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IJGI
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HealthScape: Intersections of Health, Environment, and GIS&T
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HealthScape: Intersections of Health, Environment, and GIS&T

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 7209

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
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 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. This Special Issue, titled “HealthScape: Intersections of Health, Environment, and GIS&T”, 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 1700 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 modelling
  • healthcare accessibility
  • health vulnerability
  • climate and environmental changes

Published Papers (6 papers)

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Research

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19 pages, 12973 KiB  
Article
A Novel Flexible Geographically Weighted Neural Network for High-Precision PM2.5 Mapping across the Contiguous United States
by Dongchao Wang, Jianfei Cao, Baolei Zhang, Ye Zhang and Lei Xie
ISPRS Int. J. Geo-Inf. 2024, 13(7), 217; https://doi.org/10.3390/ijgi13070217 - 22 Jun 2024
Viewed by 474
Abstract
Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this [...] Read more.
Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this study introduces a Flexible Geographically Weighted Neural Network (FGWNN) to estimate PM2.5 based on multi-source remote sensing data. FGWNN incorporates the Flexible Geographical Neuron (FGN) and Geographical Activation Function (GWAF) within the framework of Artificial Neural Network (ANN) to capture the intricate spatial non-stationary relationships among predictive variables. A robust air quality remote sensing estimation model was constructed using remote sensing data of Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), and Terrain Elevation (HGT) as inputs, and Ground-Based PM2.5 as the observation. The results indicated that FGWNN successfully generates PM2.5 remote sensing data with a 2.5 km spatial resolution for the contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared to traditional ANN and Geographically Weighted Regression (GWR) models. FGWNN holds the potential for applications in high-precision and high-resolution remote sensing scenarios. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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17 pages, 1699 KiB  
Article
Exploring the Effects of Light and Dark on Crime in London
by Ezgi Erturk, Peter Raynham and Jemima Unwin Teji
ISPRS Int. J. Geo-Inf. 2024, 13(6), 182; https://doi.org/10.3390/ijgi13060182 - 30 May 2024
Viewed by 644
Abstract
Safety from crime is a fundamental human need. In Maslow’s hierarchy, safety is one of the foundational needs of well-being. The built environment should be safe to use at all times of the day and for all groups of people. After dark, the [...] Read more.
Safety from crime is a fundamental human need. In Maslow’s hierarchy, safety is one of the foundational needs of well-being. The built environment should be safe to use at all times of the day and for all groups of people. After dark, the appearance of the outdoor environment changes dramatically, and this could impact the opportunities for crime. This study investigated the impact of daylight on the rates of different types of crime by comparing the crime rates during selected periods of daylight and darkness. The study used records of crime data from the Metropolitan Police Service. By studying crimes in the week on either side of the twice-yearly clock change, it is possible to compare periods that are dark in one week and light in the other at the same clock time. Where the time at which the crime took place was known, and using the GPS coordinates of the specific crime, the solar altitude was calculated and used to determine if it was light or dark at the time of the crime. A similar calculation was used to see if the crime would have been in the dark or light in the week on the other side of the clock change. The headline result is that there was 4.8% (OR 1.07) more crime in the dark periods than the light ones. However, this increase was not uniform across all crime types, and there were some further complications in some results due to potential changes in the behavior of some victims after dark. For the crimes of theft from a person and robbery of personal property, there was a significant increase during the dark period. The availability of light had an impact on the rate of certain crimes. Whilst this does not provide any information about the impact of street lighting on crime, it does provide some idea of by how much crime could be reduced if better lighting was provided. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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18 pages, 5697 KiB  
Article
AED Inequity among Social Groups in Guangzhou
by Feng Gao, Siyi Lu, Shunyi Liao, Wangyang Chen, Xin Chen, Jiemin Wu, Yunjing Wu, Guanyao Li and Xu Han
ISPRS Int. J. Geo-Inf. 2024, 13(4), 140; https://doi.org/10.3390/ijgi13040140 - 22 Apr 2024
Viewed by 1042
Abstract
Automated external defibrillators (AEDs) are regarded as the most important public facility after fire extinguishers due to their importance to out-of-hospital cardiac arrest (OHCA) victims. Previous studies focused on the location optimization of the AED, with little attention to inequity among different social [...] Read more.
Automated external defibrillators (AEDs) are regarded as the most important public facility after fire extinguishers due to their importance to out-of-hospital cardiac arrest (OHCA) victims. Previous studies focused on the location optimization of the AED, with little attention to inequity among different social groups. To comprehensively investigate the spatial heterogeneity of the AED inequity, we first collected AED data from a WeChat applet. Then, we used the geographically weighted regression (GWR) model to quantify the inequity level and identify the socio-economic status group that faced the worst inequity in each neighborhood. Results showed that immigrants of all ages suffer a more severe AED inequity than residents after controlling population and road density. Immigrants face more severe inequity in downtown, while residents face more severe inequity in the peripheral and outer suburbs. AED inequity among youngsters tends to be concentrated in the center of each district, while inequity among the elderly tends to be distributed at the edge of each district. This study provides a new perspective for investigating the inequity in public facilities, puts forward scientific suggestions for future AED allocation planning, and emphasizes the importance of the equitable access to AED. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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16 pages, 3853 KiB  
Article
Comparison of Different Green Space Measures and Their Impact on Dementia Cases in South Korea: A Spatial Panel Analysis
by Wulan Salle Karurung, Kangjae Lee and Wonhee Lee
ISPRS Int. J. Geo-Inf. 2024, 13(4), 126; https://doi.org/10.3390/ijgi13040126 - 9 Apr 2024
Viewed by 1213
Abstract
Dementia has become a profound public health problem due to the number of patients increasing every year. Previous studies have reported that environmental factors, including greenness, may influence the development and progression of dementia. Studies have found that exposure to green space is [...] Read more.
Dementia has become a profound public health problem due to the number of patients increasing every year. Previous studies have reported that environmental factors, including greenness, may influence the development and progression of dementia. Studies have found that exposure to green space is associated with a lower incidence of dementia. However, many definitions of green space exist, and the effects of its use may differ with the type of green space. Therefore, two types of green space measures were considered in this study to assess the differences in their impact on the prevalence of dementia among females and males. This study used five years of data (2017–2021) from 235 districts in South Korea. The two green space measures used were open space density and normalized difference vegetation index (NDVI), which were derived from satellite images. The analysis utilized a combination of traditional and spatial panel analyses to account for the spatial and temporal effects of independent variables on dementia prevalence. The spatial autocorrelation results revealed that both measures of greenness were spatially correlated with dementia prevalence. The spatial panel regression results revealed a significant positive association between NDVI and dementia prevalence, and open space had a negative association with dementia prevalence in both genders. The difference in the findings can serve as the basis for further research when choosing a greenspace measure, as it affects the analysis results, depending on the objective of the study. This study adds to the knowledge regarding improving dementia studies and the application of spatial panel analysis in epidemiological studies. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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19 pages, 6462 KiB  
Article
Hourly PM2.5 Concentration Prediction Based on Empirical Mode Decomposition and Geographically Weighted Neural Network
by Yan Chen and Chunchun Hu
ISPRS Int. J. Geo-Inf. 2024, 13(3), 79; https://doi.org/10.3390/ijgi13030079 - 2 Mar 2024
Viewed by 1509
Abstract
Accurate prediction of fine particulate matter (PM2.5) concentration is crucial for improving environmental conditions and effectively controlling air pollution. However, some existing studies could ignore the nonlinearity and spatial correlation of time series data observed from stations, and it is difficult to avoid [...] Read more.
Accurate prediction of fine particulate matter (PM2.5) concentration is crucial for improving environmental conditions and effectively controlling air pollution. However, some existing studies could ignore the nonlinearity and spatial correlation of time series data observed from stations, and it is difficult to avoid the redundancy between features during feature selection. To further improve the accuracy, this study proposes a hybrid model based on empirical mode decomposition (EMD), minimal-redundancy-maximal-relevance (mRMR), and geographically weighted neural network (GWNN) for hourly PM2.5 concentration prediction, named EMD-mRMR-GWNN. Firstly, the original PM2.5 concentration sequence with distinct nonlinearity and non-stationarity is decomposed into multiple intrinsic mode functions (IMFs) and a residual component using EMD. IMFs are further classified and reconstructed into high-frequency and low-frequency components using the one-sample t-test. Secondly, the optimal feature subset is selected from high-frequency and low-frequency components with mRMR for the prediction model, thus holding the correlation between features and the target variable and reducing the redundancy among features. Thirdly, the residual component is predicted with the simple moving average (SMA) due to its strong trend and autocorrelation, and GWNN is used to predict the high-frequency and low-frequency components. The final prediction of the PM2.5 concentration value is calculated by an artificial neural network (ANN) composed of the predictive values of each component. PM2.5 concentration prediction experiments in three representational cities, such as Beijing, Wuhan, and Kunming were carried out. The proposed model achieved high accuracy with a coefficient of determination greater than 0.92 in forecasting PM2.5 concentration for the next 1 h. We compared this model with four baseline models in forecasting PM2.5 concentration for the next few hours and found it performed the best in PM2.5 concentration prediction. The experimental results indicated the proposed model can improve prediction accuracy. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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Review

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33 pages, 10982 KiB  
Review
Assessing Contamination in Transitional Waters Using Geospatial Technologies: A Review
by Itzel Arroyo-Ortega, Yaselda Chavarin-Pineda and Eduardo Torres
ISPRS Int. J. Geo-Inf. 2024, 13(6), 196; https://doi.org/10.3390/ijgi13060196 - 12 Jun 2024
Viewed by 850
Abstract
Transitional waters (TWs) are relevant ecological and economical ecosystems that include estuaries, deltas, bays, wetlands, marshes, coastal lakes, and coastal lagoons and play a central role in providing food, protecting coastal environments, and regulating nutrients. However, human activities such as industrialization, urbanization, tourism, [...] Read more.
Transitional waters (TWs) are relevant ecological and economical ecosystems that include estuaries, deltas, bays, wetlands, marshes, coastal lakes, and coastal lagoons and play a central role in providing food, protecting coastal environments, and regulating nutrients. However, human activities such as industrialization, urbanization, tourism, and agriculture are threatening these ecosystems, which results in contamination and habitat degradation. Therefore, it is essential to evaluate contamination in TW to develop effective management and protection strategies. This study analyses the application of geospatial technologies (GTS) for monitoring and predicting contaminant distribution in TW. Cartography, interpolation, complex spatial methods, and remote sensing were applied to assess contamination profiles by heavy metals, and persistent organic compounds, and analyze contamination indices or some physicochemical water parameters. It is concluded that integrating environmental and demographic data with GTS would help to identify critical points of contamination and promote ecosystem resilience to ensure long-term health and human well-being. This review comprehensively analyzes the methods, indicators, and indices used to assess contamination in transitional waters in conjunction with GTS. It offers a valuable foundation for planning future research on pollution in these types of waters or other similar water bodies worldwide. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Association between autism spectrum disorder and environmental quality in the United States
Authors: Jianyong Wu; Alexander C. McLain; Paul Rosile; Darryl B. Hood
Affiliation: Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH, USA
Abstract: Autism spectrum disorder (ASD) has become an emerging public health problem. The cumulative effect of multiple environmental factors on the prevalence of ASD is still unknown. This study examined the association between the prevalence of ASD and the environmental quality index (EQI), an indicator of cumulative environmental quality in five major domains, including air, water, land, built, and sociodemographic variables in the United States. Poisson regression models were used to examine the associations and adjusted for race, age, and population density. The local variations of the associations were examined with Geographically Weighted Regression (GWR) models. The results show that the prevalence of ASD has a positive association with the overall EQI with a risk ratio (RR) of 1.03 and 95% confidence intervals (CI) of 1.01 – 1.06, indicating that children in counties with poor environmental quality might have a higher risk of ASD. Additionally, the prevalence of ASD has a positive association with the air index (RR=1.04, 95% CI: 1.01 – 1.06). These associations varied in different rural-urban groups and different climate regions. This study provided evidence for adverse effects of poor environmental quality, particularly air pollutants, on children’s neurodevelopment.

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