Table Of ContentSpatial analySiS in
HealtH GeoGrapHy
Geographies of Health
Series editors
allison Williams, associate professor, School of Geography and
earth Sciences, McMaster University, Canada
Susan elliott, professor, Department of Geography and environmental
Management and School of public Health and Health Systems,
University of Waterloo, Canada
there is growing interest in the geographies of health and a continued interest in
what has more traditionally been labeled medical geography. the traditional focus
of ‘medical geography’ on areas such as disease ecology, health service provision
and disease mapping (all of which continue to reflect a mainly quantitative
approach to inquiry) has evolved to a focus on a broader, theoretically informed
epistemology of health geographies in an expanded international reach. as a result,
we now find this subdiscipline characterized by a strongly theoretically-informed
research agenda, embracing a range of methods (quantitative; qualitative and the
integration of the two) of inquiry concerned with questions of: risk; representation
and meaning; inequality and power; culture and difference, among others. Health
mapping and modeling has simultaneously been strengthened by the technical
advances made in multilevel modeling, advanced spatial analytic methods and
GIS, while further engaging in questions related to health inequalities, population
health and environmental degradation.
This series publishes superior quality research monographs and edited
collections representing contemporary applications in the field; this encompasses
original research as well as advances in methods, techniques and theories. The
Geographies of Health series will capture the interest of a broad body of scholars,
within the social sciences, the health sciences and beyond.
Also in the series
The Afterlives of the Psychiatric Asylum
Recycling Concepts, Sites and Memories
edited by Graham Moon, robin Kearns and alun Joseph
Geographies of Health and Development
Edited by Isaac Luginaah and Rachel Bezner Kerr
Soundscapes of Wellbeing in popular Music
Gavin J. Andrews, Paul Kingsbury and Robin Kearns
Mobilities and Health
Anthony C. Gatrell
Spatial analysis in
Health Geography
Edited by
pavloS KanaroGloU
McMaster University, Canada
eriC DelMelle
University of North Carolina at Charlotte, USA
antonio páez
McMaster University, Canada
© Pavlos Kanaroglou, Eric Delmelle and Antonio Páez 2015
all rights reserved. no part of this publication may be reproduced, stored in a retrieval
system or transmitted in any form or by any means, electronic, mechanical, photocopying,
recording or otherwise without the prior permission of the publisher.
Pavlos Kanaroglou, Eric Delmelle and Antonio Páez have asserted their right under the
Copyright, Designs and Patents Act, 1988, to be identified as the editors of this work.
published by
ashgate publishing limited ashgate publishing Company
Wey Court East 110 Cherry Street
Union Road Suite 3-1
Farnham Burlington, VT 05401-3818
Surrey, GU9 7pt USa
england
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British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library.
The Library of Congress has cataloged the printed edition as follows:
Spatial analysis in health geography / [edited] by pavlos Kanaroglou, eric Delmelle and
Antonio Páez.
pages cm. -- (Ashgate’s geographies of health series)
includes bibliographical references and index.
ISBN 978-1-4724-1619-3 (hardback) -- ISBN 978-1-4724-1620-9 (ebook) -- ISBN
978-1-4724-1621-6 (epub) 1. Medical geography--Statistical methods. I. Kanaroglou,
Pavlos. II. Delmelle, Eric. III. Páez, Antonio.
RA791.S64 2015
614.4'2--dc23
2014036510
ISBN 9781472416193 (hbk)
ISBN 9781472416209 (ebk – PDF)
ISBN 9781472416216 (ebk – ePUB)
printed in the United Kingdom by Henry ling limited,
at the Dorset press, Dorchester, Dt1 1HD
Contents
List of Figures vii
List of Tables xi
Notes on Contributors xiii
Acknowledgements xxi
1 Introduction: Spatial Analysis and Health 1
Eric Delmelle and Pavlos S. Kanaroglou
SECTION 1 METhOdS
2 Effective Use of GIS for Spatial Epidemiology 15
Linda Beale
3 An Assessment of Online Geocoding Services for Health Research in a
Mid-Sized Canadian City 31
Patrick DeLuca and Pavlos S. Kanaroglou
4 Clustering and Co-occurrence of Cancer Types: A Comparison of
Techniques with an Application to Pediatric Cancer in Murcia, Spain 47
Antonio Páez, Fernando A. López-Hernández, Juan A. Ortega-García,
Manuel Ruiz
SECTION 2 INfECTIOuS dISEaSE
5 Spatio-Temporal Characteristics of the Medieval Black Death 71
Brian H. Bossak and Mark R. Welford
6 Space-Time Visualization of Dengue Fever Outbreaks 85
Eric Delmelle, Meijuan Jia, Coline Dony, Irene Casas, and Wenwu Tang
7 Disease at the Molecular Scale: Methods for Exploring Spatial Patterns of
Pathogen Genetics 101
Margaret Carrel
SECTION 3 ChrONIC dISEaSE
8 Modeling Spatial Variation in Disease Risk in Epidemiologic Studies 121
David C. Wheeler and Umaporn Siangphoe
vi Spatial Analysis in Health Geography
9 The Spatial Epidemiology of Mental Well-being in Dhaka’s Slums 139
Oliver Gruebner, Mobarak Hossain Khan, Sven Lautenbach,
Daniel Müller, Alexander Krämer, Tobia Lakes, Patrick Hostert
and Sandro Galea
10 Space-Time Analysis of Late-Stage Breast Cancer Incidence in Michigan 161
Pierre Goovaerts and Maxime Goovaerts
SECTION 4 ExpOSurE
11 A Method for Reducing Classical Error in Long-Term Average Air
Pollution Concentrations from Incomplete Time-Series Data 183
Matthew D. Adams and Pavlos S. Kanaroglou
12 The Geographic Distribution of Metal Dust Exposure in Syracuse, NY 197
Daniel A. Griffith
13 Participatory and Ubiquitous Sensing for Exposure Assessment in
Spatial Epidemiology 219
Michael Jerrett, Colleen E. Reid, Thomas E. McKone, and Petros Koutrakis
SECTION 5 aCCESSIbIlITy aNd hEalTh
14 Locational Planning of Health Care Facilities 243
Alan T. Murray and Tony H. Grubesic
15 Planning Towards Maximum Equality in Accessibility of NCI Cancer
Centers in the U.S. 261
Fahui Wang, Cong Fu and Xun Shi
16 Spatial Dimensions of Access to Health Care Services 275
Daniel J. Lewis
17 Nature and Death: An Individual Level Analysis of the Relationship
Between Biophilic Environments and Premature Mortality in Florida 295
Christopher J. Coutts and Mark W. Horner
Appendix 313
Index 315
List of Figures
1.1 Dengue fever cases for the city of Colombia, 2010 (geocoded at the street
intersection level), in (a). Kernel density estimation in (b), aggregated
dengue cases per neighborhood in (c), population density in (d) and
dengue fever rates in (e) 3
2.1 MAUP: scale and aggregation problem 20
2.2 The effect of classification: Directly standardized mortality rates for 2011,
London, UK 23
3.1 The City of Hamilton, indicating Urban and Rural Census Tracts 33
3.2 Observed geocodes versus what is expected based on the parcel fabric
centroids for (A) Geocoder.ca, (B) Yahoo API and (C) ArcGIS Online 37
3.3 Average distance separating geocode and parcel fabric centroid by census
tract for (A) Geocoder.ca, (B) Yahoo API and (C) ArcGIS Online 39
3.4 Average distances based on the interactive rematch in ArcGIS 42
4.1 Three examples of spatial patterns using simulated data. The x- and y-axis
represent geographical coordinates (easting and northing). Triangles,
squares, and circles represent different types of diseases. The scale in the
maps has been normalized to create a unit square 50
4.2 Multinomial spatial scan statistic and Q(m) applied to simulated examples 55
4.3 Empirical frequency of symbols (co-occurrence patterns) under k=5 and
m=3, and bootstrapped confidence intervals (90% and 95%) 58
4.4 Empirical frequency of symbols (co-occurrence patterns) under k=2
(leukemia and other) and m=3, and bootstrapped confidence intervals
(90% and 95%) 59
4.5 Co-occurring leukemia triads in Murcia (dots are cases of pediatric
cancer). Triad identifiers as in Table 4.5. 61
5.1 Medieval Black Death Outbreaks (By Year) and Medieval
Transportation Networks 74
5.2 Geographic Expansion of the Medieval Black Death. Contours represent
one-year intervals 76
5.3 Medieval Black Death Localities, Buffers (8/16 km), and Medieval
Transportation Networks in the UK 77
5.4 Epidemic initialization of the Medieval Black Death (ordinary kriging) 78
5.5 Epidemic termination of the Medieval Black Death (ordinary kriging) 78
6.1 STKDE computing framework. Boxes in bold (2) and (4) denote
computing intensive procedures, while the dashed box (3) is optional 89
viii Spatial Analysis in Health Geography
6.2 Running time for the STKDE on one CPU (a) 96 CPUs (b) based on
different cell sizes. Note the values along the Y-axis are different 91
6.3 Sensitivity of the STKDE running with variation in spatial bandwidth, on
one CPU (a) 96 CPUs (b) Note the values along the Y-axis are different 92
6.4 Transparency scheme adopted in Voxler for visualizing STKDE values
ranging from 0 to 1 (on the map) 93
6.5 Space-time distribution of dengue fever cases for the first seven month
of the year 2010 in Cali, Columbia in (a) STKDE using a cell size of 125
meters with shaded isovolumes in (b) and isovolumes in a triangulated
fashion in (c) Clusters numbers (1)-(5) are discussed in the text 94
7.1 Example of a phylogenetic tree indicating the relationships between
viruses (or bacteria) B-J, from an ancestral or root virus (A). Viruses G
and H have the greatest degree of genetic change from virus A, while B
and C are closely related to each other and to A 103
7.2 A Mantel spatial correlogram indicating genetic similarity or dissimilarity
at various distance thresholds. Points above the zero line indicate
similiarity, points below indicate dissimilarity. Filled circles indicate
statistical significance. In this case, an isolation-by-distance pattern is not
observed. Instead, samples that are further apart geographically exhibit
statistically significant similarity, while those at low geographic distances
are genetically dissimilar 107
7.3 Sample points (black circles) are used to create a triangular network
of neighbors (using Delaunay triangulation, black lines) and Voronoi
tessellations (gray lines). In Monmonier’s algorithm the genetic distance
or other measure of genetic relatedness is associated with each connection
between points on the triangular network. Barriers are then drawn between
points, along the Voronoi tessellation lines, that have the highest degree of
genetic distance. Individual barrier segments, dividing points, are linked
across the network to either form a closed loop or end at the edge of the
study area 108
7.4 In this hypothetical example, the influence of an explanatory variable on
genetic distance data varies across the dataset, with the strongest influence
observed in the west and declining association in the east 110
8.1 Two simulation study scenarios and one realization from the alternative
hypothesis for the first simulation study. The true risk area is either a circle
or triangle with an odds ratio of 3.5 and a probability of disease of 0.2
outside the elevated risk area. Case (solid) and control (unfilled) locations
are shown as circles for one realization. 127
8.2 One realization from the alternative hypothesis for the risk scenario in
the second simulation study. Data were generated with a true elevated
risk area at time 1 with an odds ratio of 3.5, a probability of disease of
0.2 outside the circle, and a 0.5 probability of moving at three later times:
time 2 (upper right), 3 (lower left) and 4 (lower right). Case (unfilled
square) and control (unfilled circle) locations are shown as circles. Cases
located in the true risk area at time 1 are shaded black 128
List of Figures ix
8.3 Spatial log-odds estimated at time of diagnosis and at 20 years before
diagnosis for models with individual spatial risk components and
cumulative spatial log-odds estimated for a model with individual spatial
risk components at time of diagnosis and 5, 10, 15, and 20 years before
diagnosis. Statistically significant local areas of elevated (lowered) risk are
outlined in black solid (white dashed) lines 132
9.1 Descriptive statistics for the 14 socio-ecological variables used in this study 143
9.2 Marginal effects for one predictor in the multivariable generalized linear
regression model in Table 9.1, while keeping all others at a constant value 148
9.3 Marginal effects for one predictor in the multivariable generalized linear
regression model in Table 9.1, while keeping all others at a constant value 149
9.4 Marginal effects for one predictor in the multivariable generalized linear
regression model in Table 9.1, while keeping all others at a constant value 150
9.5 Mental well-being (WHO-5 scores) and housing quality of men in the
slum settlement Bishil/Sarag 153
10.1 Two different representations of the Michigan space-time breast cancer
dataset 162
10.2 Maps of three county-level quantities averaged over the entire time
period 164
10.3 Annual proportions of breast cancer late-stage cases (white females 84
years and younger) that were diagnosed over the period 1985–2007 within
Wayne County 167
10.4 Time series, with the joinpoint regression model fitted, for three quantities 169
10.5 Time series, with the joinpoint regression model fitted, for the annual
number of cases that were diagnosed early and late over the Lower
Peninsula 171
10.6 Flowchart describing the different steps of the space-time analysis of
county-level rates of breast cancer late-stage diagnosis 172
10.7 Maps of two parameters of the joinpoint regression models fitted to
Michigan county-level time series of proportions of breast cancer late-
stage diagnosis 173
10.8 (A) Number of boundaries (that is, pairs of adjacent counties) with
significant differences in kriged estimates or APC values as a function of
time. (B) Average difference in travel distances to the closest screening
facilities recorded for adjacent counties over time 174
10.9 Results of the space-time cluster analysis 176
11.1 Study area map of Paris, France with monitors identified by their
ID. Circles with radii of 15, 30 and 60 km are included and center on the
Paris Centre monitor. An inset is included of the downtown region 185
12.1 Flowchart articulating the data compilation methodology for this study 198
12.2 Sampling of the Syracuse, NY, geographic landscape 199
12.3 Zoom ins from Figure 12.2a: sample parcels in Syracuse, NY 200
12.4 A scatterplot of the relationship between Box-Cox power transformed soil
(horizontal axis) and dust (vertical axis) measurements 204
