Geographically And Temporally Weighted Regression Research Articles

Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM2.5 (particulate matter with diameter ≤2.5 μm) being the main contributor. Establishing the spatial distribution of PM2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM2.5 concentrations. However, the R2 values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R2 values between the measured and remote sensed estimated PM2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a “V” -shaped oscillation change.

Analyzing carbon emissions and influencing factors in Chengdu-Chongqing urban agglomeration counties

Majority of carbon emissions originate from fossil energy consumption, thus necessitating calculation and monitoring of carbon emissions from energy consumption. In this study, we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions. We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration. Furthermore, we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation, and we used the Geographically and Temporally Weighted Regression (GTWR) model to analyze the influencing factors of carbon emissions at this scale. The results of this study are as follows: (1) from 2000 to 2019, the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease, with an average annual growth rate of 4.24%. However, in recent years, it has stabilized, and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration; (2) carbon emissions exhibited significant spatial clustering, with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration; (3) factors such as GDP, population (Pop), urbanization rate (Ur), and industrialization structure (Ic) all showed a significant influence on carbon emissions; (4) the spatial heterogeneity of each influencing factor was evident.

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model's dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

Impact of territorial spatial landscape pattern on PM2.5 and O3 concentrations in the Yangtze River delta urban agglomeration: Exploration and planning strategies

PM2.5-O3 composite pollution has become a pressing atmospheric challenge in China. Assessing the impact of territorial spatial landscape pattern on PM2.5 and O3 concentrations within the context of territorial spatial planning will provide ideas for atmospheric governance and spatial planning. Based on the agricultural-urban-ecological space system, this study first reclassifies territorial space within the Yangtze River Delta urban agglomeration (YRDUA) and calculates multilevel and multidimensional landscape metrics. Then, variables are screened by the random forest model and collinearity test. Furthermore, the spatiotemporally heterogeneous impact of selected landscape metrics on PM2.5 and O3 concentrations is explored using the geographically and temporally weighted regression (GTWR) model, with a tradeoff-synergy analysis being conducted to propose planning strategies. The conclusions show: (1) Two pollutant concentrations have both numerical and spatial correlations. The temporal stability of concentrations' responses to metrics is increasing, while the spatial heterogeneity continues to be significant. (2) Aggregation index of agricultural production space (AP_AI) and percentage of forest area (FO_PLAND) are trade-off and synergy factors of coordinated control, respectively. Largest patch index of grassland (GR_LPI) and patch density of other ecological spaces (OT_PD) are individual factors of PM2.5 reduction. (3) Spatial zoning supported by intra-zonal and inter-zonal cooperation policies, and coordinated control guidelines centered around the utilization of agricultural production space, have been advocated. This study aims to provide theoretical supplement and practical support for urban agglomeration's atmospheric governance and territorial spatial landscape pattern optimization.

Heterogeneous impacts of the high-speed railway network on urban–rural income disparity: Spatiotemporal evidence from Yangtze River Delta of China

The socioeconomic effects of China’s fast-growing high-speed railways (HSRs) have become a popular issue in recent decades. However, few studies have examined how HSRs alleviate the urban–rural income disparity temporally and spatially; the current research addresses this gap in the literature from novel network accessibility and connectivity perspectives. Using different spatiotemporal models to analyze the Yangtze River Delta in China, the main findings are as follows: (1) there is evidence that the HSR network alleviates the urban–rural income disparity and exhibits a stronger positive impact on rural residents’ income; (2) compared with HSR accessibility, HSR connectivity affects the income disparity in a more significant manner; (3) due to its advantage in capturing the spatiotemporal heterogeneities of HSR’s impact, the geographically and temporally weighted regression (GTWR) model demonstrates a higher accuracy than other models; (4) the GTWR model results show that the HSR network affects the income disparity both negatively and insignificantly in Northern Jiangsu or Southwestern Zhejiang, while this effect is significant in other regions; and (5) further empirical evidence indicates that this varying effect is in light of differences in tertiary industry development and economic growth. These findings provide a novel opinion of HSR’s socioeconomic effects and present useful empirical support for solving the serious urban–rural income disparity problem in China.

The Carbon Emission Reduction Effect and Spatio-Temporal Heterogeneity of the Science and Technology Finance Network: The Combined Perspective of Complex Network Analysis and Econometric Models

With the active promotion of the “carbon peaking and carbon neutrality” goals, science and technology finance (STF) is the important driving force of low-carbon development, and financial networks facilitate the aggregation and transformation of resources in space, so it is of great theoretical and practical significance to investigate the impact of science and technology finance networks (STFN) on carbon emissions (CE). Based on the 30 provinces of China from 2011 to 2019, this article used the STF development level in each province as the main indicator to construct the STFN. The complex network analysis and econometric models are combined, with the weighted degree values and betweenness centrality selected as typical network structure indicators incorporating into the econometric model to explore their impact on CE. Then, the Geographically and Temporally Weighted Regression (GTWR) model is applied to analyse the spatio-temporal heterogeneity of influencing factors. The results show the following: (1) From 2011 to 2019, the spatial structure of China’s STFN has changed significantly, and the status of the triangle structure consisting of Beijing–Tianjin–Hebei (BTH)–Yangtze River Delta (YRD)–Pearl River Delta (PRD) is gradually consolidated in the overall network, and the network structure tends to be stable. (2) The results of the benchmark regression show that the weighted degree value of the STFN has a significant inhibitory effect on CE, while betweenness centrality shows a certain positive effect on CE. (3) The weighted degree value has a more significant effect on CE reduction in the eastern region, while the betweenness centrality has a more significant effect on CE reduction in the central and western regions, but shows a significant promotion effect in the eastern region. (4) There is spatio-temporal heterogeneity in the effects of residents’ affluence, energy consumption, industrial structure, and environmental pollution on CE.

Exploring Travel Mobility in Integrated Usage of Dockless Bike-Sharing and the Metro Based on Multisource Data

Dockless bike-sharing (DBS) is a green and flexible travel mode, which has been considered as an effective way to address the first-and-last mile problem. A two-level process is developed to identify the integrated DBS–metro trips. Then, DBS trip data, metro passenger data, socioeconomic data, and built environment data in Shanghai are used to analyze the spatiotemporal characteristics of integrated trips and the correlations between the integrated trips and the explanatory variables. Next, multicollinearity tests and autocorrelation tests are conducted to select the best explanatory variables. Finally, a geographically and temporally weighted regression (GTWR) model is adopted to examine the determinants of integrated trips over space and time. The results show that the integrated trips account for 16.8% of total DBS trips and that departure-transfer trips are greater than arrival-transfer trips. Moreover, the integrated trips are concentrated in the central area of the city. In terms of impact factors, it is found that GDP, government count, and restaurant count are negatively correlated with the number of integrated trips, while house price, entropy of land use, transfer accessibility index, and metro passenger flow show positive relationships. In addition, the results show that the GTWR model outperforms the OLS model and the GWR model.

Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data

The COVID-19 pandemic has had profound adverse effects on public health and society, with increased mobility contributing to the spread of the virus and vulnerable populations, such as those with pre-existing health conditions, at a higher risk of COVID-19 mortality. However, the specific spatial and temporal impacts of health conditions and mobility on COVID-19 mortality have yet to be fully understood. In this study, we utilized the Geographical and Temporal Weighted Regression (GTWR) model to assess the influence of mobility and health-related factors on COVID-19 mortality in the United States. The model examined several significant factors, including demographic and health-related factors, and was compared with the Multiscale Geographically Weighted Regression (MGWR) model to evaluate its performance. Our findings from the GTWR model reveal that human mobility and health conditions have a significant spatial impact on COVID-19 mortality. Additionally, our study identified different patterns in the association between COVID-19 and the explanatory variables, providing insights to policymakers for effective decision-making.

Alleviating environmental pressure from livestock production: A “livestock-resource environmental carrying capacity” perspective in China

Scientific assessment of the relationships and influencing factors between livestock and resource-environmental carrying capacity (LS-RECC) is essential for alleviating environmental pressure. The crop-livestock balance method was used to measure China’s livestock production potential from 2005 to 2020. The improved coupling coordination degree model (I-CCDM) and exploratory spatial data analysis (ESDA) were used to analyze the spatial-temporal coordinated development characteristics of the LS-RECC. Furthermore, geographically and temporally weighted regression (GTWR) was used to identify the factors influencing the coordination development of the LS-RECC. The findings showed that (1) From 2005 to 2020, the nutrient demand for crop cultivation in China increased, and, the crop system nutrient demand exceeded the livestock nutrient supply in 2007 to 190 ×104 tons, reaching a maximum of 925 ×104 tons in 2019. However, the production potential of livestock in Ningxia, Tibet, Guizhou, Gansu, Yunnan, and Qinghai was severely overloaded. (2) The coupling coordination degree (CCD) of the LS-RECC showed a progressively increasing trend, which indicates that the sustainable development ability of China's livestock industry is constantly increasing. However, compared with that in eastern and central China, the coordinated development level of the LS-RECC in western China was lower, further indicating excessive livestock industry development in western China. (3) Environmental regulation had the strongest effect on the regional coordinated development of the LS-RECC, with obvious spatial differences. The economic development level had a positive effect, but urbanization and Engel's coefficients had the opposite effect. These results provide not only references for reducing environmental pressure on the livestock industry in China but also guidance for the sustainable development of the livestock industry in other large livestock production countries.

Toward tuberculosis elimination by understanding epidemiologic characteristics and risk factors in Hainan Province, China

BackgroundThe disease burden of tuberculosis (TB) was heavy in Hainan Province, China, and the information on transmission patterns was limited with few studies. This atudy aims to further explore the epidemiological characteristics and influencing factors of TB in Hainan Province, and thereby contribute valuable scientific evidences for TB elimination in Hainan Province.MethodsThe TB notification data in Hainan Province from 2013 to 2022 were collected from the Chinese National Disease Control Information System Tuberculosis Surveillance System, along with socio-economic data. The spatial–temporal and population distributions were analyzed, and spatial autocorrelation analysis was conducted to explore TB notification rate clustering. In addition, the epidemiological characteristics of the cases among in-country migrants were described, and the delay pattern in seeking medical care was investigated. Finally, a geographically and temporally weighted regression (GTWR) model was adopted to analyze the relationship between TB notification rate and socio-economic indicators. The tailored control suggestions in different regions for TB elimination was provided by understanding epidemiological characteristics and risk factors obtained by GTWR.ResultsFrom 2013 to 2022, 64,042 cases of TB were notified in Hainan Province. The estimated annual percentage change of TB notification rate in Hainan Province from 2013 to 2020 was − 6.88% [95% confidence interval (CI): − 5.30%, − 3.69%], with higher rates in central and southern regions. The majority of patients were males (76.33%) and farmers (67.80%). Cases among in-country migrants primarily originated from Sichuan (369 cases), Heilongjiang (267 cases), Hunan (236 cases), Guangdong (174 cases), and Guangxi (139 cases), accounting for 53%. The majority (98.83%) of TB cases were notified through passive case finding approaches, with delay in seeking care. The GTWR analysis showed that gross domestic product per capita, the number of medical institutions and health personnel per 10,000 people were main factors affecting the high TB notification rates in some regions in Hainan Province. Different regional tailored measures such as more TB specialized hospitals were proposed based on the characteristics of each region.ConclusionsThe notification rate of TB in Hainan Province has been declining overall but still remained high in central and southern regions. Particular attention should be paid to the prevalence of TB among males, farmers, and out-of-province migrant populations. The notification rate was also influenced by economic development and medical conditions, indicating the need of more TB specialized hospitals, active surveillance and other tailored prevention and control measures to promote the progress of TB elimination in Hainan Province.

LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data

Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 μg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.

Exploring the Influence of the Built Environment on the Demand for Online Car-Hailing Services Using a Multi-Scale Geographically and Temporally Weighted Regression Model

Online car-hailing is gradually shifting towards a predominant use of electric vehicles, a change that is advantageous for developing a sustainable society. Understanding the patterns of changes in online car-hailing travel can assist transportation authorities in optimizing vehicle dispatching, reducing idle rates, and minimizing resource wastage. The built environment influences the demand for online car-hailing travel. Previous studies have commonly employed the geographically weighted regression (GWR) model and the geographically and temporally weighted regression (GTWR) model to examine the relationship between the demand for online car-hailing trips and the built environment. However, these studies have ignored that the impact range of the built environment also varies with time and space. To fully consider the variations in the impact range of the built environment, this study established multi-scale geographically and temporally weighted regression (MGTWR) to examine the spatiotemporal impacts of urban built environments on the demand for online car-hailing travel. An empirical study was conducted to assess the effectiveness of the MGTWR model using point of interest (POI) data and online car-hailing order data from Haikou. The evaluation indicators showed that the MGTWR model has higher fitting accuracy than the GTWR model. Moreover, the impact of each type of POI on the demand for online car-hailing travel was analyzed by examining the temporal and spatial distribution of the regression coefficients. Additionally, we observed that transport facility POIs and healthcare service POIs exerted the most pronounced influence on the demand for online car-hailing. In contrast, the impact of shopping service POIs and catering service POIs was relatively weaker.

Impact of China’s financial development on the sustainable development goals of the Belt and Road Initiative participating countries

China’s Belt and Road Initiative (BRI) aims to strengthen regional economic and policy cooperation and achieve the rapid development among the participating countries. While the impact on the financial development of the economic growth and energy environment of BRI participating countries has garnered close attention among scholars, few studies focus on the impact of financial development on the sustainable development goals (SDGs) of the BRI participating countries. To address this gap, we utilized panel regression models to quantitatively assess the impact of China’s financial development scale, structure, and efficiency on the SDGs of the BRI participating countries, and adopted Geographically and Temporally Weighted Regression (GTWR) model to explore the spatial-temporal effects of China’s financial development scale, structure, and efficiency on the SDGs of the BRI participating countries. Our findings indicate that China’s financial development has significantly promoted the SDGs of the BRI participating countries. This study further reveals that the scale and efficiency of China’s financial development have had a more pronounced impact on the SDGs of Asian countries, low- and middle-income countries, and the Land Silk Road participating countries, compared to those of European countries, high-income countries, and the Maritime Silk Road participating countries, respectively. In contrast, the structure of financial development primarily promotes the SDGs of European and high-income BRI participating countries in the land silk belt. The role of China’s financial development in promoting the SDGs of most BRI participating countries has gradually increased over time. This study provides valuable insights for decision-makers in China to facilitate the sustainable development of BRI participating countries and foster a shared community within the BRI framework.

Spatial and temporal distribution characteristics and influencing factors of tourism eco-efficiency in the Yellow River Basin based on the geographical and temporal weighted regression model.

With economic progression in China, Yellow River Basin serves as a critical economic belt, which has also been recognized as a cradle of Chinese culture. A watershed is a complex structure of social, economic, and natural factors, and the diversity of its components determines its complexity. Studies on the spatiotemporal distribution characteristics and factors influencing the tourism eco-efficiency at the watershed scale are crucial for the sustainable regional socio-economic development, maintaining a virtuous cycle of various ecosystems, and comprehensively considering the utilization and coordinated development of various elements. Based on tourism eco-efficiency, the coordination degree of regional human-land system and the sustainable development levels can be accurately measured. With the tourism eco-efficiency in the Yellow River Basin from 2009 to 2019, the present study considers 63 cities in the Yellow River Basin as the research area by adopting the super-efficiency slacks-based measure (Super-SBM) model. Methods such as trend surface analysis, spatial autocorrelation analysis, elliptic standard deviation analysis, and hot spot analysis were used to explore their spatiotemporal distribution and evolution characteristics. The geographical and temporal weighted regression (GTWR) model was used to determine the factors influencing the tourism eco-efficiency value. The findings are as follows: ①The level of tourism eco-efficiency in the Yellow River Basin is not high, exhibiting a fluctuating upward trend. ②The tourism eco-efficiency in the Yellow River Basin shows significant spatial interdependence and agglomeration. Furthermore, the track of the center of gravity moves from northeast to southwest. ③ The tourism eco-efficiency in the Yellow River Basin is affected by various factors, with the economic development level having the greatest influence.

Coupling coordination and driving mechanisms of water resources carrying capacity under the dynamic interaction of the water-social-economic-ecological environment system

The water resources carrying capacity (WRCC) is a complex and comprehensive system that is jointly influenced by water resources, society, the economy and the ecological environment. Previous WRCC studies have primarily focused on estimating the overall level of regional WRCC. Few studies have explored the interactions among the various elements in the WRCC system and their influence on the WRCC evolution. Therefore, the purpose of this paper is, on the one hand, to explore the dynamic interactive relationships within the WRCC system from the perspectives of water resources, society, the economy and the ecological environment using a coupling coordination degree model and a panel vector autoregressive (PVAR) model, and on the other hand, to determine the evolutionary driving mechanism of the WRCC using the geographically and temporally weighted regression (GTWR) model to improve the regional WRCC. Taking 21 cities in Guangdong Province as an example, the results show that (1) the coupling coordination degree among the four WRCC subsystems in Guangdong Province shows an overall upward trend from 2009 to 2020, and the coordination between water resources utilization and other subsystems needs to be further strengthened. (2) The economic subsystem is the core of the WRCC system with reinforcing effects on both water resources and social subsystems but significant inhibitory effects on the ecological environment subsystem. Notably, the development of the ecological environment plays a crucial role in promoting social and economic development. (3) From 2009 to 2020, the development of the WRCC in Guangdong Province is initially driven by social and economic development, followed by economic development and ecological environmental protection, and then mainly by ecological environmental protection, which gradually becomes the primary driving force. This study provides a new entry point for studying the regional WRCC and formulating targeted measures for enhancing the regional WRCC.

Spatial-temporal characteristics and driving factors' contribution and evolution of agricultural non-CO2 greenhouse gas emissions in China: 1995-2021.

Comprehending the spatial-temporal characteristics, contributions, and evolution of driving factors in agricultural non-CO2 greenhouse gas (GHG) emissions at a macro level is pivotal in pursuing temperature control objectives and achieving China's strategic goals related to carbon peak and carbon neutrality. This study employs the Intergovernmental Panel on Climate Change (IPCC) carbon emissions coefficient method to comprehensively evaluate agricultural non-CO2 GHG emissions at the provincial level. Subsequently, the contributions and spatial-temporal evolution of six driving factors derived from the Kaya identity were quantitatively explored using the Logarithmic Mean Divisia Index (LMDI) and Geographical and Temporal Weighted Regression (GTWR) methods. The results revealed that the distribution of agricultural non-CO2 GHG emissions is shifting from the central provinces to the northwest regions. Moreover, the dominant driving factors of agricultural non-CO2 GHG emissions were primarily economic factor (EDL) with positive impact (cumulative promotion is 2939.61 million metric tons (Mt)), alongside agricultural production efficiency factor (EI) with negative impact (cumulative reduction is 2208.98 Mt). Influence of EDL diminished in the eastern coastal regions but significantly impacted underdeveloped regions such as the northwest and southwest. In the eastern coastal regions, EI gradually became the absolute dominant driver, demonstrating a rapid reduction effect. Additionally, a declining birth rate and rural-to-urban population migration have significantly amplified the driving effects of the population factor (RP) at a national scale. These findings, in conjunction with the disparities in geographic and socioeconomic development among provinces, can serve as a guiding framework for the development of a region-specific roadmap aimed at reducing agricultural non-CO2 GHG emissions.

Improving prediction of soil heavy metal(loid) concentration by developing a combined Co-kriging and geographically and temporally weighted regression (GTWR) model

The study of heavy metal(loid) (HM) contamination in soil using extensive data obtained from published literature is an economical and convenient method. However, the uneven distribution of these data in time and space limits their direct applicability. Therefore, based on the concentration data obtained from the published literature (2000–2020), we investigated the relationship between soil HM accumulation and various anthropogenic activities, developed a hybrid model to predict soil HM concentrations, and then evaluated their ecological risks. The results demonstrated that various anthropogenic activities were the main cause of soil HM accumulation using Geographically and temporally weighted regression (GTWR) model. The hybrid Co-kriging + GTWR model, which incorporates two of the most influential auxiliary variables, can improve the accuracy and reliability of predicting HM concentrations. The predicted concentrations of eight HMs all exceeded the background values for soil environment in China. The results of the ecological risk assessment revealed that five HMs accounted for more than 90% of the area at the "High risk" level (RQ ≥ 1), with the descending order of Ni (100%) = Cu (100%) > As (98.73%) > Zn (95.50%) > Pb (94.90%). This study provides a novel approach to environmental pollution research using the published data.

Enhancing Agricultural Ecological Efficiency in China: An Evolution and Pathways under the Carbon Neutrality Vision

Under China’s carbon neutrality vision, the integration of resource consumption and ecological protection in agricultural production requires increased attention to agroecology. This study comprehensively integrates the carbon impact within agricultural ecological efficiency (AEE). Based on the window analysis of super-SBM-undesirable and geographically and temporally weighted regression (GTWR) models, AEE and its influencing factors in China’s 31 provinces from 2000 to 2021 are measured, and then types categorized, mechanisms investigated, and optimization pathways provided. The results revealed that from 2000 to 2021, the AEE showed a growing trend, with the spatial agglomeration degree increased. Its gravity center presented a migration path of “northeast–southwest”. Driven by internal and external mechanisms, both agricultural production conditions and regional socio-economic conditions had a stronger positive effect and wider range of promotion on China’s AEE, and the positive contribution of physical environmental conditions decreased, while the inhibitory effect increased. Furthermore, this study divided three zones and emphasized that the agri-ecological power zone plays a demonstrative role in carbon sequestration and efficiency improvement, the eco-pollution balance zone needs to strike a balance between economic development and agricultural ecological development, and the agri-pollution power zone ought to reduce agricultural carbon emissions and mitigate its environmental impact.

Multiscale effects and drivers of landscape heterogeneity for water‐related ecosystem services in urban agglomerations

AbstractUnderstanding the multiscale impacts and drivers of urban agglomeration landscape patterns for ecosystem services (ESs), especially water‐related ecosystem services (WESs), is essential for the development of regional ecological management. However, the multiscale impacts and driving mechanisms of urban agglomeration landscape patterns for ESs have not been adequately explained. In this study, multivariate data were employed, and the InVEST model, trend test method, coupled GeoDetector and geographically and temporally weighted regression (GTWR) method were utilized to comprehensively explore the spatial and temporal changes in landscape patterns and WESs in the Pearl River Delta urban agglomeration (PRDUA) at various grid and administrative scales from 1990 to 2020 and to determine the driving mechanisms affecting WESs. The results indicated that the variation characteristics of landscape patterns and WESs in the PRDUA were consistent, forming a binary spatial structure of core and peripheral areas in an inverted “U” shape around the estuary of the Pearl River. The relationship between landscape patterns and WESs weakened with the increase of scale, and the correlation coefficient decreased by approximately 0.10 from 5 km to 10 km grid scale. Additionally, precipitation (PRE) was the main factor controlling WESs changes in the PRDUA, explaining more than 50% of the changes in WESs, and the regression coefficients ranged from 0.0825 to 0.1584. Changes in WESs were the result of the combined effects of natural factors, including PRE, landscape pattern, elevation, slope, and socioeconomic factors, such as population and gross domestic product (GDP). Overall, these findings could contribute to optimizing regional landscape patterns and fostering sustainable development of the ecological environment in urban agglomerations.

Spatio-Temporal Dual Kriging with Adaptive Coefficient Drift Function

Research on spatio-temporal geostatistical modeling remains a critical challenge in numerous scientific and engineering disciplines. This paper introduces a novel extension of dual kriging, called spatio-temporal dual kriging (ST-DK), in which drift functions with fixed and adaptive coefficients are established. The approach appears to be effective in modeling complex spatio-temporal dynamics, particularly when relevant auxiliary variables exert substantial influence on the target variable. To illustrate its performance, we compare the ST-DK model with the classical spatio-temporal regression kriging (ST-RK) and geographically and temporally weighted regression (GTWR) models for estimating temperature and air pressure data from Thailand in 2018. Our findings demonstrate that both the ST-DK and ST-RK models when utilizing adaptive coefficients outperform their fixed coefficient counterparts. Furthermore, the ST-DK method consistently exhibits superior performance compared to the ST-RK and GTWR methods.