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Rurality is an important indicator to reflect the development of villages and reveal internal differences in rural areas. The unbalanced development of transportation infrastructure in the current period of rapid urbanization has become one of the principal elements triggering spatial differences in rurality and changes in rural territorial characteristics. However, there are few studies on the impact of transportation infrastructure accessibility on the multidimensional characteristics of rurality from the perspective of heterogeneity. This paper analyzed the spatio-temporal characteristics of transport accessibility (TA) and the county rurality index (CRI) in Guangdong in 2005, 2010, 2015 and 2020 using an accessibility model and the rurality index and explored the clustering characteristics and interactions of TA and the CRI through exploratory spatial data analysis (ESDA) and geographic weighted regression (GWR) modeling. The findings showed that (1) TA and the CRI in Guangdong were significantly unbalanced in terms of space. The CRI showed a weakening trend in general, forming a distribution pattern of "high in the north and low in the south, high in the west and low in the east", while TA was on the rise, maintaining a stable pattern of "high in the middle and low in the periphery". (2) Both TA and the CRI in Guangdong had a Moran's I value greater than 0.6 during the study period, exhibiting strong spatial agglomeration, while the two showed a significant spatial negative correlation. (3) The influence of TA on the CRI in different dimensions showed dynamic changes in stages, with TA having a higher intensity of effect on society rurality and land rurality in 2005, while on society rurality and industry rurality in 2020. (4) This paper grouped 77 counties in Guangdong into four types of policy zonings—coordination types, lagged types of accessibility, lagged types of rurality and double lagged types—and put forward corresponding development recommendations. The study conducted in this paper contributes to an in-depth understanding of the impact of transportation infrastructure development on the multidimensional characteristics of rurality and provides a basis for policy formulation for coordinated urban–rural development and sustainable rural development. [ABSTRACT FROM AUTHOR]

It is of great practical significance to explore the relationship between urbanization and CO2 emissions for the low-carbon development of cities. However, the multiscale assessment of spatial relationship between population, land and economic urbanization and CO2 emissions is lacked. In this study, we first adopted the spatial statistical methods to evaluate the spatial pattern of China's CO2 emissions in 2019. Then, we spatially quantified China's urbanization of land, population and economy based on statistical data. Finally, we used the geo-weighted regression model to explore the spatial relationship between urbanization and CO2 emissions at the national-economic zone-province scale. The results displayed that there is obvious spatial heterogeneity in the relationship between China's urbanization and CO2 emissions. The significant positive correlation between urbanization and CO2 emissions were mainly located in the northeastern, eastern and southwestern regions, consistent with the characteristics of the Heihe–Tengchong Line. The uneven development of land, population and economic urbanization would lead to more CO2 emissions. We suggest that China should attend the balanced development of urban land, population and economy, and avoid the additional carbon emissions caused by incongruence, to further the development of low-carbon cities. [ABSTRACT FROM AUTHOR]

Climate varies along geographic gradients, causing spatial variations in the effects of energy and water on species richness and the explanatory power of different climatic factors. Species of the Quercus genus are important tree species in China with high ecological and socioeconomic value. To detect whether the effects of energy and water on species richness change along climatic gradients, this study built geographically weighted regression models based on species richness and climatic data. Variation partition analysis and hierarchical partitioning analysis were used to further explore the main climatic factors shaping the richness distribution pattern of Quercus in China. The results showed that Quercus species were mainly distributed in mountainous areas of southwestern China. Both energy and water were associated with species richness, with global slopes of 0.17 and 0.14, respectively. The effects of energy and water on species richness gradually increased as energy and water in the environment decreased. The interaction between energy and water altered the effect of energy, and in arid regions, the effects of energy and water were relatively stronger. Moreover, energy explained more variation in species richness in both the entire study area (11.5%) and different climate regions (up to 19.4%). The min temperature of coldest month was the main climatic variable forming the richness distribution pattern of Quercus in China. In conclusion, cold and drought are the critical climatic factors limiting the species richness of Quercus, and climate warming will have a greater impact in arid regions. These findings are important for understanding the biogeographic characteristics of Quercus and conserving biodiversity in China. [ABSTRACT FROM AUTHOR]

The Huashan Creek watershed is the largest water source and the main production area of honeydew in Pinghe County, whose extensive cultivation of honeydew has exacerbated soil and water pollution. However, the spatial application of remote sensing ecological index (RSEI) in this watershed and key driving factors are not clear considering the applicability of data quality and the diversity of methodological scales. To explore the RSEI and driving factors at distinct scales in Huashan Creek watershed, this study constructed the RSEI based on the environmental balance matrix at seven scales in 2020, revealed its spatial response characteristics at different scales, and analyzed the key drivers. The results show that the 240 m grid as well as rural and watershed scale convergence analyses satisfy the assessment of RSEI, whose Moran indexes are 0.558, 0.595, and 0.146, respectively. The RSEIs at different scales have significant spatial aggregation characteristics, but the overall status is moderate. The central town–riparian area with poor RSEI contrasts with the western mountainous area, which has comparatively better quality. Population has a major influence on RSEI at multiple scales (0.8), with elevation and patch index acting significantly at the village and grid scales, respectively. These findings help to identify the spatial distribution of quality and control mechanisms of RSEI in the Huashan Creek watershed and provide new insights into key scales and drivers of ecological restoration practices in the watershed. [ABSTRACT FROM AUTHOR]

Recent studies have yielded some interesting insights into the impacts of a property's walking accessibility on housing affordability and equity from the potential property owner's perspective, and limited attention was paid from the renter's perspective. This study investigates the impacts of eight types of property walking accessibility and other variables on the second-hand residential property (SHP) price and residential rental property (RRP) rent. It uses a sample of 6,603 SHPs and 3,566 RRPs that were collected in Shanghai, China, in 2021. A modified floating catchment method is used to quantify walking accessibility to eight destinations. Geographically weighted regression models are estimated to study the similarities and dissimilarities of the impacts of a property's walking accessibilities, inherent attributes, and transit time to major transportation hubs on the SHP price and RRP rent. In addition, it factors the distinctive regional and political characteristics in China, which include massive internal immigration, rapid urbanization, and household registration and housing price control policies. These results provide a more comprehensive understanding of the spatially varying impacts of property walking accessibility on housing affordability and equity. In addition, these results highlight the intensifying jobs–housing imbalance, ever-increasing commuting time and cost, and decreasing overall quality of life in metropolises, particularly among migrants and future property owners. For property developers and investors, these results demonstrate that improving the property walking accessibility might yield spatially varying returns across regions. From the perspective of planners and policymakers, these results and insights could be used to design policies and strategies, such as cooperative governance and low-income rental housing, along with walking accessibility improvement development to address the urgent need for affordable housing and equity in China. [ABSTRACT FROM AUTHOR]

The Yellow River Basin (YRB) is an important ecological security barrier in northern China, where vegetation has undergone rapid changes under the high‐intensity anthropological activities and ongoing climate change in recent decades. This study focused on the spatiotemporal evolution of fractional vegetation cover (FVC) and the driving factors during the growing season in different sub‐basins of the YRB between 2000 and 2020. The trend analysis was employed to examine the FVC spatiotemporal changes, and the geographical detector model (GDM) and the geographically weighted regression (GWR) model were utilized to explore spatial heterogeneity of natural‐human drivers on FVC. The 21‐year average FVC was 0.5780 in the growing season in the YRB, and the southeast region was greater than the northwest region. The FVC exhibited a fluctuating and rising trend of 0.44% yr−1 in the whole YRB from 2000 to 2020, and areas with improved FVC were 36.40% more than those with degraded FVC. The dominant factors influencing the growing‐season FVC were precipitation and temperature, followed by elevation and human activity intensity. The influence of natural factors on FVC showed a decreasing trend, whereas human factors exhibited an increasing trend. Natural factors promoted vegetation growth in most area of the YRB, especially in the northwest, whereas human factors had more negative effects, except for the middle parts of the basin. The spatial differentiation analyses of FVC and driving factors provide scientific guidance for ecological restoration and sustainable development in the YRB. [ABSTRACT FROM AUTHOR]

With the development of the Chinese Fengyun satellite series, Fengyun-2G (FY-2G) quantitative precipitation estimates (QPE) can provide real-time and high-quality precipitation data over East Asia. However, FY-2G QPE cannot offer precipitation information beyond the latitude band of 50°N due to the limitation of the observation coverage of the FY-2G-based satellite-borne sensor. To this end, a precipitation space reconstruction using the geographically weighted regression (GWR) coupled with a geographical differential analysis (GDA) (PSR2G) algorithm was developed, based on the land surface variables related to precipitation, including vegetational cover, land surface temperature, geographical location, and topographic characteristics. This study used the PSR2G-based reconstructed model to estimate the FY-2G QPE over Northeast China (the latitude band beyond 50°N) from December 2015 to November 2019 with a spatiotemporal resolution of 0.1°/month. The PSR2G-based reconstructed results were validated with the ground observations of 80 rain gauges, and also compared to the reconstructed results using random forest (RF) and GWR. The results show that the spatio-temporal pattern of PSR2G QPE is closer to ground observations than those of RF and GWR, which indicates that the PSR2G QPE is more competent to capture the spatio-temporal variation of rainfall over Northeast China than other two reconstruction methods. In addition, the reconstructed precipitation dataset using PSR2G has higher accuracy over study area than the FY-2G QPE below the band of 50°N. It suggested that PSR2G reconstruction precipitation strategies do not lose the precision of the original satellite precipitation data. [ABSTRACT FROM AUTHOR]

Land use change is an important factor affecting the performance of ecosystem services (ESs). Therefore, understanding the impact of land use change on ESs is of great significance for promoting the coordination of regional human–land relationships. In this study, random forest and cellular automata were used to simulate and predict the characteristics of land use change in the Yangtze River Economic Belt, and diversified land use evolution patterns were formed in combination with China's strategic development needs. The effects of habitat suitability on ESs were analyzed by using a multiscenario land use change model. The results demonstrated that the driving factors selected in this article had a good induction effect on the law of land use evolution, and the simulated land use change had high credibility. Under the mode of ecological protection and cultivated land protection, the expansion of construction land was greatly affected and was not conducive to social and economic development. Under the natural evolution mode, farmland was greatly encroached upon, and food security was greatly threatened. The regional coordination model had relative advantages, and all kinds of land use needs were met to a certain extent. The water production function of ESs was strong, but the carbon storage function was weak. The relationship between the habitat suitability index and ES changes under land use change revealed that there were significant differences in ES changes caused by ecological quality changes in mountainous and plain areas. This study provides a reference for promoting social and economic development and ecosystem integrity. Integr Environ Assess Manag 2023;19:1473–1484. © 2023 SETAC Key Points: Random forest and cellular automata were used to simulate and predict the characteristics of land use change in the Yangtze River Economic Belt, and diversified land use evolution patterns were formed in combination with China's strategic development needs.The application of national strategies to ecosystem services (ESs) analysis is described.The effects of habitat suitability on ESs were analyzed by using a multiscenario land use change model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Resources & Ecology is the property of Journal of Resources & 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.)

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. [ABSTRACT FROM AUTHOR]

The optimal selection of characteristic bands and retrieval models for the hyperspectral retrieval of soil heavy metal concentrations poses a significant challenge. Additionally, satellite-based hyperspectral retrieval encounters several issues, including atmospheric effects, limitations in temporal and radiometric resolution, and data acquisition, among others. Given this, the retrieval performance of the soil arsenic (As) concentration in Pingtan Island, the largest island in Fujian Province and the fifth largest in China, is currently unclear. This study aimed to elucidate this issue by identifying optimal characteristic bands from the full spectrum from both statistical and physical perspectives. We tested three linear models, namely Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR) and Geographically Weighted Regression (GWR), as well as three nonlinear machine learning models, including Back Propagation Neural Network (BP), Support Vector Machine Regression (SVR) and Random Forest Regression (RFR). We then retrieved soil arsenic content using ground-based soil full spectrum data on Pingtan Island. Our results indicate that the RFR model consistently outperformed all others when using both original and optimal characteristic bands. This superior performance suggests a complex, nonlinear relationship between soil arsenic concentration and spectral variables, influenced by diverse landscape factors. The GWR model, which considers spatial non-stationarity and heterogeneity, outperformed traditional models such as BP and SVR. This finding underscores the potential of incorporating spatial characteristics to enhance traditional machine learning models in geospatial studies. When evaluating retrieval model accuracy based on optimal characteristic bands, the RFR model maintained its top performance, and linear models (MLR, PLSR and GWR) showed notable improvement. Specifically, the GWR model achieved the highest r value for the validation data, indicating that selecting optimal characteristic bands based on high Pearson's correlation coefficients (e.g., abs(Pearson's correlation coefficient) ≥0.45) and high sensitivity to soil active materials successfully mitigates uncertainties linked to characteristic band selection solely based on Pearson's correlation coefficients. Consequently, two effective retrieval models were generated: the best-performing RFR model and the improved GWR model. Our study on Pingtan Island provides theoretical and technical support for monitoring and evaluating soil arsenic concentrations using satellite-based spectroscopy in densely populated, relatively independent island towns in China and worldwide. [ABSTRACT FROM AUTHOR]

Remote mountainous villages are at risk of falling back into poverty, despite having been lifted out of extreme poverty. However, there has been a lack of focus on the factors contributing to poverty-return in these villages, which making it difficult to understand the risks and their underlying causes. This study investigates the spatial distribution of 546 key assistance villages (KAVs) in the Liangshan mountainous region, a former poverty-stricken area, using the average nearest neighbor (ANN) and kernel density estimation (KDE) methods. Linear regression and geographically weighted regression (GWR) models are then employed to analyze the relationship between the KAVs' economy and potential poverty-returning factors. The results show that KAVs are primarily located in elevation ranges of 1800–2500 m (31.87%), with slopes of 6°–15° (42.67%) and 2–3 km from the township (28.94%). The distribution of KAVs exhibits distinct spatial clustering, forming four gathering areas. Several factors impact the KAVs' economy positively, including the normalized difference vegetation index (NDVI), built-up area, grassland, and education facilities, while elevation has a negative effect. The built-up area has the most critical impact on the rural economy, followed by NDVI and elevation. Additionally, education facilities and grassland areas also have significant effects. The study suggests promoting the Ex-situ Poverty Alleviation Relocation Program (ESPARP) and increasing rural built-up areas, grasslands, and educational facilities as practical measures for preventing poverty return and promoting economic development promotion in remote mountain villages. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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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Complex terrain, the sparse distribution of rain gauges, and the poor resolution and quality of satellite data in remote areas severely restrict the development of watershed hydrological modeling, meteorology, and ecological research. In this study, based on the relationship between cloud optical and physical properties and precipitation, a daily geographically weighted regression (GWR) precipitation downscaling model was constructed for the Three Rivers Source region, China, for the period from 2010 to 2014. The GWR precipitation downscaling model combined three different satellite precipitation datasets (CMORPH, IMERG, and ERA5) which were downscaled from a coarse resolution (0.25° and 0.1°) to a fine resolution (1 km). At the same time, the preliminary downscaling results were calibrated and verified by employing the geographic difference analysis (GDA) and geographic ratio analysis (GRA) methods combined with rainfall data. Finally, the analytical hierarchy process (AHP) and the entropy weight method (EW) were adopted to fuse the three downscaled and calibrated satellite precipitation datasets into the merged satellite precipitation dataset (MSP), which provides a higher quality of data (CC = 0.790, RMSE = 2.189 mm/day, and BIAS = 0.142 mm). In summary, the downscaling calibration and precipitation fusion scheme proposed in this study is suitable for obtaining high-resolution daily precipitation data in the Three Rivers Source region with a complex climate and topography. [ABSTRACT FROM AUTHOR]

Gridded gross domestic product (GDP) data are a crucial land surface parameter for many geoscience applications. Recently, machine learning approaches have become powerful tools in generating gridded GDP data. However, most machine learning approaches for gridded GDP estimation seldom consider the geographical properties of input variables. Therefore, in this study, a geographically weighted stacking ensemble learning approach was developed to generate gridded GDP data. Three algorithms—random forest, XGBoost, and LightGBM—were used as base models, and the linear regression in stacking ensemble learning was replaced by geographically weighted regression to locally fuse the three predictions. A case study was conducted in China to demonstrate the effectiveness of the proposed approach. The results showed that the proposed GDP downscaling approach outperformed the three base models and traditional stacking ensemble learning. Meanwhile, it had good predictive power on county-level GDP test data with R2 of 0.894, 0.976, and 0.976 for the primary, secondary, and tertiary sectors, respectively. Moreover, the predicted 1 km gridded GDP data had a high accuracy (R2 = 0.787) when evaluated by town-level GDP data. Hence, the proposed GDP downscaling approach provides a valuable option for generating gridded GDP data. The generated 1 km gridded GDP data of China from 2020 are of great significance for other applications. [ABSTRACT FROM AUTHOR]

Temporal and spatial transformation characteristics of China’s land-based atmospheric aerosol were obtained from the high spatial resolution MODIS MCD19-A2 data from 2011 to 2020 using remote sensing and geographically weighted regression model (GWR). This article was adopted: 1) Vertical-humidity revision: the vertical revision of aerosol optical depth (AOD) was carried out under the different spatial distributions of satellite and PM2.5 concentration data. The AOD value was obtained from the MODIS, and then divided by the atmospheric boundary layer height data, in order to obtain the AOD value close to the ground level. The humidity was revised to remove the influence of relative humidity on the AOD value. The vertically revised “dry” AOD value was divided to obtain a vertical-humidity revised “wet” AOD by the humidity impact factor, according to the calculated humidity impact factor in each city. There was an increase from 0.365 to 0.779 in the correlation coefficient between the “wet” AOD value and PM2.5 concentration data. 2) Multiple colinear tests: multiple colinear tests were carried out to verify the GWR fitting using the eight variables of the model. Variance inflation factor (VIF) was selected to test the multiple colinear all over the eight variables for the better fitting of the GWR model. 3) GWR model was established to inspect the accuracy: The processed PM2.5 and auxiliary variables data of Chinese cities were selected to establish 10 annual GWR models and the fitting of each model using the Geographically Weighted Regression function in Modeling Spatial Relationships tool of ArcGIS. SPSS software was used to verify the accuracy of the fitting in each model, with the adjusted coefficient of determination (R² ) and root mean square error (RMSE). Conclusions were drawn as follows: 1) The spatial and temporal distribution of aerosol was basically conformed to the “low in the west and high in the east, decreasing year by year”. There was an outstanding seasonal difference in the AOD value, where the highest value was 0.294 in spring, followed by 0.262 in summer, and the lower values were 0.194 and 0.223 in autumn and winter, respectively. 2) The VIF demonstrated that the strongest multicollinearity was observed in 2018, indicating the monthly scale GWR model of each city. The VIF variables were close to 1, which fully met the requirements of the GWR model. 3) The better fitting of the model was achieved, where the best year was 2013 (R²=0.933), and the worst year was 2018 (R²=0.761). There was a basically consistent trend in the spatiotemporal distribution of PM2.5 concentration and AOD in each year, indicating the high applicability of MCD19-A2 data in the GWR model. 4) In terms of PM2.5 visualization, the distribution of PM2.5 was inverted by the Spline, Inverse Distance Weighted, Kriging and Natural Neighbor interpolation in 2013. The spatial distribution and concentration range were basically consistent, compared with the “Distribution of 2013 mean PM2.5 concentrations in China” data provided by National Earth System Science Data Center. IDW and Natural Neighbor more accurately described the high-value areas of PM2.5 concentration around the Tarim Basin in Xinjiang and the Beijing-Tianjin-Hebei region. [ABSTRACT FROM AUTHOR]

Due to climate change and human activities, the eco-environment quality (EEQ) of eco-fragile regions has undergone massive change, especially in the Tibet Plateau. The Qilian Mountains (QLM) region is an essential ecological function zone in the northeastern Tibet Plateau, which plays a vital role in northwestern China's eco-environmental balance. However, EEQ changes in the QLM during the 21st century remain poorly understood. In this study, the spatiotemporal variations of the EEQ in the QLM were analyzed from 2000 to 2020 using a remote sensing ecological index (RSEI). The EEQ driving factors are identified by the geographic detector, and the spatial influence of critical factors is represented by a geographically weighted regression model. The results show low EEQ in the QLM. From 2000 to 2020, the EEQ initially slightly improved, then deteriorated, and finally gradually recovered. Spatially, the EEQ shows an increasing trend from northwest to southeast. Moran's I of EEQ remains at around 0.95, representing high spatial aggregation. "High–High" and "Low–Low" clustering features dominate in the local spatial autocorrelation, indicating the EEQ of the QLM is polarized. Precipitation is the dominant positive factor in the EEQ, with a q statistics exceeding 0.644. Furthermore, the key factors (precipitation, distance to towns, distance to roads) affecting EEQ in different periods vary significantly in space. From results we can draw the conclusion that the natural factors mainly control the spatial patterns of EEQ, while the human factors mainly impact the temporal trend of EEQ, the EEQ in the QLM has been significantly improved since 2015. Our findings can provide theoretical support for future eco-environmental protection and restoration in the QLM. [ABSTRACT FROM AUTHOR]

Drought is the most widespread natural disaster in the world. How to monitor regional drought scientifically and accurately has become a hot topic for many scholars. In this paper, Geographically Integrated Dryness Index (GIDI) was integrated from an assortment source including Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI) (as the dependent variable) based on geographically weighted regression method. Besides, the comprehensive drought situation and changing trends in China from 2001 to 2019 were also examined. The results showed that (1) GIDI has excellent performance in monitoring medium- and long-term droughts and the monitoring results shows 2003, 2016, and 2019 were relatively wet years, while 2007, 2009, and 2011 were major drought years, and spring and March were the most frequent droughts season and month, respectively, and (2) except for the middle and upper reaches of the Yellow River and Northeastern China, which have a tendency to become wet, other places have a tendency to fluctuating dry. This study took advantage of simple and efficient methods to integrate existing indices to obtain a new index for monitoring a wider range of droughts, taking into account the physical mechanism of drought formation and the time scale of drought development, so it can scientifically evaluate the spatial and temporal distribution characteristics of drought and changes. [ABSTRACT FROM AUTHOR]

The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning, resource allocation, government decision-making, disaster assessment, ecological protection, and other sustainability research. However, the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series, while GHS-POP only utilizes land use data with limited accuracy. In view of the limited remote sensing images of long time series, it is necessary to combine existing multi-source remote sensing data for population spatialization research. In this research, we developed a nighttime light desaturation index (NTLDI). Through the cross-sensor calibration model based on an autoencoder convolutional neural network, the NTLDI was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) data. Then, the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL. Furthermore, the change characteristics and the driving factors of China's population spatial distribution are analyzed. The large-scale, long-term population spatialization results obtained in this study are of great significance in government planning and decision-making, disaster assessment, resource allocation, and other aspects. [ABSTRACT FROM AUTHOR]

Carbon emissions from land use (LUCE) is an increasingly serious problem in China, and the direction of carbon reduction in urban agglomerations is still unclear. This study aimed to identify the key factors that influence county-level LUCE and their response to LUCE in Beijing-Tianjin-Hebei (BTH) region. Based on multi-source data, our study calculated county-level LUCE and its spatiotemporal variations in BTH from 1992 to 2018. Random forest (RF) and geographically weighted regression (GWR) models were applied to discuss influential factors' impact and spatial response on LUCE by quantifying relative importance and spatial heterogeneity. The results show that countylevel LUCE in BTH increased by 1300.04×104t, with an average annual growth rate of 46.11% during 1992-2018, and exhibited prominent spatial autocorrelation, with higher-value counties mostly in the core areas and lower-value counties in the suburbs within BTH. The RF estimates were better than the multiple linear regression (MLR) estimates with an improvement in R2 from 0.56 to 0.98. Population, urbanization, energy intensity, industry proportion, road network density and technical progress had effective explanatory power for county-level LUCE. In addition, GWR estimation shows that the main influencing factors demonstrated significant spatial heterogeneity, and the impacts of population, urbanization, economic, energy structure and road factors in the central core counties were significantly higher than in the northwestern and southern regions. Industrial structure and technology have the greatest impact in the southern and northeast counties. This study explores different causes of LUCE in different counties of urban agglomerations and the continuation of low-carbon control measures in BTH is required. [ABSTRACT FROM AUTHOR]

Effected by spatial difference and types of environmental regulation, enhanced environmental regulation does not necessarily lead to reduce net carbon emissions (NCE). Based on panel data of 30 province-level administrative regions in China from 2003 to 2018, this paper analyzed the impact of heterogeneous environmental regulations on NCE using geographically weighted regression (GWR). The results showed that (1) the total NCE of each province in China presented an increasing trend followed by fluctuation, with obvious spatial aggregation characteristics. (2) The intensity of four environmental regulations, command-and-control (CAC), market-incentive (MI), public-participation (PP), and voluntary environmental program of firm (VEPF) tended to increase slightly over the study period with decreasing spatial heterogeneity. (3) CAC environmental regulations had a "green paradox" effect on NCE, meaning an improving CAC environmental regulation would surprisingly increase its negative effects and contribute to NCE growth. The effect of MI environmental regulation on NCE shifted from overall promotion to partly promotion and even suppression. PP and VEPF environmental regulation would reduce NCE. The suppressive effect of PP environmental regulation grew over time, while VEPF did not. Finally, based on the research conclusion, some policy suggestion are proposed for differentiated management, diverse system of governance, and collaborative governance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Understanding the relationships between commuting demand and built environment (BE) features is advantageous in alleviating travel pressure during the commuting period. Numerous works have explored these relationships under either a single spatial unit or travel modes. However, few efforts have attempted to examine how variations in both spatial units and transport modes affect the relationships. To address the above issues, we investigate the impact of the modifiable areal unit problem (MAUP) on the modeling results across three partitioning schemes (i.e., hexagonal-based, square-based, and administrative boundary) and nine spatial units under four commuting modes (i.e., taxi, bike-sharing, ride-hailing, and bus). A geographically weighted regression is applied to estimate commuting demands considering spatial autocorrelation and heterogeneity. The experiment is conducted using smart card data and vehicle GPS trajectories collected in Shenzhen City, China. The contributions of this study include the following three parts. First, it evaluates the pros and cons of different spatial units for commuting demand modeling. Second, it summarizes the effects of the MAUP on relationships among land use, demographic, and traffic network characteristics and commuting demand according to the partitioning scale and schemes. Third, it examines the similarities and differences in the effect of the MAUP on commuting demand modeling among four travel modes. The findings of this study contribute to the division of traffic analysis zones and the allocation of commuting trips. [ABSTRACT FROM AUTHOR]

Inferiority is a complex emotion of helplessness and self-deprecation. A lack of timely and effective treatment may cause serious consequences to people who experience inferiority. People with different occupational and educational backgrounds display different patterns of inferiority. Due to privacy issues, individuals who experience inferiority are often reluctant to seek face-to-face help. However, they often spontaneously share their feelings on social media, so social media can provide a large number of data on inferiority. Based on the data from Sina Weibo, the largest social media in China, this study explores the groups that are most affected by inferiority and reveals the spatiotemporal patterns of inferiority groups with different occupational and educational backgrounds based on the data from Sina Weibo, the largest social media in China. In this study, the Weibo data on inferiority-related topics published in 288 Chinese cities from 2012 to 2017 were collected, and the geospatial locations of the posts were extracted. The spatial variation of inferiority was analyzed, and the influence of the inferiority of people in different occupations and at education stages was examined. The results show that science and technology personnel, college students, and manufacturing workers are the groups most strongly affected by inferiority, and the expressed inferiority in the three groups show significant spatiotemporal non-stationarity. Excessive evaluation pressure increases the rate of inferiority among researchers and technicians, and inferiority among college students is increasing every year. In most areas in China, the increase in the density of manufacturing employees increases the risk of inferiority among these individuals. The findings of this study can help relevant organizations to better understand the regional distribution of inferiority and provide references for these organizations to develop regional treatment interventions for inferiority. [ABSTRACT FROM AUTHOR]

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In response to the 14th National Five-year Plan of China and to better explore new strategies for promoting regional coordinated green development, the eco-efficiency values of Chengdu-Chongqing Economic Circle and the corresponding temporal analysis from 2004 to 2018 were assessed in this paper using the super-SBM model and Markov chain. Meanwhile, the spatial analysis of eco-efficiency was conducted by a geographically weighted regression model. Although eco-efficiency has risen at an increasing rate, the economic development of Chengdu-Chongqing Economic Circle was still ecologically ineffective. This means there is an urgent need to improve the efficiency of resource utilization and promote technological innovation. During the study period, the evolution of the eco-efficiency presented as a "π" shape, and was accompanied by the phenomenon of "club convergence". There was also a strong tendency for eco-efficiency to maintain the original status quo, which indicates that it lacked sufficient momentum for improvement, so it was difficult to achieve a leapfrog transfer. Spatially, the eco-efficiency was distributed from northwest to southeast in a high-low-high manner. The spatial-temporal differences of eco-efficiency narrowed but the effect of agglomeration was relatively weak and there was a polarization trend. Further investigation suggests that the differences in the development level of urbanization, opening, technology, environmental regulation and advancement of industrial structure led to the spatial differences of eco-efficiency. Each city in the Economic Circle should make every effort to improve eco-efficiency accordingly, and thus to promote the green development of the whole region, so as to lay a foundation for driving the green and coordinated development of the central and western regions. [ABSTRACT FROM AUTHOR]

Since the 21st century, large cities around the world have experienced the transition from economically destructive development to a harmonious eco-environment. Understanding the dynamic relationships between human activities and urban eco-environment in this transition is a challenging and essential topic. The normalized difference vegetation index (NDVI) can reflect the urban vegetation cover status well. Socio-economic indexes can present the intensity and spatiality of human activities quantitatively. This work aims to use traditional regression models and machine learning algorithms to analyze the impact of socio-economic factors on NDVI accurately. Random forest regression (RFR) was performed to initially assess the contributions of all factors on NDVI, which was the numerical basis for feature selection. Subsequently, detailed dynamic relationship simulations were implemented using geographically weighted regression. In the case of Wuhan in China, the results showed that the goodness-of-fit of NDVI with socio-economic factors generally exceeded 50%. The influence coefficients changed from negative to positive, and 2010 was the turning point, indicating that human activities gradually played a favorable role in protecting vegetation during this transition period. The urban–rural interface, which was located between urban centers and marginal urban suburbs, was the area where human activities contributed most to vegetation. Thus, policy makers should focus on planning and managing housing construction and vegetation planting in urban–rural interface to relieve the population burden of the central area and improve the environmental conditions of the urban eco-environment subconsciously. [ABSTRACT FROM AUTHOR]

As China's second largest energy-use sector, residential consumption has a great potential for carbon dioxide (CO2) reduction and energy saving or transition. Thus, here, using the methods of social network analysis (SNA) and geographically weighted regression (GWR), we investigated the spatiotemporal evolution characteristics of China's residential CO2 emissions (RCEs) from direct energy use and proposed some policy suggestions for regional energy transition. (1) From 2000 to 2019, the total direct RCEs rose from 396.32 Mt to 1411.69 Mt; the consumption of electricity and coal were the primary sources. Controlling coal consumption and increasing the proportion of electricity generated from renewable energy should be the effective way of energy transition. (2) The spatial associations of direct RCEs show an obvious spatial network structure and the number of associations is increasing. Provinces with a higher level of economic development (Beijing, Shanghai, and Jiangsu) were at the center of the network and classified as the net beneficiary cluster in 2019. These provinces should be the priority areas of energy transition. (3) The net spillover cluster (Yunnan, Shanxi, Xinjiang, Gansu, Qinghai, Guizhou) is an important area to develop clean energy. People in this cluster should be encouraged to use more renewable energy. (4) GDP and per capita energy consumption had a significant positive influence on the growth of direct RCEs. Therefore, the national economy should grow healthily and sustainably to provide a favorable economic environment for energy transition. Meanwhile, residential consumption patterns should be greener to promote the use of clean energy. [ABSTRACT FROM AUTHOR]

As the key tool for land use management in China, land use planning (LUP) is characterized by the advantage of being state-led, quota-oriented, and easily monitored. Nevertheless, increasing research has doubted its effectiveness, particularly in controlling urban expansion, while few studies have empirically confirmed it. To this end, we construct a planning constraint index (PCI), supported by land use quota, for evaluating the pressure from LUP on urban expansion, and then analyze the spatial equilibrium of PCI. We also explore the spatial effects of PCI on urban expansion via the geographically weighted regression model (GWR) by collecting the spatial data of 280 prefecture-level cities in China from 2009 to 2016. The results show that: (1) most cities present a shortage of urban land quotas, and spatial disequilibrium exist among regions; (2) PCI has significant negative effects on urban expansion for all of the 280 cities, and spatial disparity of these effects occurs as well; (3) PCI has a stronger influence in southwest China where the economy is relatively backward. This study aims to provide a reference for land use planners and policymakers to help improve the effectiveness of LUP. [ABSTRACT FROM AUTHOR]

High spatial resolution (1 km or finer) precipitation data fields are crucial for understanding the Earth's water and energy cycles at the regional scale for applications. The spatial resolution of the Global Precipitation Measurement (GPM) mission (IMERG) satellite precipitation products is 0.1° (latitude) × 0.1° (longitude), which is too coarse for regional-scale analysis. This study combined the Geographically Weighted Regression (GWR) and the Multifractal Random Cascade (MFRC) model to downscale monthly GPM/IMERG precipitation products from 0.1° × 0.1° (approximately 11 km × 11 km) to 1 km in Hubei Province, China. This work's results indicate the following: (1) The original GPM product can accurately express the precipitation in the study area, which highly correlates with the site data from 2015 to 2017 (R2 = 0.79) and overall presents the phenomenon of overestimation. (2) The GWR model maintains the precipitation field's overall accuracy and smoothness, with even improvements in accuracy for specific months. In contrast, the MFRC model causes a slight decrease in the overall accuracy of the precipitation field but performs better in reducing the bias. (3) The GWR-MF combined with the GWR and MFRC model improves the observation accuracy of the downscaling results and reduces the bias value by introducing the MFRC to correct the deviation of GWR. The conclusion and analysis of this paper can provide a meaningful experience for 1 km high-resolution data to support related applications. [ABSTRACT FROM AUTHOR]

PM2.5 concentrations are commonly estimated using geographically weighted regression (GWR) models, but these models may suffer from multi-collinearity and over-focus on local feature problems. To overcome these shortcomings, a self-adaptive bandwidth eigenvector spatial filtering (SA-ESF) model utilizing the golden section search (GO-ESF) and genetic algorithm (GA-ESF) was proposed. The SA-ESF model was applied to estimate ground PM2.5 concentrations in the Yangtze River Delta (YRD) region of China both seasonally and annually from December 2015 to November 2016 using remotely sensing data, factory locations, and road networks. The results of the original eigenvector spatial filtering (ESF), GO-ESF, GA-ESF, and GWR models show that the GA-ESF model offers better performance and exhibits a better average adjusted R2 which is 26.6%, 15.3%, and 10.8% higher than for the ESF, GO-ESF, and GWR models, respectively. We next calculated stochastic site indicators that can describe characteristics of regional concentration from interpolated concentration maps derived from the GA-ESF and GWR models. The concentration maps and stochastic site indicators point to major differences in the PM2.5 concentrations in mountainous areas. There are notably high concentrations in those areas using the GWR model, in contrast with the GA-ESF results, indicating that there may be overfitting problems using the GWR model. Overall, the proposed SA-ESF model with the genetic algorithm technique can capture both global and local features and achieve promising results. [ABSTRACT FROM AUTHOR]

It is of great significance to measure and evaluate vegetation restoration efficiency of ecological compensation (VREoEC) in an ecological project. However, the output efficiency of ecological compensation is still unclear, and its influencing factors need further exploration. In this research, Wuqi and Zhidan, two pioneer counties in China's Grain for Green (GFG) project, were taken together as the study area, and remote sensing vegetation indices were used to reflect the vegetation coverage level, and the geographically-weighted regression model was applied to measure the VREoEC in the GFG project. Then, the classic multiple linear regression model was used to explore the influencing factors of VREoEC. The findings were as follows: (1) while the overall vegetation coverage in the study area was improved greatly, the VREoEC showed noticeable spatial trend and accumulation, and suffered from regional water-heat distribution; (2) local terrain, micro-climate conditions, rural roads density, classified highway, and intensity of ecological compensation affected the VREoEC; and (3) because local natural and socio-economic conditions can affect the VREoEC, ecological compensation measures need to be formulated according to local conditions. Thus, this study provides a methodological reference and corresponding policy suggestions. [ABSTRACT FROM AUTHOR]

Urban air pollution, especially in the form of haze events, has become a serious threat to socio-economic development and public health in most developing countries. It is of great importance to assess the frequency of urban air pollution occurrence and its influencing factors. The objective of our study is to develop consistent methodologies for constructing an index system and for assessing the influencing factors of the urban air pollution occurrence based on the Driver-Pressure-State-Impact-Response (DPSIR) framework by incorporating spatial analysis, geographical detector, and geographically weighted regression models. The 27 influencing factors were selected for assessing their influences on the urban air pollution occurrence in 337 Chinese cities. The results indicate that the spatial pattern of the urban air pollution in China was mostly consistent with the Chinese population-based Hu Line. Urban air pollution frequently occurred in North China, Central China, Northeast China, and East China, and displayed strong seasonality. The influencing factors of urban air pollution were complex and diverse, varying from season to season. Influencing factor analysis also shows that the explanatory power between any two influencing factors was greater than that of a single influencing factor of the urban air pollution. Furthermore, most influencing factors had both positive and negative effects and local effects on urban air pollution. Finally, we put forward five suggestions on reducing urban air pollution occurrence, which can provide the basis and reference for the government to make policies on urban air pollution control in China. [ABSTRACT FROM AUTHOR]

Analyzing the carbon sink of cropland and its relationship with influencing factors is of great significance to improve carbon sink and develop sustainable agriculture. However, fewer studies evaluated the spatial relationship at multiple scales. In this study, we first quantified the carbon sink of cropland in China in 2020 using empirical model. Then, we used the Geographical Weighted Regression model to quantify the relationship between carbon sink of cropland and influencing factors at the national, agricultural zone and provincial scales. Finally, we discussed potential ways to enhance carbon sink of cropland. The results found that the total carbon sink of cropland in 2020 was in surplus, with 2.56 billion tons, showing a spatial distribution of "high north and low south". There was significant spatial heterogeneity in the relationship between carbon sink of cropland and influencing factors. Labor size and agricultural inputs were most closely related to carbon sink of cropland. The areas with significant correlation were 116.99 and 108.79 million ha, respectively, or 93.17% and 86.64% of the total cropland. Carbon sink of cropland will have strong economic value, with 1.17 trillion yuan in China in 2020, which can increase farmers' per capita income by 1.34%. In order to enhance the carbon sink of cropland, we suggest that China's agricultural sector should reduce farmers' production costs, further improve the carbon trading platform for realizing the economic value of carbon sink of cropland, and then promote the sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

Ecological land can provide people with ecological products and ecological services; and it plays an important role in maintaining the health and safety of the ecosystem. With China's rapid urbanization development, ecological land has been invaded in large quantities, and damaged seriously, even resulting in loses of its ecological function. Based on land use data from 1995 to 2015, our study explores the spatial and temporal evolution of the damage or recovery of ecological land in the Yangtze River Economic Belt (YREB). Two spatial models, geographic detector and geographic weighted regression (GWR), were employed to assess the global effects and the local effects of the driving factors for ecological land change, respectively. Our study divided the ecological land change into five types based on the degree of change as severe damage, slight damage, unchanged, slight recovery, and obvious recovery. The results show that from 1995 to 2015, the total area of ecological land in the YREB increased initially and then decreased, but the overall trend was decreasing. The total damaged area was larger than the recovered area. Arable land and woodland both showed downward trends. In terms of ecological land change over the past 20 years, the type of unchanged had the largest area, followed by slight damage and slight recovery. Our study further revealed that ecological land change was the net result of the interaction of many factors, and the explanatory power between any two driving factors was greater than that of any individual driving factor. In addition, driving factors have different impacts on ecological land change in different geographical locations. This knowledge should help land managers and policymakers to be better informed when developing pertinent land use policies at the regional and local levels. The lessons can also be extended to other regions for better management of their ecological land for sustainable use. [ABSTRACT FROM AUTHOR]

Health improvement is an important social development goal for every country. By using a geographical weighted regression (GWR) model on the 5th and 6th censuses data, this paper analyzes the spatially varied influencing factors of the change in life expectancy of residents in Chinses cities. The results indicate that: (1) The initial level of life expectancy may have a negative correlation with its increase, indicating that life expectancy in different areas may eventually converge to a higher level; moreover, the degree of convergence of life expectancy in cities with different economic development levels is variant. (2) Results of geographically weighted regression model demonstrate significant spatial heterogeneity in the effects of the level of economic development, medical conditions, demographic structure, and natural environment on health improvement. Natural conditions, such as topography, dictate the change in life expectancy in most cities in the middle eastern region of China. Change of educational level is the leading factor in the vast western region while the change in birth rate is the most critical in Xinjiang. Thus, local-based strategies are critical for solving health problems, especially with a focus on promoting health conditions in middle-income and low-income areas. [ABSTRACT FROM AUTHOR]

Winter fallow farmland is increasing dramatically in some regions in China. Our goal was three-fold: (1) to develop a consistent procedure to identify winter fallow farmland from satellite images; (2) to examine the driving factors of winter fallow farmland; and (3) to identify additional determinants affecting the spatial distribution of winter fallow farmland. We applied geographically weighted regression to examine the spatial interactions of these driving factors. High percentages of winter fallow farmland in the north-eastern region were largely attributed to elevation, income, farmland area, and slope. Geography had a strong impact on the occurrence of fallow farmland. [ABSTRACT FROM AUTHOR]

Rapid urbanization in China has led to an excessive urban expansion of built-up areas, which makes quantitative research on compact city important. We adopted density and the degree of mixed land use to measure the compactness of 160 Chinese cities. Spatial autocorrelation analysis was performed to identify spatial clustering patterns, and the relationships between compactness and five variables were explored through regression models. The result shows that in nearly half of the cases, the calculated values of two indices are less than the average. The high or low values of density and the degree of mixed land use tend to be spatially clustered. The hot spot regions of density and the degree of mixed land use lie mainly in the south of China, while the north present as cold spots or the insignificant regions. Urban compactness can be affected by multifaceted factors and the relationships between compactness and five variables are not consistent throughout the areas of analysis. The GWR model can identify this phenomenon and provides a better fit than the OLS model. This study proposed a new approach to measure the compactness, and the results of GWR analysis can conducive to appropriate policy-making based on different local conditions. [ABSTRACT FROM AUTHOR]

Landscape patterns in a watershed potentially have significant influence on the occurrence, migration, and transformation of pollutants, such as nitrogen (N) and phosphorus (P) in rivers. Human activities can accelerate the pollution and complicate the problem especially in a peri-urban watershed with different types of land use. To characterize the heterogeneous correlations between landscape patterns and seasonal variations of N and P in a peri-urban watershed located upstream of Tianjin metropolis, China, observations of total nitrogen (TN) and total phosphorus (TP) at 33 locations were performed in the wet and dry seasons from 2013 to 2016. The data from individual locations were averaged for the wet and dry seasons and analyzed with geographical detector to identify influential landscape indices on seasonal water quality variations. The geographically weighted regression method, capable of analyzing heterogeneous correlations, was used to evaluate the integrated effects from different landscape indices. The results demonstrated that the location-weighted landscape contrast index (LWLI), the ratio of urban areas, and the ratio of forest areas were major influential indicators that affected TN and TP in river water. These indices also had integrated effects on variations of TN and TP together with other indices such as Shannon diversity index, landscape shape index, largest patch index, and contagion index. The integrated effects were different in the wet and dry seasons because of different effects of flushing and dilution by rainwater and the heterogeneity in landscape patterns. The LWLI had a positive relationship to water quality in the areas with high ratio of urban areas, indicating that domestic wastewater can be a major source of N and P pollution. The approaches and findings of this study may provide a reference for characterizing the major factors and integrated effects that control nonpoint source pollution in a watershed. [ABSTRACT FROM AUTHOR]

At the regional scale, many studies have been devoted to the construction of biomass models to improve the accuracy of regional biomass estimation, while only a few studies were carried out to investigate spatial influence. Therefore, the current research examined the spatial autocorrelations as well as variations between forest biomass and forest variables using 419 forest biomass plots sampled in the Guizhou Province in the year 2010. Besides, 4 global models, including the ordinary least squares model (OLS), linear mixed model (LMM), spatial lag model (SLM), spatial error models (SEM), together with geographically weighted regression model (GWR, the local model), were fitted to the associations of forest biomass with basal area, height and age of the stand. As suggested by our findings, distinct spatial autocorrelations as well as variations exist between forest biomass and these variables. OLS is not appropriate for modeling. SLM and SEM efficiently accounted for the spatial autocorrelations within model residual; however, they were unable to manage spatial heterogeneities. However, LMM and GWR, which had combined spatial variations as well as dependence during the modeling process, performed well in data fitting and response variable predicting. Of them, GWR reduced spatial heterogeneity to a greater extent than LMM. [ABSTRACT FROM AUTHOR]

Idle rural residential land (IRRL) is an important manifestation of changes in the human-land relationship during rural development. Studies on this topic form an important field in the study of sustainable land use, but quantitative analyses of IRRL in the academic community are still lacking. In this paper, we take Pinggu District, a metropolitan suburb that has experienced rapid urbanization, to analyze the spatial differentiation of IRRL and explore the spatial differentiation in the impact of different factors on IRRL. Our findings showed that IRRL was a common phenomenon in Pinggu. There was a spatial pattern of "one belt, three cores" in Pinggu District, and its scale decreased from the southeast to the northwest. Industrial areas, semimountainous ecotourism areas and urban fringe areas were the high-incidence areas of IRRL, while the idle rate of rural residential land in mountainous areas and agricultural areas on the plain was relatively low. The IRRL was the result of a combination of different factors, and there were differences among the factors and regions. The transfer of rural labor, nonagriculturalization of the industrial structure and the mode of production and lifestyle caused by urbanization and industrialization were the major driving forces, and the lagging village planning and imperfect land use system increased the risk of IRRL at the management level. Our study contributes to filling the gap in quantitative research on IRRL to enrich the research regarding the land use system by exploring the interaction between humans and land in rural areas and thus has significance for the rural restructuring and sustainable use of land in China. • The transfer of rural labour, non-agriculturalization of the industrial structure and mode of production and lifestyle caused by urbanization and industrialization were the major driving forces. • Influence of different driving factors on idle rural residential land varies in different regions, and the leading factors in different regions are also different. • The government should enhance the positive interaction of industry, population and land to promote the coordinated development of urban and rural areas. [ABSTRACT FROM AUTHOR]

Rapid urbanization in China has led to an increasing imbalance in regional development. The Guangxi Zhuang Autonomous Region, a less developed border province with unique cultural diversity, has a relatively large population (4.52 million people in 2015) under the poverty line, according to the national standard of poverty. China has launched a national campaign to reduce poverty using a wide range of new development policies and large-scale investment. However, there have been few studies on the determinants of poverty at the county level across a province. This paper aims to explore the spatial and social differences related to poverty among 109 counties by considering the spatial heterogeneity of poverty determinants. Spatial statistical models revealed that slope (Slp), GDP per capita (GDPP), the ethnic minority population ratio (EMPR), medical and technical personnel of healthcare institutions (MTP) and illiteracy rate (IR) significantly affect the patterns of the poverty rate, with a high adjusted R2 (0.67), while the poverty rate affects GDPP, IR, MTP and EMPR; i.e., the effects are interactional. Furthermore, the IR is significantly affected by the provision of schools and transportation conditions. Among these determinants, social factors may be key. The spatial patterns of these relationships demonstrate remarkable variation across the province and between minor and major groups. This quantitative evidence is enhanced by indepth interviews with selected groups. These results are expected to be useful for the anti-poverty project in Guangxi. [ABSTRACT FROM AUTHOR]

Transport is a significant sector that contributes to the increasing carbon dioxide emissions in China. Most related studies have utilized global models to investigate the factors influencing transport sector carbon dioxide emissions from the provincial level to the national level. However, this approach cannot depict the spatial nonstationary characteristics of the influencing factors. Taking the taxi subsector in Eastern China as a case study, we employed exploratory spatial data analysis to examine the spatial heterogeneity of carbon dioxide emissions from taxis (CDET) at the city level and a local model (geographically weighted regression, GWR) to observe the spatial variation in the factors influencing emissions. The results showed that the spatial distribution of CDET was uneven across the study region; the provincial-level cities and metropolises had the highest level of carbon dioxide emissions. The variables gross domestic product (GDP), total population (TP) and urban built-up area (UBA) were among the most significant determinants of emissions. Moreover, using the GWR technique instead of conventional global models, we successfully detected variation in the relationships between emissions and factors at the city level. The GWR results revealed that the positive correlation between CDET and GDP was distributed throughout the study area in 2005 and 2014, but the value of the regression coefficient did not remain the same across the study area. Both positive and negative effects of TP and UBA on CDET were identified and mapped in both study years. Based on the results, we strongly recommend that differentiated mitigation measures be adopted to reduce CDET emissions for different cities according to the spatial variation in emissions and the factors that impact them. [ABSTRACT FROM AUTHOR]

Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies. [ABSTRACT FROM AUTHOR]

Featured Application: Landslide susceptibility assessment and other geological disaster assessment issues. Landslides pose a serious threat to the safety of human life and property in mountainous regions. Susceptibility assessment for landslides is critical in landslide management strategy. Recent studies indicate that the traditional assessment models in many previous studies commonly assume a fixed relationship between influencing factors and landslide occurrence within an area, resulting in an inadequate evaluation for the local landslides susceptibility. To address this issue, in this paper we propose a spatial proximity-based geographically weighted regression (S-GWR) model considering spatial non-stationarity of landslide data for assessing the landslide susceptibility. Spatial proximity is the basic input condition for the proposed S-GWR model. The challenge lies in defining the spatial proximity expression that shows the geographical features of landslides and therefore affects the model ability of S-GWR. Our solution chooses the slope unit as spatial adjacency, rather than the grid unit in DTM. The multicollinearity between landslide influencing factors is then eliminated through variance inflation factor (VIF) method and principal component analysis (PCA). The proposed model is subsequently validated by using data in Qingchuan County, southwestern China. Spatial non-stationary is identified for landslide data. A comparison with grid unit and four traditional evaluation models is conducted. Validation results using the area under the ROC (receiver operating characteristic) curve and success rate curve indicate that the spatial proximity-based GWR model with slope unit has the highest predictive accuracy (0.859 and 0.850 respectively). [ABSTRACT FROM AUTHOR]

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