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

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 17058

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Special Issue Editors

Prof. Dr. Lan Mu

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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
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Dr. Jue Yang

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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 250 words) can be sent to the Editorial Office for assessment.

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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Related Special Issue

HealthScape: Intersections of Health, Environment, and GIS&T in ISPRS International Journal of Geo-Information (21 articles)

Published Papers (12 papers)

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26 pages, 1085 KB

Open AccessArticle

Can Urban Information Infrastructure Development Improve Resident Health? Evidence from China Health and Retirement Longitudinal Survey

by Huiling Zhao, Chenyang Yu and Zhanchuang Han

ISPRS Int. J. Geo-Inf. 2025, 14(12), 496; https://doi.org/10.3390/ijgi14120496 - 16 Dec 2025

Viewed by 297

Abstract

Taking the “Broadband China” policy (BCP) as a quasi-natural experiment, this paper utilizes nationwide tracking data from the China Health and Retirement Longitudinal Survey (CHARLS) for 2011, 2013, 2015, and 2018 and employs a Difference-in-Differences (DID) model to evaluate whether and how urban information infrastructure development affects resident health. We identify a clear and significant improvement in health outcomes attributable to BCP. After the implementation of BCP, physical health and mental health increase by 2.5% and 1.7%, respectively. Furthermore, mechanism analysis confirms that BCP enhances resident health primarily by improving information and communication technology (ICT) levels and by promoting local economic development. The positive health effect of BCP is more pronounced in regions with a better medical environment, suggesting the presence of complementary public-service capacity. At the individual level, heterogeneity tests reveal that BCP exerts a stronger positive influence on the physical health of male and rural respondents, while the benefits for older respondents are relatively smaller. At the city level, the health-promoting effect of BCP is stronger in economically less developed regions, and cities with higher administrative status exhibit more substantial health improvements. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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17 pages, 6761 KB

Open AccessArticle

Risk of Hypoxia in Short-Term Residents in Qinghai–Xizang Plateau Based on the Disaster System Theory Model

by Zemin Zhi, Qiang Zhou, Qiong Chen, Fenggui Liu, Yonggui Ma, Ziqian Zhang and Weidong Ma

ISPRS Int. J. Geo-Inf. 2025, 14(12), 489; https://doi.org/10.3390/ijgi14120489 - 10 Dec 2025

Viewed by 288

Abstract

Recognized as the world’s “Third Pole”, the Qinghai–Xizang Plateau poses significant challenges to human health due to its harsh environment. With improved transportation and a tourism boom industry bringing over 90 million low-altitude residents to the plateau annually, hypoxia has become a critical concern. This study analyzes oxygen content data (2017–2022) together with environmental variables including elevation, temperature, precipitation, and vegetation cover, using the GeoDetector method to identify key drivers of near-surface oxygen distribution. Within the framework of disaster system theory, we evaluated the risk of hypoxia among short-term residents. Results show that the near-surface oxygen distribution across the plateau is primarily regulated by climatic and topographic factors. Interactions among environmental variables markedly enhance the explanatory power for spatial variation in oxygen content, with the coupled effects of humidity, atmospheric pressure, elevation, and temperature being especially pronounced. A high hypoxia hazard prevails across the plateau, particularly in the high-altitude western, northern, and central regions. The spatial distribution of hypoxia risk is strongly shaped by human activities, with high-risk zones clustering in densely populated towns, transportation corridors, and regions of intensive tourism. This results in a distinctive coexistence of “high hazard–low exposure” and “low hazard–high exposure” patterns. These findings provide scientific insights for tourism planning, health protection, and risk management in plateau regions. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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20 pages, 6042 KB

Open AccessFeature PaperArticle

GeoSpatial Analysis of Health-Oriented Justice in Tartu, Estonia

by Najmeh Mozaffaree Pour

ISPRS Int. J. Geo-Inf. 2025, 14(12), 467; https://doi.org/10.3390/ijgi14120467 - 28 Nov 2025

Viewed by 395

Abstract

This study investigates the role of modern small-scale cities in addressing public health challenges through the lens of spatial justice, using the city of Tartu, Estonia, as a case study. Tartu has been recognized for its progressive public health initiatives, including the Tartu Health Care College, Mental Health Centre, Smoke-Free Tartu campaign, Health Trail network, Healthy School Program, and an expanding smart bike-sharing system. By employing Geographic Information Systems (GIS), we map and analyze the spatial distribution and accessibility of health-promoting infrastructure, such as healthcare facilities, green and blue spaces, health trails, and mobility services, across the urban landscape. A population-weighted accessibility assessment indicates that, although Tartu’s central districts (e.g., Kesklinn (HRI: 0.972)) are well-served, peripheral and densely populated districts such as Annelinn (HRI: 0.351) and Ropka (HRI: 0.377) exhibit notable deficits in health-related infrastructure. However, access to green infrastructure and mobility services is more evenly distributed citywide, reflecting a relatively equitable provision of non-clinical health assets. These findings highlight both the strengths and spatial gaps in Tartu’s health-oriented urban design, emphasizing the need for targeted investment in underserved areas. The study contributes to emerging studies on health-justice planning in small-scale urban contexts and demonstrates how spatial analytics can be guided to advance distributional justice in the provision of public health infrastructure. Ultimately, this research indicates the essential role of spatial analysis in guiding inclusive and data-informed health planning in urban environments. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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30 pages, 3314 KB

Open AccessArticle

Spatio-Temporal Variability and Environmental Associations of Emergency Department Demand: A Longitudinal Analysis in Zaragoza, Spain (2011–2024)

by Jorge Blanco Prieto, Marina Ferreras González and Oscar Cosido Cobos

ISPRS Int. J. Geo-Inf. 2025, 14(11), 439; https://doi.org/10.3390/ijgi14110439 - 7 Nov 2025

Viewed by 479

Abstract

Emergency department (ED) overcrowding has become a critical public health issue worldwide, driven by increasing demand and limited healthcare resources. This study analyzes the spatio-temporal variability of ED visits at Royo Villanova Hospital (Zaragoza, Spain) from 2011 to 2024, integrating clinical, demographic, environmental, and socioeconomic data. Using geospatial tools and machine learning models (XGBoost with SHAP interpretation), we identify key patterns in ED demand across time and space. Results show that the hour of the day is the most influential variable across all diagnoses, while temperature, humidity, and air pollutants (NO₂, SO₂, O₃) significantly affect respiratory and injury-related visits. Spatial analysis reveals persistent high-demand clusters in specific health zones, with proximity to the hospital playing a major role. The COVID-19 pandemic caused structural shifts in demand, particularly in pediatric care. Our findings highlight the need for tailored, diagnosis-specific predictive models and support the use of geospatial and environmental data for proactive ED resource planning. This approach enhances the capacity of health systems to anticipate demand surges and allocate resources efficiently. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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19 pages, 2731 KB

Open AccessArticle

Exploring the Spatial Relationship Between Severe Depression, COVID-19 Case Rates, and Vaccination Rates in US Counties: A Spatial Analysis Across Two Time Periods

by Yuqing Wang and Wencong Cui

ISPRS Int. J. Geo-Inf. 2025, 14(10), 376; https://doi.org/10.3390/ijgi14100376 - 25 Sep 2025

Viewed by 799

Abstract

Severe depression is shaped by complex interactions between public health crises and socioeconomic conditions, yet the spatial and temporal dynamics of these factors remain underexplored. This study investigates the impact of COVID-19 case rates, vaccination rates, and socioeconomic factors on severe depression rates across 1470 counties in the contiguous USA in 2021 and 2022. We combined Ordinary Least Squares (OLS) regression with Multiscale Geographically Weighted Regression (MGWR) to capture both global associations and local geographic variability. Results show that higher COVID-19 case rates in 2021 were associated with increased rates of severe depression in 2022, while higher vaccination rates during the same period were associated with decreased rates of severe depression. However, these associations weakened when using 2022 data, suggesting a temporal lag in the impact on mental health. MGWR analyses revealed regional disparities: COVID-19 case rates had a stronger impact in the Midwest, while vaccination benefits were more pronounced on the West Coast. Additional factors, such as unemployment, limited sunlight exposure, and the availability of mental health resources, also influenced outcomes. These findings underscore the importance of temporally and geographically nuanced approaches to public mental health interventions and support the need for region-specific strategies to address mental health disparities in the wake of public health crises. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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25 pages, 11023 KB

Open AccessArticle

Spatio-Temporal Mapping of Violence Against Women: An Urban Geographic Analysis Based on 911 Emergency Reports in Monterrey

by Onel Pérez-Fernández, Octavio Quintero Ávila, Carolina Barros and Gregorio Rosario Michel

ISPRS Int. J. Geo-Inf. 2025, 14(10), 367; https://doi.org/10.3390/ijgi14100367 - 23 Sep 2025

Viewed by 2156

Abstract

In Latin American cities, violence against women (VAW) remains critical for public health, well-being, and safety. This phenomenon is influenced by social, political, and environmental drivers. VAW is not randomly distributed; built environments—geography, ambient population, and street networks—influence criminal through spatial dependence across multiple scales. Despite growing interest in the spatial distribution of crime, few studies have explicitly explored the spatiotemporal dimensions of VAW in Monterrey. This study explores spatio-temporal patterns of VAW in Monterrey, Mexico, based on the analysis of 27,036 georeferenced and verified emergency reports from the 911 system (2019–2022). The study applies kernel density estimation (KDE), the Getis–Ord G_i* statistics, the Local Moran I index, and space–time cube analysis to identify spatial and temporal clusters of VAW. The results show concentrations of incidents during nighttime and weekends, particularly in northern and eastern sectors in Monterrey. The analysis reveals clusters in areas of high socioeconomic vulnerability. VAW in Monterrey follows predictable and cyclical patterns. These insights contribute to the design of tailored public policies and actions to improve women’s health, well-being, and safety in critical zones and timeframes. The findings also enhance international understanding of gender-based spatial violence patterns in the rapidly urbanizing contexts of the Global South. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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27 pages, 13052 KB

Open AccessArticle

A Multi-Scale Geographically Weighted Regression Approach to Understanding Community-Built Environment Determinants of Cardiovascular Disease: Evidence from Nanning, China

by Shuguang Deng, Shuyan Zhu, Xueying Chen, Jinlong Liang and Rui Zheng

ISPRS Int. J. Geo-Inf. 2025, 14(9), 362; https://doi.org/10.3390/ijgi14090362 - 18 Sep 2025

Cited by 1 | Viewed by 2024

Abstract

Clarifying how the community-scale built environment shapes the spatial heterogeneity of cardiovascular disease (CVD) prevalence is essential for precision urban health interventions. We integrated CVD prevalence data from the Guangxi Zhuang Autonomous Region Hospital (2020–2022) with 14 built-environment indicators across 77 communities in Xixiangtang District, Nanning, and compared ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR). MGWR provided the best model fit (adjusted R² increased by 0.136 and 0.056, respectively; lowest AICc and residual sum of squares) and revealed significant scale-dependent effects. Distance to metro stations, road network density, and the number of transport facilities exhibited pronounced local-scale heterogeneity, while population density, building density, healthy/unhealthy food outlets, facility POI density, and public transport accessibility predominantly exerted global-scale effects. High-risk clusters of CVD were identified in mixed-use, high-density urban communities lacking rapid transit access. The findings highlight the need for place-specific, multi-scale planning measures, such as transit-oriented development and balanced food environments, to reduce the CVD burden and advance precision healthy-city development. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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17 pages, 6488 KB

Open AccessEditor’s ChoiceArticle

A Spatial Analysis of the Association Between Urban Heat and Coronary Heart Disease

by Kyle Lucas, Ben Dewitt, Donald J. Biddle and Charlie H. Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 344; https://doi.org/10.3390/ijgi14090344 - 7 Sep 2025

Viewed by 1221

Abstract

Heart disease remains the leading cause of death in both the United States and globally. Urban heat is increasingly recognized as a significant public health challenge, particularly in its connection to cardiovascular conditions. This study, conducted in Jefferson County, Kentucky, examines the distribution of coronary heart disease rates and develops an urban heat risk index to examine underlying socioeconomic and environmental factors. We applied bivariate spatial association (Lee’s L), Global Moran’s I, and multiple linear regression methods to examine the relationships between key variables and assess model significance. Global Moran’s I revealed clustered distributions of both coronary heart disease rates and land surface temperature across census tracts. Bivariate spatial analysis identified clusters of high heart disease rates and temperatures within the West End, while clusters of contiguous suburban tracts exhibited lower heart disease rates and temperatures. Regression analyses yielded significant results for both the ordinary least squares (OLS) model and the spatial regression model; however, the spatial error model explained a greater proportion of the variation in coronary heart disease rates across tracts compared to the OLS model. This study offers new insights into spatial disparities in coronary heart disease rates and their associations with environmental risk factors including urban heat, underscoring the challenges faced by many urban communities. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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24 pages, 3580 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1000

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 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 KB

Open AccessArticle

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 741

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.

m^{2}

) 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 KB

Open AccessArticle

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

Cited by 3 | Viewed by 5774

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 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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38 pages, 1179 KB

Open AccessSystematic Review

Reproducible GIS-Based Evidence for Public Health and Urban Security: A Systematic Mapping and Review

by Washington Ramírez Montalvan, Ibeth Manzano Gallardo, Verónica Defaz Toapanta, Edison Espinosa Gallardo and Lucas Garcés Guayta

ISPRS Int. J. Geo-Inf. 2026, 15(1), 4; https://doi.org/10.3390/ijgi15010004 - 19 Dec 2025

Viewed by 139

Abstract

Geographic Information Systems (GIS) are increasingly applied to public health and urban security challenges, yet current evidence remains fragmented across methods, disciplines, and regions. This study integrates Systematic Mapping (SM) and Systematic Review (SR) within a unified PICOS–SPICE framework to consolidate existing GIS-based research. From an initial corpus of 7106 records, 65 studies met all methodological and reproducibility criteria. Scientific production shows consistent growth, peaking in 2023, with research concentrated in Asia and North America and limited representation from Africa and South America. Methodologically, the literature is dominated by accessibility assessments and spatial autocorrelation, while advanced analytical models—such as Bayesian inference and machine learning—remain scarce. GIS workflows rely mainly on ArcGIS and QGIS, complemented by open-source tools, including R, Python, and SaTScan. The fused SM + SR pipeline provides a transparent and replicable structure that highlights current strengths in spatial resolution and analytical versatility while revealing persistent gaps in data openness, reproducibility, and global equity. These findings offer a consolidated evidence base to guide future GIS research and support informed decision-making in public health and urban security. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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