Geographically Weighted Regression Model Research Articles

Study on the Response of the Summer Land Surface Temperature to Urban Morphology in Urumqi, China

Increases in urban temperature affect the urban ecological environment and human health and well-being. In urban morphology, building characteristics are important factors affecting the land surface temperature (LST). Contemporary research focuses mainly on the effects of land use, urban tissue configuration, and street networks on the LST, and the effects of building characteristics on the LST need to be further understood. The mean LST and the urban morphology indicators of a single grid were calculated via a remote sensing inversion and a spatial analysis, and a geographically weighted regression (GWR) model was established to explore the influence of the building coverage ratio (BCR), mean building height (BH_mean), floor area ratio (FAR), and mean sky view factor (SVF_mean) on the LST. The results show that the correlations between the urban morphology indicators and the LST at a scale of 100~500 m are of different degrees, and the correlations are more significant at a scale of 200 m. Therefore, the optimal spatial scale for studying the influence of urban morphology indicators on the LST is 200 m. The fitting effect of the GWR model is significantly better than that of the ordinary least squares (OLS) method, and the effects of each indicator on the thermal environment have spatial non-stationarity. The BCR, BH_mean, FAR, and SVF_mean differ in their ability to raise and lower the temperature in different spatial zones, and the order of influence is as follows: BCR > SVF_mean > FAR > BH_mean. This study will provide a reference for the urban planning of Urumqi.

Spatial modeling of geotechnical soil parameters: Integrating ground-based data, RS technique, spatial statistics and GWR model

Ensuring the availability of a cost-effective method to predict soil behavior is imperative for newly developed construction sites. This research aims to create a model that can estimate the spatial distribution of soil properties and compaction characteristics using the Inverse Distance Weighting (IDW) and Geographic Weighted Regression (GWR) methods within the study area, specifically in Koya city, situated in Erbil, Iraq. To determine these soil parameters, 27 soil samples were collected from the fields based on stratified random sampling, and then tested and analyzed in the laboratory. The IDW spatial interpolation technique and GWR method were then used to create a spatial distribution map of soil properties and compaction characteristics. In the GWR model, the calculated soil properties and compaction characteristics served as the dependent variable, while the Modified Normalized Difference Water Index (MNDWI) derived from the Landsat8 satellite image was the independent variable. This process resulted in a spatial distribution map showing the soil properties and compaction characteristics. The results indicated a strong correlation between the MNDWI water indexes and various soil parameters, including water content, liquid limit, plastic limit, optimum moisture content, and maximum dry density, with respective coefficient of determination (R2) values of 0.91, 0.97, 0.98, 0.95, and 0.96. Additionally, the assessment of the precision in this correlation indicates that the results maintain a satisfactory level of accuracy, as demonstrated by the Root Mean Square Error (RMSE) values, which are 2.86 for water content, 5.4 for liquid limit, and 3.85 for plastic limit, 2.9 for optimum moisture, and 13.86 for maximum dry density. By integrating satellite-derived MNDWI water indexes with soil parameters, a fast, accurate, and cost-effective method for estimating soil parameters and modeling their spatial distribution in the study area can be developed. Additionally, the findings suggest that the IDW method, implemented using spatial analyst tools, performed exceptionally well for mapping the study area. In conclusion, the results of this research can be utilized by land use planners, municipalities, policymakers, and engineers to develop practical and effective plans.

Spatial characterization of forest ecosystem services and human-induced complexities in Himalayan biodiversity hotspot area.

The forest ecosystem of Indian Himalayan Region offers various ecosystem services (ESs) that are crucial for the sustenance of human beings. However, the rapid expansion of human activities (HA) poses a significant threat to the provision of the forest ecosystem services (FES). For simple and definitive assessments of FES and HA, the use of indicators has become an indispensable approach. In the present study, we performed: (i) indicator-based mapping of FES and HA, and (ii) evaluated the impact of HA on FES with the aid of geospatial techniques. Village-level analysis was conducted for FES and HA in the Aglar watershed of Uttarakhand, India for 2015. Four dominant forest types in the watershed-Quercus mixed, Pinus roxburghii, Cedrus deodara, and mixed forest were considered. For spatial characterization of FES, indicators such as forest carbon stock, net primary productivity, total water retention, and sediment yield were assessed, whereas human activity index (HAI) was evaluated using indicators of HAs, namely population density, road density, farmland, and habitation ratio. The integration of normalized values of FES indicators generated multiple ecosystem services indicator (MESI), and HAI was contructed using analytical hierarchical process based assignment of weights to HA indicators. Spatial analysis techniques such as ordinary least-square regression (OLS) and geographically weighted regression (GWR) models were used to derive the spatial relationship between them. The adjusted R2 and AIC were utilized to evaluate the effectiveness of the model. The GWR model had a better fit with an adjusted R2 of 0.68 and a lower AIC of 42.940, compared to the OLS model with an adjusted R2 of 0.21 and an AIC of 60.52. The statistics showed that GWR performed better than OLS and ably captured the heteroscedasticity of the phenomena. An inverse relation was observed between MESI and HAI. The findings of the study highlight the close link between the supply of FES and the impact of human-induced disturbances over the provision of FES, which has the potential to increase over time. The study provides a scientific basis for structuring policy dialogues to coordinate the long-term regional sustainability of FES provisioned from the Himalayan regions.

Use of geographically weighted regression models to inform retail endgame strategies in South Korea: application to cigarette and ENDS prevalence

BackgroundReducing tobacco retailer availability is a key tobacco endgame policy. The development and evaluation of retail-based policies require spatial methodologies. We modelled the prevalence of adult cigarette and electronic nicotine...

Evaluation and calculation of urban carbon emission reduction potential based on spatiotemporal geographic model

With the growth of urban carbon emissions, found that the current urban carbon emission reduction potential assessment and calculation in carbon emissions data source, research index selection and cost are some problems, to promote the realization of low carbon emission reduction target, it is necessary to use space and space and geographical weighted regression model to obtain the spatial and temporal distribution of urban carbon reduction. This article combined geographic information system (GIS) technology to construct a spatial database. Based on the geographic weighted regression (GWR) model, a time dimension was introduced to construct a spatiotemporal GWR model, which was used to obtain the spatiotemporal distribution information of urban CEs and draw a spatial distribution (SD) map of urban CE reduction potential (RP). This article also conducted experimental tests on the carbon reduction potential (CRP) of Chinese cities by combining different carbon reduction (CR) measures. This paper uses three CR methods to test P of these cities respectively measure A adjust industrial structure, measure B to promote low carbon technology innovation and measure C is to optimize the urban space structure, the experimental results show that the average carbon emission intensity of measure A cities is 0.72 tons / yuan, 0.02 tons / 0,0.04 tons / yuan less than measures B and C; the average CE efficiency of measures A cities is 0.85,0.02 and 0.05 compared with measures B and C respectively. From the above data, adjusting the industrial structure can effectively improve the CE efficiency of various cities, reduce the CE, reduce the CE intensity, and promote the realization of CE reduction in cities.

Improving the Prediction of Grain Protein Content in Winter Wheat at the County Level with Multisource Data: A Case Study in Jiangsu Province of China

Wheat is an important food crop in China. The quality of wheat affects the development of the agricultural economy. However, the high-quality wheat produced in China cannot meet the demand, so it would be an important direction for research to develop high-quality wheat. Grain protein content (GPC) is an important criterion for the quality of winter wheat and its content directly affects the quality of wheat. Studying the spatial heterogeneity of wheat grain proteins is beneficial to the prediction of wheat quality, and it plays a guiding role in the identification, grading, and processing of wheat quality. Due to the complexity and variability of wheat quality, conventional evaluation methods have shortcomings such as low accuracy and poor applicability. To better predict the GPC, geographically weighted regression (GWR) models, multiple linear regression, random forest (RF), BP neural networks, support vector machine, and long-and-short-term memory algorithms were used to analyze the meteorological data and soil data of Jiangsu Province from March to May in 2019–2022. It was found that the winter wheat GPC rises by 0.17% with every 0.1° increase in north latitude at the county level in Jiangsu. Comparison of the prediction accuracy of the coefficient of determination, mean deviation error, root mean square error, and mean absolute error by analyzing multiple algorithms showed that the GWR model was the most accurate, followed by the RF model. The regression coefficient of precipitation in April showed the smallest range of variation among all factors, indicating that precipitation in April had a more stable effect on GPC in the study area than the other meteorological factors. Therefore, consideration of spatial information might be beneficial in predicting county-level winter wheat GPC. GWR models based on meteorological and soil factors enrich the studies regarding the prediction of wheat GPC based on environmental data. It might be applied to predict winter wheat GPC and improve wheat quality to better guide large-scale production and processing.

Intra-urban differences underlying leprosy spatial distribution in central Brazil: geospatial techniques as potential tools for surveillance.

This ecological study identified an aggregation of urban neighbourhoods spatial patterns in the cumulative new case detection rate (NCDR) of leprosy in the municipality of Rondonópolis, central Brazil, as well as intra-urban socioeconomic differences underlying this distribution. Scan statistics of all leprosy cases reported in the area from 2011 to 2017 were used to investigate spatial and spatiotemporal clusters of the disease at the neighbourhood level. The associations between the log of the smoothed NCDR and demographic, socioeconomic, and structural characteristics were explored by comparing multivariate models based on ordinary least squares (OLS) regression, spatial lag, spatial error, and geographically weighted regression (GWR). Leprosy cases were observed in 84.1% of the neighbourhoods of Rondonópolis, where 848 new cases of leprosy were reported corresponding to a cumulative NCDR of 57.9 cases/100,000 inhabitants. Spatial and spatiotemporal high-risk clusters were identified in western and northern neighbourhoods, whereas central and southern areas comprised low-risk areas. The GWR model was selected as the most appropriate modelling strategy (adjusted R²: 0.305; AIC: 242.85). By mapping the GWR coefficients, we identified that low literacy rate and low mean monthly nominal income per household were associated with a high NCDR of leprosy, especially in the neighbourhoods located within high-risk areas. In conclusion, leprosy presented a heterogeneous and peripheral spatial distribution at the neighbourhood level, which seems to have been shaped by intra-urban differences related to deprivation and poor living conditions. This information should be considered by decision-makers while implementing surveillance measures aimed at leprosy control.

Estimation of Above-Ground Carbon Storage and Light Saturation Value in Northeastern China’s Natural Forests Using Different Spatial Regression Models

In recent years, accurate estimation and spatial mapping of above-ground carbon (AGC) storage in forests have been crucial for formulating carbon trading policies and promoting sustainable development strategies. Forest structure complexities mean that during their growth, trees may be affected by the surrounding environment, giving rise to spatial autocorrelation and heterogeneity in nearby forest segments. When estimating forest AGC through remote sensing, data saturation can arise in dense forest stands, adding to the uncertainties in AGC estimation. Our study used field-measured stand factors data from 138 forest fire risk plots located in Fenglin County in the Northeastern region, set within a series of temperate forest environments in 2021 and Sentinel-2 remote sensing image data with a spatial resolution of 10 m. Using ordinary least squares (OLS) as a baseline, we constructed and compared it against four spatial regression models, spatial lag model (SLM), spatial error model (SEM), spatial Durbin model (SDM), and geographically weighted regression (GWR), to better understand forest AGC spatial distribution. The results of local spatial analysis reveal significant spatial effects among plot data. The GWR model outperformed others with an R2 value of 0.695 and the lowest rRMSE at 0.273, considering spatial heterogeneity and extending the threshold range for AGC estimation. To address the challenge of light saturation during AGC estimation, we deployed traditional linear functions, the generalized additive model (GAM), and the quantile generalized additive model (QGAM). AGC light saturation values derived from QGAM most accurately reflect the actual conditions, with the forests in Fenglin County exhibiting a light saturation range of 108.832 to 129.894 Mg/ha. The GWR effectively alleviated the impact of data saturation, thereby reducing the uncertainty of AGC spatial distribution in Fenglin County. Overall, accurate predictions of large-scale forest carbon storage provide valuable guidance for forest management, forest conservation, and the promotion of sustainable development strategies.

Handling Multicollinearity on Social Spatial Data Using Geographically Weighted Random Forest

Crime includes all kinds of harmful acts that violate the laws in force in Indonesia as well as social and religious norms. The crime total is the number of incidents reported to the police, obtained from public reports and events where the perpetrators were caught red-handed by the police. We can use the Poisson model to analyze the data, but the existence of spatial heterogeneity in the data makes the model less accurate. This research investigates the methods when there is spatial heterogeneity in the data by using Geographically weighted regression (GWR), Geographically Weighted Poisson Regression (GWPR) and Geographically Weighted Random Forest (GW-RF). We compare the GWR, GWPR, and GW-RF models for criminal cases in East Java in handling multicollinearity in the data. The results of this study indicate that the GW-RF model is better for modeling criminal cases with the smallest RMSE and MAPE values and an R-Square value close to 1. Based on the three most important variables in each location, they form six groups of regencies/cities in East Java, Indonesia. The variables vary between groups and the poverty severity index is not included in the three most important variables in all locations.

Spatio-Temporal Investigation of Public Transport Demand Using Smart Card Data

AbstractPolicymakers must find efficient public transport solutions to promote sustainability and provide efficient urban mobility in the course of urban growth. A growing number of research papers are applying Geographically weighted regression (GWR) to model the relationship between public transport demand and its influential factors. However, few studies have considered the rapid development of journey inference from ticket transaction data. Similarly, the potential of GWR to analyze spatio-temporal changes that reflect changes in transportation supply and thus provide a measure for evaluating the local success of transport supply changes has yet to be exploited. In this paper, we use inferred journeys from smart card inferences as the dependent variable and analyze how public transport demand responds to a set of explanatory variables, emphasizing transport supply. Consequently, GWR and its successor Multiscale Geographically Weighted Regression (MGWR) are applied to analyze the spatially varying impact of transport supply changes for seven consecutive time frames between autumn 2017 and spring 2020, allowing conclusions about local changes in transport demand, as well as the benchmarking of transport supply changes. The (M)GWR framework’s predictive power is evaluated by training the model with past transport supply data and testing the model with data from the following consecutive years. The conducted analyses reveal that the (M)GWR model, using inferred journeys and transport supply data, can retrospectively predict the impact of transport supply changes on travel behavior and thus provides conclusions about the success of transport policies.

The Social Geography of Women’s Attitudes toward Wife-beating in Ethiopia: A Contribution Towards Proper Application of Spatial Statistics

Spatial statistical measures have been applied to Ethiopia’s Demographic and Health Survey data (EDHS), mostly at the national level. However, there is concern that most applications violate basic principles of statistics regarding autocorrelation, or are not cognizant of the first law of geography which states that all things are related but near things are more related. This study investigates local variations in attitudes toward wife-beating in Ethiopia with education as the main correlate. It does so by using a spatial measure known as the geographically weighted regression (GWR) which is more appropriate in conditions of geographic non-stationarity than the ordinary least squares regression (OLS). Equally importantly, it examines the appropriateness of existing OLS-based spatial studies of EDHS data. We found that most studies inappropriately applied OLS despite findings of spatially autocorrelated data. The GWR model showed an association between acceptance of wife-beating and educational status. It also generated a list of twelve sampling clusters where most women respondents stated that wife-beating was acceptable while admitting to having had no formal education, and where local R2s exceeded 0.5 in GWR modeling involving 72 nearest neighbors per sampling cluster. An education-focused bi-variate rather than multi-variate GWR avoided issues of multicollinearity while keeping the model simple and its results actionable. Although the majority of the twelve sampling clusters are in Harari Wereda and Kilil, which got their name from members of the Harari ethnic group that are predominantly Muslim, it is difficult to pinpoint which factor or set of factors can be cited as causally associated with characteristics that placed them on the list. The study makes methodological contributions to spatial studies of sociodemographic characteristics of populations, especially those in developing countries such as Ethiopia where local factors show significant geographic variations. It also adds to the literature on applied geographically weighted regression.

How does urbanization process affect ecological landscape pattern? an empirical analysis based on scale effects

Quantifying the relationship between land use change and landscape pattern (LP) is crucial in responding global challenges such as climate change, food security, and even biodiversity loss. Here, we used multi-period land data to quantify the spatiotemporal trends in built-up land expansion (BLE) and LP in the Yellow River Basin (YRB) during 1980–2018. We fitted the correlations between these two variables using LP Index, Landscape Expansion Indices (LEI) and the Multiscale Geographically Weighted Regression (MGWR) model. We found that regional BLE was highly variable, BLE was the main driver of landscape fragmentation, and the relationship between BLE and LP scale varies, especially in terms of the edge BLE. Landscape fragmentation and aggregation spatially varies and temporally increases in complexity. The MGWR model effectively disclosed a scale effect between BLE and LP. The control variables differed in terms of their relative impact on LP evolution. The present study demonstrated the complexity of the relationship between BLE and LP change and highlighted the importance of variable selection, spatiotemporal scale, and spatial dimension in the quantification of these relationships.

The Spatio-Temporal Evolution and Driving Factors of High-Quality Development in the Yellow River Basin during the Period of 2010–2022

The Yellow River Basin is an important ecological barrier and economic development area in China, but it faces some problems such as the degradation of its ecological quality and a lagging economic level. Promoting the high-quality development of the Yellow River Basin is the only way for China’s economic construction to enter into high-quality development, and an objective evaluation of the development quality of the study area is the premise for effectively improving this development quality. Based on panel data during the period of 2010–2022, a framework of drivers, pressures, state, impact, and a response model was used to build an index system. The index of high-quality development for each province in the Yellow River Basin was calculated using the entropy TOPSIS model. Further, the descriptive statistics method and standard deviation ellipse were applied to analyze the spatio-temporal characteristics of high-quality development in the study area, and the geographical detector and spatio-temporal geographical weighted regression model were employed to reveal the driving factors for this high-quality development in the Yellow River Basin. The results showed that (1) the high-quality development index of the Yellow River Basin was steadily improved over the study period, with an average annual growth rate of 3.024%. (2) The high-value area of the high-quality development level in the study area was distributed from northwest to southeast, with the high values of each subsystem tending to be spatially stable, as well as the spatial differences of the subsystems increasing. (3) The proportion of tertiary industry, per capita disposable income, rural–urban income ratio, per capita GDP, per capita highway mileage, and population were the main factors affecting the spatio-temporal evolution of high-quality development level in the Yellow River Basin, with average q values of 0.867, 0.938, 0.852, 0.781, 0.842, and 0.763, respectively. (4) Except for the negative effect of per capita GDP, the other five driving factors all had positive effects on the high-quality development level, with average values of 0.044, 0.068, 0.227, 0.064, and 0.215, respectively.

Modeling the Percentage of Poor Population in Java Island using Geographically Weighted Regression Approach

Poverty is a multidimensional problem faced by all countries in the world. Poverty is the inability of individual or group to meet their basic needs in terms of expenditure. In poverty problem, there is a tendency that the poor will group in locations with certain characteristics. This spatial clustering indicates spatial diversity that making global regression analysis inappropriate for application. Therefore, the purpose of this research is to model the percentage of poor population in 119 districts on Java Island in 2021 using the Geographically Weighted Regression (GWR) method. The analysis results state that the GWR model with Kernel Fixed Bisquare provides superior results compared to the global regression model and able to overcome spatial heterogeneity problem. The model is able to provide a fairly high coefficient of determination, which is 70,73 percent. The GWR model identifies ten groups of districts based on the significance of the independent variables, with the majority of them (61 districts) having a significant RLS variable. This indicates that education is an important aspect that needs to be considered by local governments to alleviate poverty.

Spatial and temporal variation of ecological quality in northeastern China and analysis of influencing factors

Ecological quality and its potential influencing factors are important contents of ecological security in Northeast China. Maintaining ecological security in Northeast China is of great significance for the country's ecological construction and sustainable development strategies. In this study, the Remote Sensing Ecological Index (RSEI) was used to analyze the spatiotemporal variations of ecological quality in Northeast China from 2000 to 2022 and to identify potential related influencing factors. The results show that ecological quality fluctuated during 2000–2022, showing a weak overall downward trend, especially in areas with excellent and good ecological quality where large-scale reductions have recently occurred, and the area of RSEI decline accounts for a relatively large proportion through trend analysis. RSEI exhibits strong spatial autocorrelation, with high values concentrated in the vegetation-covered regions of the north and central regions, and low values clustered in urban areas and human activity areas in the west. Elevation, precipitation accumulation, wind speed, vapor pressure, and net primary productivity (NPP) are potential natural factors affecting ecological quality in Northeast China. The contribution and specific response relationships of different latent factors were analyzed using Deep Neural Network (DNN) and Multiscale Geographically Weighted Regression (MGWR) models. Based on the MGWR results, it was observed that the local coefficients not only vary over time and space, but also exhibit spatial distribution characteristics, including spatial gradient distribution and spatial clustering. As a large-scale and long-term spatiotemporal analysis of ecological quality, this study can provide theoretical support for medium-scale development planning, ecological environment monitoring, and environmental protection policy-making by decision-makers.

Research on the current situation and development countermeasures of Chinese sports industry companies in the context of big data

Abstract To solve various problems in the development of the sports industry and enhance the pulling effect of the sports industry on the national economy. This paper first analyzes the development scale, the number of institutions and the number of people in China’s sports industry at present through sports industry-related indicators. It further constructs an index system to evaluate the comprehensive strength of the regional sports industry. Secondly, the factors affecting the spatial differences in the comprehensive strength of the sports industry are studied, and the spatial effect test and industrial network centrality analysis are conducted. A geographically weighted regression (GWR) power-driven model was further established for empirical analysis. Finally, the estimated parameters of the model were spatially visualized, and the influence of each influencing factor on the comprehensive strength of the local sports industry was analyzed in depth. The results show that the adjusted goodness-of-fit of the GWR model proposed in this paper is 0.8929, which is closer to 1 than 0.8073 of the OLS regression model, indicating that the GWR model has a better fit. Meanwhile, the AICc value of the GWR model is 5.4177, which is also smaller than that of the OLS regression model. The GWR model has a significant positive contribution to the development of the sports industry in each region in terms of the explanatory variables proposed government input, scientific and technological innovation and talent pool, which are all important to enhance the economic benefits of the sports industry in each region.

Spatio-Temporal Evolution and Influencing Factors of Integrated Urban–Rural Development in Northeast China under the Background of Population Shrinkage

Population shrinkage has had a certain negative impact on urban and rural development in many aspects. The impact of population shrinkage on urban–rural integration has become one of the core scientific issues that needs to be addressed in the current research on promoting the goal of common prosperity in China. Northeast China is a typical region in China that is experiencing a decrease in population and economic activity. Investigating the integrated development of urban and rural areas in this region is highly important for revitalizing Northeast China. This research paper focuses on 32 prefecture-level cities in Northeast China and utilizes spatial correlation analysis and the Geographical Weighted Regression model to uncover the evolving spatial patterns and influential factors affecting integrated urban–rural development in the context of population decline. The findings revealed the following: (1) The level of integrated urban–rural development in Northeast China continues to rise despite the shrinking population. During the comprehensive population decline stage, the growth rate of the urban–rural coupling coordination degree surpasses that of the initial stage. The areas with high values of urban–rural coupling coordination degree shift from northeastern Heilongjiang to four sub-provincial cities. The spatial correlation between urban–rural coupling and coordinated development weakens, with the main type being low–low agglomeration. (2) Factors such as economic development level, labor force size, urbanization level, level of openness, urban–rural accessibility, and proportion of built-up areas significantly correlate with urban–rural coupling and coordination. The influence of each factor varies in magnitude and direction across different locations. Labor force size and urban–rural accessibility have the most-significant impact on integrated urban–rural development, with labor force size having a positive effect and urban–rural accessibility having a negative effect. The impact of the economic development level follows a pattern of initial increase and subsequent decrease as the population shrinks. (3) Although population decline does not hinder integrated urban–rural development in Northeast China, it is closely connected to changes in the factors influencing such development. To capitalize on the development opportunities presented by national policies, Northeast China should adopt a model of urban–rural development that promotes rural growth through cities. This entails attracting talented individuals to return, enhancing the flow of urban–rural development elements in both directions, and creating a spatial development pattern characterized by “big city, big agriculture, and big ecosystem”. By doing so, the revitalization of Northeast China can be achieved.

COĞRAFİ AĞIRLIKLI REGRESYON İLE YÜKSEK TEKNOLOJİ İHRACATININ MEKANSAL ANALİZİ

The development of High-Technology Exports (HTE) contributes to the increase of competitiveness and growth in national production, the development of commercial and economic cooperation with foreign countries, and the diversification of the economy. In addition, high technology exports reduce the current account deficit of the country's economy. Therefore, high-tech industries have become indispensable for economic development in a country. The aim of this study is to examine the existence of spatial (neighbourhood) relations of countries in high-technology exports. In this study, Exploratory Regression analysis was applied to determine the model that best explains HTE among the variables. Moran Index was used for spatial autocorrelation, Geographical Weighted Regression (GWR) method for spatial regression analysis, and Ordinary Least Squares (OLS) method, to compare with the GWR method. The GWR model established in this study showed that countries have a weak or strong spatial relationship with their neighboring countries in terms of HTE. It has been observed that this spatial relationship is strong in the countries neighboring China, which ranks first in HTE, while the spatial relationship is weak in countries where HTE is low.

Exploring Spatial Variations and Factors associated with Walking Practice in Korea: An Empirical Study based on Geographically Weighted Regression

Walking practice is a representative indicator of the level of physical activity of local residents. Although the world health organization addressed reduction in prevalence of insufficient physical activity as a global target, the rate of walking practice in Korea has not improved and there are large regional disparities. Therefore, this study aimed to explore the spatial variations of walking practice and its associated factors in Korea. A secondary analysis was conducted using Community Health Outcome and Health Determinants Database 1.3 from Korea Centers for Disease Control and Prevention. A total of 229 districts was included in the analysis. We compared the ordinary least squares (OLS) and the geographically weighted regression (GWR) to explore the associated factors of walking practice. MGWR 2.2.1 software was used to explore the spatial distribution of walking practice and modeling the GWR. Walking practice had spatial variations across the country. The results showed that the GWR model had better accommodation of spatial autocorrelation than the OLS model. The GWR results indicated that different predictors of walking practice across regions of Korea. The findings of this study may provide insight to nursing researchers, health professionals, and policy makers in planning health programs to promote walking practices in their respective communities.

Spatial analysis of streamflow trends in burned watersheds across the western contiguous United States

AbstractDespite increasing magnitude and frequency of wildfire, understanding hydrological processes contributing to changes in streamflow is not well examined for the entire western contiguous United States (WCONUS). This study provides insight into whether considering spatially varying watershed characteristics, including burn severity patterns, can better explain streamflow trends at broad spatial and temporal scales. Standard geographically weighted regression (GWR) and multi‐scalar (MS) GWR were benchmarked against ordinary least squares (OLS) regression to determine if spatially varying coefficients could better explain spatially varying patterns and processes of peak flow, low flow, center timing of flow, and flashiness trends in burned watersheds. In most cases, GWR reduced spatial autocorrelation and provided insight into regional factors explaining streamflow trends. Both OLS and GWR models did not find burn severity‐based metrics significant in explaining streamflow trends, except the peak flow trends model that included the spatial arrangement of high severity patches quantified using landscape shape index (LSI). Positive peak flow trends coincided with increasing LSI values in Northern California and negative peak flow trends with increasing LSI in the southern WCONUS. This research could guide regionally specific post‐fire response and inform the development of new spatial metrics that could better represent post‐fire hydrological processes.