Progress in spatial analysis

Advances in Spatial Science
Editorial Board
Manfred M. Fischer
Geoffrey J.D. Hewings
Peter Nijkamp
Folke Snickars (Coordinating Editor)

For further volumes:
/>

Antonio Páez Julie Le Gallo
Ron N. Buliung Sandy Dall’erba
l

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Editors

Progress in
Spatial Analysis
Methods and Applications

123


Editors
Professor Antonio Páez
School of Geography
and Earth Sciences

1280 Main Street West
McMaster University
Hamilton, Ontario L8S 4K1
Canada


Professor Julie Le Gallo
Université de Franche-Comté CRESE
45 D, Avenue de l’Observatoire
25030 Besançon Cedex, France


Professor Ron N. Buliung
Department of Geography
University of Toronto at Mississauga
3359 Mississauga Road North
Mississauga, Ontario L5L 1C6
Canada


Professor Sandy Dall’erba
Department of Geography
and Regional Development
University of Arizona
P.O. Box 210076
Tucson, AZ 85721, USA


Advances in Spatial Science ISSN 1430-9602
ISBN 978-3-642-03324-7

e-ISBN 978-3-642-03326-1
DOI: 10.1007/978-3-642-03326-1
Springer Heidelberg Dordrecht London New York
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For Patricia, Leonardo, and Luanna (AP)
For Tara, Meera, and Emily (RB)


Foreword

Space is one of the fundamental categories by means of which we perceive and
experience the world around us. Behaviour takes place in space, and the geographical context of behaviour is important in shaping that behaviour. While space by
itself explains very little, spatial processes and the spatial patterning of behaviour
have long been viewed as a key to understanding, explaining, and predicting much
of human behaviour.

Whether or not spatial analysis is a separate academic field, the fact remains
that, in the past 20 years, spatial analysis has become an important by-product of
the interest in and the need to understand georeferenced data. The current interest
in the mainstream social sciences to geography in general, and location and spatial
interaction in particular is a relatively recent phenomenon. This interest has generated an increasing demand for methods, techniques, and tools that allow an explicit
treatment of space in empirical applications. Thus, spatial analysis tends to play
an increasingly important role in measurement, hypothesis generation, and validation of theoretical constructs, activities that are crucial in the development of new
knowledge. The fact that the 2008 Nobel Prize in economics was awarded to Paul
Krugman indicates this increasing attention being given to spatially related phenomena and processes. Given the growing number of academics currently doing research
on spatially related subjects, and the large number of questions being asked about
spatial processes, the time has come for reflecting on the progress made in spatial
analysis.
As an editor of the book series, I highly welcome the present edited volume
on Progress in Spatial Analysis with a focus on theory and methods, and thematic
applications across several academic disciplines. The effort is a worthy intellectual descendent of previous volumes in the series, including Anselin and Florax’s
New Direction in Spatial Econometrics in 1995, Fischer and Getis’ Recent Developments in Spatial Analysis in 1997, and Anselin, Florax, and Rey’s Advances in
Spatial Econometrics in 2004.
I am pleased to realize the mixture of very well-established leaders in the field
of spatial analysis and a new generation of scholars who, one hopes, will continue to carry the torch ignited more than 50 years ago at the dawn of Quantitative
Geography and Regional Science. In this spirit, it is my hour to formally proffer
the welcome to this edited volume, and to the effort poured into bringing major

vii


viii

Foreword

developments and applications into a single source representing recent publications

in spatial analysis. I anticipate that this volume will become a valuable reference, as
the previous offerings in the series.
Vienna
May, 2009

Manfred M. Fischer



Contents

Progress in Spatial Analysis: Introduction .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .
Antonio P´aez, Julie Le Gallo, Ron N. Buliung, and Sandy Dall’Erba

1

Part I Theory and Methods
Omitted Variable Biases of OLS and Spatial Lag Models . . . . . . . . . .. . . . . . . . . . . 17
R. Kelley Pace and James P. LeSage
Topology, Dependency Tests and Estimation Bias in Network
Autoregressive Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 29
Steven Farber, Antonio P´aez, and Erik Volz
Endogeneity in a Spatial Context: Properties of Estimators . . . . . . .. . . . . . . . . . . 59
Bernard Fingleton and Julie Le Gallo
Dealing with Spatiotemporal Heterogeneity:
The Generalized BME Model .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 75
Hwa-Lung Yu, George Christakos, and Patrick Bogaert
Local Estimation of Spatial Autocorrelation Processes . . . . . . . . . . . . .. . . . . . . . . . . 93
Fernando L´opez, Jes´us Mur, and Ana Angulo
Part II Spatial Analysis of Land Use and Transportation Systems

“Seeing Is Believing”: Exploring Opportunities for the
Visualization of Activity–Travel and Land Use Processes
in Space–Time . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .119
Ron N. Buliung and Catherine Morency
Pattern-Based Evaluation of Peri-Urban Development
in Delaware County, Ohio, USA: Roads, Zoning
and Spatial Externalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .149
Darla K. Munroe

ix


x

Contents

Demand for Open Space and Urban Sprawl: The Case of Knox
County, Tennessee . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .171
Seong-Hoon Cho, Dayton M. Lambert, Roland K. Roberts, and
Seung Gyu Kim
Multilevel Models of Commute Times for Men and Women . . . . . . .. . . . . . . . . . .195
Edmund J. Zolnik
Walkability as a Summary Measure in a Spatially
Autoregressive Mode Choice Model: An Instrumental Variable
Approach .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .217
Frank Goetzke and Patrick M. Andrade
Part III

Economic and Political Geography

Employment Density in Ile-de-France: Evidence from Local
Regressions.. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .233
Rachel Guillain and Julie Le Gallo
The Geographic Dimensions of Electoral Polarization
in the 2004 U.S. Presidential Vote . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .253
Ian Sue Wing and Joan L. Walker
Gender Wage Differentials and the Spatial Concentration
of High-Technology Industries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .287
Elsie Echeverri-Carroll and Sof´ıa G. Ayala
Fiscal Policy and Interest Rates: The Role of Financial
and Economic Integration.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .311
Peter Claeys, Rosina Moreno, and Jordi Suri˜nach
Part IV Spatial Analysis of Population and Health Issues
Spatial Models of Health Outcomes and Health Behaviors:
The Role of Health Care Accessibility and Availability .. . . . . . . . . . . .. . . . . . . . . . .339
Brigitte S. Waldorf and Susan E. Chen
Immigrant Women, Preventive Health and Place in Canadian
CMAs . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .363
Kelly Woltman and K. Bruce Newbold
Is Growth in the Health Sector Correlated with Later-Life
Migration? . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .381
Dayton M. Lambert, Michael D. Wilcox, Christopher D. Clark,
Brian Murphy, and William M. Park


Contents

Part V

xi

Regional Applications

Evolution of the Influence of Geography on the Location
of Production in Spain (1930–2005) .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .407
Coro Chasco Yrigoyen and Ana M. L´opez Garc´ıa
Comparative Spatial Dynamics of Regional Systems . . . . . . . . . . . . . . .. . . . . . . . . . .441
Sergio J. Rey and Xinyue Ye
Growth and Spatial Dependence in Europe . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .465
Wilfried Koch
Author Index. . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .483
Subject Index . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .489



List of Figures

Topology, Dependency Tests and Estimation Bias in Network Autoregressive
Models
Steven Farber, Antonio P´aez, and Erik Volz
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10

Figure 11
Figure 12
Figure 13

LR test rejection frequency for difference levels of spatial
dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The impact of sample size on rejection frequency . . . . . . . . . . . . . . .
Rejection frequency curves for two different sample sizes . . . . . . .
The impact of mean degree on small networks . . . . . . . . . . . . . . . . .
The impact of mean degree on large networks . . . . . . . . . . . . . . . . . .
The impact of clustering on rejection frequency . . . . . . . . . . . . . . . .
The impact of matrix density on rejection frequency . . . . . . . . . . . .
Dependence parameter estimation bias for different levels
of dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The impact of sample size on dependence parameter estimation
bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The impact of mean degree on dependence parameter estimation
bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The effect of clustering on dependence parameter estimation bias
The relationship between matrix density and estimation bias . . . . .
Goodness of fit scatterplots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

40
41
41
42
42
43
44
46

46
47
48
49
53

Endogeneity in a Spatial Context: Properties of Estimators
Bernard Fingleton and Julie Le Gallo
Figure 1

Figure 2

Exogenous variable spatial distribution (a) and augmented spatial
Durbin parameter distribution (b, c and d) resulting from
Monte-Carlo simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Monte-Carlo distributions of the X parameter in (17) estimated
by fitting (18) and (11) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

xiii


xiv

List of Figures

Dealing with Spatiotemporal Heterogeneity: The Generalized BME Model
Hwa-Lung Yu, George Christakos, and Patrick Bogaert
Figure 1

Figure 2

Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Simulated random field realizations (top row); estimated field
using GBME (middle row); and estimated field using GK (bottom
row) at times t D 0 (left column), t D 1 (middle column), and
t D 2 (right column) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hard data (black circles), soft data in the form of uniform
distributions (white circles), across space-time . . . . . . . . . . . . . . . . .
Space-time distributions of the value of spatial order . (Left)
t D 0, (Middle) t D 1, and (Right) t D 2 . . . . . . . . . . . . . . . . . . . . . .
Space-time distributions of the value of temporal order . (Left)
t D 0, (Middle) t D 1, and (Right) t D 2 . . . . . . . . . . . . . . . . . . . . . .
Histograms of the estimation errors of the GBME (continuous
line) and GK (dashed line) methods . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hard data (black circles) and uniform distributed data (white
circles) across space-time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Histograms of the estimation errors of the GBME (continuous
line) and GK (dashed line) methods . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hard data (black circles), and Gaussian-distributed data (white
circles) across space-time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Histograms of the estimation errors of the GBME (continuous
line) and GK (dashed line) methods . . . . . . . . . . . . . . . . . . . . . . . . . . .

82

82
83
83
84
85
86
87
87

Local Estimation of Spatial Autocorrelation Processes
Fernando L´opez, Jes´us Mur, and Ana Angulo
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Spatial regimes used in the experiment . . . . . . . . . . . . . . . . . . . . . . . .
Spatial distribution of r . Lattice 7 7. / . . . . . . . . . . . . . . . . . . . . . .
Spatial distribution of r . Lattice 20 20 . . . . . . . . . . . . . . . . . . . . . .
Spatial distribution of r under the break. East–West structure . . .
Spatial distribution of r under the break. Center–Periphery
structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The doughnut effect and the Zoom estimation . . . . . . . . . . . . . . . . . .

100
106
107
110

111
112

“Seeing Is Believing”: Exploring Opportunities for the Visualization
of Activity–Travel and Land Use Processes in Space–Time
Ron N. Buliung and Catherine Morency
Figure 1
Figure 2
Figure 3

Critical dimensions and interactions between activity-travel and
land-use systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
The Greater Toronto Area (GTA) and Greater Montreal Area
(GMA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Chronology of the spatial location of the mobile population
during an average weekday in the GMA (1998) . . . . . . . . . . . . . . . . 127


List of Figures

Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13

Figure 14

xv

Chronology of the spatial location of the mobile population
during a typical weekday in the GTA & Hamilton (2001) . . . . . . .
GTA trip density excluding high density CBD traffic zones (2001
TTS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
People accumulation profile in the Central Business District
(GMA) segmented by region of home location (1998) . . . . . . . . . .
2003 Car accumulation profile (CAP), four districts (x: time
of day, y: number of cars) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Monitoring of the number of cars parked in a specific area during
a typical weekday . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Demographic structure with segmentation related to transit use
(1987 & 1998 OD surveys), central Montreal . . . . . . . . . . . . . . . . . .
Geopolitical and network based conceptualizations of urban areas
Network Occupancy Index (top) and Transit Network Occupancy
Index (bottom) estimated for 100 traffic analysis zones . . . . . . . . .
Weighted Gaussian bivariate kernel estimation . . . . . . . . . . . . . . . . .
Geovisualization of power retail capacity in the Greater Toronto
Area (1997–2005) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Centrographic estimation and geovisualization of power centre
expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

128
129
130
132
133

133
135
137
140
142
143

Pattern-Based Evaluation of Peri-Urban Development in Delaware
County, Ohio, USA: Roads, Zoning and Spatial Externalities
Darla K. Munroe
Figure 1
Figure 2

Figure 3

Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
Graphical illustration of variations in edge-to-area ratio
and the corresponding landscape shape index (LSI).
(a) A square patch made up of nine individual squares
of dimension 2 2. (b) A non-square patch made up
of the same nine individual squares, arranged less squarely.
(c) A non-square patch made up of nine individual squares,
arranged nearly linearly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
Landscape pattern analysis of Delaware County, 1988–2003. (a)
Percent developed area (of total land) and Euclidean nearest
neighbor distance edge-to-edge between contiguous parcels (km).
(b) The number of patches (contiguous parcels sharing a common
boundary) and the landscape shape index (higher D greater
proportional edge in the landscape) . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

Demand for Open Space and Urban Sprawl: The Case of Knox County,
Tennessee
Seong-Hoon Cho, Dayton M. Lambert, Roland K. Roberts, and Seung Gyu Kim
Figure 1
Figure 2

Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Transaction parcel with surrounding open space and 1.0-mile
buffer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181


xvi

Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

List of Figures

Marginal implicit price of open space (10,000 square foot
increase in open space) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Price elasticity of open-space demand . . . . . . . . . . . . . . . . . . . . . . . . .
Income elasticity of open-space demand . . . . . . . . . . . . . . . . . . . . . . .
Lot size elasticity of open-space demand . . . . . . . . . . . . . . . . . . . . . .
Finished-area elasticity of open-space demand . . . . . . . . . . . . . . . . .
Housing-density elasticity of open-space demand . . . . . . . . . . . . . . .

185
186
186
187
187
188

Multilevel Models of Commute Times for Men and Women
Edmund J. Zolnik
Figure 1
Figure 2

Population size of MSAs (n D 43) by region . . . . . . . . . . . . . . . . . . 200
Regional differences in commute times from men-only,
women-only, and pooled men–women multilevel models . . . . . . . 211

Walkability as a Summary Measure in a Spatially Autoregressive
Mode Choice Model: An Instrumental Variable Approach
Frank Goetzke and Patrick M. Andrade
Figure 1

Map with the household locations of all the included trips . . . . . . . 222

Employment Density in Ile-de-France: Evidence from Local Regressions
Rachel Guillain and Julie Le Gallo
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Figure 6
Figure 7
Figure 8
Figure 9

Departments and communes in Ile-de-France. Scale: 1:9,000 . . . .
CBD, new towns and highways. Scale: 1:9,000 . . . . . . . . . . . . . . . . .
Geographic distribution of the density gradient for total
employment. Scale 1:9,000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Geographic distribution of the density gradient for industrial
employment. Scale 1:9,000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Geographic distribution of the density gradient for high-order
services employment. Scale 1:9,000 . . . . . . . . . . . . . . . . . . . . . . . . . .
Geographic distribution of the density gradient for high-tech
employment. Scale 1:9,000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Geographic distribution of the density gradient for standard
services employment. Scale 1:9,000 . . . . . . . . . . . . . . . . . . . . . . . . . .
Geographic distribution of the density gradient for
finance-insurance employment. Scale 1:9,000 . . . . . . . . . . . . . . . . .
Geographic distribution of the density gradient for consumer
services employment. Scale 1:9,000 . . . . . . . . . . . . . . . . . . . . . . . . . .

236
237
244
245
245
246
246
247

247

The Geographic Dimensions of Electoral Polarization in the 2004 U.S.
Presidential Vote
Ian Sue Wing and Joan L. Walker
Figure 1
Figure 2

Electoral polarization: a conceptual framework . . . . . . . . . . . . . . . . . 255
Box plot of descriptive statistics of the dataset . . . . . . . . . . . . . . . . . . 258


List of Figures

xvii

Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

268
270
276
278
279
281

Local Moran’s I significance maps of votes and key covariates . .
Log-odds of voting republican by county clusters . . . . . . . . . . . . . . .
Geographically weighted regression results . . . . . . . . . . . . . . . . . . . .
Local Moran’s I significance maps of GWR odds elasticities . . . . .
GWR odds elasticities of voting republican by county lusters . . . .
GWR odds elasticities: global and local correlations . . . . . . . . . . . .

Fiscal Policy and Interest Rates: The Role of Financial and Economic
Integration
Peter Claeys, Rosina Moreno, and Jordi Suri˜nach
Figure 1

Baseline model, spatial model estimates .W D distance matrix/ . . 326

Spatial Models of Health Outcomes and Health Behaviors: The Role
of Health Care Accessibility and Availability
Brigitte S. Waldorf and Susan E. Chen
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Spatial linkages of a health production function (HPF) . . . . . . . . . .
Cumulative distribution of physicians relative to the cumulative
population distribution across Indiana counties, 2003 . . . . . . . . . . .
Spatial distribution of elderly CVD mortality (left) and elderly
cancer mortality (right) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Spatial distribution of maternal smoking rates (left) and rates of
prenatal care (right) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Spatial distribution of nurses per person (left) and access
to hospital care (right) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

344
347
353
353
354

Is Growth in the Health Sector Correlated with Later-Life Migration?
Dayton M. Lambert, Michael D. Wilcox, Christopher D. Clark, Brian Murphy,
and William M. Park
Figure 1

Figure 2
Figure 3

Figure 4

Distribution of quantile proportions of total in-migrants
composed of individuals in the 55–69 (top panel) and 70C age
cohorts (bottom panel) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Semivariograms of residual error structure . . . . . . . . . . . . . . . . . . . .
Top panel, unshaded counties are those with rurality indices
Ä0:52; bottom panel, counties with rurality indices 0:49. Both
are associated with positive change in the professional
concentration of MD’s and the office-based MD sub-group . . . . . .
Marginal effects of selected demographic and socio-economic
variables on changes in location quotients measuring different
medical professions across a rural–urban continuum . . . . . . . . . . . .

388
395

397

398


xviii

List of Figures

Evolution of the Influence of Geography on the Location of Production
in Spain (1930–2005)
Coro Chasco Yrigoyen and Ana M. L´opez Garc´ıa
Figure 1
Figure 2
Figure 3

Figure 4
Figure 5
Figure 6

Choropleth maps of relative GDP per area (1 D national
GDP=km2 ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kernel density estimates of log relative GDP per area . . . . . . . . . . .
Moran scatterplot of log relative GDP per area in 2005 (left). Map
with the selection of provinces ever located in Quadrant 1, plus
Madrid and Valencia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Evolution of the impact of second nature forces on GDP density .
Evolution of the impact of second nature on GDP density
in two regimes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Evolution of the variance decomposition of regressions
in Table 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

418
420

422
427
431
435

Comparative Spatial Dynamics of Regional Systems
Sergio J. Rey and Xinyue Ye
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10

Figure 11

Figure 12

Figure 13

Per capita incomes in the United States, 1978 and 1998 . . . . . . . . .
Per capita incomes in China, 1978 and 1998 . . . . . . . . . . . . . . . . . . .
Convergence and spatial independence in the United States and
China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Regionalization system in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Regionalization system in the United States . . . . . . . . . . . . . . . . . . . .
Inter-regional inequality share in China and the United States . . .
Local Moran Markov transition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
LISA time path (left: China; right: the United States) . . . . . . . . . . .
Covariance networks in China and the United States (thick
segments indicate similar temporal linkages) . . . . . . . . . . . . . . . . . . .
Spider graphs of Zhejiang province (China) and California (the
United States) (the links indicate similar temporal linkages and
the thicker segments highlight spatial joins) . . . . . . . . . . . . . . . . . . . .
Spatial dynamics in China (top left view: the length of LISA time
paths (1); top right view: the tortuosity of LISA time paths (2);
bottom left view: the instability of LISA time paths (3); bottom
right view: space–time integration ratio of temporal dynamics) . . .
Spatial dynamics in the United States (top left view: the length of
LISA time paths (1); top right view: the tortuosity of LISA time
paths (2); bottom left view: the instability of LISA time paths
(3); bottom right view: space–time integration ratio of temporal
dynamics) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Convergence classification in China and the United States . . . . . . .

446
447

448
449
449
449
450
452
455

456

457

458
459


List of Tables

Omitted Variable Biases of OLS and Spatial Lag Models
R. Kelley Pace and James P. LeSage
Á
Table 1 Mean ˇOo and E ˇOo as function of spatial dependence
.ˇ D 0:75; D 0:25/ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Topology, Dependency Tests and Estimation Bias in Network
Autoregressive Models
Steven Farber, Antonio P´aez, and Erik Volz
Table 1
Table 2

Impact of matrix density on likelihood ratio . . . . . . . . . . . . . . . . . 35

Results of rejection frequency logistic regression . . . . . . . . . . . . 50

Endogeneity in a Spatial Context: Properties of Estimators
Bernard Fingleton and Julie Le Gallo
Table 1
Table 2
Table 3
Table 4
Table 5
Table 6
Table 7
Table 8
Table 9

Spatial Durbin: 2sls-SHAC estimator bias and RMSE for b1 ;
omitted variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OLS-SHAC estimator bias and RMSE for b1 ; ignoring omitted
variable. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OLS-SHAC and 2sls-SHAC estimator bias and RMSE for b1 .
OLS-SHAC and 2sls-SHAC estimator bias and RMSE for b1 .
OLS-SHAC and 2sls-SHAC estimator bias and RMSE for b1 .
OLS-SHAC estimator bias and RMSE for ; simple model;
simultaneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
IV-SHAC estimator bias and RMSE for ; spatial Durbin
model; simultaneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OLS-SHAC estimator bias and RMSE for ; simple model;
measurement error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
IV-SHAC estimator bias and RMSE for ; spatial Durbin
model; measurement error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

65
65
67
67
67
69
70
71
71

xix


xx

List of Tables

Dealing with Spatiotemporal Heterogeneity: The Generalized BME Model
Hwa-Lung Yu, George Christakos, and Patrick Bogaert
Table 1
Table 2
Table 3

Examples of S -KB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Examples of soft data with integration domain D and operator
„S – see, Equation (10) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
Summary of theoretical GBME properties. . . . . . . . . . . . . . . . . . . 81

Local Estimation of Spatial Autocorrelation Processes
Fernando L´opez, Jes´us Mur, and Ana Angulo

Table 1
Table 2
Table 3
Table 4
Table 5
Table 6
Table 7

Coefficients used in the simulation . . . . . . . . . . . . . . . . . . . . . . . . .
Diagnostic statistics in the static model. No spatial effects.
Lattice: 7 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Diagnostics statistics in the static model. No spatial effects.
Lattice: 20 20 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Testing the SLM, under the hypothesis of stability . . . . . . . . . . .
Zoom estimation under the null hypothesis. Some descriptive
statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Zoom estimation when the DGP is unstable in . Descriptive
statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Percentage of cells correctly classified . . . . . . . . . . . . . . . . . . . . . .

100
101
102
103
107
108
113

Pattern-Based Evaluation of Peri-Urban Development in Delaware
County, Ohio, USA: Roads, Zoning and Spatial Externalities

Darla K. Munroe
Table 1
Table 2
Table 3
Table 4

Landscape pattern analysis, 1988–2003 . . . . . . . . . . . . . . . . . . . . .
Descriptive statistics, peri-urban agricultural parcels,
and parcels developed, 1988–2003 . . . . . . . . . . . . . . . . . . . . . . . . .
Results of complementary log–log model of urban conversion,
1988–2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Landscape pattern analysis of actual and predicted
development patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

160
162
163
164

Demand for Open Space and Urban Sprawl: The Case of Knox County,
Tennessee
Seong-Hoon Cho, Dayton M. Lambert, Roland K. Roberts, and Seung Gyu Kim
Table 1
Table 2
Table 3

Variable names, definitions, and descriptive statistics . . . . . . . . . 175
Comparison of performance among OLS, GWR,
and GWR-SEM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Parameter global estimates of global (OLS) models . . . . . . . . . . 184

List of Tables

xxi

Multilevel Models of Commute Times for Men and Women
Edmund J. Zolnik
Table 1

Table 2

Table 3
Table 4

Descriptive statistics for household-level dependent
and independent variables for men-only, women-only,
and pooled men–women subsamples . . . . . . . . . . . . . . . . . . . . . . .
Descriptive statistics for MSA-level independent variables
for men-only, women-only, and pooled men–women
subsamples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Household-level coefficients and standard errors for men-only,
women-only, and pooled men–women multilevel models . . . . .
MSA-level coefficients and standard errors for men-only,
women-only, and pooled men–women multilevel models . . . . .

204

205
206

207

Walkability as a Summary Measure in a Spatially Autoregressive
Mode Choice Model: An Instrumental Variable Approach
Frank Goetzke and Patrick M. Andrade
Table 1
Table 2
Table 3
Table 4

Descriptive statistics of all included variables . . . . . . . . . . . . . . .
Linear probability regression model results . . . . . . . . . . . . . . . . . .
Logit regression model results . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Observed and forecasted walking mode share for the whole
dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

223
224
226
227

Employment Density in Ile-de-France: Evidence from Local Regressions
Rachel Guillain and Julie Le Gallo
Table 1
Table 2
Table 3
Table 4
Table 5

Distribution of employment in Ile-de-France . . . . . . . . . . . . . . . .

Spatial autocorrelation LM tests for model (3),
total employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ML estimation results for global employment density
functions (1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ML estimation results for global employment density
functions (2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
LM tests (maximum) of spatial autocorrelation and locational
heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

239
240
241
241
243

The Geographic Dimensions of Electoral Polarization in the 2004 U.S.
Presidential Vote
Ian Sue Wing and Joan L. Walker
Table 1

Spatial Durbin model results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272


xxii

List of Tables

Gender Wage Differentials and the Spatial Concentration
of High-Technology Industries
Elsie Echeverri-Carroll and Sofia G. Ayala

Table 1
Table 2
Table 3

Determinants of (log of) individual hourly wages for male
workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299
Determinants of (log of) individual hourly wages for female
workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301
Decomposition of the gender wage gap . . . . . . . . . . . . . . . . . . . . . 305

Fiscal Policy and Interest Rates: The Role of Financial and Economic
Integration
Peter Claeys, Rosina Moreno, and Jordi Suri˜nach
Table 1
Table 2
Table 3
Table 4
Table 5
Table 6
Table 7

Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Baseline model, pooled and panel estimates; and spatial panel
lag model (W-matrix D distance) . . . . . . . . . . . . . . . . . . . . . . . . . .
Baseline model, spatial panel error model
(W-matrix D distance) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Augmented model, spatial panel lag model, spatial fixed
effects, specifications (W-matrix D distance). See (4) . . . . . . . .
Baseline model, spatial panel lag, country groups
(W-matrix D distance) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Baseline model, spatial panel lag model, various weight
matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Augmented model, spatial panel lag model, spatial fixed
effects, specifications (W-matrix D distance). See (4) . . . . . . . .

319
320
322
323
328
330
333

Spatial Models of Health Outcomes and Health Behaviors: The Role
of Health Care Accessibility and Availability
Brigitte S. Waldorf and Susan E. Chen
Table 1
Table 2
Table 3
Table 4
Table 5
Table 6
Table 7

Physicians and nurses per 100,000 residents in 2004 . . . . . . . . .
Variable definitions and descriptive statistics . . . . . . . . . . . . . . . .
Spatial autocorrelation (Moran’s I ) of variables across Indiana
counties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Outcomes as a function of primary care availability (NURSE)
Behaviors as a function of primary care availability (NURSE)

Outcome as a function of accessibility of hospital care
(HOSPITAL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Behavior as a function of accessibility of hospital care
(HOSPITAL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

346
348
352
355
356
357
358


List of Tables

xxiii

Immigrant Women, Preventive Health and Place in Canadian CMAs
Kelly Woltman and K. Bruce Newbold
Table 1
Table 2
Table 3
Table 4
Table 5

Definition and coding of covariates . . . . . . . . . . . . . . . . . . . . . . . .
Multilevel logistic regression models: lifetime Pap uptake . . . .
Summary of variance (standard error) components, multilevel
logistic regression, lifetime Pap uptake . . . . . . . . . . . . . . . . . . . . .

Multilevel logistic regression models: regular Pap testing . . . . .
Summary of variance (standard error) components, multilevel
logistic regression, regular Pap use . . . . . . . . . . . . . . . . . . . . . . . . .

368
370
372
374
376

Is Growth in the Health Sector Correlated
with Later-Life Migration?
Dayton M. Lambert, Michael D. Wilcox, Christopher D. Clark, Brian Murphy,
and William M. Park
Table 1
Table 2
Table 3

Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386
Model specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394
Regression results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396

Evolution of the Influence of Geography on the Location of Production
in Spain (1930–2005)
Coro Chasco Yrigoyen and Ana M. L´opez Garc´ıa
Table 1
Table 2
Table 3
Table 4
Table 5

Table 6
Table 7
Table 8

Variable list for the Spanish provinces . . . . . . . . . . . . . . . . . . . . . .
Descriptive Statistics of Relative GDP per area . . . . . . . . . . . . . .
Normality and spatial autocorrelation tests of log relative GDP
per area. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Second nature on first nature OLS regression results . . . . . . . . .
Instruments and endogeneity tests in second nature effect
regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OLS regression results of GDP per area on second nature
variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
OLS regression results of GDP/area on second nature in two
spatial regimes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
First and second nature joint effect on GDP density . . . . . . . . . .

414
419
420
424
427
428
430
433

Comparative Spatial Dynamics of Regional Systems
Sergio J. Rey and Xinyue Ye
Table 1
Table 2

Table 3
Table 4
Table 5

Local Moran transition matrix in China (ND/D) . . . . . . . . . . . . .
Local Moran transition matrix in the United States (ND/D) . . .
Spatial dynamics in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Spatial dynamics in the United States . . . . . . . . . . . . . . . . . . . . . . .
Relative mobility of classic and local Moran Markov in China
and the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

451
451
453
454
459


xxiv

Table 6
Table 7

List of Tables

Local Moran transition probability matrix in China . . . . . . . . . . 459
Local Moran transition probability matrix in the United States 459

Growth and Spatial Dependence in Europe
Wilfried Koch

Table 1
Table 2
Table 3
Table 4
Table 5
Table 6

OLS and spatial error model (level model) . . . . . . . . . . . . . . . . . .
OLS and spatial error model (level model) . . . . . . . . . . . . . . . . . .
Spatial Durbin model (level model) . . . . . . . . . . . . . . . . . . . . . . . . .
OLS and spatial error model (convergence model) . . . . . . . . . . .
OLS and spatial error model (convergence model) . . . . . . . . . . .
Spatial Durbin model (convergence model) . . . . . . . . . . . . . . . . . .

473
474
475
478
479
480