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IJGI
Special Issues
HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition)
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HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition)

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1508

Special Issue Editors

Prof. Dr. Lan Mu

E-Mail Website
Guest Editor
Department of Geography, University of Georgia, Athens, GA 30602, USA
Interests: geographic information science (GIScience); GIScience for health and environment; geovisualization and cartography; spatial analysis and modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Jue Yang

E-Mail Website
Guest Editor
School of Public Health, Brown University, Providence, RI 02903, USA
Interests: health geography; GIScience; human mobility; physical activity; green space
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Health challenges are deeply associated with physical, socioeconomic, and virtual environmental factors. GIScience has been reshaping our perceptions of population, public and global health, and their intricate connections with the environment for over fifty years. GI technologies, paired with improving artificial intelligence (AI), provide an enlightening compilation of groundbreaking research at this nexus, with their robustness in data-driven and machine learning (ML) approaches. 

Building on the success of our first edition of the Special Issue (https://www.mdpi.com/journal/ijgi/special_issues/G9YU275HD5), “HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition)” continues to explore cutting-edge advancements in this field. This Special Issue is rooted in geospatial thinking and aims to encapsulate the dynamic convergence of GIS&T with geographical, epidemiological, environmental, and health research, shedding light on the multifaceted ways our environment influences health outcomes.

Within this Special Issue, we invite original contributions in the following areas:

  • Geographical analysis and modeling for health and the environment (physical, socioeconomic, and virtual);
  • Frontiers of GIS&T and AI technologies for health data and research;
  • Socioeconomic, physical, and virtual environmental health and exposure analysis;
  • Physical and virtual healthcare accessibility and inequities;
  • Health vulnerabilities amidst climate and environmental changes;
  • GIS&T and AI-technology-driven health policy and decision support.

Prof. Dr. Lan Mu
Dr. Jue Yang
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. 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 1900 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

  • HealthScape
  • GIScience
  • geospatial thinking
  • artificial intelligence (AI) and machine learning (ML)
  • environmental factors (physical, socioeconomic, and virtual)
  • geographical analysis and modeling
  • healthcare accessibility
  • health vulnerability
  • climate and environmental changes

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

Related Special Issue

Published Papers (3 papers)

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Research

26 pages, 3580 KiB  
Article
Delineating Urban High–Risk Zones of Disease Transmission: Applying Tensor Decomposition to Trajectory Big Data
by Tianhua Lu and Wenjia Zhang
ISPRS Int. J. Geo-Inf. 2025, 14(8), 285; https://doi.org/10.3390/ijgi14080285 - 23 Jul 2025
Viewed by 41
Abstract
Risk zone delineation and mobility behavior control constitute critical measures in pandemic containment. Numerous studies utilize static demographic data or dynamic mobility data to calculate the high–risk zones present in cities; however, these studies fail to concurrently consider activity and mobility patterns of [...] Read more.
Risk zone delineation and mobility behavior control constitute critical measures in pandemic containment. Numerous studies utilize static demographic data or dynamic mobility data to calculate the high–risk zones present in cities; however, these studies fail to concurrently consider activity and mobility patterns of populations in both space and time, which results in many studies only being able to employ static geostatistical analytical methods, neglecting the transmission risks associated with human mobility. This study utilized the mobile phone signaling data of Shenzhen residents from 2019 to 2020 and developed a CP tensor decomposition algorithm to decompose the long-sequence spatiotemporal trajectory data to detect high risk zones in terms of detecting overlapped community structures. Tensor decomposition algorithms revealed community structures in 2020 and the overlapping regions among these communities. Based on the overlap in spatial distribution and the similarity in temporal rhythms of these communities, we identified regions with spatiotemporal co-location as high–risk zones. Furthermore, we calculated the degree of population mixing in these areas to indicate the level of risk. These areas could potentially lead to rapid virus spread across communities. The research findings address the shortcomings of currently used static geographic statistical methods in delineating risk zones, and emphasize the critical importance of integrating spatial and temporal dimensions within behavioral big data analytics. Future research should consider utilizing non-aggregated individual trajectories to construct tensors, enabling the inclusion of individual and environmental attributes. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))
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31 pages, 2250 KiB  
Article
Spatial and Temporal Correlations of COVID-19 Mortality in Europe with Atmospheric Cloudiness and Solar Radiation
by Adrian Iftime, Secil Omer, Victor-Andrei Burcea, Octavian Călinescu and Ramona-Madalina Babeș
ISPRS Int. J. Geo-Inf. 2025, 14(8), 283; https://doi.org/10.3390/ijgi14080283 - 22 Jul 2025
Viewed by 97
Abstract
Previous studies reported the links between the COVID-19 incidence and weather factors, but few investigated their impact and timing on mortality, at a continental scale. We systematically investigated the temporal relationship of COVID-19 mortality in the European countries in the 1st year of [...] Read more.
Previous studies reported the links between the COVID-19 incidence and weather factors, but few investigated their impact and timing on mortality, at a continental scale. We systematically investigated the temporal relationship of COVID-19 mortality in the European countries in the 1st year of pandemic (March–December 2020) with (i) solar insolation (W/m2) at the ground level and (ii) objective sky cloudiness (as decimal cloud fraction), both derived from satellite measurements. We checked the correlations of these factors within a sliding window of two months for the whole period. Linear-mixed effect modeling revealed that overall, for the European countries (adjusted for latitude), COVID-19 mortality was substantially negatively correlated with solar insolation in the previous month (std. beta −0.69). Separately, mortality was significantly correlated with the cloudiness in both the previous month (std. beta +0.14) and the respective month (std. beta +0.32). This time gap of ∼1 month between the COVID-19 mortality and correlated weather factors was previously unreported. The long-term monitoring of these factors might be important for epidemiological policy decisions especially in the initial period of potential future pandemics when effective medical treatment might not yet be available. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))
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18 pages, 16726 KiB  
Article
Spatial Accessibility to Healthcare Facilities: GIS-Based Public–Private Comparative Analysis Using Floating Catchment Methods
by Onel Pérez-Fernández and Gregorio Rosario Michel
ISPRS Int. J. Geo-Inf. 2025, 14(7), 253; https://doi.org/10.3390/ijgi14070253 - 29 Jun 2025
Viewed by 735
Abstract
Healthcare accessibility is among the most critical challenges affecting millions, reflecting profound geospatial disparities in Latin America. This study aims to evaluate healthcare service geospatial accessibility patterns, comparing the geospatial coverage between public and private healthcare facilities in Santiago district, Panama. We first [...] Read more.
Healthcare accessibility is among the most critical challenges affecting millions, reflecting profound geospatial disparities in Latin America. This study aims to evaluate healthcare service geospatial accessibility patterns, comparing the geospatial coverage between public and private healthcare facilities in Santiago district, Panama. We first apply the Two-Step Floating Catchment Area (2SFCA) method and its extended variant (E2SFCA) to calculate geospatial accessibility indexes at public and private healthcare facilities. We then use Getis–Ord Gi* and Local Moran geospatial statistical analysis to identify significant clusters of high and low accessibility. The results reveal that public healthcare facilities still offer higher geospatial coverage than private healthcare facilities, with higher geospatial accessibility in the central zone and lower geospatial accessibility in the south zone of Santiago. These findings highlighted the location of new healthcare facilities in zones with lower geospatial accessibility coverage. This study provides reproducible methodological tools for other geographical contexts. It also contributes to improving decision-making and formulating public policies to reduce spatial disparities in healthcare services in Panama and other Caribbean and Latin American countries. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))
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