Special Issue "Geospatial Approaches for Understanding the Social, Economic and Environmental Impacts of COVID-19"

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

Deadline for manuscript submissions: closed (31 May 2021).

Special Issue Editors

Prof. Dr. Ed Manley
E-Mail Website
Guest Editor
School of Geography, University of Leeds, Leeds, LS2 9JT, UK
Interests: agent-based modelling; machine learning; route choice; urban analytics; traffic simulation
Special Issues and Collections in MDPI journals
Dr. Eric Delmelle
E-Mail Website
Guest Editor
Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Interests: spatial analysis and modeling; epidemiology; uncertainty; spatial optimization; geovisualization; GIS algorithms
Special Issues and Collections in MDPI journals
Prof. Dr. Mark Birkin
E-Mail Website
Guest Editor
School of Geography and Leeds Institute for Data Analytics (LIDA), University of Leeds, Leeds, UK
Interests: individual-based modelling; urban analytics; retail geography
Prof. Dr. Mark Gahegan
E-Mail Website
Guest Editor
School of Computer Science, University of Auckland, Auckland, New Zealand
Interests: spatial analysis; visualization; semantics and pragmatics; e-science; geocomputation; epidemiology
Prof. Dr. Mei-Po Kwan
grade E-Mail Website
Guest Editor
Department of Geography and Resource Management, and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong, China
Interests: geographies of health and wellbeing; human mobility; geospatial methods
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The COVID-19 pandemic has been the most serious threat to global public health in over 100 years, subsequently resulting in the implementation of severe restrictions on social behaviour, movement, and economic activity. These policies, while dealing with the immediate public health crisis, have consequences that impact widely across society, affecting systems that typically operate with relative stability (housing, transport, health, environment, consumer spending, etc.). It is important that, as we move through the crisis and onto the next stage of recovery, we understand how the social, economic and environmental implications have varied and continue to vary over geographic space.

In this Special Issue, we would like to collate some of the finest examples of the application of advanced geospatial methods towards understanding the impacts of COVID-19. These impacts should relate to the social, economic or environmental impact of COVID-19, including the subsequent imposition of restrictive policy, the knock-on impacts across social systems generally studied in isolation as well as its variation over space and time.

We invite papers that address these topics from a broad spectrum of data sources (mobile phone data, social media data, remote sensing, etc.) and geospatial methods, including machine learning, big data analytics, space-time modelling and simulation, environmental modelling, and data visualisation. In particular, we would be keen to see examples of where the crisis and its particularities have resulted in the development of novel methodologies and collaborations across diverse disciplines.

Prof. Ed Manley
Assoc. Prof. Eric Delmelle
Prof. Mark Birkin
Prof. Mark Gahegan
Prof. Mei-Po Kwan
Guest Editors

 

Keywords

  • Social disruption
  • Policy impact
  • Spatial complexity
  • Economic disruption
  • Spatial heterogeneity
  • Geospatial methods
  • Environmental modelling
  • Spatial modelling
  • Agent-based modelling
  • Geovisualisation

Published Papers (6 papers)

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Research

Article
Global Contraction and Local Strengthening of Firms’ Supply and Sales Logistics Networks in the Context of COVID-19: Evidence from the Development Zones in Weifang, China
ISPRS Int. J. Geo-Inf. 2021, 10(7), 477; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10070477 - 11 Jul 2021
Viewed by 331
Abstract
The stagnation of multinational and cross-regional goods circulation has created significant disruptions to manufacturing supply chains due to the outbreak of the COVID-19 pandemic. To explore the impact of COVID-19 on the circulation of manufacturing industry products at different geographical scales, we drew [...] Read more.
The stagnation of multinational and cross-regional goods circulation has created significant disruptions to manufacturing supply chains due to the outbreak of the COVID-19 pandemic. To explore the impact of COVID-19 on the circulation of manufacturing industry products at different geographical scales, we drew upon a case study of development zones in the city of Weifang in China to analyze the characteristics of firms’ logistics networks in these development zones, and how these characteristics have changed since the outbreak of the COVID-19 pandemic. The data used in this study were collected from fieldwork conducted between 26 August 2020 and 15 October 2020, and included the supply originations of firms’ manufacturing sources and the sales destinations of their goods. We chose the two-mode network analysis method as our study methodology, which separates the logistics networks into supply networks and sales networks. The results show the following: First, the overall structure of firms’ logistics networks in Weifang’s development zones is characterized by localization. In the context of the COVID-19 pandemic, the local network links have further strengthened, whereas the global links have seriously declined. Moreover, the average path length of both the supply and sales logistics networks has slightly decreased, indicating the increased connectivity of the logistics networks. Second, in terms of the network node centrality, the core nodes of the supply logistics networks are the development zones and the city in which the firms are located, whereas the core nodes of the sales logistics networks are the core companies in the development zones. However, since the outbreak of the COVID-19 pandemic, the centrality of supply originations and sales destinations at the local scale has increased, whereas the centrality of supply originations and sales destinations at the global scale has decreased significantly. Third, the influencing factors of such changes include controlling personnel and goods circulation based on national boundaries and administrative boundaries, forcing the logistics networks in the development zones to shrink to the local scale. Moreover, there are differences in the scope of spatial contraction between supply logistics networks and the sales logistics networks. Full article
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Article
Understanding the Drivers of Mobility during the COVID-19 Pandemic in Florida, USA Using a Machine Learning Approach
ISPRS Int. J. Geo-Inf. 2021, 10(7), 440; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10070440 - 28 Jun 2021
Viewed by 465
Abstract
As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, [...] Read more.
As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county), as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases. Full article
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Article
The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States
ISPRS Int. J. Geo-Inf. 2021, 10(6), 387; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060387 - 04 Jun 2021
Viewed by 279
Abstract
(1) Background: Human mobility between geographic units is an important way in which COVID-19 is spread across regions. Due to the pressure of epidemic control and economic recovery, states in the United States have adopted different policies for mobility limitations. Assessing the impact [...] Read more.
(1) Background: Human mobility between geographic units is an important way in which COVID-19 is spread across regions. Due to the pressure of epidemic control and economic recovery, states in the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating epidemic policies. (2) Methods: We utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (excluding the District of Colombia) with daily new cases at the county level from 22 January 2020 to 20 August 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation test and stepwise OLS regression with socioeconomic factors. (3) Results: The K-means clustering divided the time-varying spatial autocorrelation curves of the 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with the variables of median age, population density, and proportions of international immigrants and highly educated population, but negatively correlated with the birth rate. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and highly educated population proportion. (4) Conclusions: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population; high-density populated states need to strengthen regional mobility restrictions; and the highly educated population should reduce unnecessary regional movement and strengthen self-protection. Full article
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Article
On the Use of ‘Glyphmaps’ for Analysing the Scale and Temporal Spread of COVID-19 Reported Cases
ISPRS Int. J. Geo-Inf. 2021, 10(4), 213; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040213 - 01 Apr 2021
Viewed by 586
Abstract
Recent analysis of area-level COVID-19 cases data attempts to grapple with a challenge familiar to geovisualization: how to capture the development of the virus, whilst supporting analysis across geographic areas? We present several glyphmap designs for addressing this challenge applied to local authority [...] Read more.
Recent analysis of area-level COVID-19 cases data attempts to grapple with a challenge familiar to geovisualization: how to capture the development of the virus, whilst supporting analysis across geographic areas? We present several glyphmap designs for addressing this challenge applied to local authority data in England whereby charts displaying multiple aspects related to the pandemic are given a geographic arrangement. These graphics are visually complex, with clutter, occlusion and salience bias an inevitable consequence. We develop a framework for describing and validating the graphics against data and design requirements. Together with an observational data analysis, this framework is used to evaluate our designs, relating them to particular data analysis needs based on the usefulness of the structure they expose. Our designs, documented in an accompanying code repository, attend to common difficulties in geovisualization design and could transfer to contexts outside of the UK and to phenomena beyond the pandemic. Full article
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Article
The Impact of COVID-19 on Crime: A Spatial Temporal Analysis in Chicago
ISPRS Int. J. Geo-Inf. 2021, 10(3), 152; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030152 - 10 Mar 2021
Viewed by 827
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has had tremendous and extensive impacts on the people’s daily activities. In Chicago, the numbers of crime fell considerably. This work aims to investigate the impacts that COVID-19 has had on the spatial and temporal patterns of [...] Read more.
The coronavirus disease 2019 (COVID-19) pandemic has had tremendous and extensive impacts on the people’s daily activities. In Chicago, the numbers of crime fell considerably. This work aims to investigate the impacts that COVID-19 has had on the spatial and temporal patterns of crime in Chicago through spatial and temporal crime analyses approaches. The Seasonal-Trend decomposition procedure based on Loess (STL) was used to identify the temporal trends of different crimes, detect the outliers of crime events, and examine the periodic variations of crime distributions. The results showed a certain phase pattern in the trend components of assault, battery, fraud, and theft. The largest outlier occurred on 31 May 2020 in the remainder components of burglary, criminal damage, and robbery. The spatial point pattern test (SPPT) was used to detect the similarity between the spatial distribution patterns of crime in 2020 and those in 2019, 2018, 2017, and 2016, and to analyze the local changes in crime on a micro scale. It was found that the distributions of crime significantly changed in 2020 and local changes in theft, battery, burglary, and fraud displayed an aggregative cluster downtown. The results all claim that spatial and temporal patterns of crime changed significantly affected by COVID-19 in Chicago, and they offer constructive suggestions for local police departments or authorities to allocate their available resources in response to crime. Full article
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Article
Escaping from Cities during the COVID-19 Crisis: Using Mobile Phone Data to Trace Mobility in Finland
ISPRS Int. J. Geo-Inf. 2021, 10(2), 103; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020103 - 23 Feb 2021
Cited by 7 | Viewed by 1433
Abstract
The coronavirus disease 2019 (COVID-19) crisis resulted in unprecedented changes in the spatial mobility of people across societies due to the restrictions imposed. This also resulted in unexpected mobility and population dynamics that created a challenge for crisis preparedness, including the mobility from [...] Read more.
The coronavirus disease 2019 (COVID-19) crisis resulted in unprecedented changes in the spatial mobility of people across societies due to the restrictions imposed. This also resulted in unexpected mobility and population dynamics that created a challenge for crisis preparedness, including the mobility from cities during the crisis due to the underlying phenomenon of multi-local living. People changing their residences can spread the virus between regions and create situations in which health and emergency services are not prepared for the population increase. Here, our focus is on urban–rural mobility and the influence of multi-local living on population dynamics in Finland during the COVID-19 crisis in 2020. Results, based on three mobile phone datasets, showed a significant drop in inter-municipal mobility and a shift in the presence of people—a population decline in urban centres and an increase in rural areas, which is strongly correlated to secondary housing. This study highlights the need to improve crisis preparedness by: (1) acknowledging the growing importance of multi-local living, and (2) improving the use of novel data sources for monitoring population dynamics and mobility. Mobile phone data products have enormous potential, but attention should be paid to the varying methodologies and their possible impact on analysis. Full article
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