GWR Model Research Articles

PENERAPAN MODEL GEOGRAPHICALLY WEIGHTED PANEL REGRESSION PADA TINGKAT KEMISKINAN DI PROVINSI KALIMANTAN SELATAN

South Kalimantan Province is one of the provinces in Indonesia which has the lowest poverty rate or percentage of poor people on the island of Kalimantan, even in Indonesia. The percentage of poor people in South Kalimantan Province in March 2022 was 4.49% or in the 2nd lowest poverty position in Indonesia, below the Bangka Belitung Islands Province and above the Bali Province. Geographically Weighted Panel Regression (GWPR) is a local regression model with repeated data at location points for each observation at different times. This study aims to estimate the GWPR model parameters and test the significance of the GWPR model parameters to determine the factors that influence poverty in South Kalimantan Province. The independent variables used affect the dependent variable in the form of the Percentage of Poor Population, namely Life Expectancy, Open Unemployment Rate, Economic Growth, Average Years of Schooling and Number of Crimes. The analysis in this study is descriptive analysis using thematic maps, panel data regression analysis to determine the global model and GWPR by combining the panel data model with the GWR model. The results of this study show that the fixed effect model is a global model and the fixed bisquare weighting function is the best weighting function for estimating the GWPR model. Based on the GWPR model formed, there are 7 model groups based on significant independent variables. Hulu Sungai Utara and Hulu Sungai Tengah districts are districts where poverty in these areas is influenced by many variables compared to other regions in South Kalimantan Province.

Spatial variation and geographical weighted regression analysis to explore open defecation practice and its determinants among households in Ethiopia.

In Ethiopia, recent evidence revealed that over a quarter (27%) of households (HHs) defecated openly in bush or fields, which play a central role as the source of many water-borne infectious diseases, including cholera. Ethiopia is not on the best track to achieve the SDG of being open-defecation-free by 2030. Therefore, this study aimed to explore the spatial variation and geographical inequalities of open defecation (OD) among HHs in Ethiopia. This was a country-wide community-based cross-sectional study among a weighted sample of 8663 HHs in Ethiopia. The global spatial autocorrelation was explored using the global Moran's-I, and the local spatial autocorrelation was presented by Anselin Local Moran's-I to evaluate the spatial patterns of OD practice in Ethiopia. Hot spot and cold spot areas of OD were detected using ArcGIS 10.8. The most likely high and low rates of clusters with OD were explored using SaTScan 10.1. Geographical weighted regression analysis (GWR) was fitted to explore the geographically varying coefficients of factors associated with OD. The prevalence of OD in Ethiopia was 27.10% (95% CI: 22.85-31.79). It was clustered across enumeration areas (Global Moran's I = 0.45, Z-score = 9.88, P-value ≤ 0.001). Anselin Local Moran's I analysis showed that there was high-high clustering of OD at Tigray, Afar, Northern Amhara, Somali, and Gambela regions, while low-low clustering of OD was observed at Addis Ababa, Dire-Dawa, Harari, SNNPR, and Southwest Oromia. Hotspot areas of OD were detected in the Tigray, Afar, eastern Amhara, Gambela, and Somali regions. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions were explored as having high rates of OD. The GWR model explained 75.20% of the geographical variation of OD among HHs in Ethiopia. It revealed that as the coefficients of being rural residents, female HH heads, having no educational attainment, having no radio, and being the poorest HHs increased, the prevalence of OD also increased. The prevalence of OD in Ethiopia was higher than the pooled prevalence in sub-Saharan Africa. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions had high rates of OD. Rural residents, being female HH heads, HHs with no educational attainment, HHs with no radio, and the poorest HHs were spatially varying determinants that affected OD. Therefore, the government of Ethiopia and stakeholders need to design interventions in hot spots and high-risk clusters. The program managers should plan interventions and strategies like encouraging health extension programs, which aid in facilitating basic sanitation facilities in rural areas and the poorest HHs, including female HHs, as well as community mobilization with awareness creation, especially for those who are uneducated and who do not have radios.

Exploring the relationship between mortality and potential risk factors in US 50 states: A population perspective using partial least squares and geographically weighted regression models

While Alzheimer's disease becomes prevalent in elder population and attracts investment of thousands of billions for its research, its pathogenesis remains unknown. The relationship between multiple potential risk factors (Overall Health, Caregiving, cognitive decline, Nutrition/Physical Activity/Obesity, Screenings and Vaccines, mental health, Smoking and Alcohol Use) and Alzheimer's disease mortality in the 50 US states in 2020 was explored by developing multiple linear regression models, partial least squares regression models, and geographically weighted regression models in this article. In this experiment, through multiple linear regression models, we found eight significant demographic indicator variables, and to solve the covariance problem, we successfully constructed the pls model, and using the regression coefficients in the equation, we screened out the most important variables for the model, TOC06 and sex. In addition, we then explored the characteristics of the spatial distribution of mortality in Alzheimer's disease according to TOC06 and sex variables using the GWR model.

Spatially-aware station based car-sharing demand prediction

In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable transportation. Research on short-term prediction and optimization methods has improved operations and fleet control of car-sharing services; however, long-term projections and spatial analysis are sparse in the literature. We propose to analyze the average monthly demand in a station-based car-sharing service with spatially-aware learning algorithms that offer high predictive performance as well as interpretability. Our study utilizes a rich set of socio-demographic, location-based (e.g., POIs), and car-sharing-specific features as input, extracted from a large proprietary car-sharing dataset and publicly available datasets. We first compare the performance of different modeling approaches and find that a global Random Forest with geo-coordinates as part of input features achieves the highest predictive performance with an R-squared score of 0.87 on test data. While a local linear model, Geographically Weighted Regression, performs almost on par in terms of out-of-sample prediction accuracy. We further leverage the models to identify spatial and socio-demographic drivers of car-sharing demand. An analysis of the Random Forest via SHAP values, as well as the coefficients of GWR and MGWR models, reveals that besides population density and the car-sharing supply, other spatial features such as surrounding POIs play a major role. In addition, MGWR yields exciting insights into the multiscale heterogeneous spatial distributions of factors influencing car-sharing behaviour. Together, our study offers insights for selecting effective and interpretable methods for diagnosing and planning the placement of car-sharing stations.

A novel index for assessing the rural population hollowing at fine spatial resolutions based on Tencent social media big data: A case study in Guangdong Province, China

Evaluating the rural population hollowing (RPH) caused by the massive out-migration of rural laborers is essential for China’s rural land use policy formulation and rural revitalization. Existing studies mainly assessed the RPH based on survey data or statistical data. The former is mainly based on selected local villages and towns, and cannot be replicated on a large scale, while the latter is usually at the county or city scale and cannot reveal the cyclical migration of rural labors at fine spatial resolutions such as townships. To fill this gap, this paper proposed a social media data based-rural population hollowing (SM-RPH) index, which was calculated by Tencent User Density big data on holidays and non-holidays and was used to characterize the "migratory bird" process of rural laborers. Taking Guangdong Province, China as the case, this study employed the proposed novel index to evaluate the RPH at the county and township scales. This was integrated with a GWR model to explore how the important contributing factors of RPH vary across the province. The results showed a trend of transition from the cold spot areas in the core regions of PRD to the hot spot areas around the PRD. Additionally, this study revealed that distance to the nearest prefecture-level city, total nighttime lighting per capita, fiscal expenditure per unit of GDP and road network density were dominant factors in affecting rural population hollowing, and the influence of these four variables vary considerably throughout the province. Further, k-means cluster was used to group the hollowing towns according to the local coefficients of the influencing factors, and policy zoning was suggested to provide differentiated countermeasures and suggestions for rural revitalization. This methodological framework can be easily applied to other regions, and is expected to be used to evaluate the return characteristics of migrant workers in real time to help monitor the progress of rural revitalization in China and provide decision support for policy makers.

Data-Driven Modeling of Human Development Index in Eastern Indonesia's Region Using Gaussian Techniques Empowered by Machine Learning

The Human Development Index (HDI) is a statistical measure used to measure and evaluate the progress and quality of human life in a country. For the Government of Indonesia, HDI is important because it is used to create or develop effective policies and programs. In addition, HDI is also used as one of the allocators in determining the General Allocation Fund. The 2022 HDI data released by BPS shows that there has been an increase in the HDI in each district/city over the last 12 years, including in the regions of Eastern Indonesia. High and low HDI values are influenced by several factors, and there are indications that there is spatial diversity where surrounding areas tend to have HDI levels that are not far from the area. The Geographically Weighted Regression method is used in this study because it takes into account spatial aspects. However, the GWR model must be built repeatedly if there is regional expansion. Therefore, a GWR model that applies machine learning methods is needed where the model is built and tested using different datasets, namely training data and test data, so that the model can predict new data better. The results obtained are that the GWR model with test data has a better R-Square value when compared to the GWR model previously trained using training data, which is 0.9946702, based on the linear regression model shows the results that the most influential factor on HDI in Eastern Indonesia is expected years of schooling (X2).

Spatio-temporal pattern and driving factors of tourism ecological security in Fujian Province

The scientific assessment of tourism ecological security holds significant theoretical and practical importance in promoting the sustainable development of the tourism economy and the ecological environment in the region. This study focuses on 84 counties within Fujian Province as its research scope. By utilizing the DPSIR model and integrating the remote sensing ecological index (RSEI), we have established an evaluation index system for assessing tourism ecological security in the region. The tourism ecological security index was quantitatively determined using the SBM-DEA model. Subsequently, a combination of methods, including spatial autocorrelation, exploratory regression analysis, and the GWR model, was employed to explore the spatial and temporal evolution patterns, as well as driving factors affecting tourism ecological security from 2010 to 2019. The findings reveal the following insights: (1) Overall, the level of tourism ecological security within Fujian Province is low, with a decreasing disparity in the security index across different regions. The tourism ecological security mainly falls within the “unsafe” and “relatively unsafe” categories; however, regions with higher security levels are exhibiting an expanding trend. Moreover, the tourism ecological security of each county is improving, with Southern and Central Fujian outperforming other regions. (2) Notably, there is significant spatial correlation among the tourism ecological security of each county in Fujian Province, indicating pronounced agglomeration characteristics. (3) Key drivers contributing to the spatial–temporal disparities in tourism ecological security encompass tourist-related disturbances, income levels of local farmers and herders, tourism-generated income, and government interventions. Among these factors, higher income levels for farmers and herders, increased tourism income, and proactive government intervention have a positive impact on tourism ecological security, whereas tourist disturbances exert a negative influence. Additionally, the impact of each factor on tourism ecological security displays noticeable spatial heterogeneity.

The GWR model-based regional downscaling of GRACE/GRACE-FO derived groundwater storage to investigate local-scale variations in the North China Plain

Groundwater storage and depletion fluctuations in response to groundwater availability for irrigation require understanding on a local scale to ensure a reliable groundwater supply. However, the coarser spatial resolution and intermittent data gaps to estimate the regional groundwater storage anomalies (GWSA) prevent the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GARCE-FO) mission from being applied at the local scale. To enhance the resolution of GWSA measurements using machine learning approaches, numerous recent efforts have been made. With a focus on the development of a new algorithm, this study enhanced the GWSA resolution estimates to 0.05° by extensively investigating the continuous spatiotemporal variations of GWSA based on the regional downscaling approach using a regression algorithm known as the geographically weighted regression model (GWR). First, the modified seasonal decomposition LOESS method (STL) was used to estimate the continuous terrestrial water storage anomaly (TWSA). Secondly, to separate GWSA from TWSA, a water balance equation was used. Third, the continuous GWSA was downscaled to 0.05° based on the GWR model. Finally, spatio-temporal properties of downscaled GWSA were investigated in the North China Plain (NCP), China's fastest-urbanizing area, from 2003 to 2022. The results of the downscaled GWSA were spatially compatible with GRACE-derived GWSA. The downscaled GWSA results are validated (R = 0.83) using in-situ groundwater level data. The total loss of GWSA in cities of the NCP fluctuated between 2003 and 2022, with the largest loss seen in Handan (−15.21 ± 7.25 mm/yr), Xingtai (−14.98 ± 7.25 mm/yr), and Shijiazhuang (−14.58 ± 7.25 mm/yr). The irrigated winter-wheat farming strategy is linked to greater groundwater depletion in several cities of NCP (e.g., Xingtai, Handan, Anyang, Hebi, Puyang, and Xinxiang). The study's high-resolution findings can help with understanding local groundwater depletion that takes agricultural water utilization and provide quantitative data for water management.

Influence of the built environment on taxi travel demand based on the optimal spatial analysis unit.

When discussing the influence of the built environment on taxi travel demand, few studies have considered the effect of the modifiable areal unit problem (MAUP) or the influence of the "5D" dimensions of the built environment (It refers to the consideration of the built environment from five dimensions of density, diversity, design, destination accessibility and distance to transit.) on taxi travel demand. Moreover, discussion of the nonlinear and linear relationships between taxi demand and environment variables is also lacking. To address these gaps, we constructed a "5D" dimension index system of built environment variables. The influence of the MAUP on the model results was discussed using the optimal parameter-based geographical detector (OPGD) model, and the optimal spatial analysis unit was selected. The OPGD and multiscale geographically weighted regression (MGWR) models were used to reveal the influence of different dimensions of the built environment on taxi travel demand from global and local perspectives, respectively. Finally, the central urban area of Xi'an was analyzed as an example. The results show the following: (1) Most built environment variables are sensitive to the influence of MAUP. (2) It is better to divide the space into regular hexagons than squares, and the optimal spatial analysis unit in this study is a regular hexagon grid with sides of 900m. (3) From a global perspective, the distance to the city center, commercial residence POI density, transportation facility POI density, and population density have the greatest influence on the demand for taxi travel. (4) From a local perspective, the MGWR model considering spatial heterogeneity and scale differences is superior to the GWR model, and the influence of built environment variables exhibited spatial heterogeneity. The proposed optimal spatial analysis unit can provide a basis for taxi demand forecasting and scheduling. This study provides a reference for urban planners and traffic managers to offer optimization strategies related to the built environment, promote healthy development of the taxi industry, and solve the problems of the urban transportation system.

GEOGRAPHICALLY WEIGHTED RIDGE REGRESSION MODELING AT THE OPEN UNEMPLOYMENT RATE IN WEST KALIMANTAN

Geographically Weighted Ridge Regression (GWRR) is a development of ridge regression by adding a weighting element as additional information. GWRR is used to overcome spatial data containing local multicollinearity by modifying the ridge regression and GWR. The purpose of this study was to construct the GWRR model and analyze the effectiveness of the GWRR model in overcoming multicollinearity problems in spatial data. The research data used consists of response variables, namely the value of the open unemployment rate $(Y)$ in districts/cities of West Kalimantan Province in 2021 and the explanatory variables used are district/city minimum wage $(X_1)$, population $(X_2)$, percentage of poor population $(X_3)$, labor force $(X_4)$, and average years of schooling $(X_5)$. The analysis process was first carried out by using multiple linear regression modeling and GWR modeling. After that, centering and scaling transformations were carried out on the data, followed by GWRR modeling to overcome the problem of multicollinearity in spatial data. After all the models were obtained, a model comparison was made in terms of the MSE value. Based on the results of the study, 14 GWRR models were obtained by dividing into three regional groups based on the factors that affect the open unemployment rate for each district/city in West Kalimantan Province. In the comparison of the models used, when viewed from the MSE value, the GWRR model has the highest MSE value compared to the linear regression model and the GWR model. Even though the MSE value in the GWRR model is the largest among the three models, in the GWRR model the problem of multicollinearity can be resolved.

Analysis of the factors influencing economic efficiency in the structural change of e-commerce industry

Abstract With the changing structure of the e-commerce industry, the analysis of economic efficiency influencing factors helps to optimize the layout of the e-commerce industry. This paper analyzes the spatial correlation of regional e-commerce economic development by constructing a spatial weight matrix, thus proposing a geographically weighted regression model combining spatial correlation and spatial measurement and constructing a statistical GWR data sample of 11 prefecture-level cities in Jiangxi Province. Then, the DEA model was used to quantify the economic efficiency of the e-commerce industry in these 11 prefecture-level cities, and on this basis, the influencing factors of the e-commerce development efficiency and their related role coefficients were studied based on the GWR model. The average value of the e-commerce industry efficiency of the 11 prefecture-level cities in 2018 was 0.656, which did not reach DEA validity, and the comprehensive technical efficiency of the e-commerce industry varied widely across the regions, and the efficiency of the e-commerce industry in many places The most significant impact on the overall efficiency is the efficiency of the e-commerce industry. The population size, which reaches 0.856 on average, is the most significant impact on comprehensive efficiency. The information facility base is the most significant impact on pure technical efficiency, which averages 0.823. This study effectively quantifies the role of different influencing factors on the economic efficiency of different sides of the e-commerce industry and can provide an effective reference for the optimization of the structural layout of the e-commerce industry.

Influential factors of tuberculosis in mainland China based on MGWR model.

Tuberculosis (TB), as a respiratory infectious disease, has damaged public health globally for decades, and mainland China has always been an area with high incidence of TB. Since the outbreak of COVID-19, it has seriously occupied medical resources and affected medical treatment of TB patients. Therefore, the authenticity and reliability of TB data during this period have also been questioned by many researchers. In response to this situation, this paper excludes the data from 2019 to the present, and collects the data of TB incidence in mainland China and the data of 11 influencing factors from 2014 to 2018. Using spatial autocorrelation methods and multiscale geographically weighted regression (MGWR) model to study the temporal and spatial distribution of TB incidence in mainland China and the influence of selected influencing factors on TB incidence. The experimental results show that the distribution of TB patients in mainland China shows spatial aggregation and spatial heterogeneity during this period. And the R2 and the adjusted R2 of MGWR model are 0.932 and 0.910, which are significantly better than OLS model (0.466, 0.429) and GWR model (0.836, 0.797). The fitting accuracy indicators MAE, MSE and MAPE of MGWR model reached 5.802075, 110.865107 and 0.088215 respectively, which also show that the overall fitting effect is significantly better than OLS model (19.987574, 869.181549, 0.314281) and GWR model (10.508819, 267.176741, 0.169292). Therefore, this model is based on real and reliable TB data, which provides decision-making references for the prevention and control of TB in mainland China and other countries.

Factors influencing the spatiotemporal variation in the value of ecosystem services in Anxi county

Understanding the value of ecosystem services is imperative for the welfare of current and future generations. Anxi town is situated within the mountainous terrain of Fujian province, China and characterized by a diverse range of ecosystems encompassing forests, rivers, and mountains. These ecosystems offer a wide range of ecosystem services. The value of ecosystem services has been declining in the county for the past twenty-years. However, limited is known on the spatial changes and factors affecting the value of these Therefore, current study assessed the spatial changes in the value of ecosystem services (provisioning, regulating, supporting, and cultural) in 24 townships situated in the county for three time periods, i.e., 1999, 2009, and 2019 and factors affecting these services. The results revealed that annual decline in the value of ecosystem services was higher in 1999–2009 than 2009–2019. Longjuan, Xianghua, Gande, Futian, Changkeng, Xiping, Taozhou and Huqiu townships observed the highest annual decline in the value of ecosystem services ranging from 20.1 to 68.1 million yuan. The GWR model indicated that vegetation index had the highest impact on the value of ecosystem services in the county. Overall, the value of ecosystem services was negatively correlated with the urbanization rate and distance from the road with some exceptions, whereas altitude had a positive impact. All townships (except Xianghua, Futian, and Taozhou) had negative values for water supply services. Hence, water supply is a significant ecological issue in the county. It is concluded that tea farmers in the county should be educated about water and soil conservation, investment should be done in conservation measures, regional ecological environments should be protected, and ecological tea planting techniques should be developed in the county to halt further decrease in the value of ecosystem services.

Exploring the Relationships between Land Surface Temperature and Its Influencing Determinants Using Local Spatial Modeling

In recent years, a growing body of research has investigated the factors influencing land surface temperature (LST) in different cities, employing diverse methodologies. Our study aims to be one of the few to examine the socio-environmental variables (SV) of LST with a holistic approach, especially in primate cities in developing countries, which are particularly vulnerable to the impacts of climate change. In this context, the study preliminarily identifies the SV of LST while investigating the most vulnerable areas related to extreme LST at the neighborhood level. The combined 11 variables are analyzed using spatial modeling methods (GWR and MGWR). The MGWR model outperforms the GWR model with an adjusted R2 of 0.96. The results showed that: (1) the 65+ population is negatively associated with LST in 95% of neighborhoods; the socioeconomic index–LST relationship is negative in 65% of neighborhoods. (2) In 90% of the neighborhoods where the relationship between LST and the built environment ratio is positive, the socioeconomic level decreases while household size increases in 98% of the neighborhoods. (3) In 62% of the neighborhoods where the relationship between the 65+ population and LST is negative, the relationship between the socioeconomic level and LST is negative. This study aids decision-makers and planners in managing urban resources to reduce extreme LST exposure region by region and recommending multiscale policies to control determinant influences on LST.

Geographically Weighted Regression Analysis on Cases of Malnutrition Under Five in the West Sumatra

Malnutrition is a condition experienced by toddlers due to low nutrition or nutritional needs that have not been met. Effort to improve health status by improving the nutritional status of toddlers. The purpose of this research is clarify the Geographically Weighted Regression model is the best model when compared to the multiple linear regression model. The data used was obtained data from the West Sumatra Provincial Helath Office for 2020. The dependent variable used is the percentage of cases of malnutrition and there are several independent variables, namely the percentage of children under five who were given vitamin A, the percentage of babies who were exclusively breastfeed, and babies born with low birth weight. The results showed that the gwr model can explain the diversity of cases of malnutrition of children under five by 99% with a total squared error of 0.002 compared with multiple linear regression model which is able to explain the diversity of cases of malnutrition among children under five by 43% with a total squared errors of 0.585. It is concluded that the gwr model is the best model.

Geographically Weighted Panel Regression for Modeling The Percentage of Poor Population in West Sumatra

Geographically Weighted Panel Regression (GWPR) model applies panel regression to spatial data, and parameter estimation is carried out using spatial weight at each observation point. The purpose of this study is to determine the GWPR model and the factors that influence the percentage of poor people in each district/city in West Sumatra Province from 2015 to 2021. And the adaptive bisquare kernel function was used to provide spatial weighting, and Cross-Validation (CV) criteria were used to identify the optimal bandwidth. The research data was secondary data sourced from the official website and West Sumatra published books in Sumatera Barat Dalam Angka from 2015 to 2021. The GWR model and the FEM panel data regression model are combined to create the GWPR model. The results of this study is there are a differences between models and factors that affecting the poor percentages in 19 districts/cityes of West Sumatra.

Urban Equity Measure of Tree Canopy: A case study of New York City

The urban tree canopy is widely recognized as an urban resource that contributes to the physical and mental health of residents, yet tree inequality is widespread. To achieve the goal of promoting tree equity and filling the gap in relevant academic research, this study develops a 2-step 9CNN model to obtain highly accurate urban tree canopy (UTC) data from high-resolution satellite images. New York City tree canopy data is obtained by this new method and correlates with New York City socioeconomic data to examine UTC's horizontal and vertical equity. OLS regression correlations of per capita UTC area with the number of elderly, poor, low-educated, and disabled were used to measure vertical equity. To avoid the interference of spatial autocorrelation, GWR models were used to test for spatial differences in vertical equity. The results of the horizontal equity study show that there is far less UTC space per capita in New York's urban centers than in the fringe areas. Vertical equity in UTC is poor across all of vulnerable groups and is particularly pronounced in some regions. These findings can subsequently help to develop policies for UTC equity and environmental justice concerns.

Exploring the spatiotemporal heterogeneity of freeway secondary crashes using GTWR model

Understanding the effect of relevant variables on secondary crashes could provide reliable traffic intervention evidence for decision makers, which ultimately would contribute to the prevention of secondary crashes. Road crashes present obvious spatiotemporal heterogeneity, and previous studies (e.g., geographically weighted regression [GWR] based method) only analyzed the spatial heterogeneity without considering the timescale of related variables. This study proposes a geographically and temporally weighted regression model (GTWR), which adds a time variation to the conventional GWR model. The GTWR model is used to explore the spatiotemporal effects of relevant variables on the secondary crashes on expressways. The empirical study using expressways in Anhui, China, illustrates that the density of rainy days, snow and ice, and occupied middle lanes are negatively associated with secondary crashes. Then the temporal variation characteristics of each influencing factor at different timescales (e.g., weekdays, weekends, and time of day such as night, morning, afternoon, and evening) are analyzed, which reveals the spatial distribution characteristics of the influencing factors in each time period. The finding indicates that the explanatory variables have heterogeneous effects on the frequency of secondary crashes. The model comparison further demonstrates that the proposed GTWR model outperforms the ordinary least squares (OLS) and GWR methods in data fitting and spatiotemporal modeling. The research findings can aid in prevention of secondary crashes on freeways.

Spatial effects of public health laboratory emergency testing institutions under COVID-19 in China

BackgroundThe transmission of 2019 novel coronavirus (COVID-19) has caused global panic in the past three years. Countries have learned an important lesson in the practice of responding to COVID-19 pandemic: timely and accurate diagnosis is critical. As an important technology of virus diagnosis, nucleic acid testing (NAT) is also widely used in the identification of other infectious diseases. However, geographic factors often constrain the provision of public health services such as NAT services, and the spatial nature of their resource allocation is a significant problem.MethodsWe used OLS, OLS-SAR, GWR, GWR-SAR, MGWR, and MGWR-SAR models to identify the determinants of spatial difference and spatial heterogeneity affecting NAT institutions in China.ResultsFirstly, we identify that the distribution of NAT institutions in China shows a clear spatial agglomeration, with an overall trend of increasing distribution from west to east. There is significant spatial heterogeneity in Chinese NAT institutions. Secondly, the MGWR-SAR model results show that city level, population density, number of tertiary hospitals and number of public health emergency outbreaks are important factors influencing the spatial heterogeneity of NAT institutions in China.ConclusionsTherefore, the government should allocate health resources rationally, optimise the spatial layout of testing facilities, and improve the ability to respond to public health emergencies. Meanwhile, third-party testing facilities need to focus on their role in the public health emergency response system as a market force to alleviate the inequitable allocation of health resources between regions. By taking these measures to prepare adequately for possible future public health emergencies.

Downscaling of the Land Surface Temperature Data Obtained at four Different Dates in a Year Using the GWR Model: A Case Study in Antakya, Turkey

Downscaling of the Land Surface Temperature Data Obtained at four Different Dates in a Year Using the GWR Model: A Case Study in Antakya, Turkey