ABSTRACT
As the novel coronavirus disease (COVID-19) has been rapidly spreading across the world, scholars have started paying attention to risk factors that affect the occurrence of the infectious disease. While various urban characteristics have been shown to influence the outbreak, less is known about whether COVID-19 is more likely to be transmitted in areas with a greater number of incidents of previous infectious diseases. This study examines a spatial relationship between COVID-19 and previous infectious diseases from a spatial perspective. Using the confirmed cases of COVID-19 and other types of infectious diseases across South Korea, we identified spatial clusters through regression and spatial econometric models. We found that COVID-19-confirmed case rates tended to be clustered despite no similarity with the spatial patterns of previous infectious diseases. Existing infectious diseases from abroad were associated with the occurrence of COVID-19, while the effect diminished after controlling for the spatial effect. Our findings highlight the importance of regional-level infectious disease surveillance for the effective prevention and control of COVID-19.
ABSTRACT
COVID-19, the novel coronavirus that has disrupted lives around the world, continues to challenge how humans interact in public and shared environments. Repopulating the micro-spatial setting of an office building, with virus spread and transmission mitigation measures, is critical for a return to normalcy. Advice from public health experts, such as maintaining physical distancing from others and well-ventilated spaces, are essential, yet there is a lack of sound guidance on configuring office usage that allows for a safe return of workers. This paper highlights the potential for decision-making and planning insights through location analytics, particularly within an office setting. Proposed is a spatial analytic framework addressing the need for physical distancing and limiting worker interaction, supported by geographic information systems, network science, and spatial optimization. The developed modeling approach addresses dispersion of assigned office spaces as well as associated movement within the office environment. This can be used to support the design and utilization of offices in a manner that minimizes the risk of COVID-19 transmission. Our proposed model produces two main findings: (1) that the consideration of minimizing potential interaction as an objective has implications for the safety of work environments, and (2) that current social distancing measures may be inadequate within office settings. Our results show that leveraging exploratory spatial data analyses through the integration of geographic information systems, network science, and spatial optimization, enables the identification of workspace allocation alternatives in support of office repopulation efforts. © 2022 held by the owner/author(s).
ABSTRACT
COVID-19, the novel coronavirus that has disrupted lives around the world, continues to challenge how humans interact in public and shared environments. Repopulating the micro-spatial setting of an office building, with virus spread and transmission mitigation measures, is critical for a return to normalcy. Advice from public health experts, such as maintaining physical distancing from others and well-ventilated spaces, are essential, yet there is a lack of sound guidance on configuring office usage that allows for a safe return of workers. This paper highlights the potential for decision-making and planning insights through location analytics, particularly within an office setting. Proposed is a spatial analytic framework addressing the need for physical distancing and limiting worker interaction, supported by geographic information systems, network science, and spatial optimization. The developed modeling approach addresses dispersion of assigned office spaces as well as associated movement within the office environment. This can be used to support the design and utilization of offices in a manner that minimizes the risk of COVID-19 transmission. Our proposed model produces two main findings: (1) that the consideration of minimizing potential interaction as an objective has implications for the safety of work environments, and (2) that current social distancing measures may be inadequate within office settings. Our results show that leveraging exploratory spatial data analyses through the integration of geographic information systems, network science, and spatial optimization, enables the identification of workspace allocation alternatives in support of office repopulation efforts.
ABSTRACT
Overdispersed count data arise commonly in disease mapping and infectious disease studies. Typically, the level of overdispersion is assumed to be constant over time and space. In some applications, however, this assumption is violated, and in such cases, it is necessary to model the dispersion as a function of time and space in order to obtain valid inferences. Motivated by a study examining spatiotemporal patterns in COVID-19 incidence, we develop a Bayesian negative binomial model that accounts for heterogeneity in both the incidence rate and degree of overdispersion. To fully capture the heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects in both the mean and dispersion components of the model. The random effects are assigned bivariate intrinsic conditionally autoregressive priors that promote spatial smoothing and permit the model components to borrow information, which is appealing when the mean and dispersion are spatially correlated. Through simulation studies, we show that ignoring heterogeneity in the dispersion can lead to biased and imprecise estimates. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis-Hastings steps. We apply the model to a study of COVID-19 incidence in the state of Georgia, USA from March 15 to December 31, 2020.
ABSTRACT
The convergence of sports and tourism industries is a vital direction for the coordinated development of industries, and a vital means to build a quality life circle suitable for living, working and traveling in the urban agglomeration of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). By using the coupling coordination degree model and exploratory spatial data analysis and establishing an evaluating indicator system for the converged development of sports and tourism industries, this paper measures and analyzes the convergence development level, spatial-temporal evolution, and spatial correlation of the two industries in 11 cities of GBA from 2011 to 2020. The results showed that the synthetical development level of the two industries in urban agglomeration of GBA was steadily rising, with significant differences in regional development, showing the east coast of GBA > the north coast of GBA > the west coast of GBA. The growth trend of industrial convergence degree is obvious, but the overall coupling coordination degree is not high, basically in the late maladjustment stage and transition stage. The convergence of the two industries shows a positive aggregation distribution in space, and the degree of agglomeration is rising. Cities around the Pearl River Estuary mostly belong to the “high-high” spatial association type, with obvious spillover effect, and become a significant growth pole for the converged development of the two industries in GBA. Cities in the periphery of GBA and the west coast of GBA mostly belong to the “low-low” and “low-high” spatial association types. Finally, sustainable development strategies are put forward from four aspects: spatial layout coordination, industrial division coordination, exchange platform coordination, and regional policy coordination, so as to promote the highly converged and coordinated development of the sports and tourism industries in the urban agglomeration of GBA.
ABSTRACT
Urban vitality analysis is an important means to measure the attractiveness and cohesion of urban development, which is of great significance to urban operation situation awareness, fine governance and planning design. Taking China Singapore Tianjin Eco-city as the research area, based on multisource temporal-spatial data from January 2020 to February 2021 provided by the smart city data aggregation platform, such as the data of urban transportation, catering waste recycling volume, books borrowed and returned, this paper analyzed the urban vitality index from five aspects of society, transportation, commerce, culture-education and tourism. After spatializing data into geographic grids, method EW-TOPSIS (Entropy Weighted Technique for Order Preference by Similarity to an Ideal Solution) has been utilized to calculate the comprehensive vitality index. The results showed that: from temporal perspective, the vitality of each index was significantly different due to the impact of Corona Virus Disease 2019(COVID-I9) and seasons;the comprehensive vitality decreased sharply to the lowest at the beginning of the COVID-19 while it increased rapidly and then stabilized after the weakening of the prevention and control policies;the comprehensive vitality value of Eco-city reached the peak of the whole year during the National Day Golden Week affected by tourism vitality. From spatial perspective, the distribution of Eco-city vitality level is unbalanced, showing a trend of outward diffusion centered on the urban starting area and the main tourist attractions. © 2021 IEEE.
ABSTRACT
The Malaysian government implemented The Movement Control Order (MCO) on 18 March 2020 to control the spread of the COVID-19 outbreak. However, the third wave that started in September 2020 during the Recovery Movement Control Order (RMCO) phase saw a continuous increase in the number of cases. In this study, the exploratory spatial data analysis (ESDA) was used to analyse the existence of COVID-19 spatial clusters. Moran's index was used to map the spatial autocorrelation (cluster) to showcase the spreading patterns of the COVID-19 pandemic in Malaysia. The study results indicated significant changes in the COVID-19 hotspots over time. At the beginning of 2020, the state of Selangor and Sarawak were the first locality to become a significant COVID-19 hotspot. Furthermore, this research showed all affected areas during the study period. Overall, a non-random distribution of COVID-19 occurrences was detected, thus suggesting a positive spatial autocorrelation. Many parties are affected by the COVID-19 pandemic, especially those involved in healthcare provision, financial assistance allocation, and law enforcement. Other sectors such as the economy, education, and religion are also affected. Therefore, the findings from this study will provide useful information to all the related governmental and private agencies, as well as policymakers and researchers. © 2021 Penerbit UTM Press. All rights reserved.
ABSTRACT
BACKGROUND: As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale. METHODS: By sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build environment. A spatial trend analysis and Local Indicators of Spatial Autocorrelation were used to characterise the geography of age-adjusted COVID-19 incidence rates across Germany, followed by iterative modelling using Bayesian Additive Regression Trees (BART) to identify and measure candidate explanatory variables. Partial dependence plots were derived to quantify and contextualise BART model results, followed by the parameterisation of a GAM to assess correlations. RESULTS: A strong south-to-north gradient of COVID-19 incidence was identified, facilitating an empirical classification of the study area into two epidemic subregions. All preliminary and final models indicated that location, densities of the built environment, and socioeconomic variables were important predictors of incidence rates in Germany. The top ten predictor variables' partial dependence exhibited multiple non-linearities in the relationships between key predictor variables and COVID-19 incidence rates. The BART, partial dependence, and GAM results indicate that the strongest predictors of COVID-19 incidence at the county scale were related to community interconnectedness, geographical location, transportation infrastructure, and labour market structure. CONCLUSIONS: The multimethod ESDA approach provided unique insights into spatial and aspatial non-stationarities of COVID-19 incidence in Germany. BART and GAM modelling indicated that geographical configuration, built environment densities, socioeconomic characteristics, and infrastructure all exhibit associations with COVID-19 incidence in Germany when assessed at the county scale. The results suggest that measures to implement social distancing and reduce unnecessary travel may be important methods for reducing contagion, and the authors call for further research to investigate the observed associations to inform prevention and control policy.