Applied Geography (Sevenoaks, England)[JOURNAL]
Sun
108 papers
Promissory shock, broken future: COVID-19 and state-led speculations in biotechnology and pharmaceutical industries in South Korea.
Lim SH
Appl Geogr · 2021 Nov · PMID 36536775 · Full text
This research examines institutional responses to shocking events, in this case, the COVID-19 pandemic and beyond. I argue that our analysis should consider state-led nationalism in finance and financialization especiall...
This research examines institutional responses to shocking events, in this case, the COVID-19 pandemic and beyond. I argue that our analysis should consider state-led nationalism in finance and financialization especially when new modes of financial accumulation can be correlated with state projects of crisis management. Also, in dealing with shocking events, which are an inevitable aspect of capitalism, I claim nationalistic deregulations and speculation stimulated by institutional discourse can put ordinary people into permanently unpayable debt and reshape social exclusion. Drawing from interpretative policy analysis, I examine how early COVID-19 management by the Korean government took advantage of sloganeering of upper-K words, initiated by the Korean Wave, as discursive tools in invoking nationalistic sentiments. The instutional nationalism in the upper-case K as prefix is examined in promoting Korean biotechnology and pharmaceutical companies and their stocks. Further, I demonstrate how the accumulation strategies of this nationalistic COVID-19 management regarding bio and pharma industries were already practiced before COVID-19 in Korea, by the regulatory sandbox policy along with the Korean legitimation crisis. This set of practices has eventually accelerated the financialization of everyday life and Othering. I call for a critical lens to analyze the pressing agenda of discursive practices in institutional crisis responses.
Assessing the spatial variability of raising public risk awareness for the intervention performance of COVID-19 voluntary screening: A spatial simulation approach.
Kuo FY, Wen TH
Appl Geogr · 2022 Nov · PMID 36267149 · Full text
The rapid spread of a (re)emerging pandemic (e.g., COVID-19) is usually attributed to the invisible transmission caused by asymptomatic cases. Health authorities rely on large-scale voluntary screening to identify and is...
The rapid spread of a (re)emerging pandemic (e.g., COVID-19) is usually attributed to the invisible transmission caused by asymptomatic cases. Health authorities rely on large-scale voluntary screening to identify and isolate invisible spreaders as well as symptomatic people as early as possible to control disease spread. Raising public awareness is beneficial for improving the effectiveness of epidemic prevention because it could increase the usage and demand for testing kits. However, the effectiveness of testing could be influenced by the spatial demand for medical resources in different periods. Spatial demand could also be triggered by public awareness in areas with two geographical factors, including spatial proximity to resources and attractiveness of human mobility. Therefore, it is necessary to explore the spatial variations in raising public awareness on the effectiveness of COVID-19 screening. We implemented spatial simulation models to integrate various levels of public awareness and pandemic dynamics in time and space. Moreover, we also assessed the effects of the spatial proximity of testing kits and the ease of human mobility on COVID-19 testing at various levels of public awareness. Our results indicated that high public awareness promotes high willingness to be tested. This causes the demand to not be fully satisfied at the peak times during a pandemic, yet the shortage of tests does not significantly increase pandemic severity. We also found that when public awareness is low, concentrating on unattractive areas (such as residential or urban fringe areas) could promote a higher benefit of testing. On the other hand, when awareness is high, the factor of distances to testing stations is more important for promoting the benefit of testing; allocating additional testing resources in areas distant from stations could have a higher benefit of testing. This study aims to provide insights for health authorities into the allocation of testing resources against disease outbreaks with respect to various levels of public awareness.
Near real time monitoring and forecasting for COVID-19 situational awareness.
Stewart R, Erwin S, Piburn J … +7 more , Nagle N, Kaufman J, Peluso A, Christian JB, Grant J, Sorokine A, Bhaduri B
Appl Geogr · 2022 Sep · PMID 35945952 · Full text
In the opening months of the pandemic, the need for situational awareness was urgent. Forecasting models such as the Susceptible-Infectious-Recovered (SIR) model were hampered by limited testing data and key information...
In the opening months of the pandemic, the need for situational awareness was urgent. Forecasting models such as the Susceptible-Infectious-Recovered (SIR) model were hampered by limited testing data and key information on mobility, contact tracing, and local policy variations would not be consistently available for months. New case counts from sources like John Hopkins University and the NY Times were systematically reliable. Using these data, we developed the novel COVID County Situational Awareness Tool (CCSAT) for reliable monitoring and decision support. In CCSAT, we developed a retrospective seven-day moving window semantic map of county-level disease magnitude and acceleration that smoothed noisy daily variations. We also developed a novel Bayesian model that reliably forecasted county-level magnitude and acceleration for the upcoming week based on population and new case count data. Together these formed a robust operational update including county-level maps of new case rate changes, estimates of new cases in the upcoming week, and measures of model reliability. We found CCSAT provided stable, reliable estimates across the seven-day time window, with the greatest errors occurring in cases of anomalous, single day spikes. In this paper, we provide CCSAT details and apply it to a single week in June 2020.
Examining the association between urban green space and viral transmission of COVID-19 during the early outbreak.
Zhai W, Yue H, Deng Y
Appl Geogr · 2022 Oct · PMID 35936827 · Full text
Even though exposure to urban green spaces (UGS) has physical and mental health benefits during COVID-19, whether visiting UGS will exacerbate viral transmission and what types of counties would be more impacted remain t...
Even though exposure to urban green spaces (UGS) has physical and mental health benefits during COVID-19, whether visiting UGS will exacerbate viral transmission and what types of counties would be more impacted remain to be answered. In this research, we adopted mobile phone data to measure the county-level UGS visitation across the United States. We developed a Bayesian model to estimate the effective production number of the pandemic. To consider the spatial dependency, we applied the geographically weighted panel regression to estimate the association between UGS visitation and viral transmission. We found that visitations to UGS may be positively correlated with the viral spread in Florida, Idaho, New Mexico, Texas, New York, Ohio, and Pennsylvania. Especially noteworthy is that the spread of COVID-19 in the majority of counties is not associated with green space visitation. Further, we found that when people visit UGS, there may be a positive association between median age and viral transmission in New Mexico, Colorado, and Missouri; a positive association between concentration of blacks and viral transmission in North Dakota, Minnesota, Wisconsin, Michigan, and Florida; and a positive association between poverty rate and viral transmission in Iowa, Missouri, Colorado, New Mexico, and the Northeast United States.
Examining the impact of COVID-19 vaccination rates on differential access to critical care.
Cromley G, Lin J
Appl Geogr · 2022 Aug · PMID 35757493 · Full text
The measurement of potential access to health care has focused primarily on what might be called "place-based" access or the differential access among geographic locations rather than between different populations. The v...
The measurement of potential access to health care has focused primarily on what might be called "place-based" access or the differential access among geographic locations rather than between different populations. The vaccination program to inoculate the population against the effects of the COVID-19 virus has created two different at-risk populations. This research examines the impact of COVID-19 vaccination rates on access to critical care for persons fully-vaccinated versus those not fully-vaccinated. In this situation, additional tools are necessary to understand: 1) if there is a significant difference in accessibility between different populations, 2) the magnitude of this difference and how it is distributed across accessibility levels, and 3) how the differences between groups are distributed across the state. A study of access to intensive care unit (ICU) beds by these two populations for the state of Illinois found that although there was a statistically significant difference in access, the magnitude of differences was small. A more important difference was being located in the Chicago Area of the state. The not-fully vaccinated in the Chicago Area had higher than expected spatial access due to the lower need for ICU beds by a higher percentage of fully vaccinated people.
Pandemic response in rural Peru: Multi-scale institutional analysis of the COVID-19 crisis.
Cáceres Cabana YD, Malone A, Zeballos EZ … +5 more , Huamani Huaranca NO, Tinta MT, Gonzales Beltrán SA, Arosquipa AA, Pinedo D
Appl Geogr · 2021 Sep · PMID 35702546 · Full text
The first wave of the COVID-19 pandemic was devastating in Peru, which suffered a high death rate and severe economic disruption. These results occurred despite ambitious response measures, revealing widespread instituti...
The first wave of the COVID-19 pandemic was devastating in Peru, which suffered a high death rate and severe economic disruption. These results occurred despite ambitious response measures, revealing widespread institutional weaknesses across the country's levels of government. We analyze responses across the four levels of government, with emphasis on local governance in rural areas, to understand how institutions and contexts shape crisis management outcomes. We focus on the Arequipa region, drawing from 44 interviews with officials and community members. We found that the crisis provoked a reversion to the norm across multiple scales, though with significant differentiation. The national government fell back on a centralized, militarized approach that effectively reclaimed power but was ineffective in confronting the pandemic. Counter the overarching recentralization trend, in rural peripheries where state power was always partial, norms of informal local governance were reinforced and intensified. The de facto autonomy in rural areas elicited a mix of paralysis and improvisation, with outcomes that varied widely from place to place and over time. These bifurcated results in the face of crisis reveal important weaknesses in Peru's governance structures and institutions and show how pre-existing habits and norms were reproduced in the face of crisis, rather than reformed or transcended.
Rapid surveillance of COVID-19 by timely detection of geographically robust, alive and emerging hotspots using Particle Swarm Optimizer.
Wadhwa A, Thakur MK
Appl Geogr · 2022 Jul · PMID 35645430 · Full text
A novel virus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a pandemic called Coronavirus disease 2019 (COVID-19). According to the World Health Organization, COVID-19 was first...
A novel virus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a pandemic called Coronavirus disease 2019 (COVID-19). According to the World Health Organization, COVID-19 was first detected in Wuhan city in December 2019 and has affected 216 countries with 9473214 confirmed cases and 484249 deaths globally as on June 26th, 2020. Also, this outbreak continues to grow in many countries like the United States of America (U.S.), Brazil, India, and Russia. To ensure rapid surveillance and better decision-making by government authorities in different countries, it is vital to identify alive and emerging hotspots within a country promptly. State-of-the-art methods based on space-time scan statistics (like SaTScan) are not geographically robust. Also, due to the enumeration of many Spatio-temporal cylinders, the computation cost of Spatio-temporal SaTScan (ST-SaTScan) is very high. In the applications like COVID-19 where we need to detect the emerging hotspots daily as soon as the new count of cases gets updated, ST-SaTScan seems inefficient. Therefore, this paper proposes a Particle Swarm Optimizer-based scheme to timely detect geographically robust, alive, and emerging COVID-19 hotspots in a country. Timely detection can help government officials design better control strategies like increasing testing in hotspots, imposing stricter containment rules, or setting up temporary hospital beds. Performance of ST-SaTScan and proposed scheme have been analyzed for four worst-hit U.S. states for the incubation period of 14 days between June 11th, 2020, and June 24th, 2020. Results indicate that the proposed scheme detects hotspots of a higher likelihood ratio (a measure to indicate the significance of hotspot) than ST-SaTScan in significantly less time. We also applied the proposed scheme to detect the emerging COVID-19 hotspots in all states of the U.S. During the study period, the proposed scheme has detected 104 emerging COVID-19 hotspots.
Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China.
Zhou S, Zhou S, Zheng Z … +2 more , Lu J, Song T
Appl Geogr · 2022 Jun · PMID 35469327 · Full text
Risk assessment of the intra-city spatio-temporal spreading of COVID-19 is important for providing location-based precise intervention measures, especially when the epidemic occurred in the densely populated and high mob...
Risk assessment of the intra-city spatio-temporal spreading of COVID-19 is important for providing location-based precise intervention measures, especially when the epidemic occurred in the densely populated and high mobile public places. The individual-based simulation has been proven to be an effective method for the risk assessment. However, the acquisition of individual-level mobility data is limited. This study used publicly available datasets to approximate dynamic intra-city travel flows by a spatio-temporal gravity model. On this basis, an individual-based epidemic model integrating agent-based model with the susceptible-exposed-infectious-removed (SEIR) model was proposed and the intra-city spatio-temporal spreading process of COVID-19 in eleven public places in Guangzhou China were explored. The results indicated that the accuracy of dynamic intra-city travel flows estimated by available big data and gravity model is acceptable. The spatio-temporal simulation method well presented the process of COVID-19 epidemic. Four kinds of spatial-temporal transmission patterns were identified and the pattern was highly dependent on the urban spatial structure and location. It indicated that location-based precise intervention measures should be implemented according to different regions. The approach of this research can be used by policy-makers to make rapid and accurate risk assessments and to implement intervention measures ahead of epidemic outbreaks.
Measurement of contagion spatial spread probability in public places: A case study on COVID-19.
Chen L, Liu X, Hu T … +4 more , Bao S, Ye X, Ma N, Zhou X
Appl Geogr · 2022 Jun · PMID 35418716 · Full text
The scale and scope of the COVID-19 epidemic have highlighted the need for timely control of viral transmission. This paper proposed a new spatial probability model of epidemic infection using an improved Wasserstein dis...
The scale and scope of the COVID-19 epidemic have highlighted the need for timely control of viral transmission. This paper proposed a new spatial probability model of epidemic infection using an improved Wasserstein distance algorithm and Monte Carlo simulation. This method identifies the public places in which COVID-19 spreads and grows easily. The Wasserstein Distance algorithm is used to calculate the distribution similarity between COVID-19 cases and the public places. Further, we used hypothesis tests and Monte Carlo simulation to estimate the spatial spread probability of COVID-19 in different public places. We used Snow's data to test the stability and accuracy of this measurement. This verification proved that our method is reliable and robust. We applied our method to the detailed geographic data of COVID-19 cases and public places in Wuhan. We found that, rather than financial service institutions and markets, public buildings such as restaurants and hospitals in Wuhan are 95 percent more likely to be the public places of COVID-19 spread.
Do spatiotemporal units matter for exploring the microgeographies of epidemics?
Zhang S, Wang M, Yang Z … +1 more , Zhang B
Appl Geogr · 2022 May · PMID 35399592 · Full text
From the onset of the COVID-19 pandemic in 2020, studies on the microgeographies of epidemics have surged. However, studies have neglected the significant impact of multiple spatiotemporal units, such as report timestamp...
From the onset of the COVID-19 pandemic in 2020, studies on the microgeographies of epidemics have surged. However, studies have neglected the significant impact of multiple spatiotemporal units, such as report timestamps and spatial scales. This study examines three cities with localized COVID-19 resurgence after the first wave of the pandemic in mainland China to estimate the differential impact of spatiotemporal unit on exploring the influencing factors of epidemic spread at the microscale. The quantitative analysis results suggest that future spatial epidemiology research should give greater attention to the "symptom onset" timestamp instead of only the "confirmed" data and that "spatial transmission" should not be confused with "spatial sprawling" of epidemics, which can greatly reduce comparability between epidemiology studies. This research also highlights the importance of considering the modifiable areal unit problem (MAUP) and the uncertain geographic context problem (UGCoP) in future studies.
Geospatial evaluation of COVID-19 mortality: Influence of socio-economic status and underlying health conditions in contiguous USA.
Akinwumiju AS, Oluwafemi O, Mohammed YD … +1 more , Mobolaji JW
Appl Geogr · 2022 Apr · PMID 35261415 · Full text
Since its outbreak, COVID-19 disease has claimed over one hundred thousand lives in the United States, resulting to multiple and complex nation-wide challenges. In this study, we employ global and local regression models...
Since its outbreak, COVID-19 disease has claimed over one hundred thousand lives in the United States, resulting to multiple and complex nation-wide challenges. In this study, we employ global and local regression models to assess the influence of socio-economic and health conditions on COVID-19 mortality in contiguous USA. For a start, stepwise and exploratory regression models were employed to isolate the main explanatory variables for COVID-19 mortality from the ensemble 33 socio-economic and health parameters between January 1st and 16th of September 2020. Preliminary results showed that only five out of the examined variables (case fatality rate, vulnerable population, poverty, percentage of adults that report no leisure-time physical activity, and percentage of the population with access to places for physical activity) can explain the variability of COVID-19 mortality across the Counties of contiguous USA within the study period. Consequently, we employ three global and two local regression algorithms to model the relationship between COVID-19 and the isolated socio-economic and health variables. The outcomes of the regression analyses show that the adopted models can explain 61%-81% of COVID-19 mortality across the contiguous USA within the study period. However, MGWR yielded the highest R (0.81) and lowest AICc values (4031), emphasizing that it is the most efficient among the adopted regression models. The computed average adjusted R values show that local regression models (mean adj. R = 0.80) outperformed the global regression models (mean adj. R = 0.64), indicating that the former is ideal for modeling spatial causal relationships. The GIS-based optimized cluster analyses results show that hotspots for COVID-19 mortality as well as socioeconomic variables are mostly delineated in the South, Mid-West and Northeast of contiguous USA. COVID-19 mortality exhibited positive and significant association with black race (0.51), minority (0.48) and poverty (0.34). Whereas, the percentage of persons that attended college was negatively associated with poverty (-0.51), obesity (-0.50) and diabetes (-0.45). Results show that education is crucial to improve socio-economic and health conditions of the Americans. We conclude that investing in people's standard of living would reduce the vulnerability of an entire population.
Spatial variability of COVID-19 and its risk factors in Nigeria: A spatial regression method.
Bayode T, Popoola A, Akogun O … +3 more , Siegmund A, Magidimisha-Chipungu H, Ipingbemi O
Appl Geogr · 2022 Jan · PMID 34880507 · Full text
The novel and unprecedented Coronavirus disease (COVID-19) pandemic has negatively impacted most nations of the world within a short period. While its disproportionate social and spatial variability has been established,...
The novel and unprecedented Coronavirus disease (COVID-19) pandemic has negatively impacted most nations of the world within a short period. While its disproportionate social and spatial variability has been established, the reality in Nigeria is yet to be studied. In this paper, advanced spatial statistical techniques were engaged to study the burden of COVID-19 and its risk factors within the first quarter (March-May) of its incidence in Nigeria. The spatial autocorrelation (Moran's I) test reveals a significant but marginal cluster of COVID-19 occurrence in Nigeria ( = 0.11, p < 0.05). A model comparison between ordinary least square (OLS) and spatial error model (SER) was explored having checked for multicollinearity in the dataset. The OLS model explained about 64% (adjusted R = 0.64) of variation in COVID-19 cases, however with significantly clustered residuals. The SER model performed better with randomly distributed residuals. The significant predictors were population density, international airport, and literacy ratio. Furthermore, this study addressed the spatial planning implications of the ongoing disease outbreak while it advocates transdisciplinary approach to urban planning practices in Nigeria.
Green infrastructure for coastal flood protection: The longitudinal impacts of green infrastructure patterns on flood damage.
Sohn W, Bae J, Newman G
Appl Geogr · 2021 Oct · PMID 34621098 · Full text
The importance of implementing green infrastructure (GI) for flood protection is supported by multiple substantial cross-sectional analyses. Yet, limited longitudinal research has been conducted which addresses how to ma...
The importance of implementing green infrastructure (GI) for flood protection is supported by multiple substantial cross-sectional analyses. Yet, limited longitudinal research has been conducted which addresses how to maintain and improve the configuration of GI in order to minimize the cost of losses resulting from flooding. Structural damage from devastating storm events has repeatedly imposed substantial financial burdens on local governments in coastal regions. This study longitudinally examines the impacts of changes in GI patterns on flood damage cost in coastal Texas areas. Major flood events in the 36 Texan coastal watershed counties along the Gulf of Mexico were monitored from 2000 to 2017. Along with non-spatially weighted panel data models, we developed an advanced statistical model controlling for spatially correlated errors in flood loss and predicting flood loss with a set of time-series socioeconomic and environmental control variables. The results of the spatial panel data model reveal that long-term maintenance of larger, more irregular, more dispersed, less fragmented, and less connected patterns of GI will help to reduce county-level flood damage costs per capita over time. Most importantly, protecting larger patches within a closer proximity was found to be of the utmost importance for retaining the flood regulation services provided by GI. These findings suggest that planners and natural resource managers should enhance supportive land use policies to preserve existing GI and strategically locate new implementations in order to achieve long-term flood protection.
Mapping the geodemographics of racial, economic, health, and COVID-19 deaths inequalities in the conterminous US.
Grekousis G, Wang R, Liu Y
Appl Geogr · 2021 Oct · PMID 34511662 · Full text
A large number of studies have examined individual-level factors that increase COVID-19 fatalities. However, no research has focused on the geodemographic classification of the most susceptible communities to COVID-19. I...
A large number of studies have examined individual-level factors that increase COVID-19 fatalities. However, no research has focused on the geodemographic classification of the most susceptible communities to COVID-19. In this cross-sectional ecological study, we used local fuzzy geographically-weighted clustering to create the socioeconomic profile of the US counties in relation to COVID-19 death rates. We demonstrate that living in a county which has households with lower income, people with a lack of health insurance, a high African-American percentage, and lower education level, lead to 27.12% higher COVID-19 death rates than the national median, and 72.56% higher compared to the least vulnerable counties. Compared to counties with a high COVID-19 death rate, counties with a low COVID-19 death rate have 44.90% higher annual median household income and nearly double house worth (89.51% more). Results show that the effects of the COVID-19 pandemic are not universal and that the minoritised and impoverished populations suffer more. Our analysis can effectively pinpoint the most vulnerable counties and importantly allows for understanding the socioeconomic context in which tailored interventions can be applied to mitigate COVID-19 deaths.
How will COVID-19 impact Australia's future population? A scenario approach.
Charles-Edwards E, Wilson T, Bernard A … +1 more , Wohland P
Appl Geogr · 2021 Sep · PMID 36536836 · Full text
The impact of COVID-19 has been massive and unprecedented, affecting almost every aspect of our daily lives. This paper attempts to quantify the impact of COVID-19 on the future size, composition and distribution of Aust...
The impact of COVID-19 has been massive and unprecedented, affecting almost every aspect of our daily lives. This paper attempts to quantify the impact of COVID-19 on the future size, composition and distribution of Australia's population by projecting a range of scenarios. Drawing on the academic literature, historical data and informed by expert judgement, four scenarios representing possible future courses of economic and demographic recovery are formulated. Results suggest that Australia's population could be 6 per cent lower by 2040 in a scenario than in the scenario, primarily due to a huge reduction in international migration. Impacts on population ageing will be less severe, leading to a one percentage point increase in the proportion of the population aged 65 and over by 2040. Differential impacts will be felt across Australian States and Territories, with the biggest absolute and relative reductions in growth occurring in the most populous states, Victoria and New South Wales. Given the ongoing nature of the crisis at the time of writing, there remains significant uncertainty surrounding the plausibility of the proposed scenarios. Ongoing monitoring of the demographic impacts of COVID-19 are important to ensure appropriate planning and recovery in the years ahead.
Why the Navajo Nation was hit so hard by coronavirus: Understanding the disproportionate impact of the COVID-19 pandemic.
Wang H
Appl Geogr · 2021 Sep · PMID 36536835 · Full text
The COVID-19 (SARS-CoV-2) pandemic of 2019-2020 has incurred astonishing social and economic costs in the United States (US) and worldwide. Native American reservations, representing a unique geography, have been hit muc...
The COVID-19 (SARS-CoV-2) pandemic of 2019-2020 has incurred astonishing social and economic costs in the United States (US) and worldwide. Native American reservations, representing a unique geography, have been hit much harder than other parts of the country. This study seeks to understand the reasons for the disproportionate impact of the pandemic on Native American communities by focusing on the Navajo Nation - the largest Native American reservation in the US. I first reviewed the historical pandemics experienced by Native Americans. Guided by the literature review, an institutional analysis focusing on the Navajo Nation suggests a lack of both institutional resilience and healthcare preparation. The analysis further identified four factors that could help explain the Navajo's slow response to the COVID-19 pandemic: prevalence of underlying chronic health conditions, lack of institutional resilience, the relationship between the federal government and tribal governments, and lack of social trust. Relevant policy implications are discussed. For instance, to better prepare Native American communities for shocking events like the COVID-19 pandemic in the future, policymaking should integrate informal institutions to build efficient formal institutions for self-governance. Promoting public health education and establishing collaborations between Native and non-Native communities are also necessary long-run strategies.
Abrupt changes, institutional reactions, and adaptive behaviors: An exploratory study of COVID-19 and related events' impacts on Hong Kong's metro riders.
Zhou J, Wu J, Ma H
Appl Geogr · 2021 Sep · PMID 36536834 · Full text
Abrupt socioeconomic changes have become increasingly commonplace. In face of these, both institutions and individuals must adapt. Against the backdrop of the COVID-19 pandemic, suddenness, scale, and impacts of which ar...
Abrupt socioeconomic changes have become increasingly commonplace. In face of these, both institutions and individuals must adapt. Against the backdrop of the COVID-19 pandemic, suddenness, scale, and impacts of which are unprecedented as compared to its counterparts in history, we first propose transferable measures and methods that can be used to quantify and geovisualize COVID-19 and subsequent events' impacts on metro riders' travel behaviors. Then we operationalize and implement those measures and methods with empirical data from Hong Kong, a metropolis heavily reliant on transit/metro services. We map out where those impacts were the largest and explores its correlates. We exploit the best publicly available data to assemble probable explanatory variables and to examine quantitatively whether those variables are correlated to the impacts and if so, to what degree. We find that both macro- and meso-level external/internal events following the COVID-19 outbreak significantly influenced of metro riders' behaviors. The numbers of public rental housing residents, public and medical facilities, students' school locations, residents' occupation, and household income significantly predict the impacts. Also, the impacts differ across social groups and locales with different built-environment attributes. This means that to effectively manage those impacts, locale- and group-sensitive interventions are warranted.
COVID-19 exacerbates unequal food access.
Kar A, Motoyama Y, Carrel AL … +2 more , Miller HJ, Le HTK
Appl Geogr · 2021 Sep · PMID 36536833 · Full text
Inequality to food access has always been a serious problem, yet it became even more critical during the COVID-19 pandemic, which exacerbated social inequality and reshaped essential travel. This study provides a holisti...
Inequality to food access has always been a serious problem, yet it became even more critical during the COVID-19 pandemic, which exacerbated social inequality and reshaped essential travel. This study provides a holistic view of spatio-temporal changes in food access based on observed travel data for all grocery shopping trips in Columbus, Ohio, during and after the state-wide stay-at-home period. We estimated the decline and recovery patterns of store visits during the pandemic to identify the key socio-economic and built environment determinants of food shopping patterns. The results show a disparity: during the lockdown, store visits to dollar stores declined the least, while visits to big-box stores declined the most and recovered the fastest. Visits to stores in low-income areas experienced smaller changes even during the lockdown period. A higher percentage of low-income customers was associated with lower store visits during the lockdown period. Furthermore, stores with a higher percentage of white customers declined the least and recovered faster during the reopening phase. Our study improves the understanding of the impact of the COVID-19 crisis on food access disparities and business performance. It highlights the role of COVID-19 and similar disruptions on exposing underlying social problems in the US.
The city turned off: Urban dynamics during the COVID-19 pandemic based on mobile phone data.
Romanillos G, García-Palomares JC, Moya-Gómez B … +5 more , Gutiérrez J, Torres J, López M, Cantú-Ros OG, Herranz R
Appl Geogr · 2021 Sep · PMID 36536832 · Full text
Due to the rapid expansion of the COVID-19 pandemic, many countries ordained lockdowns, establishing different restrictions on people's mobility. Exploring to what extent these measures have been effective is critical in...
Due to the rapid expansion of the COVID-19 pandemic, many countries ordained lockdowns, establishing different restrictions on people's mobility. Exploring to what extent these measures have been effective is critical in order to better respond to similar future scenarios. This article uses anonymous mobile phone data to study the impact of the Spanish lockdown on the daily dynamics of the Madrid metropolitan area (Spain). The analysis has been carried out for a reference week prior to the lockdown and during several weeks of the lockdown in which different restrictions were in place. During these weeks, population distribution is compared during the day and at night and presence profiles are obtained throughout the day for each type of land use. In addition, a spatial multiple regression analysis is carried out to determine the impact of the different land uses on the local population. The results in the reference week, pre-COVID-19, show how the population in activity areas increases in each time slot on a specific day and how in residential areas it decreases. However, during the lockdown, activity areas cease to attract population during the day and the residential areas therefore no longer show a decrease. Only basic essential commercial activities, or others that require the presence of workers (industrial or logistics) maintain some activity during lockdown.
How health capabilities and government restrictions affect the COVID-19 pandemic: Cross-country differences in Europe.
Dzúrová D, Květoň V
Appl Geogr · 2021 Oct · PMID 34456395 · Full text
The COVID-19 pandemic in the first months of 2020 posed an unprecedented threat to the health of the world's population. In this longitudinal design study, we elaborated the typology of 27 European countries based on the...
The COVID-19 pandemic in the first months of 2020 posed an unprecedented threat to the health of the world's population. In this longitudinal design study, we elaborated the typology of 27 European countries based on the complete beginnings of the ongoing COVID-19 pandemic based on health indicators and contextual variables. Two-step analysis using factor scores to run a cluster analysis identifying 5 consistent groups of countries. We then analyze the relationship between the GHS predictive index, the restrictions and health care expenditures within countries categorized into 5 clusters. An analysis of the early stages of a pandemic confirmed that in countries where anti-pandemic measures were rapidly and consistently in place, the spread of the virus was suppressed more rapidly and the first wave of pandemics in these countries was incomparably more benign than in countries with later responses and milder restrictive measures.
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