ABSTRACT
The year 2020 has marked the spread of a global pandemic, COVID-19, challenging many aspects of our daily lives. Different organizations have been involved in controlling this outbreak. The social distancing intervention is deemed to be the most effective policy in reducing face-to-face contact and slowing down the rate of infections. Stay-at-home and shelter-in-place orders have been implemented in different states and cities, affecting daily traffic patterns. Social distancing interventions and fear of the disease resulted in a traffic decline in cities and counties. However, after stay-at-home orders ended and some public places reopened, traffic gradually started to revert to pre-pandemic levels. It can be shown that counties have diverse patterns in the decline and recovery phases. This study analyzes county-level mobility change after the pandemic, explores the contributing factors, and identifies possible spatial heterogeneity. To this end, 95 counties in Tennessee have been selected as the study area to perform geographically weighted regressions (GWR) models. The results show that density on non-freeway roads, median household income, percent of unemployment, population density, percent of people over age 65, percent of people under age 18, percent of work from home, and mean time to work are significantly correlated with vehicle miles traveled change magnitude in both decline and recovery phases. Also, the GWR estimation captures the spatial heterogeneity and local variation in coefficients among counties. Finally, the results imply that the recovery phase could be estimated depending on the identified spatial attributes. The proposed model can help agencies and researchers estimate and manage decline and recovery based on spatial factors in similar events in the future.
ABSTRACT
Efficient contact tracing is a crucial step in preventing the spread of COVID-19. However, the current methods rely heavily on manual investigation and truthful reporting by high-risk individuals. Mobile applications and Bluetooth-based contact tracing methods have also been adopted, but privacy concerns and reliance on personal data have limited their effectiveness. To address these challenges, in this paper, a geospatial big data method that combines person reidentification and geospatial information for contact tracing is proposed. The proposed real-time person reidentification model can identify individuals across multiple surveillance cameras, and the surveillance data is fused with geographic information and mapped onto a 3D geospatial model to track movement trajectories. After real-world verification, the proposed method achieves a first accuracy rate of 91.56%, a first-five accuracy rate of 97.70%, and a mean average precision of 78.03% with an inference speed of 13 ms per image. Importantly, the proposed method does not rely on personal information, mobile phones, or wearable devices, avoiding the limitations of existing contact tracing schemes and providing significant implications for public health in the post-COVID-19 era.
ABSTRACT
The study is aimed at finding a universal methodology for using geospatial data for the purpose of tourism statistics and at analyzing the characteristics of tourism and the touristic trips in Russian territories (Krasnodar region as an example) using data from mobile operators. The authors analyzed aggregated and depersonalized data of mobile operators, generated from cellular networks operation data. Data on cellular subscribers were adjusted for the market shares of mobile operators to obtain indicators that characterize the aggregate number of tourists and excursionists. The analysis allowed the authors, firstly, to offer methodological approaches for working with geospatial data and, secondly, to reveal that in 2019-2020 the coronavirus pandemic did not have a significant negative impact on the number of tourists and excursionists which is most likely explained by the switching of citizens from foreign trips to domestic travel. Any impact of the pandemic on the tourist's portrait was also not revealed. At the same time, geospatial data made it possible to highlight the preferences of Russian tourists in the Krasnodar region to stay in rented housing or live with relatives, friends or in their own housing instead of hotels and inns. During the pandemic these preferences were more explicit, which can be explained by some requirements or recommendations in collective accommodations that may not exist in the private sector. In general, geospatial data allow for a more complete and detailed accounting of tourist and excursion flows in a territory for initiation of support measures for transport infrastructure, tourism development within the framework of state and departmental programs, targeted event planning and creation of related services as part of the tourism strategic development and territory planning. The article also presents discussion about possible modifications of the research methodology for the analysis of, firstly, the types of tourist trips;secondly, the contribution of tourism and particular events to the economy of the territory.
ABSTRACT
The emergence of the novel corona virus disease (COVID-19) since 2019 has been a cause of significant concern for people throughout the world. While tremendous effort has been put in to it by healthcare facilities, both public and private, it would not be a stretch to state that the resources allotted were not enough to handle the floods of covid and the non-covid patients at the same time. As the entire world was under lockdown, it was considerably tougher for people to move around. This meant getting check-ups for covid was fairly tough. Thus, building up many hospital camps around a city became important. In this article, the locations of different healthcare institutions and residential flats in and around the city of Bhubaneswar were analysed. Clusters were generated out of highly dense regions utilising a number of unsupervised learning density based clustering techniques and the best model was picked among them. Folium leaflet maps in Python were used to show the clusters created from the best performing clustering method. This would allow us to collect crucial information identifying areas in severe need of medical attention. Thus, resources can be divided evenly among the population with the information acquired. © 2022 ACM.
ABSTRACT
In the midst of the 21st century, the world is threatened by an emerging infection called Coronavirus, which the World Health Organization (WHO) has dubbed COVID-19. At the end of January 2021, the WHO Emergency Committee declared the COVID-19 outbreak a "Public Health Emergency of International Concern". This study analyzed environmental factors affecting respiratory diseases, especially the relationship with COVID-19, using geospatial information. The study area comprises the metropolitan region of Santiago. The geospatial data used as a cartographic base for the development of the maps are extracted from the Library of the National Congress of Chile/BCN. © 2021 by the Author(s).
ABSTRACT
The abundance of available information on social networks can provide invaluable insights into people’s responses to health information and public health guidance concerning COVID-19. This study examines tweeting patterns and public engagement on Twitter, as forms of social networks, related to public health messaging in two U.S. states (Washington and Louisiana) during the early stage of the pandemic. We analyze more than 7M tweets and 571K COVID-19-related tweets posted by users in the two states over the first 25 days of the pandemic in the U.S. (Feb. 23, 2020, to Mar. 18, 2020). We also qualitatively code and examine 460 tweets posted by selected governmental official accounts during the same period for public engagement analysis. We use various methods for analyzing the data, including statistical analysis, sentiment analysis, and word usage metrics, to find inter-and intra-state disparities of tweeting patterns and public engagement with health messaging. Our findings reveal that users inWashington were more active on Twitter than users in Louisiana in terms of the total number and density of COVID-19-related tweets during the early stage of the pandemic. Our correlation analysis results for counties or parishes show that the Twitter activities (tweet density, COVID-19 tweet density, and user density) were positively correlated with population density in both states at the 0.01 level of significance. Our sentiment analysis results demonstrate that the average daily sentiment scores of all and COVID-19-related tweets inWashington were consistently higher than those in Louisiana during this period. While the daily average sentiment scores of COVID-19-related tweets were in the negative range, the scores of all tweets were in the positive range in both states. Lastly, our analysis of governmental Twitter accounts found that these accounts’messages were most commonly meant to spread information about the pandemic, but that users were most likely to engage with tweets that requested readers take action, such as hand washing. Author
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Algorithms for home location inference from mobile phone data are frequently used to make high-stakes policy decisions, particularly when traditional sources of location data are unreliable or out of date. This paper documents analysis we performed in support of the government of Togo during the COVID-19 pandemic, using location information from mobile phone data to direct emergency humanitarian aid to individuals in specific geographic regions. This analysis, based on mobile phone records from millions of Togolese subscribers, highlights three main results. First, we show that a simple algorithm based on call frequencies performs reasonably well in identifying home locations, and may be suitable in contexts where machine learning methods are not feasible. Second, when machine learning algorithms can be trained with reliable and representative data, we find that they generally out-perform simpler frequency-based approaches. Third, we document considerable heterogeneity in the accuracy of home location inference algorithms across population subgroups, and discuss strategies to ensure that vulnerable mobile phone subscribers are not disadvantaged by home location inference algorithms. © 2022 Owner/Author.
ABSTRACT
The outbreak of the 2019 novel coronavirus disease (COVID-19) has infected 4 million people worldwide and has caused more than 300, 000 deaths worldwide. With infection and death rates on rise, COVID-19 poses a serious threat to social functioning, human health, economies, and geopolitics. Geographic information systems and big geospatial technologies have come to the forefront in this fight against COVID-19 by playing an important role by integrating multisourced data, enhanced and rapid analytics of mapping services, location analytics, and spatial tracking of confirmed, forecasting transmission trajectories, spatial clustering of risk on epidemiologic levels, public awareness on the elimination of panic spread and decision-making support for the government and research institutions for effective prevention and control of COVID-19 cases. Big geospatial data has turned itself as the major support system for governments in dealing with this global healthcare crisis because of its advanced and innovative technological capabilities from preparation of data to modeling the results with quick and large accessibility to every spatial scale. This robust data-driven system using the accurate and prediction geoanalysis is being widely used by governments and public health institutions interfaced with both health and nonhealth digital data repositories for mining the individual and regional datasets for breaking the transmission chain. Profiling of confirmed cases on the basis of location and temporality and then visualizing them effectively coupled with behavioral and critical geographic variables such as mobility patterns, demographic data, and population density enhance the predictive analytics of big geospatial data. With the intersection of artificial intelligence, geospatial data enables real-time visualization and syndromic surveillance of epidemic data based on spatiotemporal dynamics and the data are then accurately geopositioned. This chapter aims to reflect on the relevance of big geospatial data and health geoinformatics in containing and preventing the further spread of COVID-19 and how countries and research organizations around the world have used it as accurate, fast, and comprehensive dataset in their containing strategy and management of this public health crisis. China and Taiwan are used as case studies as in how these countries have applied the computational architecture of big geospatial data and location analytics surveillance techniques for prediction and monitoring of COVID-19-positive cases. © 2022 Elsevier Inc.
ABSTRACT
Agricultural sustainability has important value for boosting regional growth. In recent years, the unprecedented expansion of rice–crayfish field (RCF) in the rural areas of mid-China has raised great concerns in terms of its spatiotemporal dynamics and socioeconomic impact. With Jianli City in mid-China as a case, this study aimed to (1) comprehensively investigate the land-use change in RCF with combined remote sensing and geospatial data analysis, (2) delineate the variations of RCF and socioeconomic benefits from 2010 to 2019 and (3) explore the influencing factors and driving mechanism by using a multiscale geographically weighted regression model. Results illustrated that the RCF development in Jianli City showed an overall uptrend between 2000 and 2019. The area of RCF in 2019 expanded by 599.95% from 2015 levels (from 10,350 ha to 72,445 ha). These extensively expanded RCFs were mainly converted from paddy fields and are distributed around the water area. In terms of socioeconomic benefits, the economic income of villagers increased, whilst the number of out-migrant workers decreased. RCF development effectively contributed to regional economic growth and reduced rural depopulation, thereby facilitating rural transformation from traditional agricultural to characteristic agriculture. The findings clearly showed the spatiotemporal dynamics of RCF and its positive impact on the socioeconomic development of rural areas, thus providing evidence for formulating targeted rural revitalisation policies to achieve rural sustainability. • The spatiotemporal dynamics of rice–crayfish field (RCF) in mid-China is explored. • The positive relationship between RCF and socioeconomic is illustrated. • Multiscale geographically weighted regression uncovers the scale effect and spatial heterogeneity of influencing factors. • RCF injects vitality into sustainable agriculture and rural revitalisation. [ FROM AUTHOR];Copyright of Applied Geography is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
The world was shocked by an unprecedented outbreak caused by coronavirus disease 2019 (COVID-19). In Malaysia, it started with the largest number of COVID-19 cases with the first wave of infection on 25 January 2020. The objectives of this paper are to obtain the perspective of the respondents about the need for web-mapping in the form of mapping the geospatial data in Malaysia and to visualize the current online datasets of COVID-19 disease case clusters. The study area would cover the entire Malaysia since a rapidly increasing number of citizens were affected by this virus. To be specific, this study focused on the active clusters of COVID-19 in Malaysia. The data were freely shared in real-time by referring to the Ministry of Health (MOH) channel. The hotspots map were explored using the Map Editor by Cloud GIS. The approach has been illustrated using a dataset of whole Malaysia which are locally transmitted confirmed cases in four phases of COVID-19 wave in Malaysia. This study is significant to raise public awareness of the virus, especially among Malaysian citizens. It can provide an accurate estimation of the cluster tracking of the COVID-19 system by using geospatial technology. Therefore, people are more concerned and followed all the Standard Operating Procedure (SOP) provided by the government to prevent the spread of COVID-19. © 2021, Russian Geographical Society. All rights reserved.
ABSTRACT
Background: The outbreak of coronavirus 2019 (COVID-19) which emerged in December 2019 spread rapidly and created a public health emergency. Geospatial records of case data are needed in real time to monitor and anticipate the spread of infection. Methods: This study aimed to identify the emerging hotspots of COVID-19 using a geographic information system (GIS)-based approach. Data of laboratory-confirmed COVID-19 patients from March 15 to June 12, 2020, who visited the emergency department of a tertiary specialized academic hospital in Dubai were evaluated using ArcGIS Pro 2.5. Spatiotemporal analysis, including optimized hotspot analysis, was performed at the community level. Results: The cases were spatially concentrated mostly over the inner city of Dubai. Moreover, the optimized hotspot analysis showed statistically significant hotspots (p < 0.01) in the north of Dubai. Waxing and waning hotspots were also observed in the southern and central regions of Dubai. Finally, there were nonsustaining hotspots in communities with a very low population density. Conclusion: This study identified hotspots of COVID-19 using geospatial analysis. It is simple and can be easily reproduced to identify disease outbreaks. In the future, more attention is needed in creating a wider geodatabase and identifying hotspots with more intense transmission intensity.
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To examine HIV service interruptions during the COIVD-19 outbreak in South Carolina (SC) and identify geospatial and socioeconomic correlates of such interruptions, we collected qualitative, geospatial, and quantitative data from 27 Ryan White HIV clinics in SC in March, 2020. HIV service interruptions were categorized (none, minimal, partial, and complete interruption) and analyzed for geospatial heterogeneity. Nearly 56% of the HIV clinics were partially interrupted and 26% were completely closed. Geospatial heterogeneity of service interruption existed but did not exactly overlap with the geospatial pattern of COVID-19 outbreak. The percentage of uninsured in the service catchment areas was significantly correlated with HIV service interruption (F = 3.987, P = .02). This mixed-method study demonstrated the disparity of HIV service interruptions in the COVID-19 in SC and suggested a contribution of existing socioeconomic gaps to this disparity. These findings may inform the resources allocation and future strategies to respond to public health emergencies.