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Remote Sensing
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Application of Geospatial Analysis in Urban Environmental Health
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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: closed (30 September 2022) | Viewed by 7002

Special Issue Editors

Dr. Ashraf Dewan

E-Mail Website
Guest Editor
School of Earth and Planetary Sciences, Spatial Sciences Discipline, Curtin University, Perth, Australia
Interests: climate risk assessment; urban environment; health geography; disaster and emergency management; human–environment interaction
Special Issues, Collections and Topics 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 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. 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 2700 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 (2 papers)

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Research

21 pages, 4568 KiB  
Article
Forecasting of Built-Up Land Expansion in a Desert Urban Environment
by Shawky Mansour, Mohammed Alahmadi, Peter M. Atkinson and Ashraf Dewan
Remote Sens. 2022, 14(9), 2037; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092037 - 23 Apr 2022
Cited by 15 | Viewed by 2454
Abstract
In recent years, socioeconomic transformation and social modernisation in the Gulf Cooperation Council (GCC) states have led to tremendous changes in lifestyle and, subsequently, expansion of urban settlements. This accelerated growth is pronounced not only across vegetated coasts, plains, and mountains, but also [...] Read more.
In recent years, socioeconomic transformation and social modernisation in the Gulf Cooperation Council (GCC) states have led to tremendous changes in lifestyle and, subsequently, expansion of urban settlements. This accelerated growth is pronounced not only across vegetated coasts, plains, and mountains, but also in desert cities. Nevertheless, spatial simulation and prediction of desert urban patterns has received little attention, including in Oman. While most urban settlements in Oman are located in desert environments, research exploring and monitoring this type of urban growth is rare in the scientific literature. This research focuses on analysing and predicting land use–land cover (LULC) changes across the desert city of Ibri in Oman. A methodology was employed involving integrating the multilayer perceptron (MLP) and Markov chain (MC) techniques to forecast spatiotemporal LULC dynamics and map urban growth patterns. The inputs were three Landsat images from 2010 and 2020, and a series of covariate layers based on transforms of elevation, slope, population settlements, urban centres, and points of interest that proxy the driving forces of change. The findings indicated that the observed LULC changes were predominantly rapid across the city during 2010 to 2020, transforming desert, bare land, and vegetation into built-up areas. The forecast showed that area of land conversion from desert to urban would be 5666 ha during the next two decades and 7751 ha by 2050. Similarly, vacant land is expected to contribute large areas to urban expansion (2370 ha by 2040, and 3266 ha by 2050), although desert cities confront numerous environmental challenges, including water scarcity, shrinking vegetation cover, and being converted into residential land. Massive urban expansion has consequences for biodiversity and natural ecosystems—particularly in green areas, which are expected to decline by approximately 107 ha by 2040 (i.e., 10%) and 166 ha by 2050. The outcomes of this research provide fundamental guidance for decision-makers and planners in Oman and elsewhere to effectively monitor and manage desert urban dynamics and sustainable desert cities. Full article
(This article belongs to the Special Issue Application of Geospatial Analysis in Urban Environmental Health)
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23 pages, 14253 KiB  
Article
Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms
by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki and Soo-Mi Choi
Remote Sens. 2021, 13(16), 3222; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163222 - 13 Aug 2021
Cited by 16 | Viewed by 3040
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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