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Implementation of GIS (Geographic Information Systems) in Health Care
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Implementation of GIS (Geographic Information Systems) in Health Care

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3627

Special Issue Editors

Dr. Yongwan Chun

E-Mail Website
Guest Editor
School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Richardson, Texas 75080, USA
Interests: GIS; spatial statistics; spatial interaction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Daniel A. Griffith

E-Mail Website
Guest Editor
School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA
Interests: spatial statistics; GIS; spatial epidemiology; quantitative urban geography
Special Issues, Collections and Topics in MDPI journals
Dr. Amy E. Hughes

E-Mail Website
Guest Editor
Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
Interests: cervical cancer screening; food security & medical outcomes; improving and studying healthcare disparities

Special Issue Information

Dear Colleagues,

Understanding spatial patterns of phenomena that occur in geographic space can offer crucial insights for them that are unlikely to be discovered with non-spatial data analyses. Geographic Information Systems (GIS) is a well adopted instrument to explore and examine such spatial patterns in disciplines that benefit from investigating spatial patterns of their subjects, such as health geography and spatial epidemiology. However, GIS often has been limited in health care applications, for example, to data preparation, spatial pattern and clustering description, spatial autocorrelation measurement, and spatial accessibility measurement.

This special issue aims to publish research papers that contribute to extending the utility of GIS in health care and/or public health research. Specifically, this special issue encourages researchers to submit papers that present novel GIS applications to investigate and enhance research on health care system and related public health issues. In addition, it welcomes research outcomes that present methodological innovations in Geographic Information Sciences (GISc) that advances the investigation of health care and public health issues from a new perspective.

Research areas may include (but are not limited to) the following:

  • Location modeling of health care and health care systems;
  • Spatial accessibility to health care;
  • Health care spatial analytics;
  • Health disparities;
  • Health services planning;
  • Emergency response;
  • Public health surveillance;
  • Environmental risk factors for health care and public health;
  • Personal sensing techniques for risk factors;
  • Spatial health big data analysis;
  • Machine learning and deep learning approaches for health care analysis;
  • Novel GIS applications for health care and public health.

We look forward to receiving your contributions.

Dr. Yongwan Chun
Prof. Dr. Daniel A. Griffith
Dr. Amy E. Hughes
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. Healthcare is an international peer-reviewed open access semimonthly 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 2700 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

  • health care
  • GIS
  • spatial accessibility
  • spatial equity
  • location modeling
  • health screening
  • health monitoring
  • public health surveillance
  • health services planning
  • spatial big data
  • spatial analytics
  • machine learning

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

14 pages, 4390 KiB  
Article
Spatial Disparities in Access to Dialysis Facilities in Texas: An Analysis of End-Stage Renal Data in 1974–2020
by Dongeun Kim, Yongwan Chun and Daniel A. Griffith
Healthcare 2024, 12(22), 2284; https://doi.org/10.3390/healthcare12222284 - 15 Nov 2024
Viewed by 1057
Abstract
Background/Objectives: This study investigates the spatial disparities in access to dialysis facilities across Texas. The objective is to analyze how urbanization and socio-economic/demographic factors influence these disparities, with a focus on differences between urban and rural areas. Methods: The enhanced two-step floating catchment [...] Read more.
Background/Objectives: This study investigates the spatial disparities in access to dialysis facilities across Texas. The objective is to analyze how urbanization and socio-economic/demographic factors influence these disparities, with a focus on differences between urban and rural areas. Methods: The enhanced two-step floating catchment area method is employed to calculate accessibility scores to dialysis facilities across the state. Additionally, Moran eigenvector spatial filtering is utilized to analyze the influence of urbanization and socio-economic/demographic factors on accessibility disparities. Results: The Moran eigenvector spatial filtering analysis revealed a significant level of spatial autocorrelation in accessibility scores, particularly highlighting disparities between urban and rural areas. Urban regions, especially major metropolitan areas, achieved higher accessibility scores due to the dense concentration of dialysis facilities. In contrast, rural areas, notably in western and northern Texas, exhibited lower accessibility, underscoring the challenges faced by residents in these regions. The model further identified urbanization and the percentage of the elderly population as critical covariates affecting accessibility, with urban counties showing higher accessibility and elderly populations in rural areas facing significant challenges. Conclusions: These findings emphasize the importance of considering spatial dependencies in healthcare accessibility studies. They suggest the need for targeted policy interventions to address the identified disparities, particularly in underserved rural regions, to improve access to dialysis facilities for vulnerable populations. Full article
(This article belongs to the Special Issue Implementation of GIS (Geographic Information Systems) in Health Care)
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24 pages, 2472 KiB  
Article
Spatial Allocation Rationality Analysis of Medical Resources Based on Multi-Source Data: Case Study of Taiyuan, China
by Lujin Hu and Shengqi Cai
Healthcare 2024, 12(16), 1669; https://doi.org/10.3390/healthcare12161669 - 21 Aug 2024
Viewed by 1549
Abstract
Reasonably allocating medical resources can effsectively optimize the utilization efficiency of such resources. This paper took Taiyuan City as an example and established a model to evaluate the rationality of medical resource spatial allocation, incorporating two key dimensions: the spatial layout and the [...] Read more.
Reasonably allocating medical resources can effsectively optimize the utilization efficiency of such resources. This paper took Taiyuan City as an example and established a model to evaluate the rationality of medical resource spatial allocation, incorporating two key dimensions: the spatial layout and the supply and demand of medical resources. In terms of the spatial layout, three indexes were included: Firstly, the service coverage rates of different levels of medical institutions, based on residents’ medical orientations, were calculated using network analysis methods. Secondly, the Huff-2SFCA method was improved to calculate the accessibility of medical resources for four different modes of transportation. Then, the Health Resource Agglomeration Degree (HRAD) and Population Agglomeration Degree (PAD) were used to quantify the equity of medical resources. In terms of the supply and demand of medical resources, one index was included: the supply–demand ratio of medical resources during sudden public health events, which was calculated using the number of beds per thousand people as an indicator. These four indexes were weighted using the entropy weight method to obtain the rationality grade of medical resource spatial allocation in Taiyuan City. The study found that the rationality evaluation level of medical resource allocation in the central urban area of Taiyuan City followed a “concentrically decreasing” pattern. The rating ranged from “very reasonable” to “less reasonable”, with the area of each level expanding gradually. The areas rated within the top two categories only accounted for 19.92% of the study area, while the area rated as “less reasonable” occupied 38.73% of the total area. These results indicate that the model accounted for residents’ travel for various medical orientations and the availability of resources during public health emergencies. It considered both the spatial layout and supply and demand of medical resources, offering recommendations for the precise allocation of urban medical resources. Full article
(This article belongs to the Special Issue Implementation of GIS (Geographic Information Systems) in Health Care)
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