppropriate to overcome the inconsistency between two sets of maps Maantay (2007) also provided evidence for the advantages of CEDS in the Bronx, New York City, over the traditional filtered areal weighting approach. In his study, the asthma hospitalization rate (AHR) inside the buffer of limited access highways (LAH) calculated by the filtered areal weighting approach is lower than not only the mean AHR for the entire Bronx, but also the AHR outside the buffer of LAH which rejects the assumption that people living near LAH are more likely to be hospitalized for asthma than those living far from LAH, that is, the correlation between AHR and proximity to major air pollution sources. However, when using CEDS to replace the filtered areal weighting approach, t he results support the hypothesis again where the AHR inside the buffer of LAH is higher than both the AHR outside the buffer of LAH and the average level for the entire Bronx. Some problems remain in my study. The quality issues in the Census data and Tax Parcel data is still unavoidable. Ural (2011) brought up the possible inconsistencies between the number of population and houses in some blocks; for example, 139 people are assigned to five singlefami ly houses in the block 4006 of Census tract 52 in West Lafayette, Indiana, which is m ost probably false. That is just one of many types of possible inconsistencies among Census data. Other types of inconsistencies found thr ough the data examination in my study include: 1) some blocks with smallsize or irregular shapes are actually just medians or parking areas along the street but considered as blocks and have a certain amount of population on them; 2) the temporal mismatch between Census and Tax Parcel s urvey may lead to some amount of

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65 population on vacant residential houses, like the 3.23% of the population in Gainesville is distributed on vacant residential lots and another like i n Miami, the population density on more than half of vacant residential houses is not zero, especially along the beach; 3) the houses among the agricultural land usually fail to be recognized on the Tax Parcel maps, so the population counts within an agricultural dominated block can only be evenly distributed across the entire agricultural area, which is not a problem in earlier studies where the study area is limited to the city (Maantay et al. 2007; Ural et al. 2011); 4) some inhabitable parcels have no population on them because of being under construction. For example, the density of the mixed use type is 0.16 persons/100m2. I randomly select ed a mixed use parcel without population and examined it on Google Earth from a v ertical and side view (Figure 7 2 ). The parcel that might have been approved to be used as a combination of stores and residential houses was being under construction at the time of being surveyed. One of the largest unsolved problems in dasymetric mapping is the variation of intra class density; for example, the area of a single family house in the city is oft en smaller than its counterpart in the suburb or in the country due to expensive land prices. National Land Cover Database (NLCD), a national scope landcover mapping program may provide a fit data source to relieve this issue to some extent, which has be en utilized in some studies (Langford et al. 1991; Reibel and Agrawal 2007; Zandbergen and Ignizio 2010; Zandbergen 2011). In NLCD dataset, the developed areas are divided into four classes based on the percentage of impervious surfaces: open space (<20%), low intensity (20% to 49%), medium intensity (50% to 79%) and high intensity (80% to 100%). To utilize NLCD data works under the assumption that those four classes

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66 aforementioned are a good proxy for the degree of urbanization, then the density of the sin gle family houses in low intensity and high intensity areas could be separated from each other. The variability of population density on the same type of parcels in different counties, though not severe, still exists due to diverse socioeconomic background However, to address this issue might be quite timeconsuming.

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67 Fig ure 7 1. Overlay of bus routes and property types A B Figure 7 2. The vertical (A) and side (B) view of a mixed use parcel

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68 LIST OF REFERENCES Aubrecht, C., K. Steinnocher M. Hollaus, and W. Wagner. Integrating earth observation and GIScience for high resolution spatial and functional modeling of urban land use. Computers, Environment and Urban Systems 33(1): 1525. Bhaduri, B., E. Bright P. Coleman and M. L. Urban. 2007 LandScan USA: A high resolution geospatial and temporal modeling approach for populati on distribution and dynamics. GeoJournal 69: 103 117. Biba, S., K.M. Curtin, and G. Manca. 2010. A new method for determining the population with walking access to transit. International Journal of Geographical Information Science 24(3): 347364. Bielecka, E. 2005. A dasymetric population density map of Poland. In: Proceedings of the 22nd Int ernational Cartographic Conference, July 9 15. A Coruna, Spain. Dobson, J., E. Bright, P. Colema n, R. Durfee, and B. Worley. 2000 LandScan: A global pop ulation database for estimating populations at ri sk. Photogrammetric Engineering and Remote Sensing 66(7): 849 857. Dong, P., S. Ramesh, and A. Nepali. 2010. Evaluation of small area population estimation using LiDAR, Landsat TM and parcel data. International Journal of Remote Sensing 31(21): 55715586. Hamilton, J.T., and W K. Viscusi. 1999 How costly is "clean"? An analysis of the benefits and costs of Superfund site remediations Journal of Policy Analysis and Management 18(1): 227. Vojvodinas population by using dasymetric method. Spatium 24: 4550. Langford, M., D.J. Maguire, and D. Unwin. 1991. The areal interpolation problem: Estimating population using remote sensing in a GIS framework. In: Masser, I., and M. Blakemore (eds), Handling geogra phic information: Methodology and potential applications London, U.K.: Longman. Langford, M. 2007. Rapid facilitation of dasymetric based population interpolation by means of raster pixel maps. Computers, Environment and Urban Systems 31: 1932. Li, T., and J. Corcoran. 2010. Testing Dasymetric Techniques to Spatially Disaggregate Regional Population Forecasts for South East Queensland. Urban Research Program Research Paper 28, Griffith University, Brisbane. Lin, J., R. Cromley, and C. Zhang. 2011. Using geographically weighted regression to solve the areal interpolation problem. Annals of GIS 17(1): 1 14.

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69 Liu, X., and K.C. Clarke. 2002. Estimation of residential population using high resolution satellite imagery. In: Maktav, D., C. Juergens, and F. Sunar Erbek (eds), Proceedings of the 3rd Symposium in Remote Sensing of Urban Areas June 1113. Istanbul, Turkey: Istanbul Technical University Press. pp. 15360. Maantay, J.A., A. Maroko, and C. Herrmann. 2007. Mapping population distribution in the urban environment: the Cadastral based Expert Dasymetric System (CEDS). Cartography and Geographic Information Science 34(2): 77 102. Martin, D. 2011. Directions in Population GIS. Geography Compass 5(9): 655665. Martin, D., Lloyd, C., Shuttleworth, I. 2011. Evaluation of gridded population models using 2001 Northern Ireland Census data. Environment and Planning A 43: 19651980 Mennis, J. 2003. Generating surface models of population using dasymetric mapping. The Professional Geographer 55: 3142. Mennis, J., and T. Hultgren. 2006. Intelligent dasymetric mapping and its application to areal interpolation. Cartography and Geographic Information Science 33(3): 179194. Mennis, J. 2009. Dasymetric mapping for estimating population in small areas. Geography Compass 3(2): 727745. Openshaw, S. 1984. Ecological fallacies and the analysis of areal census data. Environmental and Planning A 16: 1731. Qiu, F., H. Sridharan, and Y. Chun. 2010. Spatial autoregressive model for population estimation at the census block level using LIDAR derived building volume information. Cartography and Geographic Information Science 37: 239 57. Reibel, M., and A. Agrawal. 2007. Areal interpolation of population counts using preclassified land cover data. Population Research and Policy Review 26: 619633. Saporito, S., J.M. Chavers, L.C. Nixon, and M.R. McQuiddy. 2007. From here to there: Methods of allocating data between census geography and socially meaningful areas. Social Science Research 36: 897920. Ural S., E. Hussain and J. Shan 2011. Building population mappi ng with aerial imagery and GIS data. International Journal of Applied Earth Observation and Geoinformation 13(6): 841852. Sharkova, I.V. 2000. With or Without GIS? Evaluating Accuracy, Timeliness and Costs of Population Estimates for User Defined Areas. In: Proceedings of the Population Association of America Annual Meeting, March 23 25. Los Angeles, California.

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70 Shi, S., and N. Walford. 2010. An automated internet geoinformation service for integrating online geoinformation services and generating quasi r ealistic spatial population GIS maps. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38(II): 427 432. Silvn Crdenas, J.L., L. Wang, P. Rogerson, C. Wu, T. Feng, and B.D.Kamphaus. 2010, Assessing finesp atial resolution remote sensing for small area population estimation. International Journal of Remote Sensing 31: 56055634. Tapp, A.F. 2010. Areal interpolation and dasymetric mapping methods using local ancillary data sources. Cartography and Geographic Information Science 37: 215228. Wu, C., and A.T. Murray. 2007. Population estimation using Landsat enhanced Thematic Mapper imagery. Geographical Analysis 39: 2643. Wu, S., X. Qiu and L. Wang. 2005. Population estimation methods in GIS and remote sensing: A review. GIScience and Remote Sensing 42: 8096. Wu, S. 2006. Incorporating GIS and remote sensing for Census population disaggregation. Doctoral dissertation. Retrieved from ProQuest Dissertations and Theses. (Accession Order No. AAT 3221522) Wu, S., L. Wang, and X. Qiu. 2008. Incorporating GIS building data and census housing statistics for subblock level population estimation. The Professional Geographer 60(1): 12135. Xie Z.X. 2006. A framework for interpolating the population surface at the residential housing unitlevel. GIScience and Remote Sensing 43: 233251. Yang, X., G. Jiang, X. Luo, and Z. Zheng. 2012. Preliminary mapping of highresolution rural population distr ibution based on imagery from Google Earth: A case study in the Lake Tai basin, eastern China. Applied Geography 32(2): 221 227. Yuan, Y., R. M. Smith, and W. F. Limp. 1997. Remodeling Census Population with Spatial Information from Landsat TM imagery. Co mputers, Environment and Urban Systems 21(34): 245258. Zandbergen, P. A., and D.A. Ignizio. 2010. Comparison of dasymetric mapping techniques for small area population estimates. Cartography and Geographic Information Science 37: 199 214. Zandbergen, P A. 2011. Dasymetric Mapping Using High Resolution Address Point Datasets. Transactions in GIS 15(Supplement s1): 527.

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71 BIOGRAPHICAL SKETCH Peng Jia was born in Hohhot, Inner Mongolia, China. He earned his B.Eng. in e n vironmental e ngineering from the Nanjing Agricultural University (NJAU) in 2007 and his M.S. in c artography and geographic information system ( GIS ) in 2010 from the Institute of Remote Sensing Applications Chinese Academy of Sciences (IRSA, CAS), jointed w ith the National Museum of China. He entered the Department of Geography at University of Florida in August 2010 and started working in Emerging Pathogens Institute as a graduate student research assistant since May 2011. His researches focus on the applic ations of GIS remote sensing (RS) and other spatial techniques in the study of population public health, environmental health, health impact assessment and archaeology. Upon graduating in May 2012 with his M.S. in geography he will enter the Department of Geography & Anthropology at Louisiana State University to pursue his Ph.D. in geography.