Special Issue "Application of Geospatial Analysis in Urban Environmental Health"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editors

Dr. Ashraf Dewan
E-Mail Website
Guest Editor
School of Earth and Planetary Sciences, Curtin University, Bentley, WA 6102, Australia
Interests: climate change; disaster and natural hazards; health geography; resources management; coastal dynamics
Special Issues and Collections in MDPI journals
Dr. Mohammad A. Hoque
E-Mail Website
Guest Editor
School of the Environment Geography and Geosciences, University of Portsmouth, Portsmouth PO1 2UP, UK
Interests: geo-health; contaminant hydrogeology; climate change and water resources; water security and sustainable development in developing countries
Dr. Asif Ishtiaque
E-Mail Website
Guest Editor
School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA
Interests: sustainability; disaster management; public health; climate change impacts; vulnerability; adaptation; resilience

Special Issue Information

Dear Colleagues,

More than half of the world’s populations are now living in urban areas. The number is expected to swell, primarily because of rural to urban migration predominantly in developing countries, in the future. With increasing human activities, urban areas across the world are facing novel sustainability challenges. Among these challenges, public health becomes a pressing concern. Increased urbanization is expected to increase the quality of life. Nonetheless, it often poses threats to the health of urban dwellers. The urban health risk is not similar across an urban area, but it rather depends on socioeconomic status (e.g., income, occupation), dwelling locations (e.g., slums, industrial areas), and physical environment (e.g., urban parks, building heights). Studies suggest that land surface modifications play important roles in determining urban sustainability, which has compounding effects on urban health. In order to make cities healthy and ensure sustainability, it is essential to understand spatiotemporal changes of urban areas and their effects on the environmental systems. As human–environment interaction is consistently increasing, exposure of individuals to various kinds of health issues could overwhelm cities’ health system; as a result, implementation of Sustainable Development Goals (SDGs) could be challenging. However, there are opportunities to make urban system sustainable.

Data from earth observation satellites and geographic information (collectively called geospatial data) have shown great potential in understanding urban complex systems by integrating spatial representation of sources and pathways of factors affecting disease distribution, health care systems, and environmental sustainability. Currently, geospatial data along with spatial analyses are instrumental in solving urban health issues that have spatial and temporal connotation.

This Special Issue seeks contributions from a wide range of audiences, dealing with urban environmental health across the globe. It particularly invites original/review works, including but not limited to the following research topics: 

  • Methods and approaches to urban health;
  • Urban environment, including urban climate;
  • Environmental health risk assessment;
  • Urban health indicators;
  • Spatial analysis of diseases;
  • Water resources and sanitation in urban areas;
  • Urban solid waste management.
  • Urban groundwater system
  • ‘Urban SDG’ focusing on Sustainable Development Goal 11 Sustainable cities and communities

Dr. Ashraf Dewan
Dr. Mo Hoque
Dr. Asif Ishtiaque
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 papers will be 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2400 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

  • Urban health
  • Sustainable and livable cities
  • Urban climate
  • Geospatial data
  • Spatial analysis

Published Papers (1 paper)

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Research

Article
Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms
Remote Sens. 2021, 13(16), 3222; https://doi.org/10.3390/rs13163222 - 13 Aug 2021
Viewed by 285
Abstract
In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter [...] Read more.
In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas. Full article
(This article belongs to the Special Issue Application of Geospatial Analysis in Urban Environmental Health)
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