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Based on the spatial-temporal perspective of geography, this paper quantitatively measures the impact of COVID-19 on the spatial-temporal pattern of tourism network attention in Zhangjiajie, and finally summarizes the influencing factors and mechanisms. The results show as follows. (1) From the perspective of time, the online attention of tourism in Zhangjiajie shows a trend of "decline to rebound, and to stability", which reflects the temporal mobility of the effect of COVID-19 on the tourism. (2) From the spatial dimension, the scale-order of attention to the Zhangjiajie' s tourism network is relatively stable, and the effect of COVID-19 on the tourism shows a trend of "distance decay" on the whole. (3) The adjustment of tourists' perception of tourism risk, destination familiarity and location, tourists' risk tolerance and authority restriction are the influencing factors of tourism net-work attention. These factors interact with each other to drive the spatio-temporal change of tourism network attention.
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Water use was impacted significantly by the COVID-19 pandemic. Although previous studies quantitatively investigated the effects of COVID-19 on water use, the relationship between water-use variation and COVID-19 dynamics (i.e., the spatial-temporal characteristics of COVID-19 cases) has received less attention. This study developed a two-step methodology to unravel the impact of COVID-19 pandemic dynamics on water-use variation. First, using a water-use prediction model, the water-use change percentage (WUCP) indicator, which was calculated as the relative difference between modeled and observed water use, i.e., water-use variation, was used to quantify the COVID-19 effects on water use. Second, two indicators, i.e., the number of existing confirmed cases (NECC) and the spatial risk index (SRI), were applied to characterize pandemic dynamics, and the quantitative relationship between WUCP and pandemic dynamics was examined by means of regression analysis. We collected and analyzed 6-year commercial water-use data from smart meters of Zhongshan District in Dalian City, Northeast China. The results indicate that commercial water use decreased significantly, with an average WUCP of 59.4%, 54.4%, and 45.7%during the three pandemic waves, respectively, in Dalian. Regression analysis showed that there was a positive linear relationship between water-use changes (i.e., WUCP) and pandemic dynamics (i.e., NECC and SRI). Both the number of COVID-19 cases and their spatial distribution impacted commercial water use, and the effects were weakened by restriction strategy improvement, and the accumulation of experience and knowledge about COVID-19. This study provides an in-depth understanding of the impact of COVID-19 dynamics on commercial water use. The results can be used to help predict water demand under during future pandemic periods or other types of natural and human-made disturbance.
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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.
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The 2022 (M 6.8) Luding earthquake on the Xianshuihe Fault Zone (XFZ) caused severe casualties and property losses, and surface deformation and damage of which is crucial for studying the earthquake hazard assessment. However, few intensive scientific understanding has obtained to date because of widespread coronavirus transmission, strong vegetation coverage, and post-earthquake paralyzed traffic. By integrating high-resolution satellite images, large-scale geomorphic mapping, and UAV surveys, we constrain coseismic fractures and ruptures along an NW-SE-trending surface deformation zone, with discontinuous geomorphic scarps, en echelon cracks, and bulges concentrated in the areas of Yanzigou, Moxi, Menghugang, and Xingfu villages near the epicenter. Field observation also shows that the zone extends nearly parallel to the pre-existing XFZ with a length of ∼35 km with variable widths and a maximum vertical displacement of ∼100 ± 10 cm. The earthquake-induced surface coseismic effects, such as landslides, rock falls, and collapses, caused damage to the area. The amplification effect of the topography and the improper aseismic design and poor constructions may be responsible for the spatial distribution of MM Intensity IX, which is larger than other previous earthquakes that occurred in the surrounding area with a similar tectonic setting.
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As a susceptible demographic, elderly individuals are more prone to risks during sudden disasters. With the exacerbation of aging, new challenges arise for urban disaster reduction and prevention. To address this, the key is to establish a community-scale resilience assessment framework based on the aging background and to summarize factors that influence the resilience level of communities. This approach is a crucial step towards seeking urban disaster prevention and reduction from the bottom up, and serves as an important link to enhance the capacity of urban disaster reduction. This paper explores community resilience evaluation indicators under the background of aging, builds a community resilience evaluation index system based on the Pressure–State–Response, uses the entropy weight method to weigh the indicators, and carries out a resilience evaluation of 507 communities in the main urban area of Changchun. The empirical results indicate significant spatial differentiation of community resilience in the main urban area of Changchun. Moreover, the regional development is unbalanced, showing a spatial distribution pattern of weakness in the middle and strength in the periphery. The ring road network highlights the difference between the new and old urban areas. The high contribution indexes of community resilience in the main urban area of Changchun were concentrated on disaster relief materials input, community self-rescue ability, and disaster cognition ability. Finally, strategies to improve community resilience are proposed from the perspectives of stress, state, and response, emphasizing community residents' participation, conducting disaster prevention and reduction training, and improving community response-ability.
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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.
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PurposeThis study aims to investigate how the COVID-19 pandemic has impacted and changed Airbnb market in the Greater Melbourne area in terms of its temporal and spatial patterns and identify possible shifts in underlying trends in travel activities.Design/methodology/approachA panel data set of Airbnb listings in Melbourne is analysed to compare temporal patterns, spatial distribution and lengths of stay of Airbnb users before and after the COVID outbreak.FindingsThis study found that the COVID disruption did not fundamentally change the temporal cycle of the Airbnb market. Month-to-month fluctuations peaked at different levels from pre-pandemic times mainly because of lockdowns and other restrictive measures. The impact of COVID-19 disruptions on neighbourhood-level Airbnb revenues is associated with distance to CBD rather than number of COVID cases. Inner city suburbs suffered major loss during the pandemic, whereas outer suburbs gained popularity due to increased domestic travel and long stays. Long stays (28 days or more, as defined by Airbnb) were the fastest growing segment during the pandemic, which indicates the Airbnb market was adapting to increasing demand for purposes like remote working or lifestyle change. After easing of COVID-related restrictions, demand for short-term accommodation quickly recovered, but supply has not shown signs of strong recovery. Spatial distribution of post-pandemic supply recovery shows a similar spatial variation. Neighbourhoods in the inner city have not shown signs of significant recovery, whereas those in the middle and outer rings are either slowly recovering or approaching their pre-COVID levels.Practical implicationsThe COVID-19 pandemic has significantly impacted short-term rental markets and in particular the Airbnb sector during the phase of its rapid development. This paper helps inform in- and post-pandemic housing policy, market opportunity and investment decision.Originality/valueTo the best of the authors' knowledge, this is one of the first attempts to empirically examine both temporal and spatial patterns of the COVID-19 impact on Airbnb market in one of the most severely impacted major cities. It is one of the first attempts to identify shifts in underlying trends in travel based on Airbnb data.
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The Great Confinement affected the labour market, particularly the dynamics of unemployment. Based on Instituto do Emprego e For-macao Profissional (IEFP) data on registered unemployment, this article analyses the impact of the pandemic crisis in unemployment situations in mainland Portugal. The categorical and territorial distribution of the unemployed is highlighted, as well as the temporal dimen-sion of the problem. The quantitative/extensive analysis carried out, as well as the cluster analysis, indicates that the incidence of unemployment is not identical for all social groups or for the entire territory, affecting some more than others, with emphasis on some spatial concentrations, particularly in Algarve;and the variations are directly related to temporali-ties resulting from periods marked by confinement or deconfinement.
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As an important part of the ecosystem, green vegetation coverage is crucial to people's sensory and mental health. Using reliable data sets to classify and identify the green vegetation cover on the land surface and explore its spatial distribution law can provide important reference for the work of regional ecosystem managers and urban planners. The optimization of effective screening methods for green vegetation coverage areas is an important requirement to measure the surface vegetation status. UAV aerial images feature high definition, large scale, small area and high up-to-dateness. However, at present, there are few studies based on the reliable UAV aerial image system to identify green vegetation cover and further explore its spatial changes. In this study, 701 residential neighborhoods in Beijing were taken as the research objects, and the green vegetation of 7,695 sample points was identified by UAV. The green vegetation coverage was measured, and the spatial distribution pattern of green vegetation in different land surface areas was quantitatively compared. The results show that the image processing method proposed in this paper can effectively detect the boundary of green vegetation cover area from UAV aerial images, the correlation of texture segmentation is good, and the segmentation performance is better than other methods. The distribution of green vegetation cover in the research target area is uneven, with 63.79% of the research area having relatively low (Level 2) and medium (Level 3) green vegetation coverage, which indicates that the green vegetation coverage area in the research area is insufficient to meet the needs of regional ecosystem development. The characteristics of green vegetation cover in 16 districts in the study area are different, showing different spatial distribution patterns;except Xicheng District, there are 211 points without landscape in the area covered by green vegetation in 15 districts. The results can provide support for urban land surface planning and management.
ABSTRACT
Governmental attention towards the high-quality development of the Yellow River basin has brought new development opportunities for the hotel industry. This study aims to reveal the spatial-temporal evolution patterns and influencing factors of hotels in the Yellow River Basin from 2012 to 2022, based on economic, social, and physical geographic data of 190,000 hotels in the Yellow River flowing. With the help of a GIS technology system, the spatial-temporal evolution patterns of all hotels, star hotels, and ordinary hotels were explored, respectively. Then, the significant influencing factors of these patterns were revealed by using geographic detector and Person correlation analysis. The following conclusions were drawn: (1) the overall scale of the hotel industry in the Yellow River Basin expanded year by year, achieving rapid growth from 2016, and fluctuating around 2020 due to the impact of the novel coronavirus epidemic;the overall spatial distribution had significant regional differences, showing the structural characteristics of "southeast more, northwest less”;(2) there was a great difference in the degree of spatial autocorrelation agglomeration among prefecture-level cities, and the degree of agglomeration of both the hotel industry as a whole and general hotels decreased year by year, showing a random distribution in 2022;star hotels were always distributed randomly. Additionally, a strong synergistic correlation was shown between the number of ordinary hotels and the number of star hotels in local space;(3) overall, the development of the hotel industry was significantly affected by seven factors: structural force, macro force, ecological force, internal power, consumption power, intermediary power, and external power. There were differences in the forces acting on different types of hotels, which gives a pattern recognition in-depth.
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COVID-19 has had a significant impact on the global economy. The retailing sector, which relies heavily on high levels of human interaction, has experienced the worst impact. This study aimed to assess the spatial distribution of COVID-19 in Toronto and its impact on business locations from the food retail and food service sectors by investigating four retailers: Starbucks, McDonald's, Shoppers Drug Mart, and Loblaws. Kernel density estimation revealed that the spatial distribution of COVID-19 incidences in the City of Toronto is uneven, with a high density of cases present in the Downtown core. Spatial autocorrelation was performed at the global and local levels to assess the spatial pattern of Starbucks, McDonald's, Shoppers Drug Mart, and Loblaws locations. The findings revealed that retailers spatially clustered in a COVID-19 hotspot are the most impacted. Further to this analysis, a geographically weighted regression model was generated, which indicated a strong correlation between COVID-19 and low socio-economic status. This allows for a better understanding of the characteristics associated with the retail locations at risk from COVID-19, enabling retailers to make strategic adjustments to respond to a rapidly changing marketplace. © 2023, Springer Nature Switzerland AG.
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MAPPING THE DYNAMICS OF UNEMPLOYMENT: IMPACTS OF THE COVID-19 PANDEMIC IN PORTUGAL. The Great Confinement affected the labour market, particularly the dynamics of unemployment. Based on Instituto do Emprego e Formação Profissional (IEFP) data on registered unemployment, this article analyses the impact of the pandemic crisis in unemployment situations in mainland Portugal. The categorical and territorial distribution of the unemployed is highlighted, as well as the temporal dimension of the problem. The quantitative/extensive analysis carried out, as well as the cluster analysis, indicates that the incidence of unemployment is not identical for all social groups or for the entire territory, affecting some more than others, with emphasis on some spatial concentrations, particularly in Algarve;and the variations are directly related to temporalities resulting from periods marked by confinement or deconfinement. © Published under the terms and conditions of an Attribution-NonCommercial-NoDerivatives 4.0 International license.
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The present research deals with the Risk assessment of groundwater quality. 79 groundwater samples were collected from domestic and agricultural usage open and bore wells during January 2021(COVID-19 Pandemic Period). Groundwater samples were tested to determine the physicochemical parameters using standard testing procedure for the preparation of spatial distribution maps of each parameter based on the World Health Organization (WHO) standard. Multivariate statistical analysis has shown the source of groundwater pollution from secondary leaching of chemical weathering of rocks. From the Water Quality Index and bivariate plot reveals that less than 20% of the area comes under high and very high-risk zone. The types of hardness diagram showed 32.91% of the samples fall in hard brackish water as illustrated by the Piper trilinear diagram. The research outcome result shows that the least percentage of industrials effluents due to the COVID-19 pandemic, not working for all industries during lock down period.
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The present work aimed to assess the ambient levels of air pollution with particulate matter for both mass concentrations and number of particles for various fractions in Ploiesti city during the lockdown period determined by the COVID-19 pandemic (March–June 2020). The PM10 continuously monitored data was retrieved from four air quality automatic stations that are connected to the Romanian National Network for Monitoring Air Quality and located in the city. Because no other information was available for other more dangerous fractions, we used monitoring campaigns employing the Lighthouse 3016 IAQ particle counter near the locations of monitoring stations assessing size-segregated mass fraction concentrations (PM0.5, PM1, PM2.5, PM5, PM10, and TPM) and particle number concentration (differential Δ) range between 0.3 and 10 microns during the specified timeline between 8.00 and 11.00 a.m., which were considered the morning rush hours interval. Interpolation maps estimating the spatial distribution of the mass concentrations of various PM fractions and particle number concentration were drawn using the IDW algorithm in ArcGIS 10.8.2. Regarding the particle count of 0.5 microns during the lockdown, the smallest number was recorded when the restriction of citizens' movement was declared (24 March 2020), which was 5.8-times lower (17,301.3 particles/cm3) compared to a common day outside the lockdown period (100,047.3 particles/cm3). Similar results were observed for other particle sizes. Regarding the spatial distribution of the mass concentrations, the smaller fractions were higher in the middle of the city and west (PM0.5, PM1, and PM2.5) while the PM10 was more concentrated in the west. These are strongly related to traffic patterns. The analysis is useful to establish the impact of PM and the assessment of urban exposure and better air quality planning. Long-term exposure to PM in conjunction with other dangerous air pollutants in urban aerosols of Ploiesti can lead to potential adverse effects on the population, especially for residents located in the most impacted areas.
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The general consensus is that physical activity can prevent and manage lifestyle-induced chronic diseases, and moderate-to-vigorous physical activity (MVPA) has been included in several guidelines of WHO as an indicative intensity standard. Numerous studies have confirmed that improving the spatial quality of urban parks can be very helpful in supporting physical activities, and that the quality of parks is significantly related to the intensity of physical activities. However, few studies have explored the spatial characteristics of activating physical activities. Using a modified System for Observing Play and Recreation in Communities (SOPARC), this study examines the relationship between spatial characteristics and MVPA through a binary logistic regression model. The results reveal that: firstly, inconsistent with other similar studies, the most observed group in the park is the adults rather than the seniors, and the proportion of the females (51%) is higher;secondly, the distribution of MVPA in different groups shows that the seniors have less interaction with other groups, and they have a significant spatial attachment. Thirdly, in functionality, large lawn and jogging trails have been proved to be the most effective features to promote the occurrence of MVPA;among the activity, except for the significant correlation between equipped and MVPA, other attributes can be proved to encourage MVPA as well as those in comfort. In conclusion, our results can contribute to the planning and design of the urban park as well as the further management and allocation of the space and facilities under the vision of promoting public health. © 2023 by the authors.
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Coronavirus disease 2019 (COVID-19) swept the world at the beginning of 2020, and strict activity control measures were adopted in China's concentrated and local outbreak areas, which led to social shutdown. This study was conducted in southwest China from 2019 to 2021, and was divided into the year before COVID-19 (2019), the year of COVID-19 outbreak (2020), and the year of normalization of COVID-19 prevention and control (2021). A geographically and temporally weighted regression (GTWR) model was used to invert the spatial distribution of PM2.5 by combining PM2.5 on-site monitoring data and related driving factors. At the same time, a multiple linear regression (MLR) model was constructed for comparison with the GTWR model. The results showed that: (1) The inversion accuracy of the GTWR model was higher than that of the MLR model. In comparison with the commonly used PM2.5 datasets "CHAP” and "ACAG”, PM2.5 inverted by the GTWR model had higher data accuracy in southwest China. (2) The average PM2.5 concentrations in the entire southwest region were 32.1, 26.5, and 28.6 μg/m3 over the three years, indicating that the society stopped production and work and the atmospheric PM2.5 concentration reduced when the pandemic control was highest in 2020. (3) The winter and spring of 2020 were the relatively strict periods for pandemic control when the PM2.5 concentration showed the most significant drop. In the same period of 2021, the degree of control was weakened, and the PM2.5 concentration showed an upward trend.
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The severity of COVID-19 disease varies substantially between individuals, with some infections being asymptomatic while others are fatal. Several risk factors have been identified that affect the progression of SARS-CoV-2 to severe COVID-19. They include age, smoking and presence of underlying comorbidities such as respiratory illness, HIV, anemia and obesity. Given that respiratory illness is one such comorbidity and is affected by hand hygiene, it is plausible that improving access to hand washing could lower the risk of severe COVID-19 among a population. In this paper, we estimate the potential impact of improved access to hand washing on the risk of respiratory illness and its knock-on impact on the risk of developing severe COVID-19 disease across Zimbabwe. We use a geospatial model that allows us to estimate differential clinical risk at the district level. Results show that the current risk of severe disease is heterogeneous across the country, due to differences in individual characteristics and household conditions. This study demonstrates how household level improved access to handwashing could lead to reductions in the risk of severe COVID-19 of up to 16% from the estimated current levels across all districts. Taken alongside the likely impact on transmission of SARS-CoV-2 itself, as well as countless other pathogens, this result adds further support for the expansion of access to hand washing across the country. It also highlights the spatial differences in risk of severe COVID-19, and thus the opportunity for better planning to focus limited resources in high risk areas in order to potentially reduce the number of severe cases.
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The COVID-19 pandemic continues to wreak devastation on public health systems warldwide, particularly in Selangor, Malay sia, COVID-1 9 was reported from October 2020 to October 2021 at prevalent rate . In order to control and prevent: tlie spread of this pandemic, which is already underway, there is need to comprehend the spatial dimension of this disease. Therefore, the purpose of this study was to describe the patterns of COVID-a9 virus transmission in the state of Selangor. Methods: Using a Geographic Information System (GIS), and the Moran's Index (MI), spatial distribution of COVID-19 across the entire mukim was mapped and spatial statistical analysis was carried out with indications of local spatial correlations. Results: The finding revealed that the clusters were concentrated in the western and southern regions (Global Moran's I = 0.468, p = 0.05, Z = 7.01) of the state oi Selangor, thus, this research provides important information on the regional distribution and temporal dynamtcs of COV4D-Í9. Conclusion: Aa assessment ot COVID-14's geographic spread can help enhance healih care programs and resource allouation in Malaysia, specifically Selangorwhere the COVID-19 is pandemic.
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Purpose: The COVID-19 pandemic has significantly raised economic risk and uncertainty worldwide. How does COVID-19 affect urban housing markets? Is there any difference when different areas encounter COVID-19? This study aims to investigate the impacts of the pandemic on housing prices by using Beijing's housing markets data in 2020. Design/methodology/approach: The authors use transaction-level data from April to September in 2020 to conduct a hedonic price analysis of the housing markets in Beijing. The data included 70,843 transactions scraped from a real estate agent's website. The authors use the difference-in-differences approach to evaluate the impacts of the COVID-19 outbreak from the Beijing Xinfadi market (the largest and most important food wholesale market in Beijing) in 2020. Findings: This outbreak of COVID-19 caused a 6.3% drop in housing prices in Beijing from April to September in 2020. However, the impacts of COVID-19 on housing prices in different urban neighbourhoods were spatially heterogeneous. Housing prices in neighbourhoods with industries that rely on face-to-face communication were more affected by the pandemic, while those that can work remotely were less affected. Originality/value: By investigating the impacts of COVID-19 on housing prices in Beijing, this study illustrates that urban housing prices would be impacted by the pandemic, at least in the short term. While the rise and fall of housing prices were found spatially heterogeneous in Beijing, it suggests that urban neighbourhoods with specific socioeconomic characteristics and geographic locations would unfold different resilience when encountering pandemic. By using data scraping and rigorous statistical tools, the study is probably one of the first ones examining the consequences of COVID-19 in intra-urban housing markets. © 2023, Emerald Publishing Limited.
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More than 220 countries and territories are globally affected by the recent pandemic COVID-19 which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There is possibility of third wave of this pandemic as per epidemiological and public health experts. Besides that post-COVID-19 complications are alarming matter to look upon. Post-COVID-19 complications include several symptoms like as persistent fever;cough;fatigue;headache;attention disorder;dyspnea;anosmia;ageusia;chest pain discomfort;various respiratory illness;acute respiratory distress syndrome (ARDS) etc., and here the things to worry about is the development of pulmonary fibrosis after COVID-19. In some COVID-19 patients, hyper-inflammation in the form of 'cytokine storm' along with dysregulated immune response, alveolar epithelial tissue injury and wound repair collectively cause this secondary pulmonary fibrosis. Therefore, using anti-fibrotic agents e.g. pirfenidone, nintedanib and other natural compounds could be meaningful in these circumstances although their efficacy in treating COVID-19 is subject to more detailed laboratory research works. In this review article, we have discussed the progression of pulmonary fibrosis development which is triggered by COVID-19;probable solutions with anti-fibrotic agents including anti-fibrotic drugs, some well-known natural compounds, combined anti-fibrotic therapies;and the current challenges of this field.