Applications of Spatial Science and Technology in Health Research, 2nd Edition

Topic Information

Dear Colleagues,

Infectious diseases have a significant impact on global health and have added to the existing high burden of chronic disease, with a recent example being the coronavirus disease in 2019 (COVID-19).

Spatial lifecourse epidemiology has been emerging in the era of big data growth and rapid developments in geoinformation technology (mainly geospatial models, software tools, Earth observation, and geographical information systems). It is a rapidly growing approach employed to investigate the long-term effects of environmental, behavioral, psychosocial, and biological factors on health-related states and events and their underlying mechanisms.

Spatial lifecourse epidemiology calls for efforts from geospatial science to provide long-term spatial data and advanced spatial methods for revolutionizing traditional epidemiological research in addressing both infectious and chronic disease issues. The growth in the geoinformation sector, combined with the continuous availability of new geospatial epidemiological data, has resulted in increasing interest in developing innovative methods in spatial data analysis, software tools, and relevant platforms. Their availability provides important support in analyzing the characteristics of infectious diseases and in taking robust public health measures aimed at improving human health and wellbeing. As a result, geoinformation is being used in the domain of spatial (lifecourse) epidemiology to address questions relating to the geographic distribution of infectious diseases, their properties, and how to control their impact on society.

For this Topic, we welcome contributions focusing on state-of-the-art research on spatial (lifecourse) epidemiology, with a particular focus on the application of geoinformation and geospatial data analysis technologies. We seek original research and review articles on spatial (lifecourse) epidemiology regarding infectious and chronic diseases, including but not limited to diseases such COVID-19, influenza, cholera, tuberculosis, Zika virus, and Ebola. Submissions may cover any of the following topics:

• Spatial patterns of infectious diseases through quantitative analysis, such as geostatistical analysis methods;
• Prediction models of spatiotemporal transmission trends;
• Datasets and databases handling epidemiological data;
• Innovative tools and platforms in the analysis of epidemiological data;
• The impact of community interventions on epidemiology driven by socioeconomic factors;
• Relationships of environmental, socioeconomic, and/or pollution factors with infectious diseases;
• Advances in the use of geoinformation in the study of infectious diseases.

Prof. Dr. Peng Jia
Prof. Dr. Yansong Bao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Geographies geographies	2.3	3.6	2021	19.6 Days	CHF 1200	Submit
Informatics informatics	5.1	9.1	2014	32.7 Days	CHF 1800	Submit
International Journal of Environmental Research and Public Health ijerph	-	9.8	2004	24 Days	CHF 2500	Submit
ISPRS International Journal of Geo-Information ijgi	3.2	6.7	2012	34.9 Days	CHF 1900	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit

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Published Papers (3 papers)

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All IJERPH IJGI Remote Sensing Informatics Geographies Sustainability

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16 pages, 1727 KB  

Open AccessArticle

The Moderating Role of Street-View Greenery in the Relationship Between Mental Health and Life Satisfaction: An Exploratory Case Study Across Contrasting Community Contexts in Korea

by Yoohyung Joo, Jaeyoung Jung, Jiwan Hong, Sangyoon Park, Jaelim Cho, Juyeon Ko, Changsoo Kim and Joon Heo

ISPRS Int. J. Geo-Inf. 2026, 15(7), 336; https://doi.org/10.3390/ijgi15070336 - 22 Jul 2026

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Abstract

Growing evidence suggests that urban greenery is associated with improved mental health, yet how eye-level exposure functions within specific socio-environmental contexts remains underexplored. This study presents an exploratory case study utilizing semantic segmentation of street-view imagery to quantify eye-level greenery (“open greenery”) across [...] Read more.

Growing evidence suggests that urban greenery is associated with improved mental health, yet how eye-level exposure functions within specific socio-environmental contexts remains underexplored. This study presents an exploratory case study utilizing semantic segmentation of street-view imagery to quantify eye-level greenery (“open greenery”) across two contrasting community contexts: a densely developed area (Region 1) and a less developed area (Region 2) in Korea. Using interaction models reinforced by 5000-iteration bootstrap analyses, we identified the moderating role of greenery in the relationship between mental health (depression and cognitive function) and life satisfaction. Our findings indicate that the psychological benefits of greenery are highly contingent upon the interplay between individual vulnerability and regional context. Specifically, in Region 1, greenery moderated well-being for the low-cognitive function subgroup, while in Region 2, the moderating effect was most pronounced among individuals with depressive symptoms. Despite the inherent limitations of small subgroup samples, the stability of these patterns across repeated bootstrap iterations highlights meaningful “spatial intersections” where greenery plays a role in shaping psychological well-being. By adopting a case-centric approach, this study highlights that the benefits of street-view greenery are not uniform but context-dependent. These results underscore the necessity of context-aware green infrastructure strategies tailored to the specific environmental needs of vulnerable populations in diverse community settings. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research, 2nd Edition)

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24 pages, 20492 KB  

Open AccessArticle

Multi-Scale Assessment of Nighttime Heat Health Risk and Dominant Factors Using MODIS and SDGSAT-1 Observations

by Zhuang Tan, Qixia Man, Baolei Zhang, Pinliang Dong, Haiying Sun, Zhongchang Sun, Linlin Lu, Changyong Dou, Xinming Yang, Changyin Han, Cong Zhou, Xiaoqi Sun, Jian Wang and Zizhen Li

Remote Sens. 2026, 18(13), 2243; https://doi.org/10.3390/rs18132243 - 7 Jul 2026

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Abstract

Climate change is amplifying heat-related health risks, making heat health risk assessment increasingly important for sustainable urban development. However, current studies still face three challenges: (1) macro-scale and fine-scale assessments remain weakly linked; (2) nighttime heat health risk has received limited attention; and [...] Read more.

Climate change is amplifying heat-related health risks, making heat health risk assessment increasingly important for sustainable urban development. However, current studies still face three challenges: (1) macro-scale and fine-scale assessments remain weakly linked; (2) nighttime heat health risk has received limited attention; and (3) dominant factor identification remains insufficient, especially within the Local Climate Zone (LCZ) framework. To address these gaps, this study developed a hazard–exposure–vulnerability-based, multi-scale framework for assessing nighttime heat health risk in Shandong Province, China. At the macro scale, MODIS nighttime land surface temperature (1 km) was used to characterize heat hazard, and district-level risk was assessed by integrating socio-statistical data. Hotspot analysis was then applied to identify high-risk clusters and select cities for fine-scale assessment. At the fine scale, SDGSAT-1 thermal infrared data (30 m) were used to characterize intra-urban nighttime heat hazard, and block-level risk was assessed by integrating socioeconomic and urban infrastructure data. Dominant risk factors were further identified for each spatial unit. Results show that (1) at the macro scale, high nighttime heat health risk districts are concentrated mainly in southern Shandong and major urban cores; (2) at the fine scale, overall risk is higher in inland cities than in the coastal city, and the inland cities show similar spatial patterns; (3) at both district and block units, higher risk levels are associated with more complex dominant factor configurations; and (4) compact high- and mid-rise zones (LCZ 1–2) are mainly characterized by multiple dominant factors, whereas open low-rise/sparsely built zones (LCZ 6/9) are mainly characterized by no dominant factor. This study provides a multi-level perspective for heat-risk governance, offers a scientific basis for both macro-scale policy formulation and fine-scale intervention, and contributes to more sustainable and resilient cities. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research, 2nd Edition)

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22 pages, 44844 KB  

Open AccessArticle

Urban-Scale Chikungunya Risk Mapping in the Western Guangdong-Hong Kong-Macao Greater Bay Area Using Remote Sensing

by Yufeng Liu and Suhong Liu

Int. J. Environ. Res. Public Health 2026, 23(6), 730; https://doi.org/10.3390/ijerph23060730 - 30 May 2026

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Abstract

This study presents a reproducible high-resolution framework for assessing urban chikungunya environmental suitability and outbreak-related spatial heterogeneity during the 2025 outbreak in the western Guangdong–Hong Kong–Macao Greater Bay Area. Using Sentinel-2–derived environmental indicators together with a random forest–based residual correction of Landsat surface [...] Read more.

This study presents a reproducible high-resolution framework for assessing urban chikungunya environmental suitability and outbreak-related spatial heterogeneity during the 2025 outbreak in the western Guangdong–Hong Kong–Macao Greater Bay Area. Using Sentinel-2–derived environmental indicators together with a random forest–based residual correction of Landsat surface temperature, we developed a 10 m weighted additive Mosquito Habitat Suitability Index (MHSI). Index weights were empirically derived by comparing reported case locations at the street and town level with randomly sampled background points. The optimized weighting scheme indicated that humidity- and water-related conditions contributed more strongly to habitat suitability than vegetation and temperature. Reported case locations generally corresponded to higher MHSI values than background locations, suggesting that the index captures broad spatial patterns of environmental suitability. Comparison with a coarser, model-derived global chikungunya risk map was used as an external comparative consistency assessment rather than predictive validation, showing moderate agreement at the macro-spatial scale (Pearson r = 0.3421) after correction for spatial autocorrelation. Residual-difference analysis, combined with multiple points-of-interest (POI) categories, ordinary least squares (OLS), and geographically weighted regression (GWR), further suggested that human activity, transport connectivity, and healthcare accessibility may account for part of the remaining spatial mismatch not explained by environmental suitability alone. Sensitivity analyses indicated that the broad LST downscaling pattern and the exploratory GWR interpretation were reasonably stable under alternative sampling, smoothing, grid-size, and bandwidth settings. Taken together, this framework provides preliminary spatial evidence for high-resolution environmental suitability assessment and exploratory interpretation of outbreak-related spatial heterogeneity, while underscoring the need for finer-scale epidemiological data and more explicit representation of human-driven processes. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research, 2nd Edition)

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