지리가중회귀분석을 이용한 은평뉴타운 지가 분석

With the fast urbanization process, how does the vegetation environment change in one of the most economically developed metropolis, Shanghai in East China? To answer this question, there is a pressing demand to explore the non-stationary relationship between socio-economic conditions and vegetation across Shanghai. In this study, environmental data on vegetation cover, the Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery in 2003 were integrated with socio-economic data to reflect the city’s vegetative conditions at the census block group level. To explore regional variations in the relationship of vegetation and socio-economic conditions, Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models were applied to characterize mean NDVI against three independent socio-economic variables, an urban land use ratio, Gross Domestic Product (GDP) and population density. The study results show that a considerable distinctive spatial variation exists in the relationship for each model. The GWR model has superior effects and higher precision than the OLS model at the census block group scale. So, it is more suitable to account for local effects and geographical variations. This study also indicates that unreasonable excessive urbanization, together with non-sustainable economic development, has a negative influence of vegetation vigor for some neighborhoods in Shanghai.

Satellite-retrieved aerosol optical depth (AOD) has been increasingly utilized for the mapping of fine particulate matter (PM2.5) concentrations. An accurate estimation and mapping of PM2.5 concentrations depends on the high-resolution AOD data and a robust mathematical model that takes into account the spatial nonstationary relationship between PM2.5 and AOD. Take the core portion of the Beijing–Hebei–Tianjin (Jing-Jin-Ji) urban agglomeration as case study (the most seriously polluted region in China). Land use, population, meteorological variables, and simplified aerosol retrieval algorithm-retrieved AOD at 1-km resolution are employed as the predictors for the geographically weighted regression (GWR) and the ordinary least squares (OLS) model to map the spatial distribution of PM2.5 concentrations. The GWR model shows significant spatial variations in PM2.5 concentrations over the region than the traditional OLS model, which reveals relative homogeneous variations. Validation with ground-level PM2.5 concentrations demonstrates that PM2.5 concentrations predicted by the GWR model ( $R^{2} = 0.75$ , $RMSE = 10 \mu\text{g/m}^{3}$ ) correlate better than those by the OLS model ( $R^{2} = 0.53$ , $RMSE = 16 \mu\text{g/m}^{3}$ ). These results suggest that the GWR model offered a more reliable way for the prediction of spatial distribution of PM2.5 concentrations over urban areas.

Detailed information about spatial distribution of soil properties is important in ecological modelling, environmental prediction, precision agriculture, and natural resources management, as well as land-use planning. In the present study, a recently developed method called geographically weighted regression (GWR) is applied to predict spatial distribution of soil properties (pH, soil organic matter, available nitrogen, available potassium) based on topographical indicators, climate factors, and geological stratum at a regional scale. In total, 1914 soil samples collected from a depth of 0–20 cm were used to calibrate and validate the models. Performances of the GWR models were compared with the traditional, ordinary least-squares (OLS) regression. The results indicated that the GWR models made significant improvements to model performances over OLS regression, based on F-test, coefficient of determination, and corrected Akaike information criterion. GWR models also improved the reliability of the soil–environment relationships by reducing the spatial autocorrelations in model residuals. Meanwhile, the use of GWR models disclosed that the relationships between soil properties and environmental variables were not invariant over space but exhibited significant spatial non-stationarity. Accordingly, the GWR models remarkably improved the prediction accuracies over the corresponding OLS models. The results demonstrated that GWR could serve as a useful tool for digital soil mapping in areas with complex terrain.

BackgroundAngiostrongyliasis is a food-borne parasitic zoonosis. Human infection is caused by infection with the third-stage larvae of Angiostrongylus cantonensis. The life cycle of A. cantonensis involves rodents as definitive hosts and molluscs as intermediate hosts. This study aims to investigate on the infection status and characteristics of spatial distribution of these hosts, which are key components in the strategy for the prevention and control of angiostrongyliasis.MethodsThree villages from Nanao Island, Guangdong Province, China, were chosen as study area by stratified random sampling. The density and natural infection of Pomacea canaliculata and various rat species were surveyed every three months from December 2015 to September 2016, with spatial correlations of the positive P. canaliculata and the infection rates analysed by ArcGIS, scan statistics, ordinary least squares (OLS) and geographically weighted regression (GWR) models.ResultsA total of 2192 P. canaliculata specimens were collected from the field, of which 1190 were randomly chosen to be examined for third-stage larvae of A. cantonensis. Seventy-two Angiostrongylus-infected snails were found, which represents a larval infection rate of 6.1% (72/1190). In total, 110 rats including 85 Rattus norvegicus, 10 R. flavipectus, one R. losea and 14 Suncus murinus were captured, and 32 individuals were positive (for adult worms), representing an infection rate of 29.1% of the definitive hosts (32/110). Worms were only found in R. norvegicus and R. flavipectus, representing a prevalence of 36.5% (31/85) and 10% (1/10), respectively in these species, but none in R. losea and S. murinus, despite testing as many as 32 of the latter species. Statistically, spatial correlation and spatial clusters in the spatial distribution of positive P. canaliculata and positive rats existed. Most of the spatial variability of the host infection rates came from spatial autocorrelation. Nine spatial clusters with respect to positive P. canaliculata were identified, but only two correlated to infection rates. The results show that corrected Akaike information criterion, R2, R2 adjusted and σ2 in the GWR model were superior to those in the OLS model.ConclusionsP. canaliculata and rats were widely distributed in Nanao Island and positive infection has also been found in the hosts, demonstrating that there was a risk of angiostrongyliasis in this region of China. The distribution of positive P. canaliculata and rats exhibited spatial correlation, and the GWR model had advantage over the OLS model in the spatial analysis of hosts of A. cantonensis.

Soil erosion is one of the most common types of land degradation. To provide useful information for proper management, quantitative soil erosion evaluation and identifying influential factors are needed. However, rare studies have been reported on spatial modeling of soil erosion in connection with affective factors to prioritize the locality and the type of erosion control measures. Hence, the aim of this study was to (1) assess erosion-prone areas in the Talar watershed, Iran, using the revised universal soil loss equation (RUSLE) model and (2) investigate the relationship between soil erosion variability and land-use changes. Toward that, the ordinary least squares (OLS), geographically weighted regression (GWR) models, and principal component analysis (PCA) were used to analyze spatial relationships between soil erosion, land-use, and the RUSLE factors. The results of the OLS and GWR models indicated that these relationships are spatially non-stationary. GWR models had a good predictive performance than OLS with lower Akaike’s Information Criterion (from 254.31 to 276.81 in OLS and from 247.87 to 269.42 in GWR) and higher adjusted R2 values (from 0.12 to 0.54 in OLS, and from 0.36 to 0.66 in GWR). Among the variables mentioned above, LS factor, P factor, forest, and irrigated land were the most influential variables in GWR models. The results of PCA showed that PC1 and PC2 explained 66.2% of the variation in soil erosion concerning land-use and the RUSLE factors. These results provided appropriate references for managers and experts properly planning the study watershed.Graphical abstract

Dockless bike-sharing systems have become one of the important transport methods for urban residents as they can effectively expand the metro’s service area. We applied the ordinary least square (OLS) model, the geographically weighted regression (GWR) model and the multiscale geographically weighted regression (MGWR) model to capture the spatial relationship between the urban built environment and the usage of bike-sharing connected to the metro. A case study in Beijing, China, was conducted. The empirical result demonstrates that the MGWR model can explain the varieties of spatial relationship more precisely than the OLS model and the GWR model. The result also shows that, among the proposed built environment factors, the integrated usage of bike-sharing and metro is mainly affected by the distance to central business district (CBD), the Hotels-Residences points of interest (POI) density, and the road density. It is noteworthy that the effect of population density on dockless bike-sharing usage is only significant at weekends. In addition, the effects of the built environment variables on dockless bike-sharing usage also vary across space. A common feature is that most of the built environment factors have a more obvious impact on the metro-oriented dockless bike-sharing usage in the eastern part of the study area. This finding can provide support for governments and urban planners to efficiently develop a bike-sharing-friendly built environment that promotes the integration of bike-sharing and metro.

The Normalized Difference Vegetation Index (NDVI) and rainfall data were used to model the spatial relationship between vegetation and rainfall. Their correlation in previous studies was typically based on a global regression model, which assumed that the correlation was constant across space. The NDVI–rainfall association, on the other hand, is spatially non-stationary, non-linear, scale-dependent, and influenced by local factors (e.g., soil background). In this study, two statistical methods are used in the modeling, i.e., traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR), to evaluate the NDVI–rainfall relationship. The GWR was implemented annually in the growing seasons of 2000 and 2016, using climate data (Normalized Vegetation Difference Index and rainfall). The NDVI–rainfall relationship in the studied Bisha watershed (an eco-sensitive zone with a complex landscape) was found to have a stable operating scale of around 12 km. The findings support the hypothesis that the OLS model’s average impression could not accurately represent local conditions. By addressing spatial non-stationarity, the GWR approach greatly improves the model’s accuracy and predictive ability. In analyzing the relationship between NDVI patterns and rainfall, our research has shown that GWR outperforms a global OLS model. This superiority stems primarily from the consideration of the relationship’s spatial variance across the study area. Global regression techniques such as OLS can overlook local details, implying that a large portion of the variance in NDVI is unexplained. It appears that rainfall is the most significant factor in deciding the distribution of vegetation in these regions. Furthermore, rainfall had weak relationships with areas predominantly located around wetlands, suggesting the need for additional factors to describe NDVI variations. The GWR method performed better in terms of accuracy, predictive power, and reduced residual autocorrelation. Thus, GWR is recommended as an explanatory and exploratory technique when relations between variables are subject to spatial variability. Since the GWR is a local form of spatial analysis that aligned to local conditions, it has the potential for more accurate prediction; however, a larger amount of data is needed to allow a reliable local fitting.

Anxiety disorders have emerged as one of the most prevalent mental health problems and health concerns. However, previous research has paid limited attention to measuring public anxiety from a broader perspective. Furthermore, while we know many factors that influence anxiety disorders, we still have an incomplete understanding of how these factors affect public anxiety. We aimed to quantify public anxiety from the perspective of Internet searches, and to analyze its spatiotemporal changing characteristics and influencing factors. This study collected Baidu Index from 2014 to 2022 in 31 provinces in mainland China to measure the degree of public anxiety based on the Baidu Index from 2014 to 2022. The spatial autocorrelation analysis method was used to study the changing trends and spatial distribution characteristics of public anxiety. The influencing factors of public anxiety were studied using spatial statistical modeling methods. Empirical analysis shows that the level of public anxiety in my country has continued to rise in recent years, with significant spatial clustering characteristics, especially in the eastern and central-southern regions. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) spatial statistical models to examine the relationship between social, economic, and environmental factors and public anxiety levels. We found that the GWR model that considers spatial correlation and dependence is significantly better than the OLS model in terms of fitting accuracy. Factors such as the number of college graduates, Internet traffic, and urbanization rate are significantly positively correlated with the level of public anxiety. Our research results draw attention to public anxiety among policymakers, highlighting the necessity for a more extensive examination of anxiety issues, especially among university graduates, by the public and relevant authorities.

Understanding the spatial distribution of land surface temperature (LST) and its impact factors is crucial for mitigating urban heat island effect. However, few studies have quantitatively investigated the spatial non-stationarity and spatial scale effects of the relationships between LST and its impact factors at multi-scales. The main purposes of this study are as follows: (1) to estimate the spatial distributions of urban heat island (UHI) intensity by using hot spots analysis and (2) to explore the spatial non-stationarity and scale effects of the relationships between LST and related impact factors at multiple resolutions (30–1200 m) and to find appropriate scales for illuminating the relationships in a plain city. Based on the LST retrieved from Landsat 8 OLI/TIRS images, the Geographically-Weighted Regression (GWR) model is used to explore the scale effects of the relationships in Zhengzhou City between LST and six driving indicators: The Fractional Vegetation Cover (FVC), the Impervious Surface (IS), the Population Density (PD), the Fossil-fuel CO2 Emission data (FFCOE), the Shannon Diversity Index (SHDI) and the Perimeter-area Fractal Dimension (PAFRAC),which indicate the vegetation abundance, built-up, social-ecological variables and the diversity and shape complexity of land cover types. Our findings showed that the spatial patterns of LST show statistically significant hot spot zones in the center of the study area, partly extending to the western and southern industrial areas, indicating that the intensity of the urban heat island is significantly spatial clustering in Zhengzhou City. In addition, compared with the Ordinary Least Squares (OLS) model, the GWR model has a better ability to characterize spatial non-stationarity and analyze the relationships between the LST and its impact factors by considering the space-varying relationships of different variables, especially at the fine spatial scales (30–480 m). However, the strength of GWR model has become relatively weak with the increase of spatial scales (720–1200 m). This reveals that the GWR model is recommended to be applied in the analysis of UHI problems and related impact factors at scales finer than 480 m in the plain city. If the spatial scale is coarser than 720 m, both OLS and GWR models are suitable for illustrating the correct relationships between UHI effect and its influence factors in the plain city due to their undifferentiated performance. These findings can provide valuable information for urban planners and researchers to select appropriate models and spatial scales seeking to mitigate urban thermal environment effect.

The urban heat island (UHI) effect is an important topic for many cities across the globe. Previous studies, however, have mostly focused on UHI changes along either the spatial or temporal dimension. A simultaneous evaluation of the spatial and temporal variations is essential for understanding the long-term impacts of land cover on the UHI. This study presents the first evaluation and application of a newly developed spatiotemporal weighted regression framework (STWR), the performance of which was tested against conventional models including the ordinary least squares (OLS) and the geographically weighted regression (GWR) models. We conducted a series of simulation tests followed by an empirical study over central Phoenix, AZ. The results show that the STWR model achieves better parameter estimation and response prediction results with significantly smaller errors than the OLS and GWR models. This finding holds true when the regression coefficients are constant, spatially heterogeneous, and spatiotemporally heterogeneous. The empirical study reveals that the STWR model provides better model fit than the OLS and GWR models. The LST has a negative relationship with GNDVI and LNDVI and a positive relationship with GNDBI for the three years studied. Over the last 20 years, the cooling effect from green vegetation has weakened and the warming effect from built-up features has intensified. We suggest the wide adoption of the STWR model for spatiotemporal studies, as it uses past observations to reduce uncertainty and improve estimation and prediction results.

Estimating tree-related power outages for regional utility network using airborne LiDAR data and spatial statistics

Research using a global regression model might not be appropriate to find out the factors that influence strategic food prices based on spatial characteristics. To analyze the spatial effect, Geographically Weighted Regression (GWR) was employed. GWR models are better than OLS, which is indicated by the higher R2 GWR and lower AIC values. The GWR analysis provides the following findings: (1) the wholesale price most influential on the retail price of medium rice and red chili both during the main harvest and non-harvest periods; (2) the harvest pattern results in the effect of production and producer prices on the retail prices of the major harvest and non-harvest periods. Management of inter-regional distribution must be carried out to maintain supply stability and disparity in food prices between regions; (3) producer prices are integrated with trader prices in the district of production centers and surrounding areas while the integration of food prices at the consumer level occurs in the main economic center area of the region. These aspects have different effects for each region and district because the estimated parameters can be positive or negative. Testing during the harvest season (April) and non-harvest can also produce estimates that vary according to the specific characteristics of each location. Keywords: spatial analysis, Geographically Weighted Regression (GWR), retail prices, wholesale price, spatial distribution patterns

Land use regression (LUR) models are used for high-resolution air pollution assessment. These models use independent parameters based on an assumption that these parameters are accurate and invariable; however, they are observational parameters derived from measurements or modeling. Therefore, the parameters are commonly inaccurate, with nonstationary effects and variable characteristics. In this study, we propose a geographically weighted total least squares regression (GWTLSR) to model air pollution under various traffic, land use, and meteorological parameters. To improve performance, the proposed model considers the dependent and independent variables as observational parameters. The GWTLSR applies weighted total least squares in order to take into account the variable characteristics and inaccuracies of observational parameters. Moreover, the proposed model considers the nonstationary effects of parameters through geographically weighted regression (GWR). We examine the proposed model’s capabilities for predicting daily PM2.5 concentration in Isfahan, Iran. Isfahan is a city with severe air pollution that suffers from insufficient data for modeling air pollution with conventional LUR techniques. The advantages of the model features, including consideration of the variable characteristics and inaccuracies of predictors, are precisely evaluated by comparing the GWTLSR model with ordinary least squares (OLS) and GWR models. The values estimated by the GWTLSR model during the spring and autumn are 0.84 and 0.91, respectively. The corresponding average values estimated by the OLS model during the spring and autumn are 0.74 and 0.69, respectively, and the values estimated by the GWR model are 0.76 and 0.70, respectively. The results demonstrate that the proposed functional model efficiently described the physical nature of the relationships among air pollutants and independent variables.

Leishmaniasis, a parasitic disease caused by Leishmania spp., is a major public health threat. The synergistic effects of environmental and socioeconomic factors on the global distribution of leishmaniasis are unknown. Applying epidemiological data on cutaneous leishmaniasis (CL) from the Global Burden of Disease 2021 database, we used spatial autocorrelation and standard deviation ellipses to explore the spatiotemporal clustering and migration patterns of CL. Four remote sensing-retrieved environmental factors and five socioeconomic factors were selected for analysis. Spearman's correlation coefficient was used to screen for factors correlated with the prevalence of and disability-adjusted life years (DALYs) due to CL. Ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR) were used to assess the impact of the influencing factors on the prevalence of and DALYs due to CL. From 1990 to 2008, the global prevalence of and DALYs due to CL exhibited significant positive spatial autocorrelation (Z > 1.96, P < 0.05). Prevalence and DALYs both had one cold spot, located in northern Africa, and two hot spots, located in Central America and Central Asia. Temperature, infant mortality rate (IMR) and humidity were significantly positively correlated with the prevalence of and DALYs due to CL, whereas gross domestic product (GDP) and surface solar radiation (SSR) were significantly negatively correlated with the latter. The GTWR model demonstrated the best regression performance, with adjusted R2 values for prevalence reaching 0.841, 0.984, 0.839 and 0.972, and those for DALYs reaching 0.816, 0.966, 0.837 and 0.972 in Asia, Europe, the Americas and Africa, respectively. Regression coefficients further quantified the individual contributions of each factor to the prevalence of and DALYs due to CL, which could provide a scientific basis for governments to implement targeted control of CL. To our knowledge, this study is the first to analyze the global spatiotemporal distribution patterns of the prevalence of and DALYs due to CL and quantitatively study the spatiotemporal effects of environmental and socioeconomic factors on CL on a global scale. Environmental (temperature, SSR and humidity) and socioeconomic (GDP and IMR) factors were significantly correlated with the prevalence of and DALYs due to CL. The GTWR model outperformed the GWR and OLS models, further confirming the spatiotemporal effects of influencing factors on CL.

Poverty is one of the most global issues that remains a concern worldwide, including in Indonesia. Indonesia is among the top 100 poorest countries in the world, ranked 73rd, to be exact. Government wants to decrease the national poverty rate, as outlined in the 2021-2024 National Medium-Term Development Plan, with the expected percentage of poor people in Indonesia on 2024 being 6.5 to 7 percent. Unfortunately, the hope for a reduction in the poverty rate has not been achieved in several regions, such as in 4 out of 5 provinces in Kalimantan. Therefore, the analyzing factors causing poverty in the Kalimantan region is conducted using the Geographically Weighted Regression method in order to give clearly information for government to decrease the poor rate in this region. GWR (Geographically Weighted Regression) is an extension of the regression method. The equation parameters for each observation location differ from one location to another. The weighting function used were fixed gaussian, fixed bisquare, fixed tricube, adaptive gaussian, adaptive bisquare, and adaptive tricube. Based on R2 and AIC value, the best model is the model with fixed tricube function. The R2 score is 0.8952, while the AIC score is 155.83. The GWR model is better than OLS or global regression model. Thus, spatial analysis to see the factors affecting the percentage of poor people in each regency and city in Kalimantan, Indonesia has been successfully carried out.