Multiscale Effects of Multimodal Public Facilities Accessibility on Housing Prices Based on MGWR: A Case Study of Wuhan, China

The layout of public service facilities and their accessibility are important factors affecting spatial justice. Previous studies have verified the positive influence of public facilities accessibility on house prices; however, the spatial scale of the impact of various public facilities accessibility on house prices is not yet clear. This study takes transportation analysis zone of Wuhan city as the spatial unit, measure the public facilities accessibility of schools, hospitals, green space, and public transit stations with four kinds of accessibility models such as the nearest distance, real time travel cost, kernel density, and two step floating catchment area (2SFCA), and explores the multiscale effect of public services accessibility on house prices with multiscale geographically weighted regression model. The results show that the differentiated scale effect not only exists among different public facility accessibilities, but also exists in different accessibility models of the same sort of facility. The article also suggests that different facilities should adopt its appropriate accessibility model. This study provides insights into spatial heterogeneity of urban public service facilities accessibility, which will benefit decision making in equal accessibility planning and policy formulation for the layout of urban service facilities.

The Influence of Subjective and Objective Characteristics of Urban Human Settlements on Residents’ Life Satisfaction in China

Under the guidance of people-oriented development concepts, improving residents’ life satisfaction has gradually become the goal of urban governance. Based on Chinese household tracking survey data and national socio-economic statistics, this study used the entropy method, multi-layer linear regression model and geographically weighted regression model and discusses the spatial heterogeneity of the impact of objective environmental characteristics and subjective perceived characteristics of urban residential environments on residents’ life satisfaction. It is of great importance to study the mechanisms through which subjective and objective characteristics of urban human settlements influence living satisfaction among residents. It is also important to discuss how to improve living satisfaction levels through the urban human settlements and to realize high-quality urban development. The research results show that in 2018, the overall level of life satisfaction among Chinese urban residents was relatively high. However, it is still necessary to continue to optimize the urban human settlements to improve residents’ life satisfaction. The objective characteristics of the urban human settlements, such as natural environmental comfort and environmental health, have a significant positive impact on residents’ life satisfaction. Residents’ subjective perceptions of government integrity, environmental protection, wealth gap, social security, medical conditions and medical level, as well as residents’ individual gender, age and health status also have a significant impact on residents’ life satisfaction. The direction and intensity of effects of different elements of the urban human settlements and residents’ personal attributes on urban residents’ life satisfaction have different characteristics in different regions.

Assessing Spatial Heterogeneity of Factor Interactions on PM2.5 Concentrations in Chinese Cities

The identification of fine particulate matter (PM2.5) concentrations and its driving factors are crucial for air pollution prevention and control. The factors that influence PM2.5 in different regions exhibit significant spatial heterogeneity. Current research has quantified the spatial heterogeneity of single factors but fails to discuss the interactions between factors. In this study, we first divided the study area into subregions based on the spatial heterogeneity of factors in a multi-scale geographically weighted regression model. We then investigated the interactions between different factors in the subregions using the geographical detector model. The results indicate that there was significant spatial heterogeneity in the interactions between the driving factors of PM2.5. The interactions between natural factors have significant uncertainty, as do those between the normalized difference vegetation index (NDVI) and socioeconomic factors. The interactions between socioeconomic factors in the subregions were consistent with those in the whole region. Our findings are expected to deepen the understanding of the mechanisms at play among the aforementioned drivers and aid policymakers in adopting unique governance strategies across different regions.

Estimation model of PM2.5 Concentrations Based on Spatiotemporal Adaptability and Satellite Remote Sensing

Based on satellite remote sensing AOD, we can estimate and monitor the continuous changes of PM2.5, which solved the disadvantages of traditional ground station discrete monitoring. Four-dimensional spatiotemporal heterogeneity is not considered in the construction of traditional empirical regression models, such as geographically weighted regression model (GWR) and spatiotemporal geographically weighted regression model (gtwr). To solve this four-dimensional spatiotemporal nonstationarity, this article proposes and constructs a spatiotemporal adaptive fine particulate matter (PM2.5) concentration estimation model: 4D-GTWR by introducing a DEM (Digital elevation model) and time effects into a GWR model. This method solves the heterogeneity between the three-dimensional space and one-dimensional time by constructing a four-dimensional space kernel function and obtaining its weight. Based on PM2.5 ground observation data and meteorological data collected from December 2017 to February 2018 in Zhengzhou City, Henan Province, PM2.5 estimations are obtained from MODIS MYD-3K AOD data using the GWR, TWR, GTWR and 4D-GTWR models. The results showed that the MAE (mean absolute error) of the 4D-GTWR model decreased by 54.13%, 54.06% and 37.90%, compared to those of the GWR, TWR and GTWR models, respectively, and that the PM2.5 concentrations predicted by the 4D-GTWR model were closest to the measured values. The R2 (the correlation coefficient) of the 4D-GTWR model was 0.9496, which was better than those of the GWR (R2 =0.7761), TWR (R2 =0.7763) and GTWR (R2=0.8811) models. The 4D-GTWR model can not only improve the precision of PM2.5 estimations but can also reveal the four-dimensional spatial heterogeneity of PM2.5 concentrations and the differentiation of the DEM's influence on the spatial dimensions.

Quantifying spatial associations between effective green spaces and cardiovascular and cerebrovascular diseases by applying volunteered geo-referenced data

Among the top public health risks, cardiovascular and cerebrovascular diseases cause more than 1 million deaths annually globally. Urban green spaces are considered have close associations with cardiovascular and cerebrovascular diseases. However, ignoring the spatial heterogeneity of different urban green space types and considering only the configuration or compositions of urban green spaces has resulted in inconsistent and contradictory conclusions. Therefore, by introducing Tencent urban density data, four effective green spaces (EGSs) were categorized. Category 1 EGSs, which exhibit a high increasing of visitors and areas, accounted for the smallest areal percentage (0.81%). Category 2 EGSs, which exhibit a low increasing of visiting and high increasing of areas, accounted for the highest areal percentage (42.51%). Category 3 EGSs, which exhibit a high increasing of visiting and low increasing of areas, accounted for 13.70% of the total EGS areas. Category 4 EGSs, which exhibit a low increasing of visiting and areas, accounted for 3.75% of the total EGS areas. Using a geographically weighted regression model, spatial associations between EGS and cardiovascular and cerebrovascular diseases were quantified. Consequently, these spatial associations varied among EGS types and seasons. EGS configurations (perimeters of vegetation and areas of vegetation) have a more significant association with cardiovascular and cerebrovascular diseases than the composition (NDVI) of EGS. Spatial associations implying stronger relationships were observed in EGS1. The strongest association was found in summer. Enlarge the coverage of evergreen vegetation in all EGS is first considered to enhance the negative association between EGS and chronic diseases. A methodology framework was provided to classify urban green space types using multi-source data. Suggestions for how to plan different urban green spaces for developing sustainable cities have been provided in this study, which offer scientific support to urban managers and planners for effective decision making.

Spatial Variation of the Effect of Multidimensional Urbanization on PM2.5 Concentration in the Beijing–Tianjin–Hebei (BTH) Urban Agglomeration

Atmospheric PM2.5 pollution has become a prominent environmental problem in China, posing considerable threat to sustainable development. The primary driver of PM2.5 pollution in China is urbanization, and its relationship with PM2.5 concentration has attracted considerable recent academic interest. However, the spatial heterogeneity of the effect of urbanization on PM2.5 concentration has not been fully explored. This study sought to fill this knowledge gap by focusing on the Beijing–Tianjin–Hebei (BTH) urban agglomeration. Urbanization was decomposed into economic urbanization, population urbanization, and land urbanization, and four corresponding indicators were selected. A geographically weighted regression model revealed that the impact of multidimensional urbanization on PM2.5 concentration varies significantly. Economically, urbanization is correlated positively and negatively with PM2.5 concentration in northern and southern areas, respectively. Population size showed a positive correlation with PM2.5 concentration in northwestern and northeastern areas. A negative correlation was found between urban land size and PM2.5 concentration from central to southern regions. Urban compactness is the dominant influencing factor that is correlated positively with PM2.5 concentration in a major part of the BTH urban agglomeration. On the basis of these findings, BTH counties were categorized with regard to local policy recommendations intended to reduce PM2.5 concentrations.

Computer Mathematical Statistics Analysis of downscaling data of different vegetation index inversion TRMM

This paper uses enhanced vegetation index (EVI) data, normalized vegetation index (NDVI) data, DEM, aspect data, and TRMM3B43 (V7) data, based on a geographically weighted regression model (GWR), and uses a statistical downscaling method to achieve Central China Downscaling of regional TRMM data from 2010 to 2019. The research results show: (1) TRMM data has good applicability in Central China, and the R2of TRMM data and weather station measured data is above 0.8. (2) Improve the ground resolution from 0.25°×0.25° (approximately 27.5km×27.5km) to 1km×1km while ensuring the same accuracy as the original data. (3) Overall, the accuracy of EVI downscaled precipitation data in Central China is better than that of NDVI downscaled precipitation data.

Exploring the Correlation between Block Vitality and Block Environment Based on Multisource Big Data: Taking Wuhan City as an Example

Block is the basic unit for studying the urban activities of residents, and block vitality is the concrete expression of urban dynamics at the block level. The quality of the block’s residential environment is a crucial medium to satisfy the residents’ pursuit of high-quality life; good block quality is essential for fostering the block vitality and further enhancing the overall vitality of the city. This study used the distribution density of cellular signaling data to quantify block vitality and constructed a block environment index system covering four dimensions—block accessibility, block function, block development degree, and human perception of block environment—innovatively introducing the elements of block environment from the human perspective. Considering the variability of block vitality between workdays and weekends, and between downtown and suburban blocks, this study used a geographically weighted regression model to show the mechanism of the spatial and temporal influence of indicators on block vitality, as well as to suggest how to enhance block vitality at different times of the day based on the influence mechanism. This study was conducted in Wuhan, China. The findings suggest that block vitality exhibited significant spatial and temporal heterogeneity. A high-vitality block can be created by enhancing the block’s accessibility, increasing the degree of block construction, and enriching the functional density and mix of functions in the block. A pleasantly green environment with a moderate degree of openness exerted a significant impact on promoting human activity and enhancing block vitality. The creation of high-vitality blocks should also consider the differences in the impact of various elements on block vitality between weekend and workday. For example, amid the surge in travel demand for education venues on weekends, enhancing the accessibility of blocks can significantly increase the vitality of blocks on weekends. We can truly realize the people-oriented approach to build a livable and high-vitality city by adapting to local conditions and time.

Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina

Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal–geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including USAFacts, U.S. Census Bureau, and the Population Estimates Program. The percentage of males and the unemployed population were positively associated with geodistributions of COVID-19 infection (p values < 0.05) in global models throughout the time. The percentage of the white population and the obesity rate showed divergent spatial correlations at different times of the pandemic. GWR models fit better than global models, suggesting nonstationary correlations between a region and its neighbors. Characterized by temporal–geospatial patterns, disparities in COVID-19 infection rate and their PIDRs are different from the mortality and morbidity of COVID-19 patients. Our findings suggest the importance of prioritizing different populations and developing tailored interventions at different times of the pandemic.