Analyzing the Risk to COVID‐19 Infection using Remote Sensing and GIS

Kanga, Shruti; Meraj, Gowhar; Sudhanshu,; Farooq, Majid; Nathawat, Mahendra Singh; Singh, Suraj Kumar

doi:10.1111/risa.13724

Cited by 39 publications

(34 citation statements)

References 25 publications

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“…In the continental United States, Mollalo et al [ 10 ] examined GIS-based spatial modeling of the COVID-19 incident rate using a geographically weighted regression (GWR) model. Kanga et al [ 11 ] assessed the risk of COVID-19 contagion using remote sensing and GIS analysis. Urban et al [ 12 ] used GIS-based spatial modeling of COVID-19 deaths in Sao Paulo, Brazil, using the GWR model.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms

Razavi-Termeh

Sadeghi‐Niaraki

Farhangi

et al. 2021

IJERPH

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The reduction of population concentration in some urban land uses is one way to prevent and reduce the spread of COVID-19 disease. Therefore, the objective of this study is to prepare the risk mapping of COVID-19 in Tehran, Iran, using machine learning algorithms according to socio-economic criteria of land use. Initially, a spatial database was created using 2282 locations of patients with COVID-19 from 2 February 2020 to 21 March 2020 and eight socio-economic land uses affecting the disease—public transport stations, supermarkets, banks, automated teller machines (ATMs), bakeries, pharmacies, fuel stations, and hospitals. The modeling was performed using three machine learning algorithms that included random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), and logistic regression (LR). Feature selection was performed using the OneR method, and the correlation between land uses was obtained using the Pearson coefficient. We deployed 70% and 30% of COVID-19 patient locations for modeling and validation, respectively. The results of the receiver operating characteristic (ROC) curve and the area under the curve (AUC) showed that the RF algorithm, which had a value of 0.803, had the highest modeling accuracy, which was followed by the ANFIS algorithm with a value of 0.758 and the LR algorithm with a value of 0.747. The results showed that the central and the eastern regions of Tehran are more at risk. Public transportation stations and pharmacies were the most correlated with the location of COVID-19 patients in Tehran, according to the results of the OneR technique, RF, and LR algorithms. The results of the Pearson correlation showed that pharmacies and banks are the most incompatible in distribution, and the density of these land uses in Tehran has caused the prevalence of COVID-19.

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Section: Introductionmentioning

confidence: 99%

COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms

Razavi-Termeh

Sadeghi‐Niaraki

Farhangi

et al. 2021

IJERPH

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“…The results further indicate that perceived risks are important drivers for the acceptance of the government's implemented measures to control COVID-19 and for more precautionary behavior. Kanga, Meraj, Sudhanshu, Nathawat, and Singh (2021) propose a risk-based assessment framework for analyzing risk of high transmission and prevalence for COVID-19 in spatial areas, using integrated hazard and vulnerability components associated with this pandemic for effective risk mitigation. They hypothesize different COVID-19 risk indices (C19Ri) of the wards of Jaipur municipal corporation (JMC), India such as proximity to hotspots, total population, population density, availability of clean water, and associated land use/land cover and calculated them in a GIS-based multicriteria risk reduction method.…”

Section: Risk Perceptionmentioning

confidence: 99%

Global Systemic Risk and Resilience for Novel Coronavirus and COVID‐19

Mitchell

Lambert

2021

Risk Analysis

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This Special Issue is dedicated to issues and challenges related to pandemic risk and resilience, with a focus on policy and operations of global systems in the COVID‐19 pandemic. The cascading effects of emerging and reemerging infectious diseases to the global economy are a critical interest. Measures to confront the ongoing pandemic are an urgent need. Data analysis at regional and global scales is helping to prioritize response and resilience across locations of high risks. The risk sciences are available for addressing human health and infection risks; the evaluation of risk management strategies and tradeoffs; risk perception as it relates to information processing and receiving risk communication; and tracking system resilience as it relates to various imposed measures.

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“…Practice suggests that several other ICT-enhanced solutions aimed at improving the quality of walking in the smart city space exist. These include (i) systems geared toward managing crowds and pre-empting the phenomenon of mob creation [10]; (ii) sensor-based systems adjusting the pace of traffic light change to the anticipated pace of walking in the city space; (iii) sensor-based applications aimed at providing a warning regarding issues such as weather, pollution, or sidewalk defects [47,48]; and (iv) IoT-based systems aimed at minimizing the risk of exposure to illnesses such as Covid-19 infection [49]. From a micro-level perspective, the experience of walking in the smart city space may be enhanced by applications geared toward route optimization, whereby the criteria of optimality may be diverse and include sightseeing, green areas, shops, coffee places, routing and destination/location identification assistance, health benefits, and performance [50].…”

Section: Walking and Walkability: Definitions And Applicability In The Smart City Contextmentioning

confidence: 99%

Conceptualizing Walking and Walkability in the Smart City through a Model Composite w2 Smart City Utility Index

et al. 2021

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This paper explores walking and walkability in the smart city and makes a case for their centrality in the debate on the resilience and sustainability of smart cities, as outlined in the United Nations’ (UN) Sustainable Development Goals (SDGs). It is argued that, while the human/inhabitant-centric paradigm of urban development consolidates, and research on walking, walkability, and pedestrian satisfaction flourishes, the inroads of ICT render it necessary to reflect on these issues in the conceptually- and geographically-delimited space of the smart city. More importantly, it becomes imperative to make respective findings useful and usable for policymakers. To this end, by approaching walking and walkability through the lens of utility, the objective of this paper is to develop a conceptual framework in which the relevance of walking and walkability, hereafter referred to as w2, as a distinct subject of research in the smart cities debate is validated. This framework is then employed to construct a model of a composite w2smart city utility index. With the focus on the development of the conceptual framework, in which the w2 utility index is embedded, this paper constitutes the first conceptual step of the composite index development process. The value added of this paper is three-fold: First, the relevance of walking and walkability as a distinct subject of research in the realm of smart cities research is established. Second, a mismatch between end-users’ satisfaction derived from walking and their perception of walkability and the objective factors influencing walking and walkability is identified and conceptualized by referencing the concept of utility. Third, a model smart city w2 utility index is proposed as a diagnostic and prognostic tool that, in the subsequent stages of research and implementation, will prove useful for decisionmakers and other stakeholders involved in the process of managing smart cities.

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Analyzing the Risk to COVID‐19 Infection using Remote Sensing and GIS

Cited by 39 publications

References 25 publications

COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms

COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms

Global Systemic Risk and Resilience for Novel Coronavirus and COVID‐19

Conceptualizing Walking and Walkability in the Smart City through a Model Composite w2 Smart City Utility Index

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