Special Issue "Geoprocessing in Public and Environmental Health"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 August 2018).

Special Issue Editors

Assoc. Prof. Marco Helbich
E-Mail Website
Guest Editor
Department of Human Geography and Spatial Planning, Utrecht University, The Netherlands
Interests: spatial and spatiotemporal analyses; computational urban geography; GIS modeling; real estate economics; active transportation; built and natural environment; health geography
Special Issues and Collections in MDPI journals
Dr. Paulien Hagedoorn
E-Mail Website
Guest Editor
Utrecht University, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands
Interests: health geography, environmental exposures
Dr. Derek Karssenberg
E-Mail Website
Guest Editor
Utrecht University, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands
Interests: computational geography; spatial simulation modelling; complex systems; high performance computation; natural & built environments
Prof. Dr. Mei-Po Kwan
E-Mail Website
Guest Editor
Department of Geography and Resource Management, and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong, China
Interests: environmental health; human mobility; sustainable cities; urban and transportation issues in cities; GIScience
Special Issues and Collections in MDPI journals
Dr. Hannah Roberts
E-Mail Website
Guest Editor
Utrecht University, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands
Interests: GIS, environmental psychology
Dr. Simon Scheider
E-Mail Website
Guest Editor
University Utrecht, Faculty of Geosciences, Department of Human Geography and Spatial Planning, 3584 CB UTRECHT , The Netherlands
Interests: interoperability and GIS; semantic concepts in geographic analysis; Semantic Web and ontologies of geographic information; geodata retrieval and Linked Open Data (LOD); GIS workflow automation and sharing; geo-analytical question answering (QA); spatial reference systems and cognitive frames of reference (FoR); Place representation in GIS; exposure, accessibility and spatial interaction with places

Special Issue Information

Dear Colleagues,

Since the introduction of geographic information systems (GIS) a few decades ago, this technology has radically altered numerous scientific domains with public and environmental health standing out. The emerging opportunities to integrate and process spatial information into health research provide a rich ground for understanding how physical and mental health outcomes are interwoven with place (Richardson et al. 2013). Some common themes in past (Rushton 2003) and recent research agendas (Kirby et al. 2017) can be recognized, such as the visualization of spatial distributions of diseases, the detection of disease clusters, the determination of spatial accessibility of health care facilities, and the identification of disease risk across space and over time.

Despite significant progress, however, numerous challenges remain in public and environmental health research (Kwan 2012, Kirby et al. 2017, Helbich 2018). First, we progressed from a data-poor to a data-rich society. While offering a great potential to develop new hypotheses, the volume and variety of spatial data—originating in databases with different spatial resolution, temporal granularity, and different semantics—make it possible to scale-up analysis across these sources, but at the same time make geospatial health research more challenging than ever. Second, driven by progress in data collection (e.g., tracking through Global Positioning Systems, GPS) in tandem with advances in information and communications technology (e.g., smartphone-based sensing), mobility-based approaches gain momentum as well as enable mobile computation and analysis with a high spatiotemporal resolution, complementing traditional place-based approaches to assessing environmental exposures. While the latter relies on aggregated data to model environmental exposures based on people’s residential neighborhood, the former considers exposures dynamically along people`s daily travel routes and over people`s life course. Third, to handle large amounts of data and to perform computationally intensive geospatial analyses, a tight integration between databases, GIS and statistical computing that utilizes high-performance computing approaches, such as parallelization and distributed computing, are needed for efficient and scalable geoprocessing. Addressing these and many other challenges might lead to significant progress in the fields of geographic information science, public and environmental health.

This Special Issue aims to stimulate discussions on the development and application of the latest GIS and data-driven methodologies to better understand health outcomes, the underlying mechanisms, and their dynamics over time and across space. It seeks to publish original research, review papers, methodology-oriented papers, and innovative applications. Through the combination of geographic information science with public and environmental health, significant contributions are expected from transdisciplinary approaches integrating health (register) data, increasingly available environmental data together with geospatial technologies (e.g., GIS, GPS) and data analytics (e.g., machine learning, Bayesian spatial and space-time models). Research opportunities also exist in health data linkage, integration, and in GIS-based exposure modeling and locational privacy for health studies. We hope that the contributions will support evidence-based public health policies in the long term. Our interest is in papers that cover a wide spectrum of methodological and domain-specific topics on physical and mental health, including, but not limited to, the following:

  • Novel geoprocessing algorithms for health data
  • GIS-based environmental exposure modeling
  • Accessibility of health care facilities
  • Disease clustering and surveillance
  • Space-time personal environmental exposure and risk assessments
  • System science approaches through spatial simulation modeling
  • Space-time disease mapping
  • High-performance computing for disease and environment data
  • Geocoding and locational privacy
  • Data linkage and integration of health data
  • Uncertainty and the uncertain geographic context problem

Related References

Helbich, M. (2018). Toward dynamic urban environmental exposure assessments in mental health research. Environmental Research, 161, 129-135.

Kirby, R. S., Delmelle, E., & Eberth, J. M. (2017). Advances in spatial epidemiology and geographic information systems. Annals of epidemiology, 27, 1-9.

Kwan, M.-P. (2012) The uncertain geographic context problem. Annals of the Association of American Geographers, 102(5), 958-968.

Richardson, D. B., Volkow, N. D., Kwan, M. P., Kaplan, R. M., Goodchild, M. F., & Croyle, R. T. (2013). Spatial turn in health research. Science, 339, 1390-1392.

Rushton, G. (2003). Public health, GIS, and spatial analytic tools. Annual review of public health, 24, 43-56.

Dr. Marco Helbich
Dr. Paulien Hagedoorn
Dr. Derek Karssenberg
Prof. Mei-Po Kwan
Dr. Hannah Roberts
Dr. Simon Scheider
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Geoprocessing
  • Exposures
  • Clustering
  • Surveillance
  • Geocomputation
  • Simulation
  • High-performance computing
  • Geocoding
  • Locational privacy
  • Data linkage
  • Uncertainty

Published Papers (6 papers)

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Research

Open AccessArticle
Impaired Water Hazard Zones: Mapping Intersecting Environmental Health Vulnerabilities and Polluter Disproportionality
ISPRS Int. J. Geo-Inf. 2018, 7(11), 433; https://doi.org/10.3390/ijgi7110433 - 06 Nov 2018
Cited by 3
Abstract
This study advanced a rigorous spatial analysis of surface water-related environmental health vulnerabilities in the California Bay-Delta region, USA, from 2000 to 2006. It constructed a novel hazard indicator—“impaired water hazard zones’’—from regulatory estimates of extensive non-point-source (NPS) and point-source surface water pollution, [...] Read more.
This study advanced a rigorous spatial analysis of surface water-related environmental health vulnerabilities in the California Bay-Delta region, USA, from 2000 to 2006. It constructed a novel hazard indicator—“impaired water hazard zones’’—from regulatory estimates of extensive non-point-source (NPS) and point-source surface water pollution, per section 303(d) of the U.S. Clean Water Act. Bivariate and global logistic regression (GLR) analyses examined how established predictors of surface water health-hazard exposure vulnerability explain census block groups’ proximity to impaired water hazard zones in the Bay-Delta. GLR results indicate the spatial concentration of Black disadvantage, isolated Latinx disadvantage, low median housing values, proximate industrial water pollution levels, and proximity to the Chevron oil refinery—a disproportionate, “super emitter”, in the Bay-Delta—significantly predicted block group proximity to impaired water hazard zones. A geographically weighted logistic regression (GWLR) specification improved model fit and uncovered spatial heterogeneity in the predictors of block group proximity to impaired water hazard zones. The modal GWLR results in Oakland, California, show how major polluters beyond the Chevron refinery impair the local environment, and how isolated Latinx disadvantage was the lone positively significant population vulnerability factor. The article concludes with a discussion of its scholarly and practical implications. Full article
(This article belongs to the Special Issue Geoprocessing in Public and Environmental Health)
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Open AccessArticle
Winter Is Coming: A Socio-Environmental Monitoring and Spatiotemporal Modelling Approach for Better Understanding a Respiratory Disease
ISPRS Int. J. Geo-Inf. 2018, 7(11), 432; https://doi.org/10.3390/ijgi7110432 - 06 Nov 2018
Abstract
Chronic Obstructive Pulmonary Disease is a progressive lung disease affecting the respiratory function of every sixth New Zealander and over 300 million people worldwide. In this paper, we explored how the combination of social, demographical and environmental conditions (represented by increased winter air [...] Read more.
Chronic Obstructive Pulmonary Disease is a progressive lung disease affecting the respiratory function of every sixth New Zealander and over 300 million people worldwide. In this paper, we explored how the combination of social, demographical and environmental conditions (represented by increased winter air pollution) affected hospital admissions due to COPD in an urban area of Christchurch (NZ). We juxtaposed the hospitalisation data with dynamic air pollution data and census data to investigate the spatiotemporal patterns of hospital admissions. Spatial analysis identified high-risk health hot spots both overall and season specific, exhibiting higher rates in winter months not solely due to air pollution, but rather as a result of its combination with other factors that initiate deterioration of breathing, increasing impairments and lead to the hospitalisation of COPD patients. From this we found that socioeconomic deprivation and air pollution, followed by the age and ethnicity structure contribute the most to the increased winter hospital admissions. This research shows the continued importance of including both individual (composition) and area level (composition) factors when examining and analysing disease patterns. Full article
(This article belongs to the Special Issue Geoprocessing in Public and Environmental Health)
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Open AccessArticle
Spatially Explicit Age Segregation Index and Self-Rated Health of Older Adults in US Cities
ISPRS Int. J. Geo-Inf. 2018, 7(9), 351; https://doi.org/10.3390/ijgi7090351 - 27 Aug 2018
Abstract
There have been mixed findings on whether residential (spatial) age segregation causes better or worse health in older adults. These inconsistencies can possibly be attributed to two limitations in the previous studies. First, many studies have used statistical age composition to indicate the [...] Read more.
There have been mixed findings on whether residential (spatial) age segregation causes better or worse health in older adults. These inconsistencies can possibly be attributed to two limitations in the previous studies. First, many studies have used statistical age composition to indicate the residential age segregation in a community, but this statistic does not consider the spatial arrangement of the residents. Second, many national scale studies have focused on averaged (or global) associations between age segregation and senior health and have assumed that these associations represent the situation in every part of a country. Little attention has been paid to local patterns of such association in different places. To address these previously identified limitations, we calculated a spatially explicit age segregation index for each United States (US) city to replace the conventional age composition index. We derived data regarding 92,560 respondents aged 65 and above in 185 US urban areas from the Behavioral Risk Factor Surveillance System (BRFSS). We then examined global and local associations between spatial age segregation and the self-rated health of older adults across US cities. Our multilevel global analysis suggested that older adults living in age-segregated metropolitan areas experienced more mentally unhealthy days. On the other hand, the local regression analysis identified local clusters of positive associations between the age segregation and the elderly’s overall health status in western and southern metropolitan areas, but no significant associations in midwestern and northeastern cities. In short, we advocated for the use of a spatially explicit approach to deepen the understanding of the association between age segregation and senior health. The new age segregation metric and new analytic approach can offer new insights into the ongoing debate regarding aging in place. Full article
(This article belongs to the Special Issue Geoprocessing in Public and Environmental Health)
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Open AccessArticle
Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal
ISPRS Int. J. Geo-Inf. 2018, 7(7), 275; https://doi.org/10.3390/ijgi7070275 - 12 Jul 2018
Abstract
Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. [...] Read more.
Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever. Full article
(This article belongs to the Special Issue Geoprocessing in Public and Environmental Health)
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Open AccessArticle
Using GIS for Determining Variations in Health Access in Jeddah City, Saudi Arabia
ISPRS Int. J. Geo-Inf. 2018, 7(7), 254; https://doi.org/10.3390/ijgi7070254 - 28 Jun 2018
Cited by 2
Abstract
The main objective of this paper is to use Geographical Information Systems (GIS) for identifying spatial accessibility to health centers in Jeddah City, Saudi Arabia based on the drive-time analysis technique. A geo-database was created that includes the location of health centers, population [...] Read more.
The main objective of this paper is to use Geographical Information Systems (GIS) for identifying spatial accessibility to health centers in Jeddah City, Saudi Arabia based on the drive-time analysis technique. A geo-database was created that includes the location of health centers, population distribution, and road networks. ArcGIS Network Analyst and overlay analyses were selected as the analysis tools for this paper. The results of the paper indicate that health centers that are located in the northern districts of Jeddah City have fewer registered patients. There are several areas of Jeddah City that have low accessibility to health centers because they fall outside the 30 min drive-time service area. These are located mainly in western, central, and northern city districts. Local health planners in Jeddah City can use the created application to allocate additional health centers in these less-served districts. Full article
(This article belongs to the Special Issue Geoprocessing in Public and Environmental Health)
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Open AccessArticle
Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity
ISPRS Int. J. Geo-Inf. 2018, 7(4), 143; https://doi.org/10.3390/ijgi7040143 - 05 Apr 2018
Cited by 4
Abstract
Considerable research has been conducted to advance our understanding of how environmental factors influence people’s health behaviors (e.g., leisure-time physical inactivity) at the neighborhood level. However, different environmental factors may operate differently at different geographic locations. This study explores the inconsistent findings regarding [...] Read more.
Considerable research has been conducted to advance our understanding of how environmental factors influence people’s health behaviors (e.g., leisure-time physical inactivity) at the neighborhood level. However, different environmental factors may operate differently at different geographic locations. This study explores the inconsistent findings regarding the associations between environmental exposures and physical inactivity. To address spatial autocorrelation and explore the impact of spatial non-stationarity on research results which may lead to biased estimators, this study uses spatial regression models to examine the associations between leisure-time physical inactivity and different social and physical environmental factors for all counties in the conterminous U.S. By comparing the results with the conventional ordinary least squares regression and spatial lag model, the geographically weighted regression model adequately addresses the problem of spatial autocorrelation (Moran’s I of the residual = 0.0293) and highlights the spatial non-stationarity of the associations. The existence of spatial non-stationarity that leads to biased estimators, which were often ignored in past research, may be another reason for the inconsistent findings in previous studies besides the modifiable areal unit problem and the uncertain geographic context problem. Also, the observed associations between environmental variables and leisure-time physical inactivity are helpful for developing location-based policies and interventions to encourage people to undertake more physical activity. Full article
(This article belongs to the Special Issue Geoprocessing in Public and Environmental Health)
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