GIS, Spatial Analysis,

and Modeling

David J. Maguire, Michael Batty, and Michael F. Goodchild, Editors

ESRI PRESS REDLANDS, CALIFORNIA

--

Contents

Preface vu Authors xx

I GIS, Spatial Analysis, and Modeling Overview I Michael E Goodchild

2 Moving toward a GIS Platform for Spatial Analysis and Modeling I9 David J. Maguire

3 Approaches to Modeling in GIS: Spatial Representation and Temporal Dynamics 4I Michael Batty

TOOLS AND TECHNIQUES: INTRODUCTION BY DAVID J. MAGUIRE 63

4 Assessing the Uncertainty Resulting from Geoprocessing Operations 67 Konstantin Krivoruchko and Carol A. Gotway Crawford

5 Spatial Statistical Modeling in a GIS EnvironmentJ3 Luc Anselin

6 Linking General-Purpose Dynamic Simulation Models with GIS II3 Ian Miller, Stefan Knopf, aild Rick Kossik

7 Dynamic, Geospatial Landscape Modeling and Simulation I31 Thomas Maxwell and Alexey Voinov

""'t"' . .,.,,"," 3 SOCIOECONOMIC APPLICATIONS: INTRODUCTION BY MICHAEL BATTY 147

8 Urban Growth Using Cellular Automata Models I5I

Michael Batty and Yichun Xie

9 A Data Model to Represent Plans and Regulations in Urban Simulation Models I73 . Lewis D. Hopkins, Nikhil Kaza, and Varkki G. Pallathucheril

10 Urban Land-Use Transportation Models 203 Michael Wegener

II Retail and Service Location Planning 22I

Mark Bir.kin

vz CONTENTS

I2 Simulating Spatially Explicit Networks for Dispersion of Infectious Diseases 245 Ling Bian and David Liebner

I3 The Use of GIS in Transport Modeling 265 Thomas Israelsen and Rasmus Dyhr Frederiksen

I4 The Integration of Case-Based Reasoning and GIS in a Planning Support System 289 Anthony G. O. Yeh

ENVIRONMENTAL APPLICATIONS: INTRODUCTION BY MICHAEL F. GOODCHILD 315

IS Hydrologic Modeling 3I9 David R. Maidment, Oscar Robayo, and Venkatesh Merwade

I6 Environmental Modeling with PCRaster 333 Peter A. Burrough, Derek Karssenberg, and Willem van Deursen

I7 Transition Potential Modeling for Land-Cover Change 357 J. Ronald Eastman, Megan E. Van Fossen, and Luis A. Sol6rzano

I8 Modeling the Interaction between Humans and Animals in Multiple-Use Forests: A Case Study of Panther.a tigris 387 Sean Ahearn and J. L. David Smith

I9 Integration of Geographic Information Systems and Agent-Based Models of Land Use: Prospects and Challenges 403 Dawn C. Parker

20 Generating Prescribed Patterns in Landscape Models 423 Jiunn-Der (Geoffrey) Duh and Daniel G. Brown

Conclusion

2I GIS, Spatial Analysis, and Modeling: Current Status and Future Prospects 445 David J. Maguire, Michael Batty, and Michael F. Goodchild

Acronyms 457

Index 46I

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

Index

abbreviations, as model abstractions, 42

abstract cellular landscapes, 406

accuracy, 70, 126,238

agent-based models: abstract cellular landscapes, 406; cognitive learning effects, 54-56; definition, 8-9; discrete simulators, 122; dynamic simula­tion models, 128; generic models, 412-14; GIS integration, 403-14; individ­ual behavior, 47-49, 55-56; land-use change (LUC) models, 316, 403-14; micro simulation, 235-36, 248; multidimensional models, 408; software improvement recommendations, 409-10; software tools, 406-9; spa-tial interaction models, 236; urban growth models, 153, 169; user popula­tion, 405-6; See also dynamic simulation models; Recursive Porous Agent Simulation Toolkit (RePast)

aggregate-level models: definition, 7; environmental models, 316; geoprocessing operations, 69; spatial resolution, 47-49; urban growth mod­els,153

analog models, 4-5

analytical processing systems, 20-21

animal movement behaviors, 388-89, 395; See also tiger behavior models

animation viewers, 141

annealing processes, 428-29, 432-38

application programming interfaces, 22

ArcGIS: agent-based models, 408; capabilities, 12,27,30; ModelBuilder, 12, 29, 123-25,328-31; Recursive Porous Agent Simulation Toolkit (RePast), 447-48; software improvement recommendations, 285-86; spatial statistics, 102; Tracking Analyst, 12,29,450; transport models, 280-84, 450

ArcfIydro, 60, 174,315,324,327-30

arcs, 11

ArcView, 102,289,296-308, 334, 358, 448

AutoCad, 450

autocorrelation, 81,98-99, 102,426

back-propagation neural networks, 357, 367-68, 373-75, 377-81, 382

basic units, 11

Bayesian aggregation, 365

Bayesian belief networks, 85-89

Bayesian soft classification, 366-67, 373-81

Bayes Theorem, 364

bird habitat study, 86-~8

block kriging, 72

Bolivian case study, 368-82

Boltzmann cooling schedule, 433, 436-37

Boolean logic, 82-85

bottom-up models, 47, 148, 153,156-57, 165

Botton, Alain. de; 42

brand preference, 230

browser interfaces, 23

brushing, 97-98, 102

cadastral maps, 11;

calibration: cellular automata, 164, 167; definition, 14; dynamic simulation models, 127; retail/service location models, 238

California Urban Futures Model (CUFM), 209, 210, 211, 291

CA_Markov, 358, 359, 360, 362, 364, 366

caribou case study, 396-97

cartographic data visualizer (cdv), 102

case-based reasoning interfaces; 289-311; adaptation methods, 307; Approval Scores,307-8; case library, 300-301;' development control, 295-96; four RE cycle (Retrieve, Reuse, Revise, Retain), 292-93, 309; GIS integration, 290-91,299,310-11,448; graphics, 301; Hong Kong case study, 298-308; implementation, 308-9; indices, 303-6; retrieval methods, 306-7; system architecture, 296-308; tabular features, 301-3; urban planning, 148,289, 291-96

category assignment requirements, 127

CATS (Conseq~ences Assessment Tool Set), 31, 32

Cauchy c09ling schedule, 433, 436-37

cell-space models, 156-58

cellular automata: definition, 8; land cover change models, 362; limitations, 167-69; mobile agents, 55-56; spatial representations, 48-50; urban growth models,151-69

Cellular Automata (CA) tool kit, 35

Center for Spatially Integrated Social Science (CSISS), 94, 103, 315,413

centroids,50

change allocation models, 359-63

change demand models, 359, 360

change of support problem (COSP), 96, 106

choropleth maps, 11, 97

Clarke, Martin, 222

Clarke Urban Growth Model, 33

Clean Air Act (1990),212

CLUE, 3,58, 359, 360-61

CLUE-S, 359, 360-61, 366

cluster analysis, 98-99, 102

ClusterSeer, 102

cognitive learning effects, 54-56

collaborative modeling, 132

commercial-off-the-shelf (COTS) software, 27-28, 36, 411, 447

communicable diseases, 245-61

communication modeling, 42-43

complex systems: collaborative modeling, 132; entity-based models, 134-35; graphical models, 133; modular modeling, 132, 135; module specification formalism (MSF), 135; multiple space-time models, 134; parallel process-

. ing, 134; Simulation Module Markup Language (SMML), 136-38; Spatial Modeling Environment (SME), 135-39

Comprehensive R Archive Network (CRAN), 102

computational models, 5

computer-aided design (CAD) systems, 22, 42, 233, 450

conditional simulations, 79

confidence limits, 14

Consequences Assessment Tool Set (CATS), 31, 32

continuous models, 6, 7, 322

continuous simulators, 122

Conway, John, 8

Cormas platform, 407

coupled dynamics, multiple submodels, 117-18

coupled dynamics, single-system models, 116-17

INDEX

coupling, 13,212-18; See also integrated modeling; loosely-coupled architec-ture; tightly-coupled architecture

coverage models, 11

CrimeStat, 102

cross-correlation, 81-82

Cube, 276

customization capabilities, 29

cyberinfrastructure, 15

Darcy flow equation; 6

data integration, 28

data management, 22, 25, 28, 272-74, 284-85

data models, 2, 446

DCluster, 102

decision-making processes, 72, 233-34, 290-91, 404

decision support systems (DSS), 290

DELTA, 209,210,211,212

Dempster's Rule of Combination, 365

descriptive models, 45-46

diffusion effects, 53

digital elevation models (DEMs), 69, 339, 346, 350, 364, 408

digital models,S

Dinamica, 358, 359, 360, 362, 365, 366

sq

Discrete Event System Specification (DEVS) formalism, 413

discrete individual transmission models, 247

. discrete models, 6-7, 322

discrete simulators, 121-22

DRAM, 209

DRASTIC model, 2, 3

driver, SME, 137-39

dynamically chariging model structure, 118-19

dynamic modeling processes,S 5

dynamic simulation models, 113-30; accuracy, 126; agent-based models, 128; background information, 3-7, 50-51; calibration, 127; category assign-ment requirements, 127; classification, 114-22; continuous simulators, 122; coupled dynamics, multiple submodels, 117-18; coupled dynamics, single­system models, 116-17; definition, 114; discrete simulators, 121-22; discret­ization processes, 335-36; dynamically changing model structure, 118-19; dynamic spatial models, 335-36; ecosystem models, 131-45; general-pur­pose models, 120-22; GIS-centric modeling systems, 122-25; GIS-linked sys­tems, 30, 122-26; infectious disease dispersion models, 245-61; landscape pattern models, 423-38; local dynamics models, 116; modeling processes, 55; PCRaster Environmental Software, 336-37; result capture requirements, 126; simple evolution models, 115; simulator-centric'systems, 122, 125-26; software tools, 12-13; spac;e-time modeling, 5, 51-55, 335-36; stabil­ity requirements, 126; static models, 128; three-dimensional models, 128; TIGMOD, 389, 391-95, 399; time-based data requirements, 127; transport models, 268; uncertainty modeling, 127-28; urban growth models, 153, 156, 165:...66; See also agent-based models

Dynamic Urban Evolutionary Model (DUEM), 50, 148, 158-61, 448

DynESDA,102

ecosystem models, 131-45,426-27

edge expansion, 362

elastic demand, 228-29

elevation estimates, 72-74

Emme/2, 276

EMPAL, 209

enterprise application integration, 33

Enterprise Geographic Information Servers, 26, 27

entity-based models, 134-35

environmental models: agent-based models, 403-14; Bolivian case study, 368-82; caribou case study, 396-97; ecosystem models, 131-45; environmental impact submodels, 212-14; generic models, 334-35; Hidden Markov Model (HMM), 395-99; human-animal interaction models, 387-400; hydrologic models, 319-31; landscape pattern models, 423-38; land-use/land-cover change (LUCq models, 357-82, 403-14; multiple-use forest environments 387-400; Panthera tigris case study, 387-400; PCRaster Environmental ' Software, 333-54; tiger behavior models, 389-95, 397-400; transition potential models, 357-82

epidemiology applications, 245-61

errors: error propagation, 14, 71, 72-74, 86; locational errors, 70; research efforts, 29-30

ESTEEM, 289,296-308,448

estimation processes, 101

Everglades Landscape Model, 142

evolutionary algorithms, 429-30

expert systems (ES), 291-92

exploratory spatial data analysis (ESDA), 28, 97-98, 102

Exponential cooling schedule, 433, 436-37

FEARLUS, 406

finite-ele:nent models, 317

forest habitats, 387-400

four RE cycle (Retrieve, Reuse, Revise, Retain), 292-93,309

four-step transport models, 268-72, 282

FRAGSTATS, 406

framework development: elastic demand, 228-29; generic models, 43-47, 412-14; geoprocessing operations, 67-69

fuzzy (FUZCLASS) soft classifier, 367, 373-75, 377-81

fuzzy set theory, 82-85

q

Game of Life, 8, 9

general-purpose models, 120-22

genetic (evolutionary) algorithms, 429-30

GeoDA, 28, 64, 103-5

geodatabase models, 173-200

geographic automata system (GAS), 408

Geomod2, 358,359,360-61, 365

geoprocessing operations, 67-92; Bayesian belief networks, 85-89; definition, 67,68; err()r propagation, 71, 72-74, 86; framework development, 67-69; fuzzy set theory, 82-85; geostatistical simulation, 78-82; Monte Carlo simu­lation method, 78; sensitivity analysis, 74-77; uncertainty modeling, 67-74, 89-90

geostatistical data, 95

geostatistical simulation, 78-82, 354

GeoVISTA Studio, 102

Gibbs sampler, 101

GIS: abstract cellular landscapes, 406; agent-based models, 403-14; bene-fits, 28-29; generic models, 334-35; hydrologic information systems, 321-27; importance, 446-48; land-use/land-cover change (LUCC) models, 358, 403-14; limitations, 451-53; modeling techniques, 10-11; object-oriented geographic systems, 391-92; planning support systems, 290; software archi­tecture, 20-27, 30-36; software improvement recommendations, 284-85, 409-10; software tools, 12-13,20,358,406-9; space-time modeling, 11-12, 335-36; statistical functionality, 94; user population, 405-6, 448-49; See also space-time modeling; transition potential models

GIS-centric modeling systems, 31, 32-33, 122-25

GMAP, 222-26, 233

gnatcatcher habitat study, 74-77

GoldSim, 13, 30, 34, 64

graphical models, 85-89, 133

graphical user interfaces, 23, 410-11

GRJ\SS,28,358,407

gravity model, 6-7,208,222,268

Greedy cooling schedule, 433, 436-37

grid-based systems, 238-39

ground water: See hydrologic models

heuristic algorithms, 424-26, 428-38

Hidden Markov Model (HMM), 395-99

Hong Kong case study, 298-308

human-animal interaction models, 387-400

Hydrologic Engineering Center (HEC), 319, 327-30

Hydrologic Modeling System (HMS), 319, 327-30

INDEX

hydrologic models, 319-31; applications, 320; Attribute Series data, 326; geostatistical simulation, 78-80; GIS integration, 327; hydrologic cycle, 320-21; hydrologic information systems, 321-23; Map2Map, 328-31; mul­tidimensional models, 323; Raster Series data, 326-27; Time Series data, 325-26; Waste Isolation Pilot Plant (WIPP), 70-71; See also PCRaster Environmental Software

Idrisi (Clark Labs): agent-based models, 407-8; applications, 316; background information, 114; Bolivian case study, 368-82; capabilities, 27; dynamic sim­ulation models, 30; land-cover change models, 358; neural networks, 367-68; space-time modeling, 46

individual-level models, 7, 54,248-50,259-61,316,387-400

infectious disease dispersion models, 245-61; background information, 246-48; conceptual framework, 248-50; network topology, 250-51, 255-59; object-oriented attributes, 259-60; simulation design, 251-55

information models, 21-22

infrastructure modeling, 197-99

Infrastructure SimSuite, 408

Integrated Land Use, Transportation, Environment (ILUTE) model, 209, 210, 211

integrated modeling: agent-based models, 403-14; case-based reasoning inter­faces, 290-91, 299, 310-11,448; challenges, 60; coupling approaches, 33; hydrologic models, 327; land-use/land-cover change (LUCC) models, 403-14; land-use transportation models, 212-18; planning support systems, 290-91,299,310-11; software technologies, 447-51; spatial representations, 42-43; static models, 60, 128; transport models, 212~18, 275-87; urban planning, 160-67, 233-35, 290-91; See also loosely-coupled architecture; tightly-coupled architecture

integrated terrain unit (ITV), 11

Integrated Transportation and Land Use Package (ITLUP), 209, 210, 211

Interface Data Models, 319, 327

interfaces, 22-24

Intermodal Surface Transportation Efficiency Act (1991), 212

Internet applications, 236-37, 239

interpolation, 100

inverse distance interpolation, 69

IRPUD, 209, 210,211,212,216-17

irregular spatial representations, 50

Jacobian determinant, 101

JPed model, 57-60

knowledge acquisition, 293-94

knowledge-based systems (KBS), 291-96

knowledge dissemination, 29

knowledge-informed simulated annealing, 432-38

knowledge-informed spatial pattern optimization, 431

knowledge representation, 294, 295

kriging: block kriging, 72; geoprocessing operations, 69, 70, 79; spatial regres-sion analysis, 100 "

landscape pattern models, 423-38; design objectives, 426-27; ecosystem mod­els, 131-45,426-27; genetic (evolutionary) algorithms, 429-30; knowledge­informed simulated annealing, 432-38; knowledge-informed spatial pattern optimization, 431; PFF (optimization function), 432-37; prescribed patterns, 317,428-37; simulated annealing, 428-29, 432-38; spatial optimization, 424-26,428-38; Tabu search, 430-31

Land Use Evolution and Impact Assessment System, 142

land-use/land-cover change (LUCC) models, 357-82; abstract cellular land­scapes, 406; agent-based models, 316,403-14; Bolivian case study, 368-82; cellular automata, 154-56; change allocation models, 359-63; components, 358-59; Dynamic Urban Evolutionary Model (DUEM), 158-61; edge expan­sion, 362; GIS integration, 403-14; landscape pattern models, 423-38; layer models, 50-51; patch change, 362; prescribed patterns, 317, 428-37; road­related growth, 362; software improvement recommendations, 409-10; soft­ware tools, 406-9; spontaneous change, 362; transition potential models, 359-66; transportation models, 203-18; user population, 405-6

470 INDEX

land-use transportation models, 203-18; environmental impact submodels, 212:-14; feedback cycle, 206; GIS integration, 212-18; model characteristics 208-12; theoretical basis, 205-8 '

Latin Hypercube sampling pJ:"ocedure, 79

lattice data, 95, 97

LEAJJ, 174, 188, 191-95, 197~99

Leica Imagine, 28

life cycles, 22, 158-59

linear regression models, 268

linked GIS-modeling systems, 31, 33-34

linking, 97-98, 102

LISEM, 349-51

LISFLOOD, 349-51

local dynamics models, 116

local Moran's I, 99, 103

location representations, 48-49

LOGISTICREG, 358

logistic regression, 357, 366, 373-75, 377-81, 382

loosely-coupled architecture: dynamic simulation models, 51; Dynamic Urban Evolutionary Model (DUEM), 160; future research, 449; integration approaches, 33; land-use models, 148, 159-60,413; linked GIS-modeling systems, 31; MATLAB, 260; pedestrian model, 43, 57; retail/service location models, 233-35; space-time modeling, 46; transport models, 211, 216, 218, 266; urban growth models, 163-64; urban planning, 290-91; Web services model, 24; See also integrated modeling; tightly-coupled architecture

LOV, 359, 362, 365

Lowry model, 208

LTM, 358,359,360,364, 366

LUCAS, 358, 359, 361, 366

LUCIM, 406

LUCITA,406

Mahalanobis (MAHALCLASS) soft classifier, 367, 373-75, 377-81

Map2Map, 328-31

-

Map Algebra, 335, 337

Map Analysis Package, 334

Map Calculus, 335

MapInfo, 46, 57, 408

mapping capabilities, 28

market analysis applications: See retaiVservice location models

Markov, 358, 360

Markov Chain Monte Carlo (MCMC) method, 101, 103

Markov chains, 367, 396

Mathematica, 450

MATLAB, 13,50, 102,259-60,450

maximum likelihood (ML) principle, 101, 103, 366-67

Max Objective Function Value (MAXOFV), 434-37

McHarg, Ian, 4

MEPLAN, 210, 211, 212

methods of moments, 101

Metropolis-Hastings algorithm, 101

MicroMAPPAS, 235

microsimulation, 234-36, 240, 248

MicroVision, 233

MIKE BASIN, 31, 32-33

mobile agents, definition, 55-56

model-based reasoning interfaces, 292, 294

ModelBuilder, 12,29, 123-25, 328-31

modeling: definition, 2-3; GIS applications, 10-11; purpose, 3-4; sharing

47 I

methods, 15; software tools, 12-13,20; validation processes, 14; value, 15

modeling-centric systems, 31, 34-35

Model of Metropolis, 208

models, definition, 2-3

model specification and execution, 29

Modifiable Areal Unit Problem (MAUP), 10, 73

.... UIIIUIIIIIIIIII TTTT TT T IT I

472

modular modeling, 132, 135

module specification formalism (MSF), 135

Monte Carlo simulation method, 78, 354

Moore neighborhood, 157

Moran's I, 98-99, 103

Multi-Criteria Evaluation (MCE), 364

INDEX

multidimensional models: See three-dimensional models; two-dimensional models

multilevel evaluations, 425

MultiMap, 239

multipatch parameterized region growing (MPRG) algorithm, 430

multiple space-time models, 134

multiple-use forest environments, 387-400

multiplier effects, 53

multivariate simulations, 79

MUSSA, 210,211

National Center for Geographic Information and Analysis, 451

National Informatiori and Modeling System (NIMS), 232-33, 448

NatureServe Vista, 31, 32

Navier-Stokes equations, 6

Nearest Neighbor algorithm, 306-7

neighborhoods, 157

NetLogo, 450

networks, 48-50, 216, 249-59, 408

neural networks, 357, 367-68, 373-75, 377-81

NEXRAD, 328-31

Noel Kempff Mercado Natural History Museum (Bolivia), 368, 369

Notting Hill model, 56, 58

Object-Based Environment for Urban Systems (OBEUS), 408

object-component models, 23-24

2!Q

-

---!

object-oriented geographic systems, 391-92

object-oriented software, 9

Open Modelling Interface and Environment (OpenMI), 354

open-source software, 27~28, 411-12, 447

optimization algorithms, 424-26, 428-38

Panthera tigris case study, 387-400

parallel processing, 134

partial differential equations (PDEs), 6

patch change, 362

Patuxent Landscape Model, 142-43

473

PCRaster Environmental Software, 333-54; alluvial architecture, 351-54; applications, 315-16, 346-54; background information, 114; capabilities, 27; flood management, 349-51; geostatistical simulation, 354; modeling lan­guage structure, 336-38; multi-dimensional models, 338, 350; raster analy­sis, 12-13,50; scripting capabilities, 30; space-time modeling, 12,46; static models, 338; surface water runoff example, 338-46; volcanic impact on landscapes, 346-49

Pearson correlation coefficient, 80-82

pedestrian models, 55-60

Peirce Skill Score (PSS), 357, 370-71, 373-76, 380

PFF (optimization function), 432-37

pixels, 160-67

Planning Data Model (PDM), 174-88

planning geodatabase models, 173-200

planning support systems, 289-311; background information, 290; case-based reasoning interfaces, 291-96; GIS integration, 290-91, 299,310-11; Hong Kong case study, 298-308; system architecture, 296-308

PointGrid Library (PGL), 139

point patterns, 95-96

polygon spatial data, 96

population models, 246-48

portal, SME, 139-42

predictive models, 45-46, 147-48,236-37,357

474 INDEX

prescriptive models, 51, 147

probabilistic frameworks: Bayesian belief networks, 85-89; error propaga­tion, 71, 72-74, 86; fuzzy set theory, 82-85; geostatistical simulation, 78-82; Monte Carlo simulation method, 78; sensitivity analysis, 74-77; uncertainty modeling, 67-74, 89-90

process functions, 22, 25

Production, Exchange and Consumption Allocation System (PECAS), 210, 211

PROPOLIS, 212

PROSPECTS, 212

PySpace, 64, 105-6

Quenching cooling schedule, 433, 436-37

RAMAS GIS, 31, 34

random permutation, 99

random surfaces, 95

Random-Utility URBAN (RURBAN) model, 210, 211

rank/threshold procedures, 360-61

raster analysis: ArcGIS, 27; Cellular Automata (CA) took kit, 35; envi­ronmental impact submodels, 213-14; environmental models, 334-36; geoprocessing operations, 69-72; GIS capabilities, 28; hydrologic mod-els, 322, 326-27, 330; land-use/land-cover change (LUCC) models, 406-8; Maplnfo, 57; multi-dimensional models, 326-27; pedestrian model, 57, 59; pixeVraster data, 153, 162-67; remote sensing, 12; software tools, 12-13; spatial representations, 48; spatial statistics, 96; transport models, 213-14,216; uncertainty modeling, 85, 87-88; urban development models, 186; urban growth models, 153, 159-67; See also PCRaster Environmental Software .

Recreation Behavior Simulator (RBSIM) models, 408

recursive importance sampler (RIS), 101

Recursive Porous Agent Simulation Toolkit (RePast), 31, 35-36,406-8,447-48,450-51

Regional Watershed'Modeling System, 320, 328-31

regions, 11

regulations, 175, 176

475

Relative Operating Characteristic (ROC) statistic, 357, 370, 371-72, 376-82

remote sensing, 12

representative fraction, 4

residuals allocation procedures, 361

result capture requirements, 126

retail/service location models, 221-40; applications, 222~27; background information, 222; benefits, 225, 227; brand preference, 230; consumer flow, 229; elastic demand, 228-29; GIS integration, 233-35; grid-based sys­tems, 238-39; microsimulation, 234-36, 240; model characteristics, 227-31; ~model estimation process, 222-25; pricing variations, 231; retail attractive­ness, 229-31; software architecture, 231-33; store characteristics, 230-31; usefulness, 236-37; validity, 238

risk analysis, 71

River Analysis System (RAS), 319, 327-30

road-related growth, 362

R software, 102-3

rule-based interfaces, 164-65,289,293-96,310-11

SAGE toolbox, 102

San Antonio River Authority (SARA), 328

SatScan, 102

scale models, 4-5

SCATTER, 212

scripting capabilities, 29, 411, 449

sensitivity analysis: Bayesian belief networks, 88-89; flood mapping, 330; importance, 14; Modifiable Areal Unit Problem (MAUP), 10; RAMAS GIS, 34; spatial optimization, 425-26; uncertainty modeling, 63, 72, 74-78, 88-89; verification tools, 409

sharing methods, 15,25

simple evolution models, 115

simulated annealing, 428-29, 432-38

Simulation Module Markup Language (SMML), 136-38

simulator-centric systems, 122, 125-26

single-tier information architecture systems, 25, 26

SLEUTH, 31, 33, 359, 360, 362

SLUCE,407

SLUDGE, 406

SME Driver, 137-39

SME Portal, 139-42

INDEX

socioeconomic applications: infectious disease dispersion models, 245-61; land-use transportation models, 203-18; planning support systems, 289-311; retaiVservice location models, 221-40; transport models, 203-18, 265-87; urban development models, 173-200; urban growth models, 151-69

software architecture, 20-27,30-36,231-33

software tools: commercial-off-the-shelf (COTS) software, 27-28, 36,411, 447; development pathways, 410-12; dynamic simulation models, 12-13; generic models, 412-14; graphical user interfaces, 410-11; hybrid approach, 410; land-use/land-cover change (LUCC) models, 358,406-9; open-source software, 27-28, 411-12, 447; scripted interfaces, 411; single-program approach, 410; software improvement recommendations, 284-86, 409-10, 449; spatial statistics, 101-6; transport models, 272-76

SpaceStat, 101

space-time modeling: animal movement behaviors, 395; Arc Hydro, 324; conceptual framework, 43-46; discretization processes, 335-36; dynamic simulation models,S, 51-55,335-36; GIS applications, 11-12,335-36; hydrologic information systems, 321-27; importance, 30; multiple space­time models, 134; PCRaster Environmental Software, 12,46,333-54; spatial statistics, 96-97; static models, 51-52; temporal resolution,S; time­based data requirements, 127; tracking models, 12,29,450; See also infec­tious disease dispersion models

SPARTACUS, 212

spatial adjacency models, 247

spatial analysis and modeling capabilities, 28, 36, 42

spatial autocorrelation, 81, 98-99, 102,426

spatial decision support systems (SDSS), 290

spatial disaggregation, 213-14, 234

spatial interaction model, 6-7,222-40

spatially explicit models, 248-50, 259-61

spatially seemingly unrelated regressions (spatial SUR),. 105

Spatial Modeling Environment (SME), 135-43; background information, 114; infrastructure, 136-37; LEAM, 192; module specification formalism (MSF),

-

477

135; Patuxent Landscape Model, 142-43; PointGrid Library (PGL), 139; simulation configurations, 138-39; Simulation Module Markup Language (SMML), 136-37; SME Driver, 137-39; SME Portal, 139-42; STELLA, 64-65, 136-39, 192

spatial models, definition, 3

spatial optimization, 424-26, 428-38

spatial patterns, 423-38

spatial prediction, 100

spatial regression analysis, 100-103

spatial representations, 42-43, 48-50, 128, 162-67

spatial resolution, 5, 47-49, 168-69,214

spatial statistics, 93-112; background information, 94; computational issues, 97-101; data structures, 95-97; exploratory spatial data analysis (ESDA), 97-98, 102; integration requirements, 30; software tools, 101-6; space-time mod­eling, 96-97; spatial autocorrelation, 98-99, 102; spatial regression analysis, 100-103

spatial weights matrix, 96, 97, 98

spdep, 102, 103

S-Plus, 94, 102

spontaneous change, 362

SPRING, 358

S+SpatialStats, 94, 102

stability requirements, 126

STARS, 102

Stata, 102-3

static models: capabilities, 28, 452; definition, 3; environmental models, 338; hydrologic models, 331; integration requirements, 60, 128; landscape pat­tern models, 428; PCRaster Environmental Software, 338; software tools, 12; space-time modeling, 51-52; spatial representations, 128; transport models, 205,215,268; urban growth models, 153, 155-56, 162, 164-65, 167,449

STELLA: dynamic simulation models, 13, 30, 34, 51; graphical models, 133; LEAM, 192; Spatial Modeling Environment (SME), 64-65, 136-39, 192

stochastic choice procedures, 361-62

stochastic models, 248-50, 260-61, 395-99

store characteristics, 230-31

subpopulation models, 248

surface water: See hydrologic models

S~AR11,31,34,35,406-7,447

SYPR, 408

Tabu search, 430-31

Tactical Sensor and Ubiquitous Network Agent-11odeling Initiative (TSUNA11I), 408

temporal resolution, 5

tessellations, 97, 162-63

thick client systems, 25, 27

Thiessen polygons, 97, 103,338

thin client systems, 26, 27

INDEX

three-dimensional models: agent-based models, 408; challenges, 29, 450; cou­pling approaches, 233; dynamic simulation models, 128; GoldSim, 64; hydrologic models, 323; PCRaster Environmental Software, 338, 350; raster analysis, 326-27; software architecture, 25; software improvement recom­mendations, 409; spatial analysis and modeling capabilities, 36, 42; Spatial 110deling Environment (S11E), 140-41

three-tier information architecture systems, 25, 26

tiger behavior models, 389-95, 397-400

tightly-coupled architecture, 33, 211, 275, 290-91; See also integrated model­ing; loosely-coupled architecture

TIG110D, 389, 391-95, 399

time-based data requirements, 127

time geography, 249

time representations: See space-time modeling

time scale ranges, 52-53

TLU11IP, 210, 211

Tracking Analyst, 12,29,450

Traffic Analyst, 279-84

transaction processing systems, 20-21

TransCAD, 276

479

transfer functions, 79

transition potential models, 357-82; aggregation operators, 363-65; aver­aging, 364-65; back-propagation neural networks, 357, 367-68, 373-75, 377-81, 382; basic principles, 363; Bayesian soft classification, 366-67, 373-81; Bolivian case study, 368-82; change allocation models, 359-63; change demand models, 359, 360; Dempster's Rule of Combination, 365; edge expansion, 362; empirical probabilities, 363-66; fuzzy (FUZCLASS) soft classifier, 367, 373-75, 377-81; logistic regression, 357, 366, 373-75, 377-81,382; Mahalanobis (MAHALCLASS) soft classifier, 367, 373-75, 377-81; multivariate evaluations, 366-68, 373-75, 377-81; patch change, 362; Peirce Skill Score (PSS), 357, 370-71, 373-76, 380; procedure evalua­tions, 363-68; product operators, 365, 377-81; rank/threshold procedures, 360-61; Relative Operating Characteristic (ROC) statistic, 357, 370, 371-72,376-82; residuals allocation procedures, 361; road-related growth, 362; spontaneous change, 362; stochastic choice procedures, 361-62; weighted averaging, 364-65; Weights-of-Evidence, 357, 365, 377-81

Transportation Equity Act (1998), 212

transport models, 265-87; applications, 266-67; assignment models, 271; cal­culation tools, 274-75, 277, 278, 280-83, 286; custom-built packages, 278-79,284-85; data editing, 272-74, 277, 278, 280, 285; data flow, 271-72; data storage, 275-79, 283; definition, 266; four-step models, 268-72, 282; GIS integration, 279-87; integration techniques, 275-79; modal splits, 271; model types, 268; off-the-shelf packages, 276-78, 284-85; presentation tools, 275,277,278,283; scenario management, 274, 277, 278, 280, 285-86; software tools, 272-76; traditional models, 276; trip distribution, 271; trip generation, 270-71; See also urban planning

T~}nJS,31,34,210,211,212

true 3D/4D modeling, 29, 450

two-dimensional models: agent-based models, 408; challenges, 450; dynamic simulation models, 128; hydrologic models, 323; PCRaster Environmental Software, 338, 350; raster analysis, 326-27; spatial analysis and modeling capabilities, 36; Spatial Modeling Environment (SME), 140-41

two-tier information architecture systems, 25

I

uncertainty modeling: Bayesian belief networks, 85-89; cellular automata, 167-68; development control, 296; dynamic simulation models, 127-28; error propagation, 14; fuzzy set theory, 82-85; geoprocessing operations, 67-74, 89-90; geostatistical simulation, 78-82; Monte Carlo simulation method, 78; research efforts, 29-30, 451-52; sensitivity analysis, 74-77

unified modeling language (UML), 148-49, 177,413

Universal Soil Loss Equation (USLE), 3

INDEX

University Consortium on GIS (UCGIS), 29

Urban Growth Boundary (UGB), '176,194,196-97

urban planning: case-based reasoning interfaces, 148,291-96; cellular autom­ata, 156-61, 163-69; development control, 295-96; Dynamic Urban Evolutionary Model (DUEM), 50, 148, 158-61; generic models, 154-56; GIS integration, 160-67,212-18,233-35,290-91; growth patterns, 152-53; knowledge-based systems (KBS), 291-96; landscape pattern models, 423-38; land-use transportation models, 203-18; modeling applications, 147-49; planning support systems, 289-311; retaiVservice location models, 221-40; urban developmen~ models, 173-200; urban growth models, 151-69

UrbanSim, 169, 174, 188-91, 194-97,210,211

user interfaces, 22, 25

validity, 14,238

variography, 96

vector analysis, 27, 50, 162-63,334,407

verification, 14

View-Modelbase-Driver architecture, 136

VILLAGE, 407

visualization, 28; See also three-dimensional models; two-dimensional models

Visum, 276

von Neumann neighborhood, 157

Ward, John, 57

Waste Isolation Pilot Plant (WIPP), 70-71

water: See hydrologic models

Web services model, 24

weighting methods: generic models, 154-55; spatial weights matrix, 96, 97, 98; weighted averaging, 364-65; Weighted Performance Index (WPI), 434-37; Weights-of-Evidence, 357, 365, 377-81

WH Smith, 222-25

Wilson, Alan, 222

XGobi, 102

XploRe, 102