GIS-Based Analysis of the Spatial Distribution and Influencing Factors of Traditional Villages in Hebei Province, China

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.

"Home away from home” in pandemic times: how has COVID-19 changed the Airbnb market in Melbourne?

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.

Spatio-temporal dynamics of aerosol distribution in an urban environment recorded in situ by means of a bike based monitoring system

Aerosol pollution in urban areas is highly variable due to numerous single emission sources such as automobiles, industrial and commercial activities as well as domestic heating, but also due to complex building structures redirecting air mass flows, producing leeward and windward turbulences and resuspension effects. In this publication, it is shown that one or even few aerosol monitoring sites are not able to reflect these complex patterns. In summer 2019, aerosol pollution was recorded in high spatial resolution during six night and daytime tours with a mobile sensor platform on a trailer pulled by a bicycle. Particle mass loadings showed a high variability with PM10 values ranging from 1.3 to 221 µg m-3 and PM2.5 values from 0.7 to 69.0 µg m-3. Geostatistics were used to calculate respective models of the spatial distributions of PM2.5 and PM10. The resulting maps depict the variability of aerosol concentrations within the urban space. These spatial distribution models delineate the distributions without cutting out the built-up structures. Elsewise, the overall spatial patterns do not become visible because of being sharply interrupted by those outcuts in the resulting maps. Thus, the spatial maps allow to identify most affected urban areas and are not restricted to the street space. Furthermore, this method provides an insight to potentially affected areas, and thus can be used to develop counter measures. It is evident that the spatial aerosol patterns cannot be directly derived from the main wind direction, but result far more from an interplay between main wind direction, built-up patterns and distribution of pollution sources. Not all pollution sources are directly obvious and more research has to be carried out to explain the micro-scale variations of spatial aerosol distribution patterns. In addition, since aerosol load in the atmosphere is a severe issue for health and well-being of city residents more attention has to be paid to these local inhomogeneities.

Geographical Distribution and Social Justice of the COVID-19 Pandemic: The Case of Palma (Balearic Islands).

The spatial distribution of the COVID-19 infection rate in the city of Palma (Balearic Islands) is analyzed from the geolocation of positive cases by census tract and its relationship with socioeconomic variables is evaluated. Data on infections have been provided by the Health Service of the Ministry of Health and Consumption of the Government of the Balearic Islands. The study combines several methods of analysis: spatial autocorrelation, calculation of the Gini index and least squares regression, and weighted geographical regression. The results show that the pandemic comprised five waves in the March 2020-March 2022 period, corresponding to the months of April 2020, August 2020, December 2020, July 2021, and January 2022. Each wave shows a particular geographical distribution pattern, however, the second and third waves show higher levels of spatial concentration. In this sense, the second wave, affecting the peripheral neighborhoods of the eastern part of the city. The Gini index confirms geographical imbalances in the distribution of infections in the first waves of the pandemic. In addition, the regression models indicate that the most significant socioeconomic variables in the prediction of COVID-19 infection are average income, percentage of children under 18 years of age, average size of the household, and percentage of single-person households. The study shows that economic imbalances in the city have had a clear influence on the spatial pattern of pandemic distribution. It shows the need to implement spatial justice policies in income distribution to balance the effects of the pandemic.

Spatial associations of long-term exposure to diesel particulate matter with seasonal and annual mortality due to COVID-19 in the contiguous United States.

BACKGROUND: People with certain underlying respiratory and cardiovascular conditions might be at an increased risk for severe illness from COVID-19. Diesel Particulate Matter (DPM) exposure may affect the pulmonary and cardiovascular systems. The study aims to assess if DPM was spatially associated with COVID-19 mortality rates across three waves of the disease and throughout 2020. METHODS: We tested an ordinary least squares (OLS) model, then two global models, a spatial lag model (SLM) and a spatial error model (SEM) designed to explore spatial dependence, and a geographically weighted regression (GWR) model designed to explore local associations between COVID-19 mortality rates and DPM exposure, using data from the 2018 AirToxScreen database. RESULTS: The GWR model found that associations between COVID-19 mortality rate and DPM concentrations may increase up to 77 deaths per 100,000 people in some US counties for every interquartile range (0.21 µg/m3) increase in DPM concentration. Significant positive associations between mortality rate and DPM were observed in New York, New Jersey, eastern Pennsylvania, and western Connecticut for the wave from January to May, and in southern Florida and southern Texas for June to September. The period from October to December exhibited a negative association in most parts of the US, which seems to have influenced the year-long relationship due to the large number of deaths during that wave of the disease. CONCLUSIONS: Our models provided a picture in which long-term DPM exposure may have influenced COVID-19 mortality during the early stages of the disease. That influence appears to have waned over time as transmission patterns evolved.

"Home away from home” in pandemic times: how has COVID-19 changed the Airbnb market in Melbourne?

Purpose: This 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/approach: A 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. Findings: This 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 implications: The 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/value: To 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. © 2022, Emerald Publishing Limited.

Spatial and Temporal Analysis of COVID-19 Cases in West Java, Indonesia and Its Influencing Factors.

Coronavirus Disease 2019 (COVID-19) spread quickly and reached epidemic levels worldwide. West Java is Indonesia's most populous province and has a high susceptibility to the transmission of the disease, resulting in a significant number of COVID-19 cases. Therefore, this research aimed to determine the influencing factors as well as the spatial and temporal distribution of COVID-19 in West Java. Data on COVID-19 cases in West Java obtained from PIKOBAR were used. Spatial distribution was described using a choropleth, while the influencing factors were evaluated with regression analysis. To determine whether COVID-19s policies and events affected its temporal distribution, the cases detected were graphed daily or biweekly with information on those two variables. Furthermore, the cumulative incidence was described in the linear regression analysis model as being significantly influenced by vaccinations and greatly elevated by population density. The biweekly chart had a random pattern with sharp decreases or spikes in cumulative incidence changes. Spatial and temporal analysis helps greatly in understanding distribution patterns and their influencing factors, specifically at the beginning of the pandemic. Plans and strategies for control and assessment programs may be supported by this study material.

JUE insight: Demand for transportation and spatial pattern of economic activity during the pandemic.

Using traffic data from Taiwan for 2020, we quantify how the COVID-19 outbreak affected demand for public and private transportation. Despite there being no governmental restrictions, substantial shifts in travel modes were observed. During the peak of the pandemic in Taiwan within the study period (mid-March 2020), railway ridership declined by 40% to 60%, while highway traffic volume increased by 20%. Furthermore, railway ridership was well below pre-pandemic levels, though there were no locally transmitted cases in the eight-month period from mid-April to December. These changes in traffic patterns had implications for spatial patterns of economic activity: retail sales and nighttime luminosity data show that during the pandemic, economic activity shifted away from areas in the vicinity of major railway stations.

"Home away from home” in pandemic times: how has COVID-19 changed the Airbnb market in Melbourne?

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.

Census Tract Patterns and Contextual Social Determinants of Health Associated With COVID-19 in a Hispanic Population From South Texas: A Spatiotemporal Perspective.

BACKGROUND: Previous studies have shown that various social determinants of health (SDOH) may have contributed to the disparities in COVID-19 incidence and mortality among minorities and underserved populations at the county or zip code level. OBJECTIVE: This analysis was carried out at a granular spatial resolution of census tracts to explore the spatial patterns and contextual SDOH associated with COVID-19 incidence from a Hispanic population mostly consisting of a Mexican American population living in Cameron County, Texas on the border of the United States and Mexico. We performed age-stratified analysis to identify different contributing SDOH and quantify their effects by age groups. METHODS: We included all reported COVID-19-positive cases confirmed by reverse transcription-polymerase chain reaction testing between March 18 (first case reported) and December 16, 2020, in Cameron County, Texas. Confirmed COVID-19 cases were aggregated to weekly counts by census tracts. We adopted a Bayesian spatiotemporal negative binomial model to investigate the COVID-19 incidence rate in relation to census tract demographics and SDOH obtained from the American Community Survey. Moreover, we investigated the impact of local mitigation policy on COVID-19 by creating the binary variable "shelter-in-place." The analysis was performed on all COVID-19-confirmed cases and age-stratified subgroups. RESULTS: Our analysis revealed that the relative incidence risk (RR) of COVID-19 was higher among census tracts with a higher percentage of single-parent households (RR=1.016, 95% posterior credible intervals [CIs] 1.005, 1.027) and a higher percentage of the population with limited English proficiency (RR=1.015, 95% CI 1.003, 1.028). Lower RR was associated with lower income (RR=0.972, 95% CI 0.953, 0.993) and the percentage of the population younger than 18 years (RR=0.976, 95% CI 0.959, 0.993). The most significant association was related to the "shelter-in-place" variable, where the incidence risk of COVID-19 was reduced by over 50%, comparing the time periods when the policy was present versus absent (RR=0.506, 95% CI 0.454, 0.563). Moreover, age-stratified analyses identified different significant contributing factors and a varying magnitude of the "shelter-in-place" effect. CONCLUSIONS: In our study, SDOH including social environment and local emergency measures were identified in relation to COVID-19 incidence risk at the census tract level in a highly disadvantaged population with limited health care access and a high prevalence of chronic conditions. Results from our analysis provide key knowledge to design efficient testing strategies and assist local public health departments in COVID-19 control, mitigation, and implementation of vaccine strategies.

The Spatial Spillover Effect and Its Impact on Tourism Development in a Megacity in China

By utilizing the tourism development data of Beijing for the period from 2010 to 2019, this study examined the spatial pattern distribution of tourism development in Beijing using the coefficient of variation and Moran’s I index. In addition, the geographic detector method was employed to explore the impact of tourism resource investment, tourism reception facilities, and urban development level on the spatial pattern of tourism development. The results indicate that the spatial differences in tourism development in various Beijing districts are gradually expanding, mainly focusing on the differences between urban function expansion regions. The number of tourists shows a spatial distribution pattern including a core area, urban function expansion area, ecological conservation area, and new urban development area. The spatial correlation of tourism development increases gradually, and some parts show the spatial correlation form of low–high aggregation. Tourism resource investment, tourism reception facilities, and urban development level all play a significant role in promoting the spatial pattern of tourism development, among which the most obvious role is the interactive effect of tourism reception facilities, star-rated hotels, and openness. Therefore, to improve the development of Beijing’s tourism industry, the government needs to pay attention to the differences in the expansion of urban functions, the degree of contact between regions, the number of tourism reception facilities, and the level of regional openness. The significance of this research is in promoting spatial governance, coordinated development among regions, and the high-quality development of tourism in Beijing, and laying down a foundation for the introduction of spatial collaborative governance policies in other megacities in China.

Food Retail Network Spatial Matching and Urban Planning Policy Implications: The Case of Beijing, China

Food is the core of urban daily life and socio-economic activities but is rarely the focus of urban planning. The spatial layout of food retail outlets is important for optimizing the urban food system, improving land resource allocation, and encouraging healthy food consumption. Based on food retail POI data, this study employed kernel density estimation, road network centrality, spatial autocorrelation analysis, and locational entropy to analyze the spatial characteristics of supermarkets, produce markets, and small stores in an urban center in Beijing, and explored street coupling and supply-demand matching. The results indicated that within the study area: (1) supermarkets had an obvious “core-periphery” distribution, produce markets had a polycentric distribution, and small stores had a relatively uniform distribution;(2) road network centrality indices revealed a differentiated multi-core-edge distribution;(3) streets with high locational entropy values for supermarkets and produce markets were mostly concentrated in the central area, whereas the matching distribution of small stores was relatively balanced. From the perspective of urban planning, policy implications are proposed based on spatial and social equity, urban-rural differences, population structure and distribution status, and a resilient supply chain. The study findings have practical significance for guiding the development of urban food systems in a healthy, just, and sustainable direction, as well as rational urban land planning.

Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling.

The coronavirus disease of 2019 (COVID-19) pandemic is currently a global challenge, with 210 countries, including Indonesia, seeking to minimize its spread. Therefore, this study aims to determine the spatiotemporal spread pattern of this virus in Surabaya using various data on confirmed cases from 28 April to 26 October 2021. It also aims to determine the relationship between pollutant parameters, such as carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3), as well as the government's high social restrictions policy in Java-Bali. Several methods, such as the weighted mean center, directional distribution, Getis-Ord Gi*, Moran's I, and geographically weighted regression, were used to identify the spatial spread pattern of the virus. The weighted mean center indicated that the epicenter location of the outbreak moved randomly. The directional distribution demonstrated a decrease of 21 km2 at the end of the study phase, which proved that its spread has significantly reduced in Surabaya. Meanwhile, the Getis-Ord Gi* results demonstrated that the eastern and southern parts of the study region were highly infected. Moran's I demonstrate that COVID-19 cases clustered during the spike. The geographically weighted regression model indicated a number of influence zones in the northeast, northwest, and a few in the southwest parts at the peak of R2 0.55. The relationship between COVID-19 cases and air pollution parameters proved that people living at the outbreak's center have low pollution levels due to lockdown. Furthermore, the lockdown policy reduced CO, NO2, SO2, and O3. In addition, increase in air pollutants; namely, NO2, CO, SO2 and O3, was recorded after 7 weeks of lockdown implementation (started from 18 August).

The Neighborhood Contagion Focus as a Spatial Unit for Diagnosis and Epidemiological Action against COVID-19 Contagion in Urban Spaces: A Methodological Proposal for Its Detection and Delimitation.

The concept of neighborhood contagion focus is defined and justified as a basic spatial unit for epidemiological diagnosis and action, and a specific methodological procedure is provided to detect and map focuses and micro-focuses of contagion without using regular or artificial spatial units. The starting hypothesis is that the contagion in urban spaces manifests unevenly in the form of clusters of cases that are generated and developed by neighborhood contagion. Methodologically, the spatial distribution of those infected in the study area, the city of Málaga (Spain), is firstly analyzed from the disaggregated and anonymous address information. After defining the concept of neighborhood contagion focus and justifying its morphological parameters, a method to detect and map neighborhood contagion focus in urban settings is proposed and applied to the study case. As the main results, the existence of focuses and micro-focuses in the spatial pattern of contagion is verified. Focuses are considered as an ideal spatial analysis unit, and the advantages and potentialities of the use of mapping focus as a useful tool for health and territorial management in different phases of the epidemic are shown.

Pattern Recognition of the COVID-19 Pandemic in the United States: Implications for Disease Mitigation.

The novel coronavirus (COVID-19) pandemic presents a severe threat to human health worldwide. The United States (US) has the highest number of reported COVID-19 cases, and over 16 million people were infected up to the 12 December 2020. To better understand and mitigate the spread of the disease, it is necessary to recognize the pattern of the outbreak. In this study, we explored the patterns of COVID-19 cases in the US from 1 March to 12 December 2020. The county-level cases and rates of the disease were mapped using a geographic information system (GIS). The overall trend of the disease in the US, as well as in each of its 50 individual states, were analyzed by the seasonal-trend decomposition. The disease curve in each state was further examined using K-means clustering and principal component analysis (PCA). The results showed that three clusters were observed in the early phase (1 March-31 May). New York has a unique pattern of the disease curve and was assigned one cluster alone. Two clusters were observed in the middle phase (1 June-30 September). California, Texas and Florida were assigned in the same cluster, which has the pattern different from the remaining states. In the late phase (1 October-12 December), California has a unique pattern of the disease curve and was assigned a cluster alone. In the whole period, three clusters were observed. California, Texas and Florida still have similar patterns and were assigned in the same cluster. The trend analysis consolidated the patterns identified from the cluster analysis. The results from this study provide insight in making disease control and mitigation strategies.