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Methodologies and Applications of Geographic Information Science and Spatial Statistical Analysis in Public Health

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Global Health".

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 23502

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Special Issue Editors

Dr. Jane Law

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Guest Editor

1. School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
2. School of Planning, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: spatial epidemiology; healthy communities and the built environment; public health planning; environmental criminology; smart cities; spatial statistics
Special Issues, Collections and Topics in MDPI journals

Dr. Henry Hui Luan

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Guest Editor

Department of Geography, College of Arts and Sciences, University of Oregon, Eugene, OR 97403, USA
Interests: Bayesian spatial and spatiotemporal modeling and its applications in exploring inequities of urban environmental exposures and their associations with health; spatiotemporal analysis; Bayesian statistics; GIScience; public health; crime
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of Geographic Information Sciences (GIScience) and spatial statistical analysis has emerged as an innovative component in the field of Public Health. Great attention has been paid to analyzing spatial and spatiotemporal data; locations; and spatial interaction in understanding public health issues. Applying GIScience or/and spatial statistics, spatial epidemiologists, health geographers, biostatisticians and public health professionals have made significant contributions in understanding problems such as disease clusters and environmental risk factors, and targeting interventions to improve public health. Through analysis and mapping of spatial data from single or multiple sources, GIScience and spatial statistical analysis have been shown as effective tools for assisting policymakers to study diseases, environmental problems, health inequity, and health care in public health.

This Special Issue seeks papers that demonstrate original research with innovative methodologies and applications of GIScience and spatial statistical analysis in the field of public health. The aim of this call is to provide a strong overview of the GIScience and technological advances of spatial methodology in public health, invoke new ways of thinking about how public health programs can be better accomplished, and stimulate new ideas to use GIScience and spatial statistics in public health. Both methodologies for analyzing spatial data and their applications in public health will be considered for this special issue.

Prof. Jane Law
Prof. Henry Hui Luan
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 submissions that pass pre-check are 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. International Journal of Environmental Research and Public Health 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 2500 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

Geographic Information Systems (GIS)
Spatial and spatiotemporal epidemiology
Community/Neighborhood health
GIS applications in epidemiology and public health research
Bayesian disease mapping
Health geomatics
Health inequities
Health geography
Health surveillance
Public health intervention
Healthcare planning

Published Papers (6 papers)

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Research

16 pages, 5170 KiB

Open AccessArticle

Evaluating Spatial Accessibility to General Hospitals with Navigation and Social Media Location Data: A Case Study in Nanjing

by Tianlu Qian, Jie Chen, Ang Li, Jiechen Wang and Dingtao Shen

Int. J. Environ. Res. Public Health 2020, 17(8), 2752; https://doi.org/10.3390/ijerph17082752 - 16 Apr 2020

Cited by 19 | Viewed by 2921

Abstract

Spatial accessibility to general hospitals is an important indicator of the convenience and ability of residents to obtain medical services. Therefore, developing a model for measuring accessibility to general hospitals by multiple transportation modes is necessary. In this study, considering that the increase in travel time will reduce the attractiveness of general hospitals, we used the Two-Step Floating Catchment Area with the Gaussian attenuation function, in which the supply was presented by capacity of hospitals (i.e., number of beds), and the demand was presented by population in each grid derived with social media data mapping real-time locations of active users. The Gaussian Two-Step Floating Catchment Area (Ga2SFCA) simulates the attenuation tendency of the general hospital service capabilities over transit time. To obtain a highly precise understanding of accessibility to hospitals, transit time on Baidu Maps’ navigation service was used as the impedance condition, and the study area was divided into 1 square kilometer grids as the basic unit of research. Taking Nanjing city as a case study, it is found that the accessibility distribution shape changes from a multi-centered circular pattern to a multi-peak distribution, as the time threshold increases. By comparing the accessibility among 11 districts varying from main urban area to suburbs, the accessibility to general hospitals in Nanjing is significantly regionally unbalanced in both travel modes. By calculating and mapping the Modal Accessibility Gap (MAG) of the two travel modes, different modes of transportation resulted in different general hospital accessibility distributions. Generally, private car is superior in access to general hospitals to public transit in most areas. In the central area, public traffic may not contribute to the access to medical services as much as we thought, rather it plays a role in areas far from hospitals along metro lines and bus routes. Full article

(This article belongs to the Special Issue Methodologies and Applications of Geographic Information Science and Spatial Statistical Analysis in Public Health)

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20 pages, 1922 KiB

Open AccessArticle

The State of School Infrastructure in the Assembly Constituencies of Rural India: Analysis of 11 Census Indicators from Pre-Primary to Higher Education

by Akshay Swaminathan, Menaka Narayanan, Jeff Blossom, R. Venkataramanan, Sujata Saunik, Rockli Kim and S. V. Subramanian

Int. J. Environ. Res. Public Health 2020, 17(1), 296; https://doi.org/10.3390/ijerph17010296 - 1 Jan 2020

Cited by 4 | Viewed by 6040

Abstract

In India, assembly constituencies (ACs), represented by elected officials, are the primary geopolitical units for state-level policy development. However, data on social indicators are traditionally reported and analyzed at the district level, and are rarely available for ACs. Here, we combine village-level data from the 2011 Indian Census and AC shapefiles to systematically derive AC-level estimates for the first time. We apply this methodology to describe the distribution of 11 education infrastructures—ranging from pre-primary school to senior secondary school—across rural villages in 3773 ACs. We found high variability in access to higher education infrastructures and low variability in access to lower education variables. For 40.3% (25th percentile) to 79.7% (75th percentile) of villages in an AC, the nearest government senior secondary school was >5 km away, whereas the nearest government primary school was >5 km away in just 0% (25th percentile) to 1.9% (75th percentile) of villages in an AC. The states of Manipur, Arunachal Pradesh, and Bihar showed the greatest within-state variation in access to education infrastructures. We present a novel analysis of access to education infrastructure to inform AC-level policy, and demonstrate how geospatial and Census data can be leveraged to derive AC-level estimates for any population health and development indicators collected in the Census at the village level. Full article

(This article belongs to the Special Issue Methodologies and Applications of Geographic Information Science and Spatial Statistical Analysis in Public Health)

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18 pages, 728 KiB

Open AccessArticle

A Geospatial Analysis of Access to Ethnic Food Retailers in Two Michigan Cities: Investigating the Importance of Outlet Type within Active Travel Neighborhoods

by Greg Rybarczyk, Dorceta Taylor, Shannon Brines and Richard Wetzel

Int. J. Environ. Res. Public Health 2020, 17(1), 166; https://doi.org/10.3390/ijerph17010166 - 25 Dec 2019

Cited by 11 | Viewed by 3782

Abstract

To date, the research that examines food accessibility has tended to ignore ethnic food outlets. This void leaves us with a limited understanding of how such food stores may, or may not, impact food security. The study discussed herein addressed this by conducting a geospatial assessment of ethnic food outlet accessibility in two U.S. cities: Flint and Grand Rapids, Michigan. We used Geographic Information Systems (GIS) tools to create a revealed accessibility index for each food outlet, and used the index to determine access within active travel service areas. We utilized an ordinary least squares regression (OLS), and two local models: spatial autoregression (SAR) and geographically weighted regression (GWR) to enhance our understanding of global and localized relationships between outlet accessibility and type (while controlling for known covariates). The results show that the local models outperformed (R² max = 0.938) the OLS model. The study found that there was reduced access to ethnic restaurants in all service areas of Grand Rapids. However, in Flint, we observed this association in the bicycling areas only. Also notable were the influences that demographic characteristics had on access in each city. Ultimately, the findings tell us that nuanced planning and policy approaches are needed in order to promote greater access to ethnic food outlets and reduce overall food insecurity. Full article

(This article belongs to the Special Issue Methodologies and Applications of Geographic Information Science and Spatial Statistical Analysis in Public Health)

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14 pages, 2274 KiB

Open AccessArticle

Mapping Variation in Breast Cancer Screening: Where to Intervene?

by Cindy M. Padilla, François Painblanc, Patricia Soler-Michel and Veronica M. Vieira

Int. J. Environ. Res. Public Health 2019, 16(13), 2274; https://doi.org/10.3390/ijerph16132274 - 27 Jun 2019

Cited by 9 | Viewed by 2828

Abstract

Small geographic areas with lower mammography screening participation rates may reflect gaps in screening efforts. Our objective was to use spatial analyses to understand disparities in mammography screening use and to identify factors to increase its uptake in areas that need it in Lyon metropolitan area, France. Data for screened women between the ages of 50 and 74 were analyzed. Census blocks of screened and non screened women were extracted from the mammography screening programme 2015–2016 dataset. We used spatial regression models, within a generalized additive framework to determine clusters of census blocks with significantly higher prevalence of non-participation of mammography screening. Smoothed risk maps were crude and adjusted on the following covariates: deprivation index and opportunistic screening. Among 178,002 women aged 50 to 74, 49.9% received mammography screening. As hypothesized, women living in highly deprived census blocks had lower participation rates compared to less deprived blocks, 45.2% vs. 51.4% p < 0.001. Spatial analyses identified four clusters, one located in an urban area and three in suburban areas. Moreover, depending on the location of the cluster, the influence came from different variables. Knowing the impact of site-specific risk factors seems to be important for implementing an appropriate prevention intervention. Full article

(This article belongs to the Special Issue Methodologies and Applications of Geographic Information Science and Spatial Statistical Analysis in Public Health)

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21 pages, 2990 KiB

Open AccessArticle

Spatiotemporal Heterogeneity in the Distribution of Chikungunya and Zika Virus Case Incidences during their 2014 to 2016 Epidemics in Barranquilla, Colombia

by Thomas C. McHale, Claudia M. Romero-Vivas, Claudio Fronterre, Pedro Arango-Padilla, Naomi R. Waterlow, Chad D. Nix, Andrew K. Falconar and Jorge Cano

Int. J. Environ. Res. Public Health 2019, 16(10), 1759; https://doi.org/10.3390/ijerph16101759 - 18 May 2019

Cited by 14 | Viewed by 4246

Abstract

Chikungunya virus (CHIKV) and Zika virus (ZIKV) have recently emerged as globally important infections. This study aimed to explore the spatiotemporal heterogeneity in the occurrence of CHIKV and ZIKV outbreaks throughout the major international seaport city of Barranquilla, Colombia in 2014 and 2016 and the potential for clustering. Incidence data were fitted using multiple Bayesian Poisson models based on multiple explanatory variables as potential risk factors identified from other studies and options for random effects. A best fit model was used to analyse their case incidence risks and identify any risk factors during their epidemics. Neighbourhoods in the northern region were hotspots for both CHIKV and ZIKV outbreaks. Additional hotspots occurred in the southwestern and some eastern/southeastern areas during their outbreaks containing part of, or immediately adjacent to, the major circular city road with its import/export cargo warehouses and harbour area. Multivariate conditional autoregressive models strongly identified higher socioeconomic strata and living in a neighbourhood near a major road as risk factors for ZIKV case incidences. These findings will help to appropriately focus vector control efforts but also challenge the belief that these infections are driven by social vulnerability and merit further study both in Barranquilla and throughout the world’s tropical and subtropical regions. Full article

(This article belongs to the Special Issue Methodologies and Applications of Geographic Information Science and Spatial Statistical Analysis in Public Health)

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14 pages, 657 KiB

Open AccessArticle

A Time-Based Objective Measure of Exposure to the Food Environment

by Jason Y. Scully, Anne Vernez Moudon, Philip M. Hurvitz, Anju Aggarwal and Adam Drewnowski

Int. J. Environ. Res. Public Health 2019, 16(7), 1180; https://doi.org/10.3390/ijerph16071180 - 2 Apr 2019

Cited by 16 | Viewed by 3191

Abstract

Exposure to food environments has mainly been limited to counting food outlets near participants’ homes. This study considers food environment exposures in time and space using global positioning systems (GPS) records and fast food restaurants (FFRs) as the environment of interest. Data came from 412 participants (median participant age of 45) in the Seattle Obesity Study II who completed a survey, wore GPS receivers, and filled out travel logs for seven days. FFR locations were obtained from Public Health Seattle King County and geocoded. Exposure was conceptualized as contact between stressors (FFRs) and receptors (participants’ mobility records from GPS data) using four proximities: 21 m, 100 m, 500 m, and ½ mile. Measures included count of proximal FFRs, time duration in proximity to ≥1 FFR, and time duration in proximity to FFRs weighted by FFR counts. Self-reported exposures (FFR visits) were excluded from these measures. Logistic regressions tested associations between one or more reported FFR visits and the three exposure measures at the four proximities. Time spent in proximity to an FFR was associated with significantly higher odds of FFR visits at all proximities. Weighted duration also showed positive associations with FFR visits at 21-m and 100-m proximities. FFR counts were not associated with FFR visits. Duration of exposure helps measure the relationship between the food environment, mobility patterns, and health behaviors. The stronger associations between exposure and outcome found at closer proximities (<100 m) need further research. Full article

(This article belongs to the Special Issue Methodologies and Applications of Geographic Information Science and Spatial Statistical Analysis in Public Health)

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