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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 4155

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 (3 papers)

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Research

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 718
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 941
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 1382
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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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: EXPLORING THE EFFECTS OF LIGHT AND DARK ON CRIME IN LONDON
Author: Erturk
Highlights: Ambient lighting affects crime rates but not for all crimes; it affects some crime types, especially outdoor crimes. The innovative method of calculating daylight availability based on the solar altitude of the location and crimes within a 5-minute crime window provides high-accuracy results The findings help to understand how criminals' behaviour changes based on time and lightness.

Title: Assessing contamination in transitional waters using Geographic Information Systems: A review
Author: Torres
Highlights: Evaluation of pollution in transitional water using GIS have been limited studied in spite of their environmental and socioeconomic relevance Conventional pollutants have been analyzed, but persistent organic compounds, pesticides and emerging micropollutants have been scarcely studied More studies about risk assessment and environmental and human vulnerability are needed

Title: Geospatial analysis of zoonotic diseases across Africa
Authors: Samsung Lim; Keevan Naicker; Ashley Quigley; C Raina MacIntyre
Affiliation: School of Civil and Environmental Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia
Abstract: Africa has been burdened by infectious diseases over the last decade, with increasing outbreaks particularly from the zoonotic subgroup. Identifying the correlations among humans, animals and their environmental settings is critical for addressing zoonotic diseases. However, their establishment in developing nations remains a challenging problem. This problem can be tackled by EPIWATCH, an artificial intelligence driven real-time global surveillance system that aggregates open-source data and detects early signals of potential outbreaks from the open-source data. In this paper, we investigated the time series of EPIWATCH's monthly reports for all African countries during the period from 2018 to 2022 that include four zoonotic diseases, namely Ebola haemorrhagic fever, Lassa fever, Marburg virus disease, and Yellow fever. Unusual spatio-temporal groupings for the EPIWATCH data per disease and year were identified using the emerging hot spot analysis tool. Weekly occurrence records were constructed from the EPIWATCH data and joined with a collection of socio-demographic and environmental covariates. A MaxEnt model was fitted for each of the four diseases, with best cross-validation tuned model’s influential covariates identified by permute-after-calibration approach. Predominantly Western and Southern Africa showed hot spots for the years 2021-2022 for each disease. Mid-year population and normalised difference vegetation index were both within the top three most influential covariates for Ebola, Lassa fever and Marburg virus disease. However, Yellow fever had a poor cross-validation set performance with no influential covariates identified, which requires further investigation as our future work. In conclusion, the EPIWATCH data aligns with traditional case-based data from the World Health Organization (WHO) for Africa, with periods of earlier signal generation. Therefore, EPIWATCH can offer additional benefit as an early warning signal system for outbreaks in real-time, especially for African countries. In addition, when correlated, the EPIWATCH reports can be used as a proxy measure for occurrence records that can be successfully used in ecological niche models.

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