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The number of drug abuse cases in Indonesia from 2015-2020 tends to fluctuate. Factors influencing drug abuse cases in each region are thought to vary according to geographical location. This geographic effect is known as spatial heterogeneity. Spatial heterogeneity was analyzed usingGeographically Weighted Regression(GWR). This study aims to model the factors that influence drug abuse in every province in Indonesia, namely Economic Situation (X1), Association/Environment (X2), Convenience (X3) and Lack of Supervision (X4) using a Gaussian kernel. The results showed that the GWR with the Gaussian kernel is better at estimating the model because it has a higher value, namely R^2 with 90.94% and the AIC value equals 598.798979. Factors that significantly affect the number of cases of drug abuse in Indonesia are Economic Conditions, Association/Environment, Convenience and Lack of Supervision.

Abstract 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.

Shoho Castle Maps are maps of castle towns throughout Japan drawn by Kano School painters on the order of the shogun in 1644. The Shoho Castle Map of Takada, Joetsu City, Niigata Prefecture was used to visualize local distortions in historical maps and to scrutinize the mapmaking process. A novel method, geographically weighted bidimensional regression, was developed and applied to visualize the local distortions of the map. Exaggerated expressions by mapmakers that have not been identified in previous studies were revealed. That is, in addition to the castle being drawn enlarged, the town where the merchants and artisans lived was drawn larger than the castle. Therefore, the Takada Shoho Castle Map reflects mapmakers’ intentions, besides enlarging military facilities, which appear to have emphasized the pictorial composition of the map by placing the main gate to the castle at the center and drawing the map area evenly from the center in a well-balanced layout.

This study aims to explore the factors affecting variation in ozone concentration, using ozone concentration monitoring data from the Beijing–Tianjin–Hebei (Jing–Jin–Ji) region from 2015 to 2022. The characteristics of the spatial and temporal variation in ozone concentration, as well as the impact of meteorological and land-use types on this variation, were investigated. Seasonally, ozone concentrations in the Jing–Jin–Ji region were highest in summer, followed by spring and autumn, and were lowest in winter. Spatially, a characteristic trend of lower concentrations in urban centers and higher concentrations in suburban areas was found. From the results of correlation analysis and a stepwise regression model, temperature and solar radiation were found to be the primary factors affecting seasonal variations in ozone concentration, explaining 67.9% of the variance. Additionally, the results of geographically weighted regression models confirmed the spatial heterogeneity of the impact of temperature and solar radiation on ozone concentration. In urban centers, such as Beijing and Tianjin, temperature and solar radiation more strongly influenced ozone concentration. To determine the factors driving this spatial heterogeneity, a geodetector was employed to analyze the impact of various geographical factors on the spatial distribution of ozone concentration. In winter, the impact of various factors of urbanization, such as lighting index, population density, and construction land, as well as air quality factors (PM2.5 and NO2), on ozone concentration was stronger, with q values greater than 0.2. However, in autumn, the influence of these factors on ozone concentration weakened. Furthermore, the results of the two-factor interaction detection analysis showed that the q values for the interaction between urban and air quality factors ranged between 0.40 and 0.47, which were substantially higher than those for other interactions. This further confirmed the strong influence of urban activities on ozone concentration.

Anthropogenic nitrogen (N) inputs can have detrimental environmental effects, necessitating a comprehensive understanding of the nitrogen budget (NB) and its spatial correlation with the water quality. This study, utilizing a 2016 dataset, scrutinized 850 subwatersheds with diverse land covers across the Republic of Korea (ROK). Employing Geographically Weighted Regression (GWR), it examined the spatial correlations between the NBs and the quality of the groundwater and river water at the watershed scale. Robust correlations (R2 = 0.87) were observed between the groundwater quality and NBs, surpassing those of the surface water (R2 = 0.48). Sensitivity analyses highlighted the importance of high-resolution spatial data in capturing nuances within complex land covers. The integration of such data led to increases in the spatial correlations between the groundwater and river water quality of approximately 0.6–0.9 and 0.3–0.5, respectively. Notably, when the agricultural land cover exceeded 10%, significant enhancements in the spatial correlations were observed, emphasizing the pivotal role of agriculture in nutrient and water quality. At a 10% cropland ratio, the spatial correlations between the watershed-scale NBs and river/groundwater quality increased by approximately 76% and 501%, respectively. This study provides novel insights into the spatial relationships among NBs, water quality, and land use, highlighting the significance of high-resolution data and the impact of agricultural practices on watershed management. These findings contribute valuable information for developing strategies to mitigate nitrogen pollution.

Number of cases of tuberculosis (TB) was higher than that of the national level in Kashgar, China. This study aimed to analyze the spatial and temporal distribution of TB and the relationship between TB and social factors, which can provide a reference for the prevention and control of TB. We applied spatial autocorrelation analysis to study the distribution of tuberculosis in Kashgar. We used a geographically weighted regression (GWR) model to analyze the relationship between TB and social factors. A total of 100,330 cases of TB in Kashgar from 2016 to 2021 were analyzed. The number of TB cases in Kashgar was higher in the east, lower in the west, and most elevated in the center. The highest cumulative number of cases was found in Shache county. Global Moran's I ranged from -0.212 to -0.549, and local spatial autocorrelation analysis identified four clusters. According to our analysis, the incidence of tuberculosis was negatively correlated among the regions of Kashgar, and the related causes need to be analyzed in depth in future studies. Per capita gross domestic product (GDP), number of medical institutions per capita, and total population influenced the incidence of tuberculosis in Kashgar. Based on our findings, we suggest some effective measures to reduce the risk of TB infection, such as improving the living standard, developing the regional economy, and distributing health resources rationally.

Global warming caused by carbon emissions has become a major issue that countries need to address. As the largest carbon emitter globally, the construction industry is one of the major contributors to carbon emissions in China. It is of significance for carbon reduction to study carbon emission from construction industry. Based on various methods, this study explored the spatio-temporal characteristics of carbon emissions and the driving factors of construction industry. This study found, in 2007, 2010, and 2012, carbon emissions from the construction industry exhibited an increasing trend, and the indirect carbon emissions accounted for approximately 77% of the total carbon emissions overall; in addition, the regional gaps in carbon emissions are widening. The space centers of gravity of direct, indirect, and total carbon emissions showed similar rotations in the counterclockwise direction and gradually shifted to the northeast direction. Carbon emissions from the construction industry were predominantly influenced by the total population, number of employees in construction industry, labor productivity in construction industry, added value of the construction industry, energy consumption in construction industry in 2007, evolution to the mutual influence of the total population, labor productivity in construction industry, and energy consumption in construction industry in 2012. The finds can make references for the regional sustainable development. [ABSTRACT FROM AUTHOR]

Background: In Sarawak, 252 300 coronavirus disease 2019 (COVID-19) cases have been recorded with 1 619 fatalities in 2021, compared to only 1 117 cases in 2020. Since Sarawak is geographically separated from Peninsular Malaysia and half of its population resides in rural districts where medical resources are limited, the analysis of spatiotemporal heterogeneity of disease incidence rates and their relationship with socio-demographic factors are crucial in understanding the spread of the disease in Sarawak. Methods: The spatial dependence of district-wise incidence rates is investigated using spatial autocorrelation analysis with two orders of contiguity weights for various pandemic waves. Nine determinants are chosen from 14 covariates of socio-demographic factors via elastic net regression and recursive partitioning. The relationships between incidence rates and socio-demographic factors are examined using ordinary least squares, spatial lag and spatial error models, and geographically weighted regression. Results: In the first 8 months of 2021, COVID-19 severely affected Sarawak's central region, which was followed by the southern region in the next 2 months. In the third wave, based on second-order spatial weights, the incidence rate in a district is most strongly influenced by its neighboring districts' rate, although the variance of incidence rates is best explained by local regression coefficient estimates of socio-demographic factors in the first wave. It is discovered that the percentage of households with garbage collection facilities, population density and the proportion of male in the population are positively associated with the increase in COVID-19 incidence rates. Conclusion: This research provides useful insights for the State Government and public health authorities to critically incorporate socio-demographic characteristics of local communities into evidence-based decision-making for altering disease monitoring and response plans. Policymakers can make well-informed judgments and implement targeted interventions by having an in-depth understanding of the spatial patterns and relationships between COVID-19 incidence rates and socio-demographic characteristics. This will effectively help in mitigating the spread of the disease. [ABSTRACT FROM AUTHOR]

Cambodia has the most fires per area in Southeast Asia, with fire activity have significantly increased since the early 2000s. Wildfire occurrences are multi-factorial in nature, and isolating the relative contribution of each driver remains a challenge. In this study, we quantify the relative importance of each driver of fire by analyzing annual spatial regression models of fire occurrence across Cambodia from 2003 to 2020. Our models demonstrated satisfactory performance, explaining 69 to 81% of the variance in fire occurrence. We found that deforestation was consistently the dominant driver of fire across 48 to 70% of the country throughout the study period. Although the influence of low precipitation on fires has increased in 2019 and 2020, the period is not long enough to establish any significant trends. During the study period, wind speed, elevation, and soil moisture had a slight influence of 6–20% without any clear trend, indicating that deforestation continues to be the main driver of fire. Our study improves the current understanding of the drivers of biomass fires across Cambodia, and the methodological framework developed here (quantitative decoupling of the drivers) has strong potential to be applied to other fire-prone areas around the world. [ABSTRACT FROM AUTHOR]

The distribution of atmospheric CO2 is not homogenous, primarily due to variations in the CO2 budgets of regional terrestrial ecosystems. To formulate a comprehensive strategy to combat the increasing global CO2 levels and associated warming, it is crucial to consider both the distribution of atmospheric CO2 and the CO2 budgets of ecosystems. This study focused on analyzing the relationship between regional atmospheric CO2 and CO2 budgets in China from 2010 to 2017. Initially, a robust estimation model of net ecosystem CO2 exchange was developed to calculate CO2 budgets using collected emission data. Subsequently, Pearson correlation, redundancy analysis, and geographically weighted regression techniques were employed to examine the link between atmospheric CO2 levels, CO2 budgets, and other meteorological factors at various spatial and temporal scales. The findings from the redundancy analysis and geographically weighted regression indicated that the atmospheric CO2 content of each province could not be solely determined by the regional CO2 budgets. However, a significant and positive correlation between atmospheric CO2 levels and CO2 budgets was observed in non-coastal provinces for a period of six months (R2 ranging from 0.46 to 0.83). Consequently, it is essential to promote a balance between CO2 emissions and the CO2 uptake capacity of regional ecosystems. This balance would minimize positive CO2 budgets and effectively mitigate the increase in atmospheric CO2 levels. [ABSTRACT FROM AUTHOR]

Water is one of the critical needs of human life and living things. Therefore, proper planning is important for its consumption all over the world, especially in Iran. This study was conducted to evaluate the impacts of land use change on water yield in the Teraz watershed, Khuzestan province, Iran, from 1990 to 2050. Water yield was evaluated using integrated valuation of ecosystem services and tradeoffs (InVEST) tool and land use was mapped using the maximum likelihood classification in the ENVI 5.3, and CA-Markov in the TerrSet environment. Moreover, the relationship between water yield and landscape metrics, including the number of patches (NP), patch density (PD), landscape shape index (LSI), and Largest Patch Index (LPI), was considered based on the Geographically Weighted Regression (GWR) method in the Arc GIS 10.5. The results showed that the area of forest and rangeland in the studied area decreased in the last 30 years, by 3199 and 1611 ha, respectively, and the area of agriculture and construction land uses increased by 4388 and 387 ha, respectively. It is predicted that in the next 30 years, 2442 ha of forests will decrease and the area of agriculture, rangeland, and construction land uses will increase by 1651, 687, and 102 ha, respectively. It was found that the total volume of available water yield in the region is equal to 26.5 Mm3 in 2020, on average of 857 m3 ha-1, and based on that, the ranking of land uses is as follows: 1. Construction, 2. Rangeland, 3. Forest, 4. agriculture. The results of GWR confirmed that water yield had a significant and negative spatial relationships with the NP, PD, and LSI metrics (R2>=0.83, p-value>0.05), while its relationship with the LPI metric was a significant and positive relationship (R2>0.84, p-value>0.05). It can be concluded that the landscape features and land use pattern can determine the production and yield of water in the study area. [ABSTRACT FROM AUTHOR]

Residential property values are influenced by a combination of physical, socio-economic and neighbourhood factors. This study investigated the influence of public schools on residential property prices. Relatively few existing models have taken the spatial heterogeneity of different submarkets into account. To fill this gap, three types of valuation models were applied to sales data from both non-strata and strata properties, and how the proximity and quality of public schools have influenced the prices of different residential property types was examined. The findings demonstrate that an increase of one unit in the normalised NAPLAN score of primary and high schools will lead to a 3.9% and 1.4%, 2.7% and 2.8% rise in housing prices for non-strata and strata properties, respectively. It is also indicated that the application of geographically weighted regression (GWR) can better capture the varying effects of schools across space. Moreover, properties located in the catchment of high-scoring schools in northern Greater Sydney are consistently the most influenced by school quality, regardless of the property type. These findings contribute to a comprehensive understanding of the relationships between public schools and the various submarkets of Greater Sydney. This is valuable for the decision-making processes of home buyers, developers and policymakers. [ABSTRACT FROM AUTHOR]

In an era of urbanization, urban livability is a critical concern. This study explores the relationship between administrative capacity and urban livability in Türkiye's 81 provinces. Recent global attention to administrative capacity highlights its role in governance and societal progress. The research emphasizes the importance of enhancing administrative capacity for positive outcomes, including improved urban livability, and examines the impact of provincial administrative capacity on urban livability geographically. A robust administrative capacity is crucial for effective public institutions, from local to international levels. Recent research shows it fosters better institution performance, economic growth, democracy, policy effectiveness, conflict prevention, counterterrorism, and local democracy. Among such contributions administrative capacity is also key to a city's success, attracting residents and businesses, enhancing urban livability. In order to reveal such dynamic, the study covers all 81 Turkish provinces, testing geographic variations in the impact of administrative capacity on livability. It uses provincial administrative capacity and Forbes Magazine's livability index in global and geographical regression models. The results confirm a strong relationship between provincial administrative capacity and urban livability. Geographically, this impact varies across regions. In conclusion, the research reveals the importance of administrative capacity in shaping urban livability. Policymakers and urban planners should focus on enhancing administrative competence to improve residents' quality of life, fostering informed decision-making and policy formulation for livable cities. [ABSTRACT FROM AUTHOR]

A geographically weighted regression (GWR) model with fewer explanatory variables and higher prediction accuracy is required in spatial analysis and other practical applications. This article proposes an l 0 -norm variable adaptive selection method to enhance performances of a GWR by simultaneously performing model selection and coefficient optimization. Specifically, we formulate a regularized GWR model with an additional l 0 -norm constraint to shrink those unimportant regression coefficients toward zero and propose an adaptive variable selection algorithm by iteratively distinguishing the important variables from the variable set. At each location, the best variable subset and optimizing coefficient estimations are simultaneously achieved under the l 0 -GWR framework. Moreover, two novel criteria, the modified Bayesian information criterion and the interpretability of coefficient symbol, which specify the variable selection and model interpretation, respectively, are also introduced to improve the performance of the l 0 -GWR. Experiments on both simulated and actual data sets demonstrate that the proposed algorithm can significantly improve the estimation accuracy of coefficients and can also enhance the interpretative ability of the established model. [ABSTRACT FROM AUTHOR]

The spatiotemporal transformations of rural residential lands and populations reflect changes in rural human–land relations. This study uses high-precision rural residential land patches and population distribution data to detect the area, population density, and spatial heterogeneity of newly added rural residential land (NARRL) in China from 2000 to 2020 through spatial local clustering and geographically weighted regression. The patch results were summarized into county-level units for regional comparison, spatial clustering identification, and policy recommendations. The main conclusions are as follows: (1) The total rural residential area increased by 13.86% between 2000 and 2020. The average population density of NARRL (APDNARRL) at patch scale is 701.64 person/km2, significantly exceeding the 507.23 person/km2 of the remaining patches. (2) There are obvious spatial differences in the distribution of APDNARRL as per county-level statistics. There are significant differences in APDNARRL on both sides of the Hu Huanyong Line; the APDNARRL on the left is significantly lower than that on the right. (3) Spatial heterogeneity was found to be among the driving factors of APDNARRL. This study also detected the number and location of hollowing counties; it is significant for monitoring dynamic changes in rural residential lands, revealing their spatial distribution patterns and driving factors, thus improving the optimization of rural land resources. [ABSTRACT FROM AUTHOR]

Ecosystem services are the beneficial goods and services that ecosystems provide to humans. Urbanization is an important feature of human social development. While promoting economic and social development, it also brings about land degradation, resource depletion, environmental pollution and other problems, intensifying the transformation of natural ecosystems into semi-natural and artificial ecosystems, ultimately leading to the loss of ecosystem service functions and declining value. The study of the impact of urbanization on the value of ecosystem services is of critical importance for the conservation of ecosystems and sustainable development. This study examined the spatio-temporal patterns of urbanization's impacts on ecosystem service value in the Yellow River Basin from the perspective of spatio-temporal heterogeneity. Findings: (1) Both the ecosystem service value (ESV) and urbanization level (UL) in the Yellow River Basin were on the rise on the whole, but they were significantly spatially negatively correlated and mainly characterized by the high–low spatial clustering of "low ESV–high UL" and "high ESV–low UL". This negative correlation was gradually weakened with the transformation of the urbanization development mode and ecological restoration projects in the Yellow River Basin. (2) The impacts of the five urbanization subsystems on the value of ecosystem services were diverse. Landscape urbanization had a negative impact on the value of ecosystem services in all regions; economic urbanization and innovation urbanization changed from having a negative to a positive impact; and demographic urbanization and social urbanization had both a positive and a negative impact. (3) To promote the coordinated development of ecological environmental protection and urbanization in the YRB, this paper proposes to change the urbanization development model, implement ecological restoration by zoning, and formulate classified development plans. This study compensates for the shortcomings of current studies that ignore the different impacts of urbanization subsystems on ecosystem service value and lack sufficient consideration of the spatio-temporal heterogeneity characteristics of urbanization and ESVs, enriches the theoretical understanding of the interrelationships between natural and human systems in basin areas, and provides a scientific basis for the rational formulation of urban planning and ecological protection policies in the region, which is of great theoretical and practical significance. [ABSTRACT FROM AUTHOR]

The purpose of this article is to determine the factors influencing the birth rate in Romania, by incorporating explicitly the spatial factor in the proposed models. The study is justified by the dramatic fall of the birth rate over the past three decades. With a negative natural population growth and an increasing number of emigrants, the population will become older and there will be a few million less in the next decades. To achieve the objective, various spatial modelling methods were used, such as Spatial AutoRegressive Model (SAR), Spatial Error Model (SEM), Geographically Weighted Regression (GWR) and a spatial panel data model. The data granularity is at the county level for the year 2020. Results show that GDP per capita and the amount of financial support received by families for raising a child have a significant effect on the birth rate. Using a spatial approach for modelling the birth rate, we reveal demographic problems that may exist in certain areas and identify the regions that would require a policy to stimulate birth rates. [ABSTRACT FROM AUTHOR]

The industrial transfer of heavy industries such as non-metallic mineral manufacturing, metal smelting and manufacturing from the eastern coast of China to the central region is beneficial to the economic development of the central region on the one hand, but increases carbon emissions in the central region on the other hand. In February 2022, the National Development and Reform Commission approved the "14th Five-Year Plan for the Development of the Urban Agglomeration in the Middle Reaches of the Yangtze River". This indicates that the urban agglomeration of the middle reaches of the Yangtze River is an important region for implementing green development in the central area. The spatial and temporal evolution of carbon emissions and influencing factors in this region are the foundation for achieving carbon peaking and the carbon neutrality goal. This paper calculates the total carbon emissions of the cities in the urban agglomeration of the middle reaches of the Yangtze River and uses models such as spatial autocorrelation, geographically weighted regression, and Geodetector to explore the spatial–temporal pattern of carbon emissions. The results show the following: (1) The total carbon emissions of the middle reaches of the Yangtze River urban agglomeration showed fluctuations during 2010–2020, and the carbon emission reduction effect is unstable. Additionally, the carbon emissions of the middle reaches of the Yangtze River city cluster show obvious spatial variability, but the high carbon emission area is always concentrated in Wuhan, and this remains unchanged. (2) In 2010, 2014 and 2017, population size was the most important factor affecting carbon emission divergence, and in terms of interaction, the interaction between energy intensity and GDP and urbanization is the reason for the increasing carbon emissions. (3) The influence of population size on carbon emissions decreases from north to south, the influence of energy intensity on carbon emissions shows a spread from the most influential region in the northwest to the centre and then to the northeast, and the GDP per capita has little influence on the difference of carbon emissions spatial distribution. [ABSTRACT FROM AUTHOR]

The development of urbanization and the transformation of green lands into impermeable land increase temperature and create urban heat islands (UHIs). Our observations with remote sensing instruments of Landsat platforms show considerable changes in land use types in Beijing city with the shrinking of green lands, expansion of built environments, and a slight increase in the temperature during the recent four decades. Using remote sensing instruments of Landsat platforms and registered data from two meteorological stations in Beijing, this study finds the relationship between land surface temperature (LST) and the increasing conversion of cultivated lands into built-up areas. This article presents innovative research that shows the mutual correlation well and recommends revisions in the land use policies for better weather. The geographically weighted regression model (GWR) with a Gaussian weighting kernel function analyzes the impact of various urban land use types on the LST and the increase UHIs. In Beijing city, green lands show fewer standard deviations (SD) in the average temperatures equal to 0.109, while the industrial spaces exhibit a high SD equal to 0.212. The outcomes of this paper contribute to finding optimal land use policies everywhere in the world with the increasing urbanization through simulating its model for a more comfortable life. [ABSTRACT FROM AUTHOR]

The identification of ecosystem health and its influencing factors is crucial to the sustainable management of ecosystems and ecosystem restoration. Although numerous studies on ecosystem health have been carried out from different perspectives, few studies have systematically investigated the spatiotemporal heterogeneity between ecosystem health and its influencing factors. Considering this gap, the spatial relationships between ecosystem health and its factors concerning climate, socioeconomic, and natural resource endowment at the county level were estimated based on a geographically weighted regression (GWR) model. The spatiotemporal distribution pattern and driving mechanism of ecosystem health were systematically analysed. The results showed the following: (1) the ecosystem health level in Inner Mongolia spatially increases from northwest to southeast, displaying notable global spatial autocorrelation and local spatial aggregation. (2) The factors influencing ecosystem health exhibit significant spatial heterogeneity. Annual average precipitation (AMP) and biodiversity (BI) are positively correlated with ecosystem health, and annual average temperature (AMT) and land use intensity (LUI) are estimated to be negatively correlated with ecosystem health. (3) Annual average precipitation (AMP) significantly improves ecosystem health, whereas annual average temperature (AMT) significantly worsens eco-health in the eastern and northern regions. LUI negatively impacts ecosystem health in western counties (such as Alxa, Ordos, and Baynnur). This study contributes to extending our understanding of ecosystem health depending on spatial scale and can inform decision-makers about how to control various influencing factors to improve the local ecology under local conditions. Finally, this study also proposes some relevant policy suggestions and provides effective ecosystem preservation and management support in Inner Mongolia. [ABSTRACT FROM AUTHOR]

Poverty is one of the world's main problems, which has been very difficult to overcome. The poverty rate has influences on economic growth in an area. The greater the poverty rate in an area, the more difficult it is for an area to grow economically. Many factors affect the level of poverty. Geographical location, level of education, population growth in each region, significantly affect the poverty level. The many factors that affect the poverty level make it difficult for us to predict which factors will influence the level of poverty in an area, making it difficult for us to solve the problem. This research was made to predict what factors influence the level of poverty in Papua, Indonesia. This study uses a Geographically Weighted Regression (GWR) method due to the existence of spatial heterogeneity. It produces model estimates that are local for each location and different from other locations to get a model with the most influential factors in each region. The results of this study are as follows. First, agricultural employees affected the poverty rate in each location in Papua. Second, house area and households with access to safe water are significant variables in Yapen, Biak Numfor, and Supiori. [ABSTRACT FROM AUTHOR]

This paper analyses the spatial trends and patterns of the crime rates in London and explores how socio-economic characteristics affect crime rates with consideration of the geographic context across London. The 2015 London Crime Statistics and Socio-economic Characteristics datasets were used. First, we investigated the spatial patterns of crime rates through exploratory spatial analysis at the ward level. In addition, both the ordinary least square (OLS) model and geographically weighted regression (GWR) model, which allow the effects of factors to vary in spatial scales, were adopted and compared to explore the potential spatially varying effect across London. The results showed that there exists obvious spatial clustering for the crime rate in central London. Both global and local Moran's I values indicated the spatial dependence of crime at the ward level. The GWR model performed better in explaining crime rates than the OLS model. Only two factors, namely, the percentage of children aged from 0 to 15 and employment rates, had significant spatial variability in London. The influences of the percentage of children aged 0 to 15 on crime rates are constantly negative over a spatial scale; however, employment rates positively affect crime rates in the north-western areas near the centre of London. Therefore, this paper focuses more on the spatial perspective, which fills the gap in traditional crime analysis, especially on the spatially varying influence of socio-economic status. [ABSTRACT FROM AUTHOR]

Geographically weighted regression (GWR) is a classical modeling method for dealing with spatial non-stationarity. It incorporates the distance decay effect in space to fit local regression models, where distance is defined as Euclidean distance. Although this definition has been expanded, it remains focused on physical distance. However, in the era of globalization and informatization, where the phenomenon of remotely close association is common, physical distance may not reflect real spatial proximity, and GWR based on physical distance has clear limitations. This paper proposes a geographically weighted regression based on a network weight matrix (NWM GWR) model. This does not rely on geographical location modeling; instead, it uses network distance to measure the proximity between two regions and weights observations by improving the kernel function to achieve distance attenuation. We adopt the population mobility network to establish a network weight matrix, modeling China's urbanization and its multidimensional driving factors using network autocorrelation and NWM GWR methods. Results show that the NWM GWR model has more accurate fit and better stability than ordinary least squares and GWR models, and better reveals relationships between variables, which makes it suitable for modeling economic and social systems more broadly. [ABSTRACT FROM AUTHOR]

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In this paper, a new adaptive estimation approach is proposed for the spatially varying coefficient models with unknown error distribution, unlike geographically weighted regression (GWR) and local linear geographically weighted regression (LL), this method can adapt to different error distributions. A generalized Modal EM algorithm is presented to implement the estimation, and the asymptotic property of the estimator is established. Simulation and real data results show that the gain of the new adaptive method over the GWR and LL estimation is considerable for the error of non-Gaussian distributions. [ABSTRACT FROM AUTHOR]

Delineation of areas susceptible to gully erosion with high accuracy and low cost using significant factors and statistical model is essential. In the present study, a gully susceptibility erosion map (GEM) was developed using hydro-geomorphometric parameters and geographic information system in western Iran. For this aim, a geographically weighted regression (GWR) model was applied, and its results compared to frequency ratio (FreqR) and logistic regression (LogR) models. Almost twenty effective parameters on gully erosion were detected and mapped in the ArcGIS®10.7 environment. These layers and gully inventory maps (375 gully locations) were prepared using aerial photographs, Google Earth images, and field surveys divided into 70% and 30% (263 and 112 samples) ArcGIS®10.7. The GWR, FreqR, and LogR models were developed to generate gully erosion susceptibility maps. The area under the receiver/relative operating characteristic curve (AUC-ROC) was calculated to validate the generated maps. Based on the LogR model results, soil type (SOT), rock unit (RUN), slope aspect (SLA), Altitude (ALT), annual average precipitation (AAP), morphometric position index (MPI), terrain surface convexity (TSC), and land use (LLC) factors were the most critical conditioning parameters, respectively. The AUC-ROC results show the accuracy of 84.5%, 79.1%, and 78% for GWR, LogR, and FreqR models, respectively. The results show high performance for the GWR compared to LogR and FreqR multivariate and bivariate statistic models. The application of hydro-geomorphological parameters has a significant role in the gully erosion susceptibility zonation. The suggested algorithm can be used for natural hazards and human-made disasters such a gully erosion on a regional scale. [ABSTRACT FROM AUTHOR]

According to the World Bank, Taiwan ranks first in its exposure to natural hazards. We present an estimated hedonic price function that shows how differential exposure to earthquakes or flooding impacts housing prices in the Taichung metropolitan area. The 1999 Jiji earthquake affected the area, which also regularly experiences flooding due to typhoons. Geographically weighted regression estimates of implicit attribute prices show that housing in high-risk and medium-risk earthquake and flooding zones is associated with price discounts that reflect the higher risk of adverse events, especially in high-risk earthquake zones. Effects are comparable in magnitude to more frequently estimated neighbourhood attributes such as neighbourhood income or school quality. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a major factor that affects many biophysical processes in the land–atmosphere relationship. This factor is obtained from satellite images having different temporal and spatial resolutions. This study applied the geographically weighted regression (GWR) model for four different dates representing each season a year to improve the LST images obtained in coarse resolution. In this study, MODIS LST images that are available having fine temporal but coarse spatial resolution were modeled using NDBI and NDVI indices, and their spatial resolution is improved. In addition, LANDSAT 8 images were used as reference images to evaluate the accuracy of the images obtained from the models. Results of the GWR model have been evaluated by comparing it statistically with TsHARP and DisTradother commonly used methods. As a result of the comparison by using the average of four dates outputs, the GWR model (R2 = 0.73, RMSE = 0.78) was more successful than the TsHARP (R2 = 0.56, RMSE = 1.00) and DisTrad (R2 = 0.49, RMSE = 1.09) methods. The most successful downscaling performance in the GWR model was obtained in the spring season (RSR = 0.48). According to these findings, the GWR model can be used for downscaling LST images in urban areas. However, before applying this algorithm to scenarios outside of urban areas, it is recommended to use the required parameters and optimize their combinations. [ABSTRACT FROM AUTHOR]

Theft is an inevitable problem in the context of urbanization and poses a challenge to people's lives and social stability. The study of theft and criminal behavior using spatiotemporal, big, demographic, and neighborhood data is important for guiding security prevention and control. In this study, we analyzed the theft frequency and location characteristics of the study area through mathematical statistics and hot spot analysis methods to discover the spatiotemporal divergence characteristics of theft in the study area during the pre-COVID-19 and COVID-19 periods. We detected the spatial variation pattern of the regression coefficients of the local areas of thefts in Haining City by modeling the influencing factors using the geographically weighted regression (GWR) analysis method. The results explained the relationship between theft and the influencing factors and showed that the regression coefficients had both positive and negative values in the pre-COVID-19 and COVID-19 periods, indicating that the spatial distribution of theft in urban areas of Haining City was not smooth. Factors related to life and work indicated densely populated areas had increased theft, and theft was negatively correlated with factors related to COVID-19. The other influencing factors were different in terms of their spatial distributions. Therefore, in terms of police prevention and control, video surveillance and police patrols need to be deployed in a focused manner to increase their inhibiting effect on theft according to the different effects of influencing factors during the pre-COVID-19 and COVID-19 periods. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of the study is to perform the spatial analysis of the conditioning factors for the increase in the incidence rate of dengue cases in municipalities located in the Amazon biome, in the period from 2016 to 2021. Three statistical approaches were applied: Moran's index, ordinary least squares regression, and geographically weighted regression. The results revealed that the incidence rates of dengue cases cluster in two areas, both located in the south of the Amazon biome, which is associated with the Arc of Deforestation. The variable deforestation influences the increase in dengue incidence rates revealed by the OLS and GWR model. The adjusted R2 of the GWR model was 0.70, that is, the model explains about 70% of the total case variation of dengue incidence rates in the Amazon biome. The results of the study evidence the need for public policies aimed at the prevention and combat of deforestation in the Amazon region. [ABSTRACT FROM AUTHOR]

Data from monitoring programs with high spatial resolution but low temporal resolution are often overlooked when assessing temporal trends, as the data structure does not permit the use of established trend analysis methods. However, the data include uniquely detailed information about geographically differentiated temporal trends driven by large-scale influences, such as climate or airborne deposition. In this study, we used geographically weighted regression models, extended with a temporal component, to evaluate linear and nonlinear trends in environmental monitoring data. To improve the results, we tested approaches for station-wise pre-processing of data and for validation of the resulting models. To illustrate the method, we used data on changes in total organic carbon (TOC) obtained in a monitoring program of around 4800 Swedish lakes observed once every 6 years between 2008 and 2021. On applying the methods developed here, we identified nonlinear changes in TOC from consistent negative trends over most of Sweden around 2010 to positive trends during later years in parts of the country. [ABSTRACT FROM AUTHOR]

The present study analyzes spatio-temporal urban growth in Kathmandu Urban Agglomeration (KUA) and its impact on ecological infrastructures between 2000 and 2020. A geographically weighted regression (GWR) approach was used to investigate intra-urban drivers of urban growth between 2000–2010 (epoch 1) and 2010–2020 (epoch 2), and to predict built-up growth for the year 2030. The study makes crucial contributions to the urban growth modelling literature by (a) investigating six different neighborhood sizes (3 × 3 to 13 × 13) for computing proportion of built-up cells and (b) eliminating/selecting covariates using correlation check and global lasso regression. The superiority of GWR model over global regression model has been evaluated using the Akaike information criterion score and the stationarity test. The chronological urban growth analysis of KUA highlights rapid growth in mountainous landscape from 54.90 to 166 km2 during 2000–2020 and is projected to increase to 224.22 km2 during 2000–2030. This has significantly altered the forest-agriculture landscape during 2000–2020 (111.2 km2) and is expected to affect a large segment of ecological (blue-green) infrastructure (138 km2) by 2030. The findings of this study may be used for policy formulation, appropriate land use planning, and sustainable urban development. [ABSTRACT FROM AUTHOR]

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China's pursuit of sustainable and healthy economic growth requires the promotion of high-quality development. While many scholars have studied high-quality development, few have examined the coupling and coordination among its internal systems. The study aims to analyze the influencing factors of high-quality coordinated development, identify problem areas based on the five development statuses, and provide practical recommendations for optimizing the spatial layout of high-quality development in these areas. By applying a coupling coordination model and a geographically weighted regression model, the comprehensive level of high-quality coordinated development in the eastern coastal areas was evaluated. The results revealed that the majority of the eastern coastal region exhibited weak coordination and significant spatial differences in their comprehensive level. The problem cities were predominantly located in the southern and northern coastal areas. An economic foundation and innovation potential have a positive and stable impact on high-quality coordinated development. [ABSTRACT FROM AUTHOR]

Actual evapotranspiration (ET) is perhaps the most difficult quantity to directly measure among the major water balance components. Because of the high cost and labor constraints associated with the direct measurement of ET, empirical data-driven modeling has frequently been used to estimate ET. Beyond the widely used traditional type regression that has the effect of producing ‘global’ parameter estimates, assumed to be uniform throughout an area, we utilized a more localized spatially non-stationary technique – the geographically weighted regression (GWR) – to estimate mean monthly ET in the Passaic River Basin (PRB). We identified the key environmental controls of ET and developed new sets of spatially varying empirical ET models based on variable combinations that produced the best-fit model. The analysis showed that temporal and spatial variabilities in ET over the PRB are driven by climatic and biophysical factors. We found that the key controlling factors were different from month to month, with wind speed being dominant throughout the year in the study basin. A monthly mean ET index map was further generated from the model to illustrate areas where ET exceeds precipitation. This will among others enable water loss due to evapotranspiration to be accounted for in future water supply plans for the basin. HIGHLIGHTS New set of empirical monthly actual ET models was developed.; Use of spatially varying regression technique removes frequently overlooked stationarity assumption of ordinary least square models (OLS).; The GWR technique reveals any rapid changes in environmental variables over an area, necessitating further investigation.; Readily available data can be used to reasonably quantify monthly ET under mean climatic conditions.;

Air temperature records are acquired by networks of weather stations which may be several kilometres apart. In complex topographies the representativeness of a meteorological station may be diminished in relation to a flatter valley, and the nearest station may have no relation to a place located near it. The present study shows a simple method to estimate the spatial distribution of minimum and maximum air temperatures from MODIS land surface temperature (LST) and normalized difference vegetation index (NDVI) images. Indeed, there is a strong correlation between MODIS day and night LST products and air temperature records from meteorological stations, which is obtained by using geographically weighted regression equations, and reliable results are found. Then, the results allow to spatially interpolate the coefficients of the local regressions using altitude and NDVI as descriptor variables, to obtain maps of the whole region for minimum and maximum air temperature. Most of the meteorological stations show air temperature estimates that do not have significant differences compared to the measured values. The results showed that the regression coefficients for the selected locations are strong for the correlations between minimum temperature with LSTnight (R2 = 0.69–0.82) and maximum temperature with LSTday (R2 = 0.70–0.87) at the 47 stations. The root mean square errors (RMSE) of the statistical models are 1.0 °C and 0.8 °C for night and daytime temperatures, respectively. Furthermore, the association between each pair of data is significant at the 95% level (p

Surface urban heat island (SUHI) can considerably influence the urban environment and the quality of life. It is vital to examine how underlying surface properties impact seasonal and diurnal SUHIs. However, the influence of three-dimensional (3D) urban morphological parameters (UMPs) on SUHIs has not been thoroughly studied under varying climatic settings. To fill this knowledge gap, the present study investigated seasonal and diurnal changes in SUHI intensities (ΔT) in 208 cities in China from 2014 to 2016 using moderate-resolution imaging spectroradiometer (MODIS) land surface temperature products. In addition, the influence of potential factors in urban surface energy balance, including two-dimensional (2D) and 3D UMPs, socio-economic indices, urban greening, and surface albedo, on seasonal and diurnal ΔT were assessed under different climatic settings and with different city sizes using the method of Geographically Weighted Regression (GWR). Results show that negative summer daytime ΔT was observed in some cities under dry climates. Generally, in summer, the ΔT during daytime was higher than at nighttime. The 3D UMPs (i.e. building height and volume) yielded more decisive influences on ΔT than 2D UMP (i.e. building coverage). This is particularly true for the summer diurnal cycle and under dry climatic settings. Building height was found to be negatively correlated with surface temperatures, while building volume was positively correlated. Additionally, the 3D UMPs yielded more influences on winter ΔT than summer ΔT. The capability of vegetation to regulate ΔT was more potent in dry climates than in wet climates and in small cities than in large cities. Varying climates and city sizes can modify the significance of the 2D and 3D UMPs on the urban surface energy balance, suggesting that urban thermal mitigation should consider climate background and population size.

Real estate market analysis can involve many aspects. One of them is the study of the influence of various factors on prices and property values. For this type of issues, different kinds of measures and statistical models are often used. Many of them do not give unambiguous results. One of the reasons for this is the fact that the real estate market is characterized by the concept of local markets, which may be affected in different ways by economic, social, technical, environmental and other factors. Incorporating the influence of local markets, otherwise known as submarkets, into models often helps improve the precision of mass real estate valuation results. The delineation of submarket boundaries can be done in several different ways. One tool that is helpful in these types of situations are geographically weighted regression (GWR) models. The problem that may arise when using such models is related to the nature of some market factors, which may be of a qualitative nature. Because neighborhoods of individual properties may lack variability in terms of some variables, estimating GWR models is significantly difficult or impossible.

Abstract Background Dyslipidemia is a key factor causing cardio cerebrovascular diseases, and the total cholesterol (TC) is an important lipid indicator among them. Studies have shown that environmental factors have a strong association with TC levels. Previous studies only focused on the seasonal variation of TC level and the short-term effects of some environmental factors on TC level over time, and few studies explored the geographical distribution of TC level and quantified the impact of environmental factors in space. Methods Based on blood test data which was from China Health and Retirement Longitudinal Study (Charls) database, this study selected the TC level test data of middle-aged and elderly people in China in 2011 and 2015, and collected data from 665 meteorological stations and 1496 air pollutant monitoring stations in China. After pretreatment, the spatial distribution map of TC level was prepared and the regional statistics were made. GeoDetector and geographically weighted regression (GWR) were used to measure the relationship between environmental factors and TC level. Results The TC level of middle-aged and elderly in China was higher in females than in males, and higher in urban areas than in rural areas, showing a clustered distribution. The high values were mainly in South China, Southwest China and North China. Temperature, humidity, PM10 and PM2.5 were significant environmental factors affecting TC level of middle-aged and elderly people. The impact of pollutants was more severe in northern China, and TC level in southern China was mainly affected by meteorological factors. Conclusions There were gender and urban-rural differences in TC levels among the middle-aged and elderly population in China, showing aggregation in geographical distribution. Meteorological factors and air pollutants may be very important control factors, and their influencing mechanism needs further study.

Improving the vitality of cities has long been considered an important goal of planning. However, people’s understanding of how complex and diverse built environment factors affect urban vitality is still limited. In recent years, the emergence of new data provides a new perspective for the study of urban vitality. In this article, the spatio-temporal variation of urban vitality was quantitatively measured by using Baidu heat map, and the influence of built environment factors on urban vitality is further analyzed by geographically weighted regression model. The analysis was conducted at the block level, taking into account differences between weekdays and weekends. The results show that Shenzhen presents a vitality pattern of three centers and two sub-centers, and the average vitality level of weekdays is higher than that of weekends. Distance to subway station, road density, residential density, land mixed use, and compactness have significant influence on block vitality, but the influence varies from block to block, showing strong spatial heterogeneity. Commercial facility density and floor space ratio show significance only on weekends and weekdays, respectively. The findings reveal that we need to take regional differences into consideration and develop more targeted urban planning policies to facilitate block vitality.

Researchers have been trying to identify the contributory factors behind pedestrian crash occurrences through studies at both microscopic and macroscopic levels. However, built environment-related factors have primarily been examined in developed countries, resulting in a limited understanding of the phenomenon in the context of developing countries. Methodologically, these studies mostly used global regression models, which failed to incorporate spatial autocorrelation and spatial heterogeneity. Additionally, some of these studies applied spatial regression models randomly without following a comprehensive logical framework behind their selections. Our study aimed to develop a comprehensive spatial regression modeling framework to examine the relationships between pedestrian crash occurrences and the built environment at the macroscopic level in a megacity, Dhaka, the capital of a developing country: Bangladesh. Using secondary pedestrian crash data, the study applied one global non-spatial model, two global spatial regression models, and two local spatial regression models following a comprehensive spatial regression modeling framework. The factors which significantly contributed to pedestrian crash occurrences in Dhaka were employed person density, mixed and recreational land use density, primary road density, major intersection density, and share of non-motorized modes. Except for the last factor, all the other ones were positively related to pedestrian crash density. Among the five models used in this study, the multiscale geographically weighted regression (MGWR) performed the best as it calibrated each local relationship with a distant spatial scale parameter. The findings and recommendations presented in this study would be useful for reducing pedestrian crashes and choosing the appropriate modeling technique for crash analysis.

Abstract Background Delayed antenatal care is when the first visit is carried out after 12 gestational weeks. Despite the fact that many studies have been conducted on antenatal care initiation, little attention has been paid to its spatial pattern. Therefore, this study examine geographical weighted regression analysis of delayed antenatal care initiation and its factors among all reproductive-aged women in Ethiopia. Objective To assess geographical weighted regression analysis of delayed antenatal care initiation and its factors among all reproductive-aged women in Ethiopia, 2016. Methods This study was grounded on the 2016 Ethiopian Demographic Health Survey. It incorporated extracted sample size of 4740 (weighted) reproductive-aged women. ArcGIS version 10.8 and SaTScan™ version 9.7 software were employed to investigate geographic information. To distinguish factors associated with hotspot areas, local and global models were fitted. Result the geographic pattern of Delayed antenatal care initiation was clustered (Moran’s I = 0.38, p