A spatial emergy model for Alachua County, Florida
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 Material Information
Title: A spatial emergy model for Alachua County, Florida
Physical Description: Book
Language: English
Creator: Lambert, James David, 1958- ( Dissertant )
Alexander, John F. ( Thesis advisor )
Odum, H. T. ( Reviewer )
Starnes, Earl M. ( Reviewer )
Harris, Lawrence D. ( Reviewer )
Zwick, Paul D. ( Reviewer )
Publisher: State University System of Florida
Place of Publication: Florida
Publication Date: 1999
Copyright Date: 1999
 Subjects
Subjects / Keywords: Architecture thesis, Ph. D   ( lcsh )
Dissertations, Academic -- Architecture -- UF   ( lcsh )
spatial modeling
emergy
Genre: bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
non-fiction   ( marcgt )
Spatial Coverage: United States -- Florida -- Alachua County
Coordinates: 29.7 x -82.3
 Notes
Abstract: A spatial model of the distribution of energy flows and storages in Alachua County, Florida, was created and used to analyze spatial patterns of energy transformation hierarchy in relation to spatial patterns of human settlement. Emergy, the available energy of one kind previously required directly or indirectly to make a product or service, was used as a measure of the quality of the different forms of energy flows and storages. Emergy provides a common unit of measure for comparing the productive contributions of natural processes with those of economic and social processes--it is an alternative to using money for measuring value. A geographic information system was used to create a spatial model and make maps that show the distribution and magnitude of different types of energy and emergy flows and storages occurring in one-hectare land units. Energy transformities were used to convert individual energy flows and storages into emergy units. Maps of transformities were created that reveal a clear spatial pattern of energy transformation hierarchy. The maps display patterns of widely-dispersed areas with lower transformity energy flows and storages, and smaller, centrally-located areas with higher transformities. Energy signature graphs and spatial unit transformities were used to characterize and compare the types and amounts of energy being consumed and stored according to land use classification, planning unit, and neighborhood categories. Emergy ratio maps and spatial unit ratios were created by dividing the values for specific emergy flows or storages by the values for other emergy flows or storages. Spatial context analysis was used to analyze the spatial distribution patterns of mean and maximum values for emergy flows and storages. ABSTRACT (cont.): Two small-scale experiments assessed the hypothesis that width of fuel connectors would differentially affect the rate and/or success of fire spreading across rural north Florida pasturelands. The effects of the two different treatment variables 1) width (of inter-patch connectors) and 2) orientation (of the connectors relative to ambient wind) were not sufficient to emerge as important relative to more salient variables including fuel moisture and humidity, solar position, ambient temperature and wind. Even though intuitive, head fires were shown to move through the connectors significantly faster than did backfires. In addition, the variance surrounding the means of the back fire movement rates was very small. All things considered, the experiments established that structural connectivity across otherwise open landscapes does have significant effects on the behavior of prescribed fire, arguably the single most critical variable with respect to vegetated landscapes in the lower Southeastern Coastal Plain of North America.
Subject: KEYWORDS: emergy, GIS
Statement of Responsibility: by James David Lambert.
Thesis: Thesis (Ph. D.)--University of Florida, 1999.
Bibliography: Includes bibliographical references (p. 558-568).
System Details: System requirements: World Wide Web browser and PDF reader.
System Details: Mode of access: World Wide Web.
General Note: Title from first page of PDF file.
General Note: Document formatted into pages; contains ix, 569 p.; also contains graphics.
General Note: Vita.
 Record Information
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 45261454
alephbibnum - 002484132
notis - AMJ9746
System ID: UF00100731:00001

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A SPATIAL EMERGY MODEL
FOR
ALACHUA COUNTY, FLORIDA










BY

JAMES DAVID LAMBERT


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY


UNIVERSITY OF FLORIDA


1999
































Copyright 1999

By

James David Lambert
































Dedicated
To
Marilyn M. Winston
















ACKNOWLEDGEMENT


I would like to express my sincerest appreciation to my committee chairman,

Dr. John F. Alexander, and to my other committee members, Dr. H.T. Odum, Dr. Earl M.

Starnes, Dr. Lawrence D. Harris, and Dr. Paul D. Zwick for their support and patience

over the years. Special appreciation is due Dr. Howard T. Odum who has changed my

way of looking at the world forever. This work would not have been completed without

the encouragement and support of my most significant, best friend, and fellow student,

Marilyn Winston.
















TABLE OF CONTENTS


page

A C K N O W L E D G E M E N T ............................................................................................... iv

A B S T R A C T ................................................................................................................... v iii

INTRODUCTION ........................................ ......... ........................... 1

T heoretical B asis for the Study ................................. ...................... ................ 1
EM ERGY D defined .................................................... 2
Energy System s D iagram s ............................................... ................. 4
T ransform ity D efined ............................................................. ............. 6
Spatial Distribution of Energy According to Transformation Hierarchy ... 7
E energy Signatures .. ... ................................... ..................... ........... .. 9
Land Area U nit M odel D iagram .......................................... ............. 10
Technological B asis for the Study ................................................... ............. 12
Previous Studies of the Spatial Distribution of EMERGY in the Landscape ..... 13
S tu d y A re a ........................................................................................................... 1 8
Study O objectives ........................................................................................... 2 1
O organization of the Study .................................... ........................ .............. 22

M E T H O D S ...................................................................................................................... 2 5

G general D description of the M odel ................................................... .............. 25
Softw are and H ardw are U sed .................................. ...................... ............. 31
Development of the GIS Database ...................... ............. 32
General Methods for Creating Component Grids ......................................... 37
A nalytical C overages ................ .. ....................... ..................... 37
(Sub)Component Grids from Point-feature Coverages ........................ 38
(Sub)Component Grids from Line-feature Coverages ............................ 42
(Sub)Component Grids from Polygon-feature Coverages ................... 44
Specific Methods for Creating Flow Component Grids ................................ 46
Natural System Flows-Gross Primary Productivity ............................. 46
Natural System Flows-Renewable Sources .................................... 52
Urban System Flows-Water Consumption ..................................... 55
Urban System Flows-Fuels and Electricity .................................... 62
Urban System Flows-Transporation Subcomponent ............................ 62









Urban System Flows-Buildings and Agriculture Subcomponent ...... 64
Urban System Flows-Goods Consumption .................................... 72
Urban System Flows- Human Services .............................. ............. 79
Urban System Flows-Wastes Not Recycled ................................... 90
Urban System Flows- Goods Recycled ................................................. 98
Specific Methods for Creating Storage Component Grids ................................ 104
N natural System Structure ............. ..... ................ ....... ....... ......... 104
Natural System Structure-Biomass Subcomponent ......................... 105
Natural System Structure-Water Storage Subcomponent ............... 106
Natural System Structure-Organic Matter in Soils Subcomponent .... 110
U rban System Structure ................................................... ............. 112
Urban System Structure-Buildings Subcomponent ......................... 112
Urban System Structure-Roads Subcomponent ........................ 116
Urban System Structure-Utility Infrastructure Subcomponent ....... 119
Urban System Structure- People ....... ......... ................................. 122
General Methods for Creating Analytical Grids .......... .. .............. 128
L o g arith m G rid s ............... ................................................................. 12 9
Total EMERGY Consumption and Total EMERGY Storage.............. 130
T ran sform ity G rid s ................................................................................ 132
Other Analytical Grids ............................... 132
Using the Model for Analysis ............................... 134
M ap s and H istogram s ............................................................................ 134
County-wide (Sub)Component EMERGY Signatures ....................... 135
C om p arativ e Stu dies ............................................................................. 136
EMERGY Ratio Analysis ................. .............. 139
Spatial Context Analysis ............................... 140
One-kilometer Cell-size Model ................. .............. 141

R E S U L T S ...................................................................................................................... 14 2

T he G IS D database ..... ... ....................................... ....................... . ........ .. 142
New Primary Data Coverages ........................................ ............... 143
Analytical Coverages ............................... 159
Sum m ary Coverage ........................................................ ............... 181
The Spatial EMERGY Model ............................... 186
EMERGY Flow Components ............... ...................... 192
EMERGY Storage Components ....................................... .............. 271
Total EMERGY Consumption and Total EMERGY Storage......................... 330
Total EMPOWER Density Analytical Grids .......... .............. 330
Total EMSTORAGE Density Analytical Grids ......................... 332
Transform ity A nalytical G rids .................................... ....... ............. 346
County-wide (Sub)Component EMERGY Signatures ....................... 351
County-level Transformities ................. .............. 360
C om parative Studies .................................................. ............................... ..... 361
Land Use and Cover Classification Study .................... ................ 361
Planning Unit Study ............................... 412









Neighborhood Study ................. .............. 426
EM ERGY Ratio A analysis ..................................................... ... .... ............. 440
Resource Use Analytical Grids ................. .............. 440
EM POW ER Ratios ....... ........... .............. .................... 446
EMSTORAGE Ratios ................. .............. 458
Spatial Context A analysis ......................... ............................... .... ............. 470
EMPOWER Density Context Analysis ........................ 472
EMSTORAGE Density Context Analysis ................... .............. 484
One-kilometer-resolution Model .................. .............. 496

D ISCU SSION ............................................................................. ........................ 502

G general Theoretical M ethod ........................................................... ............. 503
Strengths and Weaknesses of the Land Area Unit
Spatial M odeling A approach ..................................... ............... ............. 504
Evidence of Spatial Patterns of Energy Transformation Hierarchy ............... 507
Hierarchical Patterns Associated With Individual Components.......... 507
Total EMERGY Consumption and Total EMERGY Storage.............. 512
Transformities for Total EMERGY Consumption
and Total E M ERGY Storage...... ..... ....................................... 513
Transformities, Mean Densities, and Percentages
for Land U se Classifications ....... .......... .................................... 514
Value Added by Using GIS Technology ....... ......... .................................. 518
Suggestions for Future Research ....... ....... ........ ................... 518
C lo sin g R em ark s ....... .. .................................... .......................... ............. 52 0

A P P E N D IX .................................................................................................................. 5 2 1

REFERENCES ................................................. ......................... .. 558

BIOGRAPH ICAL SKETCH .. .............................................................. ............. 569
















Abstract of Dissertation Presented to the Graduate School
Of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

A SPATIAL EMERGY MODEL
FOR
ALACHUA COUNTY, FLORIDA

By

James David Lambert

August 1999


Chairman: John F. Alexander, Jr.
Maj or Department: College of Architecture

A spatial model of the distribution of energy flows and storage in Alachua

County, Florida, was created and used to analyze spatial patterns of energy

transformation hierarchy in relation to spatial patterns of human settlement. Emergy, the

available energy of one kind previously required directly or indirectly to make a product

or service, was used as a measure of the quality of the different forms of energy flows

and storage. Emergy provides a common unit of measure for comparing the productive

contributions of natural processes with those of economic and social processes-it is an

alternative to using money for measuring value.

A geographic information system was used to create a spatial model and make

maps that show the distribution and magnitude of different types of energy and emergy









flows and storage occurring in one-hectare land units. Energy transformities were used

to convert individual energy flows and storage into emergy units.

Maps of transformities were created that reveal a clear spatial pattern of energy

transformation hierarchy. The maps display patterns of widely-dispersed areas with

lower transformity energy flows and storage, and smaller, centrally-located areas with

higher transformities. Energy signature graphs and spatial unit transformities were used

to characterize and compare the types and amounts of energy being consumed and stored

according to land use classification, planning unit, and neighborhood categories. Emergy

ratio maps and spatial unit ratios were created by dividing the values for specific emergy

flows or storage by the values for other emergy flows or storage. Spatial context

analysis was used to analyze the spatial distribution patterns of mean and maximum

values for emergy flows and storage.

The modeling method developed for this study is general and applicable to all

types of landscapes and could be applied at any scale. An advantage of this general

approach is that the results of other studies using this method will be directly comparable

with the results of this study. The results and conclusions of this study reinforce the

hypothesis that an urban landscape will develop a predictable spatial pattern that can be

described in terms of a universal energy transformation hierarchy.














INTRODUCTION


The primary goal of this study is to create a general type of spatial model of the

distribution of energy flows and storage in urban and regional landscapes that can be

used to study spatial patterns of the energy transformation hierarchy and energy

signatures in relation to spatial patterns of human settlement. EMERGY (capitalized

throughout this study to avoid confusion with the word 'energy') is used as a measure of

the quality of the different forms of energy flows and storage that are included in the

model. A secondary goal of the study is to demonstrate how recent advances in computer

and information science technologies can be applied in a way that provides new insights

into the spatial patterns of energy flow and storage.

The primary target audience for this study is urban and regional planners.

However, anyone interested in how man-dominated landscapes are spatially organized to

process and store both local, natural energy sources and imported, nonrenewable energy

sources should be interested in the results. The specific spatial model presented in this

study was implemented at a spatial resolution that makes it particularly useful for

describing, quantifying, and studying patterns that occur within spatial units of urban

systems. Although the focus for this study is on patterns of urban systems, the modeling

methods presented in this study are intended to be general and applicable to all types of

landscapes and most could be applied at any scale.











Theoretical Basis for the Study


This study is based on general and ecological systems theory developed over the

past three decades by H.T. Odum and his associates (Odum, 1971; Odum, et.al., 1976,

Odum, 1983, 1988, 1992; Hall, 1995; Odum,1996; Odum, et.al., 1998). A basic review

of the concepts and principles of this theory that are most important and relevant to this

study are presented in the following sections.



EMERGY Defined


In his latest book, Environmental Accounting EMERGY and Environmental

Decision Making (1996), H.T. Odum explains how the concept of EMERGY can be used

as the basis for an environmental accounting approach that measures and evaluates the

relative contributions to real wealth that are made by both environmental and economic

processes. In this book, Odum (1996, p.6) defines real wealth as anything that ". . is

produced and maintained by work processes from the environment. . ." He explains

how real wealth can be measured in terms of EMERGY in the following paragraph.

To build and maintain the storage of available resources, environmental work has
to be done, requiring energy use and transformation. We can quantitatively
evaluate the storage by the work done in its formation. Work of energy
transformation can be measured by the availability of the energy that is used up.
Thus, real wealth can be measured by the work previously done. EMERGY is a
scientific measure of real wealth in terms of the energy required to do the work of
production. (Odum, 1996, p. 7)


EMERGY is defined as the ". . available energy of one kind previously required

directly or indirectly to make a product or service. ." (Odum, 1996, p.7). This study

will use a variant of EMERGY, called solar EMERGY, as its primary unit of measure.









Solar EMERGY is defined by Odum (1996, p.8) as ". . the available solar energy used

up directly and indirectly to make a service or product. Its unit is the solar emjoule

(abbreviated sej)." These straightforward definitions do not do nearly as much justice to

the elegance of this concept as the following quote from Dr. Odum's book in which he

presents the concept from the perspective of "common folk".

A commonplace idea among people everywhere is that some things take more
effort than others. Long before physical science made a narrow quantitative
definition of energy, the word "energy" was used to refer to the work done. For
example, it was said that a house took more energy to build than a chair. The
universal idea among common folk was, and still is, that putting more energy into
something generates more value. The concept of EMERGY is scientifically
defined to give a quantitative measure to this ancient principle.

Because of the way it is defined, the scientific use of "energy" does not coincide
with the folk concept. The practical, operational scientific measure of energy is
the heat generated when various forms of energy are converted. The scientific
concept makes no allowance for different kinds of energy representing different
levels of effort. The scientific concept rates a calorie of sunlight, electricity,
nuclear fission, and human thinking as equal. In other words, the different levels
of prior effort involved in generating different kinds of energy are ignored ....
EMERGY is a measure that looks back upstream to record what energy went into
the train of transformation processes. The computations recognize that each type
of energy has a different upstream energy input which must be included in order
to summarize all the energy of one type that is required to generate the output.
(Odum, 1996, pp. 13-14)


With these concepts in mind, EMERGY was chosen for this study because it

measures the work contributed by both nature and society to the production and storage

of 'real wealth'. An advantage of using EMERGY for a study such as this is that the

concept provides a method for comparing storage and flows related to natural processes

with those of economic and social processes. EMERGY provides an alternative to using

money for measuring the contributory value of an element of the system. Money does

not work well for valuing the contribution of the 'free' energy sources from nature. The







4

essential contributions from these free resources are often ignored, if not forgotten, in

evaluations of economic systems because of the difficulties of valuing natural resources

(Ahmad et al., 1989; Costanza, 1991; Uno and Bartelmus, 1998).



Energy Systems Diagrams


EMERGY analysis is based on diagramming the flows and storage of energy

using a standard symbol language (Odum, 1983, 1996). These energy system diagrams

are drawn based on an understanding of several energy-related characteristics of the

system being modeled. These characteristics include: what the boundaries of the system

are, what outside energy sources contribute to the system, how energy flows interact

within a system, and what the flow pathways are between interactions and energy storage

elements of a system being modeled. The diagrams are drawn according to a strict set of

rules, defined and described by H.T. Odum (1983), so that the laws of thermodynamics

are observed.

A simple example is shown in Figure 1-1. It has flows from two types of

potential energy sources interacting in a productive process to create a storage or

structure (real wealth). The first law of energy conservation is observed in the

diagramming of the energy flows and storage (all of the energy entering the system is

accounted for-none is created or destroyed). The diagram also illustrates the second

law of thermodynamics by accounting for the fact that some energy loses its ability to do

work in the production interaction process (leaving the system in the form of dispersed

heat), and that all storage will naturally depreciate over time and must be maintained by

productive processes.


















































Figure 1-1: A simple energy system diagram showing two sources of potential energy
interacting in a productive process that builds a storage or structure. This storage is "real
wealth" as defined by Odum (1996). The diagram illustrates that, because of the second
law of thermodynamics, much of the potential energy used in the productive process is
used up (loses its ability to do work) and leaves the system in a more dispersed form
(usually as heat). The storage also loses potential energy over time. Surviving systems
will self organize to develop structure that has reinforcing feedback to the production
process (Odum, 1983).









Transformity Defined


Transformities are used to calculate the EMERGY in a product or service. H.T.

Odum describes the concept of transformity in the following quotes from his book.

The quotient of a product's EMERGY divided by its energy is defined as its
transformity (Odum, 1976, 1988). The units of transformity are emjoules per
joule .... Solar transformity is the solar EMERGY required to make one joule of
a service or product. Its units are solar emjoules per joule (sej/J). A product's
solar transformity is its solar EMERGY divided by its energy.

The more energy transformations there are contributing to a product, the higher is
the transformity. This is because at each transformation, available energy is used
up to produce a smaller amount of energy of another form. Thus, the EMERGY
increases but the energy decreases, and therefore the EMERGY per unit energy
increases sharply. . Goods and services that have required the most work to
make and have the least energy have the highest transformities. Examples are
human services and information . .most energy transformations are controlled by
inputs of high transformity, whose energy contribution is small but whose
EMERGY contribution may be large. An example is the control of a forest by
people ... (Odum, 1996, p. 11)

Transformities, as defined above, can be used to distribute all forms of energy

along a universal energy hierarchy based on the magnitude of transformity values (Odum,

1996). The concept of universal energy hierarchy is based on the second law of

thermodynamics that states that available energy is degraded in any transformation

process. The second law explains why we see hierarchy in all types of systems.

Odum (1996) points out that a familiar pattern seen in many types of systems can

be described in terms of transformities-many units of lower transformity energy

converge and interact through productive processes with other higher-transformity forms

of energy to create even fewer units of other forms of energy with even higher

transformities.







7

Spatial Distribution of Energy According to Tranformation Hierarchy

Odum and others (Odum and Brown, 1976; Constanza, 1975, Brown, 1980,

Odum, 1983, 1996; Whitfield, 1994; Huang, 1998) have proposed that the spatial

distribution of energy flows and storage tend to follow patterns based on the level of

transformity for the flow or storage (illustrated in Figure 1-2).

For example, low transformity flows tend to be widely dispersed compared to

higher transformity flows. In terms of the spatial arrangement of urban systems, these

previous studies predict that a city center will have a higher energy flow transformity

than the less developed suburbs surrounding the city center, and that rural areas outside

the city will have the lowest transformity for flows.

In the case of storage, there are usually many smaller, lower transformity

storage that are widely dispersed, and there are usually fewer of the larger, higher

transformity storage. Once again, in terms of the urban spatial pattern, this observation

suggests that a city center will have a higher storage transformity than suburbs. Because

of the large inputs of high-transformity nonrenewable resources and human services that

are required to develop and maintain all forms of urban structure, it is likely that the

transformities for urban storage will be larger than those for natural structure in the

surrounding areas.

Brown (1980, 1981) observed a hierarchical phenomenon in the sizes and

distributions of cities in the regional landscape of Florida, and described the phenomenon

in terms of energy and transformity hierarchies. Whitfield (1994) demonstrated this

spatial phenomenon in his study of the energy distribution patterns of Jacksonville,

Florida. Huang (1998) found similar patterns in a study of Taipei, Taiwan.




















































Increasing Transformity





Figure 1-2: Conceptual diagram for the spatial arrangement of various elements of a
regional landscape arranged in order of predicted transformity (adapted from Huang,
1998, and Brown, 1981).









Energy Signatures


Odum (1983) suggests that all systems self-organize to maximize power flow

according to the energy sources that are available to the system. The term 'energy

signature' has been used by Odum (1983) to describe the type and magnitude of energy

flows to a particular system. The nature and magnitude of these energy sources helps

determine the nature and magnitude of the productive processes and the storage that

occur in the system. This concept can be applied to both ecological and economic

systems. For instance, Twilley (1995) found that energy signatures could be used to

predict the morphological structure and ecological processes of mangrove ecosystems,

and Odum (1983) has used energy signatures to characterize the level of development of

countries and regions.

These energy signatures are typically presented as bar graphs with the

magnitudes of each of the various energy sources for the system accounted for in the

same units of measure (for energy or EMVERGY). Odum (1983) prefers that the sources

be arranged from left to right on the graph in order of increasing transformity to improve

understanding of the energy transformation hierarchy.

Whereas, the concept of energy signatures has been previously used to

characterize total systems, it is proposed here that they could also be used to characterize

distinct spatial units in the landscape. It is also proposed that the concept can logically be

expanded to include the magnitude of storage that are within each spatial unit in the

signature. An energy signature of areal land units that includes both energy flow and

storage magnitudes (measured in units of the same kind-in this case, EMERGY) could

be used as a multivariate land classification scheme that links classes with processes.









Land Area Unit Model Diagram


The unit model system diagram shown in Figure 1-3 is proposed as a general

basis for spatial modeling of the EMERGY flows and storage in equal-size units of land

area in an urban and/or regional system. The diagram shows how the EMERGY sources

from outside the land area unit model system flow in and interact with other flows

through production processes that maintain or build storage of EMERGY. This model

does not attempt to model flows between different land area units-each unit is

considered to be a separate unit model system with flows coming from unspecified points

outside of the unit model system and going to unspecified points outside the unit system.

The spatial model created in this study is based on this diagram. In other words,

the individual storage and flow components of the spatial model correspond to elements

of the unit model system diagram. The diagrammatic elements that have corresponding

components in the spatial model are numbered for cross-referencing in the text.

To create the spatial model, the magnitude of each energy flow into each land

area unit and each energy storage within each unit was estimated based on the

characteristics and features of each land area unit. The EMERGY associated with each

energy flow or storage was calculated using the appropriate transformities for each type

of energy flow or storage. Energy or EMERGY signatures for land area units are based

on the magnitude of each flow and storage included in the diagram.

The horizontal dimension used in this study for the land area unit model was 100-

meter squared (one-hectare). The vertical dimension of each land unit was 250 meters

from the ground to the lower boundary and 100 meters from the ground to the upper

boundary of the land area unit. The time dimension used in this study is one year.































n .........., xr . ,Recycled
1" 8 Solid Waste
Land Area Unit Boundary 7 : Wastes
ndar Not Recycled
= EMERGY Flow ........................ = EMERGY Flow = EMERGY Storage
Evaluated Not Evaluated

Figure 1-3: EVIMERGY flows and storage evaluated for each land area unit in the spatial model. Individual flow and storage elements
have been numbered to provide cross-references in the text (see Figure 2-1). Note: GPP = gross primary production.











Technological Basis for the Study


Throughout much of modern civilization, the paper map has been the preeminent

tool for studying and managing the spatial patterns of natural processes and human

settlement. However, recent advances in computer technologies and in the field of

geographic information sciences have resulted in the development of sophisticated

geographic information systems (GIS) that offer a new paradigm for the study of spatial

processes and patterns-the digital spatial model.

There has been dramatic growth in the use of GIS technology in the last decade.

Most government agencies with land planning or management responsibilities have

adopted the technology, and many have created, or are in the process of creating, GIS

databases that describe the features of the land that they study, regulate, or manage. This

phenomenon has resulted in the widespread public availability of government-developed

GIS databases (Lambert and Zwick, 1997).

Typically these GIS databases have been created to support the very particular

spatial information needs of the agency, but often they can be used for other purposes

because of the general nature of the data. For example, GIS databases developed by

property appraisers contain descriptive attributes and, most significantly, the location of

buildings that can be used to precisely map the location of estimates for the storage of

energy and EMERGY in urban structure.

Detailed municipal GIS databases that describe property, road, and utility

structures form the basis of a new opportunity to examine the spatial distribution patterns

of energy and EMERGY in more detail than has been possible using paper maps and







13

traditional techniques. At the same time that the relevant digital spatial data have become

available, the computer hardware with enough computational power to manipulate these

large, complex databases is becoming less expensive. And finally, the capabilities of the

GIS software have evolved to make working with large databases much more practical

than in the recent past.

As previously stated, a secondary goal of this study is to demonstrate how recent

advances in technology can be applied to the study of the spatial patterns and relations of

energy flows and storage in landscapes. To a large degree, this study is made possible

by the temporal convergence of three necessary ingredients-data, hardware, and

software. It is hoped that by mixing these ingredients together with EMERGY theory

that new insights will be revealed in the results.



Previous Studies of the Spatial Distribution of Energy in the Landscape


About two decades ago there were several studies conducted that used energy

transformation hierarchy theory as a basis for studying urban and regional systems

(Brown, 1973; DeBellevue, 1976; Littlejohn, 1973; Maltby, 1977; H.T. Odum, 1971;

Regan, 1977; Sipe, 1977; Sipe, et.al., 1979; Steller, 1976; Zucchetto, 1975; Kangas,

1983). All of these studies used energy systems diagrams to model aspects of the urban

system. Most of these studies used land use maps to estimate areas of different land uses.

These area estimates were used to make estimates of energy flows and storage in the

systems being studied.

Many textual references were made in these studies regarding the relationships

between energy transformation hierarchy and the spatial patterns observed in the system







14

being studied. However, if any maps were included, they were usually simple hand

drawn diagrammatic maps that were used to document these relations or land use maps

that were used to make the area calculations.

In 1981 an energy analysis workshop was conducted at the University of Florida

to assess the research needs for developing a better understanding of the relationships

between humanity and nature (Brown and Odum, 1981). Most of the primary research

needs that were identified through this process were related to the need for a better

understanding of how ecological and economic processes affect the spatial organization

of landscapes.

There were several studies conducted about two decades ago that were

specifically aimed at examining the spatial patterns of the distribution of energy flows

and storage in regional landscapes as expressed in various embodied energy units. This

body of research was the initial inspiration for this research work. All of these early

studies used similar methods to estimate energy flows and storage per unit of area.

For instance, as part of the study 'Carrying Capacity of Man and Nature in South

Florida' (Odum and Brown, 1976), Costanza(1975) studied the spatial distribution of

land use subsystems in south Florida over the period from 1900 to 1973. He calculated

the metabolism and structure for each subsystem by multiplying the energy flows and

storage occurring in each subsystem by an energy quality factor and expressed the

results in fossil fuel equivalents (FFE).

Costanza's study used methods to measure areas of each land use that would be

considered rather primitive when compared to today's technological methods. First land

use maps were prepared from air photos, then these maps were cut up into pieces and







15

weighed to determine the area of each land use in each county. These rough area

calculations were used to calculate subsystem energy flows and storage by multiplying

the area times a generalized estimate of the flow or storage per unit area for each

subsystem.

He produced coarse resolution regional maps showing the spatial distribution of

incoming energies using isopleths, and spectral distribution graphs showing mean energy

flow per unit area, referred to in this study as energy density. Energy flow per unit area

was later referred to as power density (Odum, Brown, and Costanza, 1976).

Costanza (1979) utilized the data from this earlier research to build a spatial

simulation model for south Florida. This model divided the south Florida region into 88

16 by 16-mile cells. This spatial model was linked to an energy diagram simulation

model. According to Costanza, the model did a "fairly good job" of predicting the

historical development of spatial patterns between 1900 and 1973.

Brown (1980) studied the hierarchical organization of urban and regional

landscapes using embodied energy (expressed as coal equivalents, CE) as a basis for his

calculations. He calculated the power density and volume of structure for 11 urban land

uses. Urban power densities were calculated based on the embodied energy in the

electricity and other primary fuels used, and on the embodied energy in goods and

services. The power densities for natural ecological systems were measured by

estimating the gross primary production of each type of system.

Brown calculated areas of land uses in his study areas in the same manner as

Costanza, by cutting up land use maps and weighing the pieces. Estimates of direct

energy use within urban land use classes were derived from representative samples of







16

yearly energy consumption from utility records. These average figures were then

multiplied by the number of acres of the corresponding land use class. Estimates of

volume of structure per land use were also based on representative samples. Using a ratio

of the embodied energy per dollar of assessed value of 20,000 Cal CE/$, Brown

calculated an average figure for embodied energy in urban structure per acre of each

category of land. Embodied energy in goods and services was assumed to be

proportional to the consumption of direct energy (electricity and fuel). Brown used a

ratio of 21,000 Cal CE/$ to calculate the embodied energy in goods and services.

Using these data, he produced a series of graphs that demonstrated the

hierarchical distribution land use areas within his study areas and the phenomenon of

increasing levels of embodied energy flows and storage with increasing complexity of

land use. His study also demonstrated the hierarchical distribution of cities in Florida

when measured by their total embodied energy flow and storage.

Alexander (1981) used a dynamic landscape simulation model of Flagler County,

Florida to study the effects of regional development policies on settlement patterns. His

model combined energy theory with thematic mapping methods developed by Ian

McHarg (1969). The computer simulation model he used created simple maps using

symbols ( +, x,*,--, etc.) to plot out the spatial results. This is the first example (known to

the author) where a computer-generated map was created that was based on energy

transformation theory. Another early example of using computers to generate energy

maps was a study by Richardson (1988) that used a simple, but general, dynamic

simulation model to demonstrate how systems will self-organize spatially to maximize

power flows from available sources.







17

A more recent study by Whitfield (1994) examined the spatial patterns of

EMERGY flows and storage for Jacksonville, Florida. Whitfield evaluated the resource

flows and storage for 17 different land uses in his study area. He used averaged figures

for each land use to calculate the EMPOWER density, EMERGY storage, and

transformity for each land use.

Whitfield's study includes a collection of graphs he called "resource use

signatures" that show the amount of each resource flow by land use. Most significantly,

he generated maps of the total EMERGY flows and storage for Jacksonville, as well as

spatial profiles that he called "landscape signatures". These landscape signature graphs

show the quantity of EMERGY flow or storage on the y-axis and the x-axis indicates the

distance along a straight transect through the city. From these maps and profiles, the

spatial pattern of resource use and structure in the study area is clearly evident.

In a recent study of energy hierarchies in the landscape, Huang (1998) studied the

effects of the spatial convergence of energy on the evolution of spatial patterns of Taipei,

Taiwan. He used a model (similar to Figure 1-2) to explain how Taipei is spatially

organized to converge lower transformity energy flows from the natural and agricultural

areas into areas of higher concentration, with the area of highest concentration (energy

flow density) being at the urban center of the system. Huang (1998) observed that

"... mutual non-linear interactions between population, area, and assets of each zone

result in a self-organizing spatial system, from which an urban hierarchy evolves."

In this study, Huang estimated the magnitude of 14 different renewable and

nonrenewable flows of energy and EMERGY for 1178 administrative districts in Taipei.

He did not estimate energy stored in urban or natural structure, but implied that the level







18

of urban structure develops in tandem with the level of energy density. Using the flow

values estimated for each district, Huang used statistical analysis to cluster the districts

into six energetic zones to simplify his model and describe the concept of energy

convergence based on these zones.

Huang used the latest GIS technology to make maps and conduct analysis of

spatial patterns. For instance, using the estimated flows, he generated maps of the

individual flows, the combined renewable and nonrenewable flows, and EMERGY

investment ratio maps. Several EMERGY indices were calculated for each of the

energetic zones including: transformity of the zone, total zonal EMPOWER density,

EMERGY investment ratio for the zone, and per capital EMERGY used in the zone.



Study Area


The area chosen for spatial modeling and study is Alachua County, Florida, USA.

The map in Figure 1-4 shows where Alachua County is located in the State of Florida.

The model is based on data from the years 1993 and 1994. According to the 1990 U.S.

Census of Population (U.S. Census Bureau, 1992), the county had a population of

142,081 adults and 39,515 children. The county has a service-oriented economy with

the largest employer being state and local government. There is very little industrial

manufacturing, and only a small agricultural industry (cattle, vegetables, forestry).

The largest city in the county, Gainesville, is located in the center of the county,

and there are several small towns distributed in a radial arrangement around Gainesville.

Most (94%) of the population of the county works in the county, and most of the county's

employers are located in the City of Gainesville. Gainesville is also the location of







19

several regional shopping centers and regional hospitals and health service providers.

The University of Florida is also located in Gainesville. The University exerts a strong

influence on the spatial organization of the county in terms of the location of high-density

housing, service providers, and road infrastructure.

The rural areas of the county are best described according to the eastern and

western halves of the county. The eastern portion of the county is characterized by a

fairly level landscape with many isolated small wetlands and several large lake, stream,

and wetland systems. A large portion of the eastern half of the county is used for forestry

production. There is relatively little population growth in this half of the county. In

contrast, the western half of the county is characterized by upland forest types, pastures,

and agricultural crop fields. There are no large lakes or wetlands in this half of the

county. Compared to the eastern half of the county, the western half is experiencing

dramatic residential development pressure radiating out from the City of Gainesville.

Alachua County was chosen because of the familiarity that the author has with the

area. Familiarity was an important consideration for two reasons: first, because of the

level of spatial detail obtainable by using one-hectare square areas for the land area unit

spatial model; and second, because of the abstraction of this detail resulting from

converting everything into energy and EMERGY terms. Although other potential study

areas already had the spatial data that were required for this type of study, it was decided

that it would be more advantageous to be very familiar with the detailed spatial patterns

of the study area. Unfortunately, this decision meant that considerable effort had to be

expended to develop several of the GIS databases (property parcels, buildings, roads, and

land use classifications) that would be required to conduct the study.























\ Federal Highways
State Highways
M Alachua County
State of Florida


Figure 1-4: Location of the Alachua County study area.







21

Study Objectives


The primary objectives for this study are: 1) to develop a general theoretical

approach for the spatial modeling of energy flows and storage in urban and regional

landscapes; 2) to analyze the spatial patterns of energy transformation hierarchy and

energy signatures in the study area using a specific implementation of the general model;

and, 3) to demonstrate how GIS technologies and methods can be used to add new

insight into spatial patterns of energy hierarchy by conducting analyses that would not be

reasonable to do without GIS technology.

1) Develop a general theoretical method. The first objective is to develop a

general method for the spatial modeling of energy flows and storage in an urban and

regional landscape using energy transformation hierarchy theory and the land area unit

diagram model as a theoretical basis.

The previous studies that were cited suggest that urban and regional systems may

develop similar spatial patterns in terms of energy transformation hierarchies. However,

the non-standard methods used in these studies can make direct quantitative comparisons

of the results difficult. This study will demonstrate that the concept of a land area unit

spatial model is a more general approach to modeling the spatial distribution of energy

and EMERGY flows and storage.

2) Study the spatial patterns of energy hierarchy for the study area. The second

primary objective is to study the spatial patterns of the energy transformation hierarchy in

the Alachua County study area.

Because the target audience for this study is urban planners, the specific spatial

model created for this study should be at a spatial resolution and scale that makes the







22

results most useful from the viewpoint of the planner. The resolution of the spatial model

should make it possible for planners to describe and study the energetic characteristics of

the urban landscape in relation to more commonly used land area terms such as land use

and zoning classifications and neighborhoods. The influence of prominent urban

features, such as major roads or shopping centers, should be recognizable in the results.

3) Demonstrate the value added by using GIS technologies and methods. The

third primary objective of the study is to demonstrate how GIS technologies and methods

increase the potential for studying spatial relations in terms of energy hierarchy.

The availability of very detailed GIS databases of urban structural features and

urban flows such as traffic counts and electricity usage makes very detailed spatial

analysis possible. By using GIS analysis methods, patterns of energy distribution may be

found in this detailed data that were not previously intuited. However, one may want to

ask the question in the end as to whether or not the extra detail adds enough new insight

to be worth the enormous effort required to manipulate the large amounts of data.



Organization of the Study


This study is organized around the creation, description, and use for analysis of a

spatial EMERGY model of Alachua County, Florida. The model was created by using the

values of descriptive attributes in several thematic GIS databases as the input for

algorithms that estimate the flows and storage of energy and EMERGY. Detailed

descriptions of the methods and algorithms used to create the model dominate the

methods chapter of the study. In fact, the methods chapter was designed to be a

'cookbook' for others wishing to create similar models for other cities or regions.







23

A conscious effort was made to avoid as much GIS software-specific

terminology in the methods chapter as possible. In other words, the methods were

designed to not require a specific GIS software package for implementation-in fact, the

procedures used should be available in most basic GIS software. However, some generic

GIS terminology has been used for clarity. A familiarity with basic GIS methods will be

required of someone wishing to create their own model using this approach.

The first section of the results chapter uses maps and basic statistics to describe

the results for each of the individual components of the model. The model components

are associated with a flow or storage that was represented in the land area unit model

system diagram (Figure 1-3). An EMERGY and an energy map was made for each flow

or storage represented in the model diagram. The model component maps represent

energy flow density (joules/hectare/year), EMPOWER density (solar

emjoules/hectare/year), energy storage density (joules/hectare), or EMERGY storage

density, referred to in this study as "EMSTORAGE" (solar emjoules/hectare) for the

component flow or storage.

In the second section of the results, appropriate energy and EMERGY component

flows were added together to create "Total Energy density" and "Total EMPOWER

density" maps. Transformity maps are presented that were created by dividing the "Total

EMPOWER density" map values by the "Total Energy density" map values (solar

emjoules/ joules). Similar maps were created for "Total" energy and EMERGY storage.

County-wide energy and EMERGY signature graphs are presented that were

created by summing the all of the land area unit values for each of the component flows







24

and storage. The county-wide signatures were created to characterize the whole study

area as if the County were a single land area unit.

In the third section of the results, three comparative studies are presented that

should be of particular interest to urban and regional planners. Comparisons of the

energy and EMERGY characteristics (including energy and EMERGY signatures and

transformities) are presented for land use classifications, planning units, and

representative neighborhoods.

The fourth and fifth sections in the results present analyses that would be very

difficult to conduct without using GIS technology. For instance, in the fourth section,

EMERGY ratio maps were produced by dividing the values in one EMERGY component

map by the values in another component map. Examples of the EMERGY ratio maps

include the ratio of nonrenewable to renewable EMERGY-a possible index of

environmental loading, and the ratio of EMERGY in urban structure to EMERGY in

natural structure. In the fifth section, spatial context analyses were conducted that utilize

the special capabilities of GIS technology. In the context analyses, the values of each

land area unit were compared to the values of the contiguous units to generate several

spatial context characterizations for each land area unit.

Finally, the last section of the results presents a version of the spatial model in

which the land area units are one-square kilometer (compared to one-hectare). This

version of the spatial model was created to be able to examine the effects of the size of

the land area unit on the results, and to evaluate the usefulness of a more generalized

model for planning applications.














METHODS


General Description of the Model


The spatial EMERGY model components. A general land area unit system

diagram model describing the EMERGY flows and storage for each land area unit was

presented in Figure 1-3. The spatial model that complements this diagram model

includes both energy and EMERGY components that correspond to each of the diagram's

storage and flow symbols. In the spatial model each component is modeled as a data set

called a 'grid'. A grid is a spatial data set that divides the study area into equal-size,

square land area units called 'cells'. Each cell contains a value corresponding to the

magnitude of the flow or storage that the component grid represents.

The number of grids in the spatial model is directly related to the complexity of

the diagrammatic model. For instance, urban structure can be represented by a single

storage tank in the diagram and as a single component grid in the complementary spatial

model. Or urban structure can be modeled more discretely with separate tanks in the

diagram, and separate grids in the spatial model.

In the spatial model, a 'component grid' may be composed of the sum of two or

more 'subcomponent grids.' For example, in the spatial model the 'urban built structure'

component grid is the sum of the subcomponent grids representing the storage in the

buildings, transportation infrastructure, and utility infrastructure.










A primary purpose of creating each of these subcomponent grids separately was

to facilitate the processing of the original source GIS data layers. However, in this study

subcomponent grids were also considered to be discrete elements of their associated

component flow or storage that were potentially worthy of being used independently in

planning analyses.

The concept of a subcomponent grid and it's related component grid in the spatial

model is implicit. It may help to think of a subcomponent grid representing a storage as a

compartment within a storage tank symbol in the diagram model, and a subcomponent

grid representing a flow as one of the forks along a split flow symbol. Figure 2-1

illustrates these concepts.

Tables 2-1 and 2-2 list the component and subcomponent grids included in the

spatial EMERGY model that was developed for this study. These tables include a cross

reference to the numbers used in Figure 1-3 to identify the individual flow and storage

elements in the land area unit diagram model that are included in the spatial model.

Any given component grid, or even a subcomponent grid, may also be the sum of

one or more 'intermediate component grids'. The split flows shown in Figure 1-3 for

water use (element #3), fuels used in buildings and agriculture (element #4b), and goods

used (element # 5) were calculated using 'intermediate component grids'. Many

intermediate component grids were created as intermediate steps in the creation of

component or subcomponent grids. Unlike subcomponent grids, they exist, in this study,

only to facilitate processing of original GIS data layers, and will not be considered

explicitly in the general analysis of the model or any planning analyses. The intermediate

component grids could, however, be used for more detailed analysis in future studies.











Table 2-1: Component and subcomponent (shown shaded) grids representing flows of
energy and EMERGY that were included in the spatial model.

Description of Figure 1-3 Energy EMERGY
General Individual Flows Item Component Component
Category Calculated Number Grid Name Grid Name


Renewable Transpiration 1 renew_en renew
Resources Used


Natural Systems Gross primary 2 gpp_en gpp
Metabolism productivity


Water Water used 3 wtruse_en wtruse
Used by Man for domestic,
commercial,
and agricultural
purposes

Direct Use of Sum of use in 4 fuel_en fuel
Fuels and buildings, grounds,
Electricity agriculture,
transportation
Use for
transportation 4a trn fulen trn_ful
Use in buildings,
grounds, and 4b bag_ful_en bag_ful
agriculture

Goods/Services Consumable and 5 goods_en goods
Consumption durable goods
(and services) used


Human Services 'In situ' services 6 servicemen service
from local people


Solid and Liquid Solid/liquid wastes 7 waste_en waste
Wastes that are not recycled


Recycled Recycled solid 8 recycle_en recycle
Wastes wastes










Table 2-2: Component and subcomponent (shown shaded) grids representing storage of
energy and EMERGY that were included in the spatial model.



General Description of Figure 1-3 Energy EMERGY
Category Individual Storages Item Component Component
Calculated Number Grid Name Grid Name


Natural Systems Sum of 9 natstr_en natstr
Structure biomass, surface
(includes and ground water,
agriculture, and organic matter
forestry, and in soils
urban forest)

Biomass 9a biostr_en biostr


Surface and 9b wtrstr_en wtrstr
groundwater


Organic matter in 9c soilom_en soilom
soil


Urban System Sum of 10 urbstr_en urbstr
Structure buildings, roads and
utilities infrastructure


Buildings 10a bldg_en bldg


Roads 10b road_en road


Utilities 10c util_en util
infrastructure


People Storage in humans 11 popstr_en popstr














Roads


Buildings


Utilities


Urban Structure


(a)


Figure 2-1: The relationship between a subcomponent grid and it's related component
grid in the spatial model. One can think of a storage subcomponent grid as a
compartment within a storage tank symbol (a), or a flow subcomponent grid as a split
along a flow symbol line (b).










Size of Component Grid Cells. All of the grids in the model have a cell area of

one hectare. Each square cell is 100 meters on each side. Each grid has 583 columns and

596 rows. This results in grids that have a total of 347,468 cells per grid. Only 252,581

cells have data associated with them because the area of the county does not fill the entire

square grid. Each of the cells of each (sub)component grid is spatially coincident with

cells in the other (sub)component grids.

Units of measure. The number associated with each grid cell in a (sub)component

grid represents either the total energy or EMERGY stored or the total energy or

EMERGY flow per year occurring in the geographic area of that cell.

The numbers for all component grids representing EMERGY storage are in units

of solar emjoules per hectare (sej/ha) and are referred to as EMSTORAGE density

values. The energy storage component grids are in units of joules per hectare (j/ha).

EMERGY flows are measured as the total annual flow of solar emjoules per

hectare per year (sej/ha/yr). Energy flows are measured in units of joules per hectare per

year (j/ha/yr).

EMPOWER has been defined as the EMERGY flow per unit of time and

'EMPOWER density' as the EMPOWER per unit of area (EMPOWER/area) (Odum,

1996). In this model, the numbers associated with each cell in each of the EMERGY

flow component grids represent the 'component EMPOWER density' of each cell.

Geographic extent and time period. The spatial model covers the geographic

extent of Alachua County, Florida. All calculations of EMERGY storage and flow were

based on data from either 1993 or 1994. These years were chosen because of the

availability of primary data and statistics. A single reference year for the model was not










possible due to limitations imposed by data availability. All energy or EMERGY flow

numbers only represent the total flow occurring for a period of one year, which occurred

at some time during the two-year reference period. In some cases, calculations represent

an average of data for the two years; in other cases, data were only available for one or

the other of the two years. Materials with a turnover time of less than a year are

calculated in the model as flows. Materials with longer turnover times are calculated as

storage in the model.



Software and Hardware Used


GIS and imagery processing software. The primary GIS software used for this

study was Arc/Info Version 7.1, a product of the Environmental Systems Research

Institute, Inc. (ESRI), Redlands, California. Another ESRI software product called

ArcView GIS Version 3.0 was also used extensively.

GIS software terminology. An effort was made to describe these methods in

terms that do not require the reader to be familiar with GIS software-specific jargon.

However, familiarity with a few terms is essential. In ESRI software terminology, a

vector-type GIS data layer is called a 'coverage.' There are three basic categories of

coverages based on the type of spatial feature that they represent: points, lines, or

polygons. A raster-type GIS data layer is called a 'grid' (ESRI, 1996).

Computer hardware. Because of the intensive computer processing required for

executing some of the steps in creating this model, a workstation computer (SUN Ultral

Model 170 workstation, UNIX operating system) was used to create the model and

perform the analyses. The initial development of the model required the use of










approximately 6 gigabytes of disk storage. This large amount of disk storage was

required primarily to facilitate the collection and processing oOf the raw GIS databases

and for the manipulation of this raw data to create the final model. The model in its final

form can be viewed and analyzed using far less expensive desktop PC computers.



Development of the GIS Database


The first major step in the development of the spatial EMERGY model was the

development of a GIS database that would provide the type of information needed to

estimate energy and EMERGY flows and storage listed in the previous tables. This

required the collection, review, and processing of existing data, and subsequently, the

creation of several new coverages.

Existing coverages. Over the past few years, many GIS databases have been

created by various federal, state, and local government agencies in support of their

specific missions. Many of these GIS databases for Florida have been collected and

processed so that they have a standardized digital format (ESRI coverages) and map

projection through the Florida Geographic Data Library (FGDL) project (Lambert and

Zwick, 1997). In this study the FGDL map projection, described in Figure 2-2, was used

for all existing and new coverages and grids. Approximately 100 GIS databases,

including those available as a result of the FGDL project, were reviewed for their

potential contribution to this study. Based on a review of the available databases, only a

few were eventually used directly in the creation of the final model.





























Projection ALBERS EQUAL AREA
Datum NAD27
Units METERS
Spheroid CLARKE1866
Parameters:
1st standard parallel 24 0 0.000
2nd standard parallel 31 30 0.000
central meridian 84 0 0.000
latitude of projection's origin 24 0 0.000
false eating (meters) 400000.000
false nothing (meters) 0.00000


Figure 2-2: Description of the map projection and coordinate system used for all
coverages and grids in the GIS database and model.










Several of the databases that were not used directly still served important roles as

ancillary, supporting data. For instance, municipal limits and boundaries of protected

areas help one understand why a particular observed spatial pattern might have

developed. There is also a potential for some of these databases to be used in the future

for additional analyses.

New coverages. Based on the review of the existing databases, several new

coverages had to be developed including parcels, buildings, and roads. The creation of

these new coverages required a great deal of time and effort. However, they were

indispensable inputs to the development of the model. The following sections briefly

describe the methods used to develop these new coverages.

Property parcel coverage. The property parcel coverage was created by

converting the Alachua County Property Appraiser's GIS database, in MapGrafix

software format, into the Arc/Info GIS polygon coverage format. This effort required the

development of a new software format conversion methodology (Lambert, 1996).

A property parcel coverage is a valuable primary data source for the development

of this type of model because of the attribute data associated with each polygon feature.

For instance, attributes associated with the built structures include dollar value of the

structures, and the year they were built. Other attributes indicate the primary type of

economic activity occurring on the parcel based on Florida Department of Revenue land

use codes (FDOR, 1990) and total square footage of buildings. The property parcel

coverage was also valuable as one of the intermediate data processing inputs for several

of the final components of the model.










Building coverage. The creation of the building coverage was made possible by

the creation of the property parcel coverage described above. The polygon centroids of

those polygons in the parcel coverage that had a dollar value greater than zero for built

structure were converted into a point-feature coverage.

The building coverage includes attribute data for all built structure on a property.

For instance, there are attributes for the dollar value of miscellaneous structure such as

fencing, driveways, etc. in addition to the primary building structures. Consequently, in

some cases, point features may not represent buildings, but rather only other built

structure. The attribution of the coverage allowed for consideration of these cases when

calculating fuel use in buildings, etc., however, it was a complicating factor in the

calculations, and may have introduced a few anomalies in some of the component grids.

All of the point features that were derived from polygons that were 10 acres or

less in area, were assumed to approximate the location of the built structures accurately

enough for the spatial resolution of the model. On the other hand, it was determined that

the accuracy of the location of each of the point features that were derived from parcel

polygons that were greater than 10 acres in size should be reviewed.

One objective of reviewing each of these point features was to identify those

features with obviously anomalous locations, and to manually reposition the feature to a

more likely location within the limits of it's originating polygon. Typical anomalous

conditions included point features in the middle of lakes or wetlands. A second objective

of this review was to identify, and reposition if necessary, those point features with dollar

values greater than one million. A 1995 SPOT panachromatic satellite image










(10-meter resolution) was a used as an aid to reposition each point feature to its most

probable location within its originating parcel polygon. All of this tedious work was

necessary because the spatial accuracy of the building point coverage would be reflected

in several of the final components of the model that would be derived from various

attribute data associated with the building coverage.

Road coverage. A new road coverage had to be created because the existing

coverages for roads were not spatially accurate enough for the model and did not have the

descriptive and quantitative attributes needed to calculate estimated EMERGY flows and

storage.

The initial spatial data for this new coverage was converted from several AutoCad

digital format files that were created as part of the Alachua County Control Densification

and Identification of Land Corners Project (Southern Resource Mapping Co., 1990).

This relatively spatially-accurate digital map of the roads of Alachua County was derived

from 1988-vintage aerial photography. New roads, built since 1988, were added to the

coverage using the parcel coverage as a guide.

Attribute data for functional classification, number of lanes, and estimated

average annual daily traffic was added to each road segment in the coverage (ADOT,

1995, and FDOT, 1995). These attribute data were necessary for the EMERGY storage

and flow calculations as described in later sections.

Land use and cover coverage. A county-wide land use and cover coverage was

created for this study by combining the pre-release version of the 1995 Suwannee River

Water Management District (SRWMD) land use and cover coverage (SRWMD, 1995)

with the 1990 St. John's River Water Management District (SJRWMD) (May, 1993)










land use and cover coverage. Both coverages were derived from aerial photo

interpretation, and are based on the Florida Land Use and Cover Classification System

(FLUCCS) (FDOT, 1985). This resulted in a hybrid land use and cover coverage based

on different dates. Fortunately, this did not affect the analysis appreciably because the

small portion of the county not included in the SRWMD coverage was in the southeast

portion of the County. This part of the County had not changed much since 1990 (based

on a review of the 1990 data using the SPOT panachromatic satellite imagery for 1995

for comparison).



General Methods for Creating Component Grids


The general methods used to process the primary data coverages into the

(sub)component grids of the spatial model are described in this section. Other

(sub)component-specific methods are described in later sections.



Analytical Coverages


Each of the (sub)component grids created was actually derived from an

'analytical' coverage that was created to facilitate the calculations required in this study.

These 'analytical' coverages are significantly modified versions of the existing or new

'raw' primary data coverages.

Every 'analytical coverage' has had several new data items added to its feature

attribute table to facilitate the energy and EMERGY calculations. Each analytical

coverage has also been 'overlaid' with a county-wide polygon coverage, called

'alanetl00cov', which is comprised of 100-meter square polygons corresponding to the










one-hectare cells in the final model. Other unique modifications to specific analytical

coverages are described in later sections.

Each of the model's component grids was created by first making energy and

EMERGY calculations based on attribute data contained in the analytical coverages, and

then converting the analytical coverages into grids using methods developed for each

type of coverage (i.e., point, line, polygon).

Figure 2-3 describes the general conversion steps that were used for all types of

analytical coverages, and the following sections discuss specific methods for point, line,

or polygon-type analytical coverages. The unique energy and EMERGY calculations and

methods used to create each of the intermediate, subcomponent, or component grids in

the model are described in later sections. The actual file names of each grid or coverage

created in this study have been used in this report to facilitate the use of the GIS digital

database (which will be published separately on CD-ROM) by other researchers.



(Sub)Component Grids from Point-feature Coverages


The following steps were used to convert all of the analytical coverages that were

comprised of point features into the grids used in the model. The method was designed

to convert these point coverages into grids that have individual cell values that represent

the sum of all of the energy or EMERGY values (for a particular flow or storage)

calculated for the point features found within the area of each one-hectare cell.
























































Figure 2-3: General steps used to convert all analytical coverages into the intermediate,
sub-component, or component grids.


Step One
Overlay analytical coverage with 100-meter square polygons
of the 'alanetl 00cov' coverage to transfer the
unique 'id' for each polygon (found in the 'emgrid-id' attribute item)
to the spatially coincident features in the analytical coverage.


Step Two
Calculate the amount of energy and EMERGY
that is associated with each feature in the analytical coverage
using appropriate values in the feature attribute table.


Step Three
Create a 'summary data table'
that contains values for the total energy or EMERGY
of all point, line, or polygon features that is summarized by
the 'unique id' of each 100-meter-square polygon.


Step Four
Merge the 'summary data table' with the feature attribute table of the
'summary coverage' called 'emnetl00cov' using the values in the
'emgrid-id' attribute items (the unique id of the 100-meter-square polygons)
of both tables as the relational values.


Step Five
Create the energy or EMERGY grid
using the POLYGRID Arc/INFO command
and the appropriate item in the 'emnetl00cov' feature attribute table
as the data input source for the command.











Step 1: Each analytical point coverage was overlaid with a polygon coverage

called 'alanetl00cov' using the Arc/Info IDENTITY command (ESRI, 1996). This step

only needs to be executed once for each of the analytical coverages.

The 'alanetl00cov' coverage divides the entire county into 100-meter-square

polygons that are spatially coincident with the grid cells in the final model. Each polygon

in the 'alanetl00cov' coverage has a unique identification number stored in an attribute

item called 'emgrid-id'. As a result of the overlay operation, every point feature that falls

within the area of any one the polygons in the 'alanetl00cov' coverage, as illustrated in

Figure 2-4, is attributed with the same unique identification number from the

'alanetl00cov' coverage.

Step 2: The amount of each energy or EMERGY flow or storage associated with

each point feature was calculated using the appropriate attribute items in the point feature

attribute table. The specific calculations for each type of energy or EMERGY flow or

storage are described later.

Step 3: The Arc/Info STATISTICS command (ESRI, 1996) was used to create a

summary database table of the total energy or EMERGY of all point features found

within each 100 meter-square polygon.

Step 4: This summary database table was then merged (using the unique number

in the 'emgrid-id' attribute item as the relational item value) with the polygon attribute

table of the 'emnetl00cov' summary coverage using the Arc/Info JOINITEM command

(ESRI, 1996).





















































100 meters

Figure 2-4: Each analytical coverage was overlaid with a polygon coverage that divides
the entire county into 100 meter-square polygons. These polygons are spatially
coincident with the grid cells in the final model. Each polygon in the 'alanetlOOcov'
coverage has a unique identification number stored in an attribute item called 'emgrid-
id'. As a result of the overlay operation, every point, line, or polygon feature that falls
within the area of any one the polygons in the 'alanetlOOcov' coverage is attributed with
the same unique identification number from the 'alanetlOOcov' coverage. As illustrated
above, polygon and line features are split at the points of intersection with each of the
100-meter square polygons.


-------------------------

#2


---------------------------












The 'emnetlOOcov' summary coverage has the same 100 meter-square polygons

(which are spatially coincident with the grid cells in the final model) with the same

unique numbers (in the 'emgrid-id' attribute) as the 'alanetl00cov' coverage. The

'emnetl00cov' summary coverage was used to store all of the final summarized energy

and EMERGY data for each polygon.

Step 5: Each final intermediate, subcomponent, or component grid was created

by executing the Arc/Info GRID function POLYGRID (ESRI, 1996) using the

summarized values for energy or EMERGY flows or storage from the appropriate

attribute items in the 'emnetl00cov' summary coverage as the input source for the

POLYGRID function. The values in the resulting grid represent the total energy or

EMERGY of all of the point features within each 100 meter-square area.



(Sub)Component Grids from Line-feature Coverages


The following steps were used to convert all analytical coverages that were

comprised of linear features into the grids used in the model. Similar to the previous

method employed for conversion of point features, this technique was designed to

convert line feature coverages into grids that have individual cell values that represent the

sum of all of the energy or EMERGY values calculated for the linear features found

within the area of each 100 meter-square cell.

Step 1: Each analytical line coverage was overlaid with the polygon coverage

called 'alanetl00cov' coverage using the Arc/Info IDENTITY command (ESRI, 1996).

In contrast to performing this operation on point coverages, in which the point features do










not get altered in any way other than receiving an additional attribute, this overlay

operation significantly alters the structure and number of linear features. Essentially, all

linear features are 'split' at each of the points where they intersect the border of any one

of the 100-meter-square polygons in the 'alanetl00cov' coverage (see Figure 2-4).

Each line feature in the modified analytical coverage retains the values for each of

its attributes except for the 'length' attribute. The value of the 'length' attribute is

recalculated to reflect the length of the feature that is within each 100 meter-square

polygon. This step needs to be executed only once for each analytical coverage.

Step 2: As a result of step one, each of the split linear features has an 'emgrid-id'

attribute item value indicating which of the 100 meter-square polygons in the

'alanetl00cov' coverage it is located within.

The amount of energy or EMERGY flow or storage associated with each feature

was then calculated using the appropriate attributes multiplied times the length of the

segment of the feature that is contained within each of the polygons. The specific

calculations for each type of energy or EMERGY flow or storage are described later.

Step 3: The Arc/Info STATISTICS command (ESRI, 1996) was used to create a

summary database table of the total energy or EMERGY of all the line features found

within each 100 meter-square polygon.

Step 4: This summary database table was then merged (using the unique number

in the 'emgrid-id' attribute item as the relational item value) with the polygon attribute

table of the 'emnetl00cov' coverage using the Arc/Info JOINITEM command (ESRI,

1996).










Step 5: Each final intermediate, sub-component, or component grid was created

by executing the Arc/Info GRID function POLYGRID (ESRI, 1996) using the

summarized values for energy or EMERGY flows or storage from the appropriate

attribute items in the 'emnetlOOcov' summary coverage as the input source. The values

in the resulting energy or EMERGY grid represent the total energy or EMERGY of all of

the linear features within each 100 meter-square area.



(Sub)Component Grids from Polygon-feature Coverages


The following steps were used to convert all analytical coverages that were

comprised of polygon features into the grids used in the model. This technique was

designed to convert polygon feature coverages into grids that have individual cell values

that represent the sum of all of the energy or EMERGY values calculated for the polygon

features found within the area of each 100 meter-square cell.

Step 1: Each analytical polygon coverage was overlaid with the polygon

coverage called 'alanetl00cov' coverage using the Arc/Info IDENTITY command

(ESRI, 1996). In a manner similar to the 'splitting' of linear features, and in contrast to

point feature coverages, each polygon in the coverage is 'split' along the borders of any

one of the 100 meter-square polygons. This effect is illustrated in Figure 2-4.

Each resulting polygon feature retains the values for each of its attributes except

for the 'area' attribute. The value of the 'area' attribute is recalculated to reflect the area

of the feature that is within each 100 meter-square polygon. This step only needs to be

executed once for each analytical coverage.










Step 2: As a result of step one, each of the split polygon features has an 'emgrid-

id' attribute item value indicating which of the 100 meter-square polygons it is located

within. The amount of energy or EMERGY flow or storage associated with each feature

was then calculated using the appropriate attributes multiplied times the area of the

feature that is contained within each of the 100 meter-square polygons. The specific

calculations for each type of energy or EMERGY flow or storage are described later.

Step 3: The Arc/Info STATISTICS command (ESRI, 1996) was used to create a

summary database table of the total energy or EMERGY of all the features found within

each 100 meter-square polygon.

Step 4: This summary database table was then merged (once again, using the

unique number in the 'emgrid-id' attribute item as the relational item value) with the

polygon attribute table of the 'emnetl00cov' coverage using the Arc/Info JOINITEM

command (ESRI, 1996).

Step 5: Each final component grid was created by using the Arc/Info GRID

function POLYGRID (ESRI, 1996) and the summarized values for energy or EMERGY

flows or storage from the appropriate attribute items in the 'emnetl00cov' summary

coverage as the input source. The values in the resulting intermediate, sub-component, or

component grid represent the total energy or EMERGY of all of the polygon features

within each 100 meter-square area.










Specific Methods for Creating Flow Component Grids


The following sections describe the specific calculations and methods used to

create each of the energy and EMERGY flow component and subcomponent grids for

Alachua County that were listed in Table 2-1 and diagrammed in Figure 1-3.



Natural System Flows-Gross Primary Productivity


The renewable, environmental energies available to a system include sunlight,

deep earth heat, tides, surface wind, physical and chemical energy of rain and rivers.

Through photosynthesis, plants have the ability to efficiently capture and process these

natural energy flows, thus play the role of the primary production component of the

world's natural ecosystems. Science has devised many ways of measuring the value of

the contributions made by the earth's vegetation, but one of the most basic ways of

assessing the amount of work contributed by plants is to measure their gross primary

productivity. Gross primary productivity is a measure of the total energy captured, or

organic matter created, by green plants per unit surface and time. The proportion of this

production that is left after respiration is called net primary production (Whittaker, 1970,

E. P. Odum, 1971).

Gross primary productivity is a more appropriate measure to use for this study

than net primary productivity because it still recognizes the value of production for the

maintenance of structure, as in the case of 'climax' forests, when net production is often

very low (Bernatzky, 1978, Waring and Schlesinger, 1985, Packham et al.., 1992, Tivy,










1993). Several studies similar to this one (Costanza, 1975, Brown, 1980, and Whitfield,

1994) have used gross primary productivity (GPP) for similar reasoning.

Unfortunately, GPP is difficult to measure except in laboratory experiments or by

inference from net production (Whittaker and Marks, 1975). Consequently, data on GPP

for ecosystems are very limited. A major effort to measure the primary productivity of

the world's ecosystems was conducted in the 1970s called the International Biological

Program (IBP) (National Academy of Sciences, 1975). The focus of this coordinated

research effort was to measure net primary productivity (NPP) and biomass of the

world's ecosystems (Lieth, 1975, Lieth, 1978). As a result of these efforts, many

estimates of NPP and biomass have been published from which GPP can be extrapolated

(Art and Marks, 1971, Rodin et al., 1975, Leith, 1975, Olson, 1975, Reichle, 1981).

Similar efforts continue today in recognition of the importance of the role of global forest

resources in the global carbon cycle (Apps and Price, 1996, Archibold, 1995).

Satoo (1982) pointed out that estimating GPP from NPP can, however, be difficult

because it depends on respiration rates that vary in relation to both environmental and

stand conditions. For instance, the proportion of GPP that is measured as NPP varies

with temperature, season, and age of stand. Complicating the issue further, these early

measurements are coming under scrutiny recently because they often only estimated

aboveground biomass (Long et al., 1992). Despite these difficulties in measuring GPP

accurately, it is conceptually an appropriate measure of the work contributed by the

vegetative component of natural systems.

Whereas GPP is a measure of the work contributed by natural systems, it is not a

measure of the amount of natural, renewable energies used in the production. Odum










(1983, 1996) suggests a method of deriving a reasonable measure of the EMERGY from

renewable sources that is used by terrestrial ecosystems is to use the amount of water

transpired by the ecosystem. The EMERGY in transpired water is in effect an integration

of the EMERGY in sun, wind, and rain, and avoids double-counting of these inputs

(Odum, 1996). In cases where additional EMERGY sources (which may be non-

renewable) are significant to production, the EMERGY of these sources must be added as

well to find the total EMERGY contributing to GPP. Transformities for the GPP of

natural systems can be calculated by dividing the total EMERGY contributing to GPP by

the energy in GPP.

Gross primary productivity (GPP) was used in this study as a measure of the work

contributed by natural systems. The amount of water transpired by ecosystems was used

as a measure of the amount of natural, renewable energies used in the work provided by

these ecosystems (Odum 1983, 1996). Additional non-renewable EMERGY sources that

were significant to production, were added to the total EMERGY contributing to GPP.

Transformities for the GPP of natural systems were calculated by dividing the total

EMERGY contributing to GPP by the energy in GPP. Calculations for important

ecosystems in Alachua County (as they were classified by the land use and cover

coverages) were done to determine energy in GPP, natural and cultural EMERGY flows

contributing to GPP, and transformity of GPP for each ecosystem.

The 'em landcov' analytical coverage. The coverage used to create this

component of the spatial model was the analytical coverage called 'em_landcov'. This

coverage was created in an attempt to more precisely model the variation found in

urbanized landscapes than would be possible with the original land use and cover










coverage described earlier. For instance, the areas occupied by roads are not identified in

the land use and cover coverage (except in cases of major four-lane or interstate roads).

Consequently, a method was devised to 'buffer' (using the Arc/Info BUFFER command

(ESRI, 1996)) the linear road coverage features (by a factor considering number of lanes

and average lane width) so that these paved areas, which do contribute any GPP and do

not use environmental flows in the same manner as natural systems, could be treated

differently from the areas with natural systems. Following the same reasoning, the areas

covered by the footprints of buildings were identified and buffered (according to the

square footage of the building) so that these areas could also be treated differently from

the natural systems.

Another characteristic of the original land use and cover coverage is that some

land use classification categories, such as the residential, commercial, and industrial

classes, do not refer to any type of land cover (or natural system). In these cases,

assumptions were made about the type of natural systems found in these categories.

Based on personal observation, the typical Alachua County low- and medium-density

residential yard consists of large trees with an understory that has been cleared in a zone

adjacent to the dwelling and replaced with turfgrass, ornamental shrubs, and flowers.

This maintained zone of residential landscape is supported by a significant amount of

non-renewable energy sources. In an effort to estimate the areal extent of these

maintained landscape zones, the building footprints were buffered by an additional 10

meters.

Other land use classifications, such as high-density residential, commercial, etc.,

are assumed to have proportionately less vegetation in the maintained landscape zone










than low- and medium-density residential land uses because of larger proportions of the

area used for parking lots, etc.. In the cases of more intensive land use types, such as

shopping malls and industrial sites, estimates of proportionately less vegetation were

made and applied to the entire area of the land use.

Data processing steps. The following generalized data processing steps were used

in creating the analytical coverage 'emlandcov' and the gross primary production

components of the spatial EMERGY model (element # 2 in Figure 1-3).

Step 1: The roads coverage was buffered based on an algorithm whereby the

number of lanes of each road segment was multiplied by 2.5 meters to calculate the

buffer distance used for each segment. The resulting buffered road polygon is twice as

wide as the buffer distance. For example, a 4 lane road is assigned a 10 meter buffer

distance resulting in a 20 meter wide polygon representing the paved area of the road.

The buffered road coverage was then unioned with the original land use and cover

coverage.

Step 2: The point features in the 'building' coverage that had a total building and

miscellaneous value of less than $10,000 were deleted from the building coverage. These

features were not considered for buffering as building footprints because most of these

features only represent miscellaneous 'improvements' rather than actual residential or

commercial buildings (52,411 point features remained out of a total of 57,421 features in

the building coverage).

The remaining point features were buffered according to the square footage of the

building to create the building footprint coverage. Estimates of the square footage were

made for buildings where data was not present (2,334 features) by dividing the total










value of the building by 50 (assuming $50/sq.ft.). The buffer distance used to buffer

point features is the radius of a circle. The radius of the building footprint buffer

(resulting in a circle) was calculated based on square footage of the building using the

following equation:

(.305 m/ft)(sqrt(totsqfoot/3.14)) = buffer distance in meters


Finally, the building footprint coverage was unioned with the intermediate land

use and cover coverage that was previously unioned with the buffered roads.

Step 3: The building footprint coverage was buffered by 10 meters to represent

the area of the managed landscape that surrounds the buildings. As noted earlier, this 10

meter buffer distance is based on field observations in Alachua County. In an effort to

introduce a measure of the natural spatial variation that is found in the urban landscape,

the actual size of these buffered areas were purposefully made slightly random by

introducing a coarse 'fuzzy tolerance' into the buffering algorithm. The resulting

coverage was unioned with the last version of the land use and cover coverage to yield

the final analytical coverage, called 'em_landcov', that includes areas of buffered roads,

building footprints, and maintained landscape.

Step 4: Additional attribute items were added to the 'em_landcov' feature

attribute table in order to calculate the EMERGY flows occurring through each classified

polygon in the 'em_landcov' coverage.

Estimates of GPP per hectare were made for representative ecosystems. These

estimates were converted from flows per hectare to flows per meter-squared. Each land

use or cover class was assigned a flow/m2 /yr value according to the most closely

corresponding value calculated for a representative ecosystem, or in the cases of some










commercial and industrial classifications, by assigning a value that has been

proportionately reduced by a factor to estimate the amount of vegetation typically present

on that type of land use. The flow/m2 /yr rate assigned to each polygon was multiplied

times the area of the polygon so that the total flow for each cell could be summed.

Step 5: The coverage was then processed according to the methods described

previously for converting polygonal feature coverages into the EMERGY and energy

component grids, called 'GPP' and 'GPP EN', for gross primary productivity of natural

systems.



Natural System Flows-Renenewable Sources


Renewable environmental energy flows are the basis of all natural processes and

contribute to all economic processes. Each area on the earth's surface has several types

of natural energy sources that contribute renewable energy and EMERGY to the natural

and economic systems of the area. Odum (1983, 1996) has pointed out that the

combination, frequency, and quantity of these environmental energy sources is reflected

in the character of both the area's ecosystems and economic systems.

In Alachua County, sun, wind, and rain are the predominant local sources of

natural energy. Rainwater ends up either being used for transpiration by ecosystems or

flowing into streams, lakes, and aquifers. In this study, the geopotential energy in runoff

water and streams was assumed to be a minor source of natural energy because of the

relatively flat landscape of the county.

A notable exception to this assumption is the Santa Fe River, which flows along

the northern boundary of the county. However, due to technical limitations of the










relevant coverages, this important feature could not be included in the present form of the

model. The storage of water in streams, lakes, and aquifers is however included in the

model and is discussed later.

Odum (1996, 1983) cautions against 'double-counting' in evaluating the total

EMERGY contributed by geobiospheric processes (from sun, wind, rain, waves, land

cycles, etc.) to local areas of natural systems. For instance, he points out that, from a

global perspective, sun, wind, and rain are all coproducts of the same global climatic

process. Hence, the EMERGY content of rainfall contains EMERGY from sun and wind.

In order to avoid double-counting the EMERGY of geobiospheric sources, Odum has

shown that for any local area the largest geobiospheric input of EMERGY should be used

to account for the total contribution of EMERGY from these global processes.

In consideration of this principle, calculations for representative one-hectare areas

within Alachua County were made and it was revealed that the largest geobiospheric

input of EMERGY comes from chemical potential energy of rainfall. In this model,

transpiration by ecosystems was considered to be the primary use of this rainfall within

any given one-hectare cell of the model. The rainfall that is not used in transpiration

either recycles through evaporation, or contributes to long-term groundwater and lake

storage. The chemical potential energy of the rainwater that is used in the process of

transpiration in ecosystems integrates the combined inputs of the energy in sunlight used

and wind absorbed and thus represents the total amount of EMERGY used from

renewable geobiospheric sources without double-counting. This procedure assumes land

use and its erosion is the same EMPOWER or less than the global annual budget.










The coverage used to create this component of the spatial model was the

analytical coverage called 'emlandcov' that was also used for the creation of the gross

primary production component grids.

Calculations made previously to determine the energy and EMIERGY in gross

primary production included estimates of the energy and EMIERGY in transpiration for

each of the ecosystems represented in the county. These calculated values for energy and

EMVIERGY in transpiration were also used to create this component of the model.

The previous section described how building and road footprints were added to

the 'em_landcov' analytical coverage to more precisely model the type and amount of

natural systems present in any one-hectare cell. For these calculations, transpiration flow

values were not assigned to the building and road footprint polygons. Instead, the

amount of annual sunlight energy and EMVIERGY received by each building or road

footprint polygon was used to assign a value to those polygons.

Data Processing Steps. The following data processing steps were used to create

the RENEW_EN and RENEW component grids that represent the amount of renewable

energy and EMERGY used (element # 1 in Figure 1-3).

Step 1: Additional attribute items were added to the 'em_landcov' feature

attribute table to facilitate calculations for the amount of transpiration per unit of area for

each type of ecosystem or land use, and the corresponding energy and EMVIERGY flows.

Energy and EMERGY calculations were made for the building and road footprint

polygons by multiplying the annual solar insolation rate for Gainesville, Florida ((5.95 E9

j(sej)/m2/yr)(Odum 1996)) by the area of the polygon.










Step 2: The coverage was then processed according to the methods described

previously for converting polygon feature coverages into the energy and EMERGY

component grids.



Urban System Flows-Water Consumption


This component grid models the energy and EMERGY in water used by humans

for domestic, commercial, and agricultural purposes (see element # 3 in Figure 1-3 and

note the split flow to urban and agricultural systems). Since water used in agriculture is

transpired, there is a small amount of double counting of the EMERGY calculated for

agricultural renewable resources use.

According to Marella (1992 1993), in Alachua County, an estimated 52.12

million gallons of groundwater per day (Mgd) were withdrawn from the Floridan aquifer

in 1990, while only .36 Mgd were withdrawn from surface waters. Of this total for the

county, an estimated 22.95 Mgd was withdrawn for public-supply use, and 14.75 Mgd of

this was used at residences. Thirteen utility companies provided water to a total of

142,104 customers in 1990. About 88% of these customers were served by Gainesville

Regional Utilities, and the rest were served by minor utilities in the smaller cities. Self-

supply wells supplied the majority of the water needs of those outside of the public-

supply service areas. Agriculture was the largest self-supplier, using an estimated 18.45

Mgd of water for crop irrigation and raising of livestock.

The creation of the final component grid representing the energy and EMERGY

in water used by humans for domestic, commercial, and agricultural purposes required

that several intermediate component grids be created. The 'em resbldgcov' analytical










coverage was used to estimate the quantities of water used for domestic purposes. The

point features in the 'em_resbldgcov' analytical coverage are a subset of the original

'buildings' coverage, which represent residential buildings only. The point features in

the 'em_combldgcov' analytical coverage (which include all other buildings not in the

'em_resbldgcov' coverage, i.e., commercial, institutional, agricultural, and industrial

buildings) were used to estimate the quantities of water used for commercial, industrial,

and institutional purposes. The use of these analytical point coverages was determined

to be the most appropriate, and consistent, method of calculating and locating domestic,

commercial, and industrial water use because most of the water is used inside of, or very

close to, buildings. On the other hand, agricultural water use was estimated using the

analytical polygon coverage called 'em_agwtrcov' because agricultural water use is often

distributed over larger areas.

It was determined that different transformities should be used for public-water

supply and self-supplied well water. The transformity for water supplied by public

utilities has a higher transformity, assumed to be 665714 sej/j (Odum, 1996), than self-

supplied well water, assumed to be 255242 sej/j (Odum, 1996), because of the additional

energies required to process the water and supply it to the end-user. Consequently, it was

necessary to create a coverage of approximate municipal water service boundaries, called

'munwtrbnd', that would facilitate the calculations of EMERGY in water used based on

whether or not the water was from a public water utility or a well.

Four sets of intermediate component grids (described in Table 2-3) were created

and then 'added' together to create the final water use component grids.










Table 2-3: Intermediate grids that were used to calculate the total energy and EMERGY
of water use in buildings and agriculture.



Intermediate Grids Description


RES_WTR EMERGY of domestic water used


RES_WTR_EN energy of domestic water used


COM_WTR EMERGY of Comm/Industrial/Institutional


COM_WTR_EN energy of Comm/Industrial/Institutional use


BLD_WTR Sum of RES_WTR and COM_WTR


BLD_WTR_EN Sum of RES_WTR_EN and COM_WTR EN


AGR_WTR EMERGY of agricultural water used


AGR_WTR_EN energy of agricultural water used



Note: The final EMERGY component grid called WTRUSE is the sum of BLD_WTR
and AGR_WTR. The final energy component grid called WTRUSE EN is the sum of
BLD_WTR_EN and AGR_WTR EN. The intermediate component grid called
AGR_WTR represents the left fork of the split flow in element #3 of Figure 1-3. The
intermediate component grid called BLD_WTR is the right fork of the same element.
These intermediate component grids represent how the land area unit diagram can be
made more or less complex depending on the need for detail. In other words, these
intermediate component grids could be included as subcomponent grids if desired.










Data processing steps. The following generalized data processing steps were used

in creating the intermediate grids used to create this component of the model.

Domestic water use intermediate component grid. The domestic water use grids,

called RES WTR and RES WTR EN were created as follows:

Step 1: The analytical point coverage called 'em_resbldgcov' was overlaid with

the 'munwtrbnd' coverage to identify those buildings which were part of a municipal

water service area.

Step 2: Additional attribute items were added to the 'em_resbldgcov' analytical

coverage feature attribute table to facilitate calculations for estimates of the amount of

water used (gallons) in each building, and the energy and EMERGY.

Step 3: The calculations for the amount of water used in each building were

based on the assumption that within municipal water service areas the rate of water use

was 104 gallons/day/person, and outside the service areas the rate was 162

gallons/day/person (Marella, 1992). The estimated building population data used in these

calculations was calculated as part of the development of the population and service

component grids. The following equations were used:

for public water supply;

gallons of water used = building population 37,960
( note: 104 gal/person/day 365 day/yr = 37,960 gal/per/yr)

energy (joules) = gallons 8.35 Ib/gal 453.6 g/lb 4.9 j/g

EMERGY (sej) = energy (j) 665,714 sej/j


for well water supply;

gallons of water used = building population 59,130
( note: 162 gal/person/day 365 day/yr = 59,130 gal/per/yr)










energy (joules) = gallons 8.35 Ib/gal 453.6 g/Ib 4.9 j/g

EMERGY (sej) = energy (j) 255,242 sej/j


Step 4: The coverage was then processed according to the methods described

previously for converting point feature coverages into the energy and EMERGY

intermediate component grids for residential water use.

Commercial water use intermediate component grid. The

commercial/industrial/institutional water use grids, called COM_WTR and

COM WTR EN were created as follows:

Step 1: The analytical point coverage called 'em_combldgcov' was processed to

identify those buildings in the municipal water service area.

Step 2: Additional attribute items were added to the 'em_combldgcov' coverage

to facilitate calculations for estimates of the amount of water used (gallons) in each

building, and the energy and EMERGY.

Step 3: The calculations for the amount of water used in each building were

based an assumption that the total amount of water used in all commercial, industrial, and

institutional buildings could be distributed proportionally according to the square footage

of each building. The following calculations were used to determine the proportion of

water use to be attributed to each square foot of building depending on whether the

building had public-supplied or well water:

For public water supply;

Total water used = 8.2 Mgd (Marella, 1993)

8.2 Mgd 365 days/yr = 2,993,000,000 gal/yr

2,993,000,000 gal/yr / 46,118,466 sq.ft. = 64.9 gal/sq.ft./yr










For well water supply;

Total water used = 2.29 Mgd (Marella, 1993)

2.29 Mgd 365 day/yr = 835,850,000 gal/yr

835,850,000 g/y / 8,315,778 sq.ft = 100.5 gal/sq.ft./yr


The following equations were used to calculate the gallons of water used, and the

energy and EMERGY of this water:

For municipal supply;

gallons of water used = square footage of building 64.9 gal

energy (joules) = gallons 8.35 Ib/gal 453.6 g/lb 4.9 j/g

EMERGY (sej) = energy (j) 665,714 sej/j


For well water supply;

gallons of water used = square footage of building 100.5 gal

energy (joules) = gallons 8.35 Ib/gal 453.6 g/lb 4.9 j/g

EMERGY (sej) = energy (j) 255,242 sej/j


Step 4: The coverage was then processed according to the methods described

previously for converting point feature coverages into the energy and EMERGY

intermediate component grids for commercial/industrial/ institutional water use.

Agricultural water use intermediate component grid. The agricultural water use

grids, called AGR_WTR and AGR_WTR_EN were created as follows:

Step 1: The analytical polygon coverage called 'em_agwtrcov' was created to

identify those areas of the county being used for various agricultural purposes. Those










types of agriculture using significant amounts of irrigation water (vegetables and

ornamentals) or water for livestock were identified and located.

Step 2: Additional attribute items were added to the 'em_agwtrcov' coverage to

facilitate calculations for estimates of the amount of water used (gallons) by agriculture,

and the energy and EMERGY.

Step 3: The estimates used here for the amount of water used per unit of area by

agriculture are based on the average quantities used by typical crops and animals grown

in Alachua County and predicted aggregated use are consistent with estimates for total

county use made by Marella (1992). For all vegetable and field crops, tree crops, and

intensive livestock operations, the water use rate was estimated to be 45 gallons/m2/year.

For all ornamental nurseries the water use rate was estimated to be 360 gallons/m2/year.

Water used was assumed to be from wells.

The following equations were used:

gallons of water used = area (m2) 45 gal/m2/yr

or,

gallons of water used = area (m2) 360 gal/m2/yr



energy (joules) = gallons 8.35 Ib/gal 453.6 g/Ib 4.9 j/g

EMERGY (sej) = energy (j) 255,242 sej/j


Step 4: The coverage was then processed according to the methods described

previously for converting polygon feature coverages into the energy and EMERGY

intermediate component grids for agricultural water use.










Urban System Flows-Fuels and Electricity


The EMERGY component grid, called FUEL, represents the total energy and

EMERGY of fuel and electricity used within each cell of the model (element # 4 in

Figure 1-3). It was created by adding together the transportation subcomponent grid

(element # 4a in Figure 1-3), called TRN_FUL, and the buildings and agriculture

subcomponent grid called BAG_FUL (element # 4b in Figure 1-3). The energy

component grid called FUEL_EN was created by adding together the TRN_FUL_EN and

BAG_FUL EN subcomponent grids.



Urban System Flows-Transportation Subcomponent


A large proportion of any urban area's total fossil fuel consumption is used for

transportation (Energy Information Administration, 1994). In addition to estimating the

spatial distribution of this use, the amount of fossil fuel used for transportation per unit of

area and time may serve as an indicator of the relative amount of materials (goods) that

are being carried through a given location for ultimate consumption at another

unspecified location.

The primary coverage used to create this component of the spatial model was the

analytical coverage called 'em_roadcov'. To facilitate the estimation of motor fuel use,

each road segment was assigned an estimate of the average annual daily traffic flow

(AADT). Two sources of AADT data were used to create this database. The primary

source was the Alachua County Department of Transportation Traffic Survey Database

(Alachua County, 1995). This tabular database contains sample daily traffic counts for










all of the major urban and rural county roads. These traffic counts were made at specific

known locations along roads, such as intersections or crossroads. Therefore, for the

purposes of this study, it was necessary to extrapolate these point measurements to

estimates for the traffic flow that occurred along the corresponding linear segment of

each road. The other source of AADT data was the Road Characteristics Inventory GIS

Database maintained by the FDOT (FDOT, 1996). This spatial database had AADT

estimates for linear road segments (although this database was also originally derived

from point measurements).

Data processing steps. The following generalized data processing steps were used

in creating this subcomponent of the model.

Step 1: Additional attribute items were added to the analytical coverage, called

'em_roadcov', to enable the calculation of the energy and EMERGY flows of motor fuels

used in each road segment found within each one hectare cell of the model.

Step 2: The energy and EMERGY flows for each road segment were calculated

by using the AADT and an assumed average fuel consumption rate for vehicles in 1993

(Energy Information Administration, 1994). The average fuel consumption rate was

converted from miles per gallon units to liters per meter as shown below.

(1609.34 meters/mile)(20 mpg)/(3.7854 liters/gal) = 8503 meters/liter

(8503 meters/liter)-1 = .000117605 liters/meter


The following equation was used to calculate the liters of fuel used per year along

each road segment.

(Length, meters)(.000117605 liters/meter)(AADT)(365 days) =
liters/yr/segment










Finally, these material flows were converted into energy and EMERGY flows

using the energy factor and transformity of fossil fuels (Odum, 1996) and the equations

shown below.

(liters/yr/segment)(4.52 x 107 J/liter) = energy, j/yr/segment

(energy, j) (6.6 x 104 sej/J) = sej/yr/segment


Step 3: The coverage was then processed according to the methods described

previously for converting linear feature coverages into the EMERGY and energy

subcomponent grids, called TRN_FUL and TRN_FUL_EN, for fossil fuel use in

transportation.



Urban System Flows-Buildings and Agriculture Subcomponent


This subcomponent grid represents the flow of energy and EMERGY in the

electricity and fossil fuels used in all types of buildings and in agriculture (see element #

4b in Figure 1-3 and note the split flow going both to agriculture and building use).

Electricity consumption accounts for approximately 86% of all types of fuel use

in buildings in Florida, fossil fuels (natural gas, fuel oils, and LPG) account for 14% of

the residential site energy consumption (EIA, 1993), and electricity consumption

accounts for approximately 76% of commercial site energy consumption (EIA, 1994).

In Alachua County, the majority of fossil fuel use in buildings occurs within the

Gainesville Regional Utilities (GRU) natural gas service area. Knowing this, ideally this

component grid should be created by first calculating the energy and EMERGY from the

estimated use of electricity, using a transformity of 200,000 sej/j (Odum, 1996), and then










calculating the energy and EMERGY of fossil fuels, using a transformity of 48,000 sej/j

(for natural gas which is the primary fossil fuel used in buildings in Alachua County)

(Odum, 1996), and finally by adding the amounts of energy or EMERGY calculated for

each of the flows together. However, there is a lack of data regarding the location of the

approximately 20,000 GRU customers that received natural gas service in the 1992-93

era (GRU, 1996). Therefore, it was necessary to calculate the energy and EMERGY

representing both electricity and fuel use in buildings by predicting the use of electricity

in each building as if natural gas supplies were not available.

Although this method introduces some error, particularly in the calculations of

EMERGY, the amount of error was deemed to be acceptable for the purposes of this

study. One reason for this is that the primary uses for fossil fuels in residences are for

space and water heating, and in the 'typical' Florida home fossil fuels only account for

about 14% of total energy consumption (EIA, 1993). The majority of residential energy

consumption occurs through the uses of electricity for appliances, lighting, and air

conditioners, and these uses are also found in homes with natural gas service. Therefore,

the effect on the estimates introduced by this assumption only apply to a minor

percentage of the total energy and EMERGY flows, and only in those buildings with gas

service.

In order to estimate the amount of electricity used in each building using the data

available for this study, different usage rates were assigned according to the size and/or

type of building. Residential buildings were calculated using one of two different usage-

rates equations according to the square footage of the building. The reasoning for this

was that a strictly linear equation that calculates the same number of kilowatt-hours per










square foot for all sizes of buildings is not realistic. A more appropriate calculation was

derived from the finding by the Energy Information Administration (EIA) that for

residential buildings about 75% of the electricity consumed is used for appliances and

water heating (EIA, 1993). These basic uses of electricity are found in homes of all sizes

and dollar values. Thus, it is more appropriate to assume a base level of electricity use

for all residential buildings and then calculate additional usage for heating and cooling

based on square footage. Variability due to age of home (new homes are more energy

efficient) and household income (poor households tend to have older, less efficient

appliances) was not included in these calculations.

Various types of commercial and institutional buildings have different typical

energy intensity (kWh/sq.ft.) characteristics that have been established by national and

regional surveys (EIA, 1992, 1991). For this study, each non-residential building was

assigned to an energy intensity class according to the type of commercial or institutional

activity being conducted in the building. The estimated total electricity used in each

building was then calculated by multiplying the appropriate energy intensity, according to

that building's energy intensity class, times the square footage of the building.

To establish the acceptability of these assumptions, the individual building energy

use estimates were summed for all of the buildings within the county and compared with

the known amounts of all types of energy (in kWh units) supplied by all utilities in 1993

(GRU, 1996, BEBR, 1995). The method used predicts the total electricity/heating fuel

energy use to within 4.7% of the actual amount of energy known to be supplied to all

customers.










The creation of the final component grid representing the energy and EMERGY

in electricity and other fuels used by humans for domestic, commercial, and agricultural

purposes required that several intermediate component grids be created in a manner

similar to the method used to create the water use component grid. Four of the

intermediate grids were created using the analytical point coverages called

'em_resbldgcov' and 'em_combldgcov'. The 'em_resbldgcov' coverage was used to

estimate of the electricity used in residential buildings, and the 'em_combldgcov'

coverage was used to estimate the electricity used in commercial, industrial, and

institutional buildings. Agricultural fuel use was estimated using the analytical polygon

coverage called 'emlandcov'.

Four sets (an energy and an EMERGY grid in each set) of intermediate

component grids were created and then 'added' together to create the final electricity and

fuel use subcomponent grids. These intermediate grids are listed in Table 2-4.

The final EMERGY subcomponent grid, called 'BAG_FUL', is the sum of

BLD_ELC and AGR_FUL, and the final energy component grid, called

'BAG FUL EN', is the sum of BLD ELC EN and AGRFUL EN.

Data processing steps. The following three sets of generalized data processing

steps were used in creating the intermediate component grids that were used to create this

subcomponent of the spatial EMERGY Model.

Domestic electricity use intermediate component grid. The residential electricity

use intermediate component grids, were created as follows:










Step 1: Additional attribute items were added to the 'em_resbldgcov' analytical

coverage to facilitate calculations for the amount of electricity used in each building, and

the corresponding energy and EMERGY flows.

Step 2: The calculations for the amount of electricity used in each building were

based on the assumptions about base levels of use that were discussed above. Residential

buildings were divided into two groups for the calculations. The first group included all

buildings with a total square footage of less than 1000 square feet and all multi-family

residential buildings. The multi-family buildings were included based on the assumption

that: although the total square footage of multi-family buildings was usually greater than

1000 square feet, that this total was most often comprised of individual residential units

of less than 1000 square feet with base levels of usage comparable to individual

dwellings.

The following equation was used to calculate the amount of electricity used in

buildings of this group:

Electricity Used (kWh/year) = total square feet 9 kWh/sq.ft./year


The second group includes all other residential buildings (those with more than

1000 square feet). The base level of electricity usage was estimated to be 10,000

kWh/year/building, and additional usage was estimated for heating and cooling uses

based on the square footage of the building (EIA, 1993). The following equation was

used for this group:

Electricity Used, kWh/year =
(10,000 kWh/year) + (total square feet 2.25 kWh/sq.ft./year)










Table 2-4: Intermediate grids that were used to calculate the total energy and EMERGY
of electricity and fuel use in buildings and agriculture.



Intermediate Grids Description


RES_ELC EMERGY of residential electricity used


RES_ELC_EN energy of residential electricity used


COM_ELC EMERGY of Commercial/
Industrial/ Institutional electricity used


COM_ELC_EN energy of Commercial/
Industrial/ Institutional electricity used


BLDELC Sum of RESELC and COMELC


BLDELCEN Sum of RES_ELC_EN and COMELC EN


AGR_FUL EMERGY of agricultural fuels used


AGR_FUL_EN energy of agricultural fuels used



Note: The final EMERGY sub-component grid called BAG_FUL is the sum of
BLD_ELC and AGR_FUL. The final energy sub-component grid called BAG_FUL EN
is the sum of BLD_ELC_EN and AGRFUL EN. The intermediate component grid
called AGR_FUL represents the left fork of the split flow of element #4b in Figure 1-3.
The intermediate component grid called BLD_ELC is the right fork of the split flow of
element #4b.










Finally, the energy and EMERGY of the electricity used was calculated using the

following equations:

Energy, j/year = (kWh/yr) (860 kcal/kWh) (4186 J/kcal)

EMERGY, sej/year = (energy, j/yr) (200,000 sej/j)


Step 3: The coverage was then processed according to the methods described

previously for converting point feature coverages into the EMERGY and energy

intermediate component grids, called RESELC and RESELC EN, for electricity use in

residential buildings.

Commercial electricity use intermediate component grid. The commercial/

industrial/institutional electricity use intermediate component grids were created as

follows:

Step 1: Additional attribute items were added to the 'em_combldgcov' analytical

coverage to facilitate calculations for the amount of electricity used in each building, and

the corresponding energy and EMERGY flows.

Step 2: The calculations for the amount of electricity used in each building were

based on the application of different usage rates for different types of buildings.

Electricity usage rates, in kWh per square foot, were assigned to each building type

(using the Department of Revenue (DOR) use code associated with the building)

according to average usage rates, which were determined by regional surveys conducted

by the Energy Information Administration (EIA, 1991, 1992, 1994). The following

equation was used, applying the appropriate usage rate, to calculate each building's

annual electricity use:










Electricity Used (kWh/year) = total square feet kWh/sq.ft./year


The energy and EMERGY of the electricity used was calculated using the

following equations:

Energy, j/year = (kWh/yr) (860 kcal/kWh) (4186 J/kcal)

EMERGY, sej/year = (energy, j/yr) (200,000 sej/j)


Step 3: The data layer was then processed according to the methods described

previously for converting point feature coverages into the EMERGY and energy

intermediate component grids, called COM_ELC and COM_ELC_EN, for electricity use

in commercial, industrial, and institutional buildings.

Agricultural use intermediate component grid. Agricultural uses of fuel that were

closely associated with a building, such as nursery and greenhouse production, were

calculated in the commercial/industrial/ institutional use grid. The agricultural fuel use

intermediate component grids, called AGRFUL and AGR_FUL_EN, include fuels used

for pasture and row crop production, and maintenance of residential lawns and gardens,

and were created as follows:

Step 1: Additional attribute items were added to the 'emlandcov' analytical

(polygon) coverage to facilitate calculations for the amount of electricity and fossil fuels

used per unit of area for each type of agricultural land use, and the corresponding energy

and EMERGY flows.

Step 2: Estimated fuel energy usage rates for each type of agricultural production

were used that were based on previous calculations done to determine contributions to

gross primary production in natural and agricultural systems and other data (Fluck, 1992,










and 1992b). The following equations were used, applying the appropriate usage rates, to

calculate the energy and EMERGY of the fuels used for the total area of each occurrence

of an agricultural land use:

Energy, j/year = (j/m2/yr) (area, m2)

EMERGY, sej/year = (sej/m2/yr) (area, m2)


Step 3: The coverage was then processed according to the methods described

previously for converting polygon feature coverages into the EMERGY and energy

intermediate component grids, called AGRFUL and AGR_FUL_EN, for fuel use in

agriculture.



Urban System Flows-Goods Consumption


This component grid models the flows of energy and EMERGY of both

consumable and durable goods consumed in residential, commercial, and institutional

buildings and as a result of agricultural production (element #5 in Figure 1-3). The

EMERGY of these goods implicitly includes the EMERGY of services to deliver them to

the point of their consumption (Odum, 1996). This model does not try to track the flow

of goods through wholesale and retail establishments. Durable goods (furniture, cars,

equipment, etc.) are included in these calculations by combining estimates of annual

depreciation of durable goods (in other words, annual consumption) with estimates of the

amounts of consumable goods used annually in each building.

The creation of this component grid required that several intermediate component

grids be created in a manner similar to the methods used to create the water use and fuel










use component grids. Four of the intermediate grids were created using the analytical

point coverages called 'em_resbldgcov' and 'em_combldgcov'. The 'em_resbldgcov'

coverage was used to estimate the goods consumed in residential buildings, and the

'em_combldgcov' coverage was used to estimate the goods consumed in commercial,

industrial, and institutional buildings. Agricultural goods consumption was estimated

using the analytical polygon coverage called 'emlandcov'.

Four sets (an energy and an EMERGY grid in each set) of intermediate

component grids were created and then 'added' together to create the final goods

consumption component grid. These intermediate grids are described in Table 2-5. The

final EMERGY component grid, called 'GOODS', is the sum of 'BLD_GDS' and

'AGR GDS'.

Data processing steps. The following three sets of generalized data processing

steps were used in creating the intermediate grids used to create this component of the

spatial EMERGY Model.

Domestic goods consumption intermediate component grid. The residential

goods consumption grids, called RES_GDS and RES_GDS EN were created as follows:

Step 1: The analytical point coverage called 'em_resbldgcov' was overlaid with

the 'em_census' analytical coverage to add the data required to estimate the income of

each individual single family residential-type building. Additional attribute items were

added to the 'em_resbldgcov' coverage to facilitate calculations of energy and EMERGY

flows.










Table 2-5: Intermediate grids that were used to calculate the total energy and EMERGY
of goods consumption in residential, commercial/public buildings and agriculture.



Intermediate Grids Description


RES_GDS EMERGY of residential-based goods consumed


RES_GDS_EN energy of residential-based goods consumed


COM_GDS EMERGY of Commercial/Industrial/
Institutional-based goods consumption


COM_GDS_EN energy of Commercial/Industrial/
Institutional-based goods consumption


BLD_GDS Sum of RES_GDS and COMGDS


BLDGDSEN Sum of RESGDSEN and COMGDS EN


AGR_GDS EMERGY of goods consumed in
agricultural production


AGR_GDS_EN energy of goods consumed in
agricultural production



Note: The final EMERGY sub-component grid called GOODS is the sum of BLD_GDS
and AGR GDS. The final energy sub-component grid called GOODS_EN is the sum of
BLD_GDS EN and AGR_GDS_EN. The intermediate component grid called
AGR_GDS represents the left fork of the split flow of element #5 in Figure 1-3. The
intermediate component grid called BLD_GDS is the right fork of the split flow of
element #5.










Step 2: The 1990 census data (U.S. Bureau of the Census, 1992) were processed

to find the aggregate income for each census 'blockgroup'. The aggregate income was

apportioned to each building by first determining the total square footage of all

residential buildings in each blockgroup and then assigning each building a proportional

amount of the blockgroup aggregate income according to the square footage of the

building. The aggregate income figures provided by the census data were also adjusted

to 1994 levels (BEBR, 1995). The following equation was used to estimate the income

for each building:

Building Income ($/year) = ((blockgroup aggregate income, $/year)
/ (blockgroup total square feet)) square feet of building


Step 3: The estimated dollar amount of goods consumed in each building was

then calculated based on each building's income estimate using data from the U.S.

Department of Labor (1995). This data represents the sum of national average estimated

expenditures ($) for the following general categories: food at home, household supplies,

apparel, amortized vehicle, entertainment, equipment, appliances and furnishings

purchases, personal care supplies, and miscellaneous supplies. Each building was

assigned a dollar value for goods consumption based on the income range of the building.

Step 4: The EMERGY of the annual goods consumption in each building was

calculated using the value of 1.37 E12 sej/$ (Odum, 1996) in the following equation:

EMERGY, sej/year = (dollars of goods/yr) (1.37 E12 sej/$)


The energy of the annual goods consumption in each building was calculated

based on methods and data used by Brown (1980). In this case the assumption was made










that the average cost of a pound of goods was $5 and each pound of goods contained

1500 kcal of energy. The following equation was used:

Energy, j/year = (dollars of goods/yr) (300 kcal/$) (4186 J/kcal)


Step 5: The coverage was then processed according to the methods described

previously for converting point feature coverages into the EMERGY and energy

intermediate component grids, called RES_GDS and RES_GDS_EN, for goods

consumption in residential buildings.

Commercial goods consumption intermediate component grid. In this model the

commercial/industrial/institutional goods consumption grids, called COM_GDS and

COM_GDS_EN, are intended to estimate the amount of goods consumed annually on site

in the process of conducting the particular type of business associated with each building.

The calculations for estimating the amount of goods consumed in each building were

based on the application of different consumption rates, in dollars of goods consumed per

square foot of building per year, for the different types of businesses associated with each

of the buildings. These consumable and durable goods would typically include office

supplies, and amortized depreciation of office furniture, appliances, and industrial

equipment. These grids are not intended to represent goods in stock, that are intended for

wholesale or retail sale to other consumers. In other words, goods are mapped at their

point of consumption. These intermediate component grids were created as follows:

Step 1: Additional attribute items were added to the 'em_combldgcov' analytical

coverage to facilitate calculations of energy and EMERGY flows in

commercial/industrial/institutional buildings.










Step 2: Goods consumption rates, in dollars per square foot per year, were

assigned to each building type using the Department of Revenue (DOR) use code

associated with the building. These rates were calculated for commercial businesses

according to an estimated percentage of gross annual sales expended on goods (both

consumable and amortized durable goods) during a year for each type of business

(USDOC, 1996a, 1996b, 1996c).

Data on county-wide annual gross sales for each type of business (BEBR, 1993,

and 1995) was multiplied by the estimated percentage of gross sales representing goods

consumed in conducting each type of business. This total expenditure on goods was then

divided by the total square footage of all corresponding buildings to get the estimated rate

of goods consumption per square foot for those buildings associated with each type of

business.

Annual goods expenditures by schools, colleges and local/state/federal

government were based on an estimated percentage of gross annual government

expenditures in each category (BEBR, 1995). Gross annual expenditures in each

category were multiplied by the appropriate percentage rate representing goods consumed

and then divided by the aggregate total square footage of the appropriate type of

buildings.

The following equation was used, applying the appropriate consumption rate, to

calculate the annual goods consumption occurring in each building:

Goods consumed ($/year) = total square feet dollars/sq.ft./year










Step 3: To be consistent with the calculations used for domestic goods

consumption, the energy and EMERGY of the annual goods consumption in each

commercial/institutional building was calculated using the same assumptions and the

following equations:

EMERGY, sej/year = (dollars of goods/yr) (1.37 E12 sej/$)

Energy, j/year = (dollars of goods/yr) (300 kcal/$) (4186 J/kcal)


Step 4: The coverage was then processed according to the methods described

previously for converting point feature coverages into the EMERGY and energy

intermediate component grids called, COM_GDS and COM_GDS_EN, representing

goods consumption in commercial/institutional buildings.

Agricultural goods consumption intermediate component grid. In this model the

intermediate component grids called AGR GDS and AGR_GDS_EN are intended to

estimate the amount of goods consumed annually in the process of pasture and row crop

production, and maintenance of residential lawns and gardens. Consumption of goods

that are closely associated with a building, such as nursery and greenhouse production,

was calculated in the commercial/ institutional intermediate component grid. These grids

were created as follows:

Step 1: Additional attribute items were added to the 'em_landcov' analytical

(polygon) coverage to facilitate calculations for the amount of goods consumed per unit

of area for each type of agricultural land use, and the corresponding energy and

EMERGY flows.










Step 2: Estimated goods consumption rates for each type of agricultural

production were used. These rates were based on previous calculations done to

determine contributions to gross primary production in natural and agricultural systems

and other data (Fluck, 1992, and 1992b).

The following equations were used, applying the appropriate usage rates, to

calculate the energy and EMERGY of the goods consumed for the total area of each

occurrence of an agricultural land use:

Energy, j/year = (j/m2/yr) (area, m2)

EMERGY, sej/year = (sej/m2/yr) (area, m2)


Step 3: The coverage was then processed according to the methods described

previously for converting polygon feature coverages into the EMERGY and energy

intermediate component grids called, AGR_GDS and AGR_GDS EN, for goods

consumption in agricultural production.



Urban System Flows-Human Services


This component grid models the energy and EMERGY of in-situ human services

(those services provided within the area of each grid cell). It does not include the

EMERGY from services that was implicitly included in the previous calculations for the

EMERGY of goods consumed (see element #6 in Figure 1-3).

The creation of this component grid presented a significant challenge since, unlike

buildings, people do not stay in the same location all day. The general approach taken for

this study was to estimate the percentages of time people spend while at home, work or










school, and while 'shopping', and then spatially distribute the corresponding services

according to appropriate locations.

Based on this general approach, several intermediate component grids were

created to facilitate the spatial distribution of these services. Six sets (an energy and an

EMERGY grid in each set) of intermediate component grids were created and then

'added' together to create the final human services component grid. These intermediate

grids are listed in Table 2-6. The final EMERGY component grid, called 'SERVICE', is

the sum of the BLD_SRV and AGR SRV intermediate component grids, and the final

energy component grid, called 'SERVICE_EN', is the sum of the BLD_SRV_EN and

AGR_SRV_EN intermediate component grids.

All of the intermediate grids, except for the agricultural services grid, were

created using the analytical point coverages called 'em_resbldgcov' and

'em_combldgcov'. The 'em_resbldgcov' coverage was used to estimate of the services

provided while people were at their residences. The 'em_combldgcov' coverage was

used to estimate the services provided while people were at workplaces, schools, or were

shopping. The use of these analytical point coverages was determined to be the most

appropriate method of calculating and locating most human services because most people

spend the majority of their time in and around buildings.

The analytical polygon coverage called 'em_landcov' was used to calculate the

agricultural services because these services tend to be spatially dispersed.

Data processing steps. The following generalized data processing steps were used

in creating the intermediate grids used to create this component of the spatial EVIMERGY

model.










Table 2-6: Intermediate grids that were used to calculate the total energy and EMERGY
of human services.


Intermediate Grids Description

EMERGY of services occurring in
RES_SRV residential buildings


RES_SRV_EN energy of services in residential buildings
EMERGY of human services occurring in
WRK_SRV school and work buildings


WRK_SRV_EN energy of services in schoolwork buildings
EMERGY of human services occurring in
SHP_SRV buildings as a result of shopping


SHP_SRV_EN energy of services while shopping


COMSRV WRK SRV + SHPSRV


COMSRVEN WRK SRV EN + SHPSRVEN


BLDSRV RES SRV + COMSRV


BLDSRVEN RES SRV EN + COMSRVEN
EMERGY of human services provided to
AGR_SRV agricultural production


AGR_SRV_EN energy of services in agricultural production


Note: The final EMERGY component grid, called 'SERVICE', is the sum of the
BLD_SRV and AGR_SRV intermediate component grids, and the final energy
component grid, called 'SERVICE_EN', is the sum of the BLD_SRV_EN and
AGR_SRV_EN intermediate component grids.










Services-at-home intermediate component grid. In this study, the assumption was

made that people spend 67% of each 24-hour day at home. The intermediate component

grids, called RES_SRV and RES_SRV EN, representing EMERGY and energy of

services provided at home were created as follows:

Step 1: The analytical point coverage called 'em_resbldgcov' was overlaid with

the 'em_census' data layer to add the data required to estimate the population of each

individual residential-type building.

The census data (U.S. Bureau of the Census, 1992) were processed to identify two

population classes: the total number of adults (over 18 years old) and the total number of

children. Since the census data represent the total population, by age class, for an area

called a 'census block', these population numbers had to be distributed proportionately

across all of the buildings, represented as point features, which were found in each block.

This apportioning of the population was accomplished by first determining the

number of 'residential units' occurring in each building (the number of units in multi-

family buildings is an attribute in the property parcel coverage) and the total number of

units occurring in each census block.

Then, a calculation was performed for each building as follows: the total number

of people (in each age class) of the corresponding census block was divided by the total

number of residential units in that block; this number was then multiplied by the number

of residential units in the building. The result of these calculations was an estimate of

each building's population of both adults and children (people under 18 years old).

Step 2: Additional attribute items were added to the 'em_resbldgcov' coverage to

facilitate calculations for the energy in services. The energy in human services from









adults and children occurring in each residential building were calculated using the

following equations:

Energy in adult services, j/yr/bldg = (# of adults) (2500 kcal/day)


Energy in child services, j/yr/bldg


Total energy in services, j/yr/bldg


* (365 days/year) (4186 j/kcal) (.67)

= (# of children) (2500 kcal/day)
* (365 days/year) (4186 j/kcal) (.67)

= (Adult Services, j/yr/bldg)
+ (Child Services, j/yr/bldg)


Step 3: Additional attribute items were also added to the 'em_resbldgcov'

coverage to facilitate calculations for the EMERGY in services from humans based on

different transformities according to level of education (Odum, 1996). For adults, the

average proportion of high school and college-educated persons (U.S. Census Bureau,

1992) was used to calculate the EMERGY of services using two different transformities.

All services from children use the same transformity. The following equations were

used:

for college-educated adults;

EMERGY, sej/yr/bldg = ((# adults) (.25)) (2500 kcal/day)
(365 days/year) (4186 j/kcal) (.67, %time) (7.33 E7 sej/j)


for high school-educated adults;

EMERGY, sej/yr/bldg = ((# adults) (.75)) (2500 kcal/day)
(365 days/year) (4186 j/kcal) (.67, %time) (2.46 E7 sej/j)


for children;

EMERGY, sej/yr/bldg = (# children) (2500 kcal/day) (365 days/year)
(4186 j/kcal) (.67, %time) (8.90 E6 sej/j)










for the total EMERGY;

Total EMERGY, sej/yr/bldg = EMERGY from college-educated adults
+ EMERGY from high-school educated adults
+ EMERGY from children


Step 4: The 'em_resbldgcov' analytical coverage was then processed according

to the methods described previously for converting point feature coverages into the

EMERGY and energy intermediate component grids, called RES_SRV and

RES_SRV_EN, for residential human services.

Services-at-work/school intermediate component grid. In this study, the

assumption was made that people spend 25% of each day at work or school. The

analytical point coverage called 'em_combldgcov' was used to create this intermediate

component grid. This coverage contains all of the buildings that were not classified as

residential. The intermediate component grids, called WRK_SRV and WRK_SRV_EN,

representing EMERGY and energy of services provided at work or school were created

as follows:

Step 1: Additional attribute items were added to the 'em_combldgcov' coverage

to facilitate an estimate of the average daytime population of each building. These

calculations were based on estimates of the number of employees, or students, per square

foot of each building type according to the Department of Revenue Use Code. These

estimates were calculated by dividing the total number of employees/students for each

type of business/school (BEBR, 1993) by the total square footage of all of the buildings

in each type of business/school. The average daytime building population was calculated

as follows:










Building Population = (employees or students/sq.foot)
(sq. feet of building)


Step 2: Additional attribute items were added to the 'em_combldgcov' coverage

to facilitate calculations for the energy in human services. Only the building population

of primary and secondary schools were used for the calculation of children's services.

The energy in human services from adults and children occurring in each building were

calculated using the following equations:

Energy in adult services, j/yr/bldg = (# of adults) (2500 kcal/day)
(365 days/year) (4186 j/kcal) (.25)

Energy in child services, j/yr/bldg = (# of children) (2500 kcal/day)
(365 days/year) (4186 j/kcal) (.25)

Total energy in services, j/yr/bldg = (Adult Services, j/yr/bldg)
+ (Child Services, j/yr/bldg)


Step 3: Additional attribute items were also added to the 'em_combldgcov'

coverage to facilitate calculations for the EMERGY in services from humans at

work/school. These calculations are also based on different transformities according to

level of education as was done for services occurring at residences. Different types of

buildings were assumed to have different percentages of college and high school

educated adults, and children depending on whether the building was associated with

professional services, manufacturing, etc. The following equations (which are essentially

the same ones used for residential services, except for the percentage of time and the

varying percentages of college and high school-educated adults) were used:









for college-educated adults;

EMERGY, sej/yr/bldg = ((# adults) (%college)) (2500 kcal/day)
(365 days/year) (4186 j/kcal)
(.25, %time) (7.33 E7 sej/j)


for high school-educated adults;

EMERGY, sej/yr/bldg = ((# adults) (%high school) (2500 kcal/day)
(365 days/year) (4186 j/kcal)
(.25, %time) (2.46 E7 sej/j)


for all children;

EMERGY, sej/yr/bldg = (# children) (2500 kcal/day) (365 days/year)
(4186 j/kcal) (.25, %time) (8.90 E6 sej/j)


for the total EMERGY;

Total EMERGY, sej/yr/bldg = EMERGY from college-educated adults
+ EMERGY from high-school educated adults
+ EMERGY from children


Step 4: The 'em_combldgcov' analytical coverage was then processed according

to the methods described previously for converting point feature coverages into the

EMERGY and energy intermediate component grids, called WRK_SRV and

WRK_SRV_EN, for human services provided while at work or school.

Shopping' intermediate component grid. In this study, the assumption was made

that people spend an average of 8% of each day 'shopping' at retail or service

establishments. This works out to about 2 hours per day, and implicitly includes time

spent driving to and from these 'shopping' places. This intermediate grid was created in

an attempt to recognize that people spend time at locations other than work and home.










The energy and EMERGY in human services provided by people while shopping

at these locations was estimated in the intermediate component grids called SHP_SRV

and SHP_SRV_EN. These grids were created as follows:

Step 1: Additional attribute items were added to the analytical point coverage

called 'em_combldgcov' to facilitate the calculations. Only those buildings that were

assumed to be associated with the retail or service industries were qualified for inclusion

in these calculations. The square footage of each shopping-qualified building was

divided by the total square footage of all qualified buildings to determine the percentage

each building represents of the total county-wide qualified building square footage.

Step 2: The primary calculations used to create this grid are based on two

assumptions: first, it was assumed that every person, including children, spends 8% of

their time shopping, and second, it was assumed that the percentage of the total shopping

time of all people in the county that occurs in each qualified building was proportional to

the percentage that building represents of the total county-wide shopping-qualified

building square footage. The total population of the county, 142,081 adults and 39,515

children, was based on U.S. Census Bureau data (1990). Based on these assumptions, the

following equations were used to calculate the energy in shopping services:

Energy in adult services, j/yr/bldg = (% of total shopping sq. footage)
(total number of adults in county) (.08, %time)
(2500 kcal/day)* (365 days/year) (4186 j/kcal)


Energy in child services, j/yr/bldg = (% of total shopping sq. footage)
(total number of children in county) (.08, %time)
(2500 kcal/day)* (365 days/year) (4186 j/kcal)

Total energy in services, j/yr/bldg = (Adult Services, j/yr/bldg)
+ (Child Services, j/yr/bldg)









Step 3: Additional attribute items were also added to the 'em_combldgcov'

coverage to facilitate calculations for the EMERGY in services from humans while

shopping. These calculations are based on the assumptions about education levels used

previously for the residential services intermediate component grid. The following

equations were used:

for college-educated adults;

EMERGY, sej/yr/bldg = (% of total shopping sq. footage)
(# of college educated adults in county)
(.08, %time) (2500 kcal/day) (365 d/year)
(4186 j/kcal) (7.33 E7 sej/j)


for high school-educated adults;

EMERGY, sej/yr/bldg = (% of total shopping sq. footage)
(# of high school educated adults in county)
(.08, %time)* (2500 kcal/day) (365 d/year)
(4186 j/kcal) (2.46 E7 sej/j)


for children;

EMERGY, sej/yr/bldg = (% of total shopping sq. footage)
(# of children in county) (.08, %time)
(2500 kcal/day) (365 days/year)
(4186 j/kcal) (8.90 E6 sej/j)


for the total EMERGY;

Total EMERGY, sej/yr/bldg = EMERGY from college-educated adults
+ EMERGY from high school educated adults
+ EMERGY from children


Step 4: The 'em_daypopcov' analytical coverage was then processed according

to the methods described previously for converting point feature coverages into the










EMERGY and energy intermediate component grids, called SHP_SRV and

SHP_SRV_EN, for human services provided while shopping.

Agricultural services intermediate component grid. Human services related to

agricultural production, which were closely associated with a building, such as nursery

and greenhouse production, were calculated in the intermediate component grid for

work/school services. This grid includes services that are more appropriately modeled as

occurring over areal rather than point locations. These services include those provided to

pasture, forestry, and row crop production, and maintenance of residential lawns and

gardens. The intermediate component grids are called AGR SRV and AGR SRV_EN

and were created as follows:

Step 1: Additional attribute items (were added to the 'em_landcov' polygon

coverage to facilitate calculations for the amount of human services provided per unit of

area for each type of agricultural land use, and the corresponding energy and EMERGY

flows.

Step 2: Estimated service rates for each type of agricultural production were used

based on previous calculations done to determine contributions to gross primary

production in natural and agricultural systems.

The following equations were used, applying the appropriate usage rates, to

calculate the energy and EMERGY of the services provided for the total area of each

occurrence of an agricultural land use:

Energy, j/year = (j/m2/yr) (area, m2)

EMERGY, sej/year = (sej/m2/yr) (area, m2)










Step 3: The coverage was then processed according to the methods described

previously for converting polygon feature coverages into the EMERGY and energy

intermediate component grids, called AGR SRV and AGR SRV EN, for human

services provided in agricultural production.



Urban System Flows-Wastes Not Recycled


This component grid models the flows of energy and EMERGY of the solid and

liquid wastes that were generated in residential, commercial, and institutional buildings

that were not recycled (see element #7 in Figure 1-3).

Data from several sources (FDEP, 1995, USEPA, 1997, and TIA, 1991) on the

municipal solid waste (MSW) stream for Alachua County (for the period of July 1994 to

June 1995) was summarized and used as the basis for estimates of municipal solid waste

(MSW) flows being generated at the sites of various types of buildings. Land-filled

MSW was categorized into: general wastes (urban or rural), yard wastes (urban or rural),

and construction debris. Liquid waste estimates were based on a percentage of water

consumed using data calculated previously for the water use component grid.

The creation of this component grid required that several intermediate component

grids be created in a manner similar to the method used to create previous component

grids. Separate solid and liquid waste intermediate component grids were created using

different methods. The 'em_resbldgcov' coverage was used to estimate the solid and

liquid wastes generated in residential buildings, and the 'em_combldgcov' coverage was

used to estimate the solid and liquid wastes generated in commercial, industrial, and










Table 2-7: Intermediate grids that were used to calculate the total energy and EMERGY
of municipal solid wastes (MSW) and liquid waste flows that were not recycled.


Intermediate Grids Description


RES_MSW EMERGY of MSW originating
in residential buildings

RES_MSW_EN energy of MSW originating
in residential buildings

RES_LWS EMERGY of liquid wastes originating
in residential buildings

RES_LWS_EN energy of liquid wastes originating
in residential buildings

COM_MSW EMERGY of MSW originating
in commercial/institutional buildings

COM_MSW_EN energy of MSW originating
in commercial/institutional buildings

COM_LWS EMERGY of liquid wastes originating
in commercial/institutional buildings

COM_LWS_EN energy of liquid wastes originating
in commercial/institutional buildings

BLDMSW RES MSW + COM MSW


BLDMSWEN RES MSW EN + COMMSWEN


BLDLWS RESLWS + COMLWS


BLD_LWSEN RESLWSEN + COMLWSEN




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