ABSTRACT
Food deserts (FD) have attracted attention after the post-COVID-19 pandemic, primarily due to adverse health and other implications of living in areas designated as food deserts. Most studies have focused on various aspects of the impact of food deserts, including the nutritional and health risks of living in FDs. Spatial integration and analysis of the GIS data in food provide a powerful way to expose the issues of creating deserts and how they change over space and time. This study aims to investigate the socioeconomic factors influencing food deserts using geospatial analyses. Guilford, Bladen, and Rutherford Counties in North Carolina were selected as case studies due to their higher percentage of the population with limited healthy food access. This study used open-source data, such as the USDA's Crop Land Layer (CDL) land cover maps, census data, and the Food Access Research Atlas. This research provides a geostatistical analysis of FDs based on income/expenditure, population, vehicle, and food aid. The study results generally showed that geospatial technologies are vital for investigating FDs. The results will assist policy makers and other responsible agencies in formulating appropriate intervention policies tailored to meet the demands of these counties.
ABSTRACT
COVID-19 is caused by the novel coronavirus SARS-CoV-2. First started in Wuhan, COVID-19 has spread everywhere, including Indonesia. COVID-19 can cause severe pneumonia, severe acute respiratory distress syndrome (ARDS) symptoms, and multiple organ failure. According to the WHO, COVID-19 generally has an incubation period of 5-6 days, ranging from 1 to 14 days. However, in Jakarta, the cases have decreased significantly since the implementation of PPKM (Restrictions of Activity), running since early July 2021. The government claimed that the PPKM rule has significantly impacted COVID-19 cases, decreasing every day, especially in Jawa-Bali Region. In addition, the Vaccination rate in Indonesia also played a significant part in decreasing COVID-19 cases, with Jakarta currently standing with 9 million people fully vaccinated per December 2021. To monitor the development of COVID-19 in Jakarta and provide information to the public about health facilities, especially hospitals in Jakarta, in this study, the distribution area of COVID-19 cases will be mapped with CHIME using ArcGIS Online tools. The analysis results obtained based on the mapping results that most cases were in the Cengkareng area, and the area with the most hospitals werein East Jakarta. © 2022 IEEE.
ABSTRACT
Traditional villages are a valuable cultural asset that occupy an important position in Chinese traditional culture. This study focuses on 206 traditional villages in Hebei Province and aims to explore their spatial distribution characteristics and influencing factors using ArcGIS spatial analysis. The analysis shows that traditional villages in Hebei Province were distributed in clusters during different historical periods, and eventually formed three core clusters in Shijiazhuang, Zhangjiakou and Xingtai-Handan after different historical periods. Moreover, the overall distribution of traditional villages in Hebei Province is very uneven, with clear regional differences, and most of them are concentrated in the eastern foothills of the Taihang Mountains. To identify the factors influencing traditional villages, natural environmental factors, socio-economic factors, and historical and cultural factors are considered. The study finds that socio-economic and natural environmental factors alternate in the spatial distribution of traditional villages in Hebei Province. The influence of the interaction of these factors increases significantly, and socio-economic factors have a stronger influence on the spatial distribution. Specifically, the spatial distribution of traditional villages in Hebei Province is influenced by natural environmental factors, while socio-economic factors act as drivers of spatial distribution. Historical and cultural factors act as catalysts of spatial distribution, and policy directions are external forces of spatial distribution. Overall, this study provides valuable insights into the spatial distribution characteristics and influencing factors of traditional villages in Hebei Province, which can be used to develop effective strategies for rural revitalisation in China.
ABSTRACT
Due to improper management, industrialization and urbanization resulted in poorer surface and river water quality flowing through the city. Still, complete lockdown in the country resulted in improved surface water quality. Hence, a study has been performed to analyze these changes held during COVID-19 lockdown using a combination of different parameters derived from spatial data. The study includes analyses of significant water bodies, surface water bodies through out the city;the survey has proven that the lockdown situation that occurred due to the pandemic has resulted in improved water quality which has been determined based on water bodies analysis done for 12 major water bodies, and by the study performed it has been observed that the area of the nonturbid water has increased by 0.148 sq. km after the lockdown situation occurred. The study will be helpful to assess the impacts of lockdown on water bodies to take the sustainable measures which can be taken shortly for the improved regulation of pollutants and other contaminants based on positive effects on the surface water quality. © 2023 Elsevier Inc. All rights reserved.
ABSTRACT
This edited volume on the biopolitics and shock economy of COVID-19 crisis embraces a wide spectrum of topics such as shock economy, medical perspectives on COVID-19, application of geospatial technology, infectivity, immunity, and severity of the disease, as well as ontology of the disease emergence as important factors for adoption of relevant biopolitical measures, sociocultural obstacles, COVID-19-induced transaction costs, social support and resilience of inhabitants of marginalized areas, as well as business resilience factors, entrepreneurship, and digital transformation. Through each chapter of this book, the authors, with their expertise in the theme they picked, have attempted to unfold some emerging aspects in the COVID-19 crisis which could benefit not only the academics but also the institutional, social, economic, developmental, and health policy-makers as well as the health practitioners on the ground. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
This study explored geospatial technologies currently used by various researchers, industries, health professionals, etc., in the fight against the global pandemic of COVID19. The use of dashboards is among the prominent innovative geospatial mapping technologies implemented by several bodies such as the Johns Hopkins University's Center for Systems Science and Engineering (JHU CSSE) dashboard, WHO dashboard, HealthMap dashboard, etc. Dashboards have been useful in providing information on the dynamics of the pandemic spread. The use of geospatial big data and Web map viewers has also gained much ground. The application of various statistical and epidemiological models and tools has equally been utilized to simulate the dynamics of the pandemic in new dimensions. Time series model forecast has played a major role in the modelling of medical facilities, while location/allocation modelling has contributed to resources management for best sites to situate testing centres, emergency units, medical centres, etc. ESRI industry has also developed a number of innovative solutions in the context of indoor assessment of facilities for the new normal, airport smart strategies and mobile tracking technologies, etc. Geospatial technology is critical in the fight against the pandemic, and it is imperative to state that efforts to maximize the technology should be advocated. The study provided limitations to the applications of geospatial technology and recommendation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
According to the World Health Organization (WHO), the elderly and people with comorbidities are most vulnerable to COVID-19 infection. With this, the challenges and threats posed to the vulnerable population require targeted interventions. While public health surveillance methods had developed recent advances to meet users' information needs, the volume and complexity of infectious disease data had increased, resulting in increasing difficulty to facilitate risk communication with the public and for decision-makers to make informed measures to protect the public's health. Moreover, the implementation of COVID-19 spatiotemporal disease surveillance strategies specifically targeting the vulnerable population in the Davao Region had been previously unexplored. This paper investigated the COVID-19 incidence in the Davao Region from 03 Mar 2020, the earliest recorded date of onset, to 31 Aug 2021 using geospatial tools. The variables were visualized through choropleth maps and graduated symbols, and subsequently examined through spatial autocorrelation and hotspot analysis. Hotspots across the region were observed to be in high-density areas. These areas pose greater risks of infection due to the presence of a high concentration of cases. However, high case fatality rates were found in far-flung municipalities where access to COVID-19 healthcare facilities is a dilemma. In the COVID-19 setting and future disease outbreaks similar to COVID-19, results from this study may provide insights to government offices and other related agencies to improve healthcare systems and programs such as providing and initiating tailor-fitted isolation and consultation mechanisms appropriate to the vulnerable population in a community. [ FROM AUTHOR] Copyright of Philippine Journal of Science is the property of Science & Technology Information Institute 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
Nowadays, spatial geographic data analysis and GIS related software are more and more applied to the planning of urban public facilities. Under the COVID-19, people pay more attention to the protection of medical facilities for people's health, and a reasonable distribution of hospital facilities is conducive to people's health. Taking Haikou City as an example, this research will optimize the location of hospital space layout according to the existing third-level first-class general hospitals in Haikou City by using GIS software, road analysis, spatial analysis, and other methods. The results show that the existing hospitals in Haikou are too concentrated in the central urban area, the overall distribution of medical facilities is lack of balance, and there is a serious lack of medical facilities in new urban development areas and suburbs. According to the comparison between population density analysis and traffic analysis and the service scope of existing hospitals, the author finds out the scope of hospitals that need to be supplemented, and then calculates the scope of service area after taking several random points within the scope, and finally finds the one with the largest service scope is the optimal location. The results obtained by optimizing the site selection can provide a scientific reference for the rational layout of medical facilities in Haikou City in the future. © 2023 SPIE.
ABSTRACT
The present study focuses on the quantitative investigation of economic vulnerability and assesses the perception survey carried out among 75 residents of Katwa Municipality, an up-growing urban area in Purba Barddhaman District, West Bengal. To estimate the socio-economic vulnerability during the lockdown, standardised factor scores have been calculated in the analysis of principal components and GIS-based mapping has been employed also. Among the total working population of the surveyed household, about 20% have changed their occupation, wages have been reduced by 35 and 35% have lost their occupation during the lockdown period. The highly socio-economic vulnerability has been observed in the wards where households and population are also high. The regression coefficient shows that the increasing trend of marginal other workers has the significantly (p < 0.1) highest marginal effect on the socio-economic vulnerability. The Likert scale measuring perception indicated that the majority of the respondents agreed with their socio-economic vulnerabilities caused by lockdown during the pandemic situation. The significant outcome of the study indicates the initiation of diversified income generation opportunities and deliberates its contribution to the formation of location-specific planning for the socio-economic development and integrative management of the study area. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Data collection and large-scale urban audits are challenging and can be time consuming processes. Geographic information systems can extract and combine relevant data that can be used as input to calculation tools that provide results and quantify indicators with sufficient spatial analysis to facilitate the local decision-making process for building renovations and sustainability assessment. This work presents an open-access tool that offers an automated process that can be used to audit an urban area in order to extract relevant information about the characteristics of the built environment, analyze the building characteristics to evaluate energy performance, assess the potential for the installation of photovoltaics on available building rooftops, and quantify ground permeability. A case study is also presented to demonstrate data collection and processing for an urban city block, and the relevant results are elaborated upon. The method is easily replicable and is based on open data and non-commercial tools.
ABSTRACT
The new Acute Respiratory Syndrome, COVID-19, has affected the health and the economy worldwide. Therefore, scientists have been looking for ways to understand this disease. In this context, the main objective of this study was the spatialization of COVID-19, thinking in distinguishing areas with high transmissibility yet, verifying if these areas were associated with the elderly population occurrence. The work was delineated, supposing that spatialization could support the decision-making to combat the outbreak and that the same method could be used for spatialization and prevent other diseases. The study area was a municipality near Sao Paulo Metropolis, one of Brazil's main disease epicenters. Using official data and an empirical Bayesian model, we spatialized people infected by region, including older people, obtaining reasonable adjustment. The results showed a weak correlation between regions infected and older adults. Thus, we define a robust model that can support the definition of actions aiming to control the COVID-19 spread.
ABSTRACT
The global spread of the coronavirus has generated one of the most critical circumstances forcing healthcare systems to deal with it everywhere in the world. The complexity of crisis management, particularly in Iran, the unfamiliarity of the disease, and a lack of expertise, provided the foundation for researchers and implementers to propose innovative solutions. One of the most important obstacles in COVID-19 crisis management is the lack of information and the need for immediate and real-time data on the situation and appropriate solutions. Such complex problems fall into the category of semi-structured problems. In this respect, decision support systems use people's mental resources with computer capabilities to improve the quality of decisions. In synergetic situations, for instance, healthcare domains cooperating with spatial solutions, coming to a decision needs logical reasoning and high-level analysis. Therefore, it is necessary to add rich semantics to different classes of involved data, find their relationships, and conceptualize the knowledge domain. For the COVID-19 case in this study, ontologies allow for querying over such established relationships to find related medical solutions based on description logic. Bringing such capabilities to a spatial decision support system (SDSS) can help with better control of the COVID-19 pandemic. Ontology-based SDSS solution has been developed in this study due to the complexity of information related to coronavirus and its geospatial aspect in the city of Tehran. According to the results, ontology can rationalize different classes and properties about the user's clinical information, various medical centers, and users' priority. Then, based on the user's requests in a web-based SDSS, the system focuses on the inference made, advises the users on choosing the most related medical center, and navigates the user on a map. The ontology's capacity for reasoning, overcoming knowledge gaps, and combining geographic and descriptive criteria to choose a medical center all contributed to promising outcomes and the satisfaction of the sample community of evaluators.
ABSTRACT
The spatiotemporal variation of the death and tested positive cases is poorly understood during the respiratory coronavirus disease 2019 (COVID-19) pandemic. On the other hand, COVID-19's spread was not significantly slowed by pandemic maps. The aim of this study is to investigate the connection between COVID-19 distribution and airborne PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm). Long-term exposure to high levels of PM2.5 is significantly connected to respiratory diseases in addition to being a potential carrier of viruses. Between April 2020 and March 2021, data on COVID-19-related cases were gathered for all prefectures in Japan. There were 9159, 109,078, and 451,913 cases of COVID-19 that resulted in death, severe illness, and positive tests, respectively. Additionally, we gathered information on PM2.5 from 1119 air quality monitoring stations that were deployed across the 47 prefectures. By using the statistical analysis tools in the Geographical Information System (GIS) software, it was found that the residents of prefectures with high PM2.5 concentrations were the most susceptible to COVID-19. Additionally, the World Health Organization-Air Quality Guidelines (WHO-AQG) relative risk (RR) of 1.04 (95% CI: 1.01-1.08), which was used to compute the PM2.5-caused deaths, was employed as well. Approximately 1716 (95% CI: 429-3,432) cases of PM2.5-related deaths were thought to have occurred throughout the study period. Despite the possibility that the actual numbers of both COVID19 and PM2.5-caused deaths are higher, humanitarian actors could use PM2.5 data to localize the efforts to minimize the spread of COVID-19.
Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Relief Work , Humans , COVID-19/epidemiology , Air Pollutants/analysis , Environmental Monitoring/methods , Particulate Matter/analysis , Air Pollution/analysis , Environmental Exposure/analysisABSTRACT
A growing number of various studies focusing on different aspects of the COVID-19 pandemic are emerging as the pandemic continues. Three variables that are most commonly used to describe the course of the COVID-19 pandemic worldwide are the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered. In this paper, using the multiscale geographically weighted regression, an analysis of the interrelationships between the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered were conducted. Furthermore, using maps of the local R2 estimates, it was possible to visualize how the relations between the explanatory variables and the dependent variables vary across the study area. Thus, analysis of the influence of demographic factors described by the age structure and gender breakdown of the population over the course of the COVID-19 pandemic was performed. This allowed the identification of local anomalies in the course of the COVID-19 pandemic. Analyses were carried out for the area of Poland. The results obtained may be useful for local authorities in developing strategies to further counter the pandemic.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , COVID-19 Vaccines , Poland/epidemiology , Pandemics , SARS-CoV-2 , Spatial RegressionABSTRACT
The significance of the COVID-19 pandemic has resulted in the availability of an unprecedented amount of data having become available unlike in any comparable health emergency before. Global situation updates were made available on a daily basis. This provides the unique opportunity to gain a better understanding of the underlying spatial patterns that developed during the spread of the virus. This contribution makes use of these data and provides a geographical overview of the spread of the COVID-19 pandemic in 2020, the first year of the (known) spread of the virus. A main emphasis is put on the utilisation of innovative data visualisation approaches by deploying cartogram techniques as a method to emphasise the underlying quantities of global cases and deaths. The cartographic analysis is accompanied by a critical reflection on the sometimes problematic nature of the data and the patterns that have emerged from it. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
Having broken out in late 2019, COVID-19 has resulted in a once-in-a-century health emergency that has rapidly evolved into a global socio-economic crisis. As of March 2022, more than 450 million people were infected by the SARS-CoV-2 virus, the cause of COVID-19, resulting in more than six million deaths (WHO, Coronavirus disease (COVID-19) situation dashboard, 2022). The medical systems of many countries have been stretched to the verge of collapse and more than half of the global labor force has stood down. Not only has the pandemic doubled the number of people at risk of starvation to 270 million (Nature, 589:329-330, 2021), but it also pushed 100 million people into poverty in 2020, triggering the worst global recession since World War II (Blake and Wadhwa, 2020 year in review: the impact of COVID-19 in 12 charts, 2020), and increasing the risk of exposure to other pandemics related to ecosystem degradation (IPBES, Workshop report on biodiversity and pandemics of the intergovernmental platform on biodiversity and ecosystem services. Retrieved from Bonn, Germany, 2020;Yin et al., Geogr Sustain 2(1):68-73, 2021). The normal functioning of many organizations has also been hampered by the pandemic and disruptions to the global travel and tourism industry have been unprecedented. By way of an example, travel restrictions led to the postponement of the 2020 34th International Geographical Congress to the following year and, ultimately, the decision was made to transition to an entirely online format for the event. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
Nowadays, the leisure time of the inhabitants is increasing, meaning that urban green spaces are becoming more and more valuable, as people visit these places more often and for longer periods to spend their spare time there. In the aftermath of the Covid pandemic, these open spaces are playing an even more important role in social interaction. The features used by different groups of the society (sociotopes), the aesthetics and the atmosphere of green spaces all have an impact on the experiences gained in the area, all these may determine our perception and opinion of the given space. The present research aims to show, through the example of Stockholm's green spaces, the factors responsible for good or negative experiences and whether there is any correlation between these and the urban green space types. Stockholm was the first city to win the European Green Capital Award in 2010, showing its leadership in environmental thinking and being a role model for other European, including Hungarian (Budapest, Pécs) cities that have also applied for the European Green Capital Award but were not shortlisted as finalists. Stockholm can also be described as 'part water, part green belt, part city', as stated in an official document published by the European Commission (2010, p. 21). Stockholm ranks highly among European cities in terms of green space accessibility (Buckland–Pojani 2022), Green City Index score and environmental policy (Akande et al. 2019), as well as in terms of public satisfaction with the quality of life in the city (Okulicz-Kozaryn 2013), therefore may serve as a best practice model for other cities. A categorisation of Stockholm's green spaces has been carried out in this study, using Ibes' (2015) paper as a basis. According to this, six categories can be distinguished by using ten variables. The green space categories identified in the analysis show differences in the number of sociotope functions and differences in the temporal (time of day, season, length of stay), frequency (visits to the area) and economic (cost of services in the area) aspects of these categories. © 2023, Teruleti Statisztika. All Rights Reserved.
ABSTRACT
Global pandemics pose a threat to the sustainable development of urban health. As urban spaces are important places for people to interact, overcrowding in these spaces can increase the risk of disease transmission, which is detrimental to the sustainable development of urban health. Therefore, it is crucial to identify potential epidemic risk areas and assess their risk levels for future epidemic prevention and the sustainable development of urban health. This article takes the main urban area of Harbin as the research object and conducts a cluster spatial analysis from multiple perspectives, including building density, functional density, functional mix, proximity, intermediacy, and thermal intensity, proposing a comprehensive identification method. The study found that (1) functional density is the most significant influencing factor in the formation of epidemic risks. Among various urban functions, commercial and public service functions have the strongest impact on the generation and spread of epidemic risks, and their distribution also has the widest impact range. (2) The spaces with higher levels of epidemic risk in Harbin are mainly distributed in the core urban areas, while the peripheral areas have relatively lower levels of risk, showing a decreasing trend from the center to the periphery. At the same time, the hierarchical distribution of urban space also has an impact on the spatial distribution of the epidemic. (3) The method proposed in this study played an important role in identifying the spatial aggregation of epidemic risks in Harbin and successfully identified the risk levels of epidemic distribution in the city. In spatial terms, it is consistent with high-risk locations of epidemic outbreaks, which proves the effectiveness and feasibility of the proposed method. These research findings are beneficial for measures to promote sustainable urban development, improve the city's epidemic prevention capabilities and public health levels, and make greater contributions to the sustainable development of global public health, promoting global health endeavors.
ABSTRACT
Spatial thinking is an integral skill for geography students to develop. Whilst many spatial competencies have been identified by researchers, and the merits of GIS seemingly ubiquitous in the published literature, little work has been done into how students' spatial thinking skills can be assessed. Therefore, further investigation into the relationship between spatial thinking and performance and attainment is needed. This research investigates the impact using a geographic information system (GIS) has on students' spatial thinking skills and attempts to assess the extent using a framework. This was done through the design and implementation of two GIS-based interventions. This small-scale evaluation used qualitative methods to investigate students' and teachers' views. Student work was also analysed using the framework developed for the assessment of spatial thinking skills. The findings suggest that the use of a GIS does enhance, and in most cases improves students' spatial thinking skills, but, that spatial thinking is hard to quantify and difficult to measure progress in. Another benefit that using a GIS affords is the creation of engaging, contemporary and interactive lessons, using real data, from which students derive a lot of geographical value. [ FROM AUTHOR] Copyright of International Research in Geographical & Environmental Education is the property of Taylor & Francis Ltd 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
Burning of fossil fuels in the form of coal or gasoline in thermal power plants, industries, and automobiles is a prime source of nitrogen dioxide (NO2), a major air pollutant causing health problems. In this paper, spatio-temporal unevenness of NO2 concentrations via both spaceborne Sentinel-5P and ground-based in situ data have been studied for the period of 2017-2021. Annual and seasonal distribution of TROPOMI-NO2 depict consistency over the Jharkhand region, highlighting six hotspot regions. As compared to 2019, a notable dip of 11% in the spatial annual average TROPOMI-NO2 was achieved in 2020, which were elevated again by 22% in 2021 as the lockdown gradually goes out of the picture. Among eight ground-monitoring stations, Tata and Golmuri stations always displayed a higher level of TROPOMI-NO2 ranges up to 15.2 ×1015molecules.cm-2 and 16.9 ×1015molecules.cm-2 respectively, as being located in the highly industrialised district of Jamshedpur. A big percentage reduction of up to 30% in TROPOMI-NO2 has been reported in Jharia and Bastacola stations in Dhanbad in the lockdown phase of 2020 compared to 2019. Good agreement between TROPOMI-NO2 and surface-NO2 has been achieved with R = 0.8 and R = 0.71 during winter and post-monsoon respectively. Among four meteorological parameters, TROPOMI-NO2 was majorly found to be influenced by precipitation, having R = 0.6-0.8 for almost all stations. More advanced satellite algorithms and ground-based data may be used to estimate NO2 in places where monitoring facilities are limited and thus can help in air pollution control policy.