Multiscale Geographically Weighted Regression Research Articles

Spatial heterogeneity analysis on distribution patterns and underlying determinants of historical rural settlements: a case study in Taihang Baxing

AbstractAs vital carriers of cultural heritage, rural areas are facing the threats of degradation and urbanization during rapid urbanization processes. In recent years, China has formulated and implemented a series of national policies, regulations, and heritage protection lists to safeguard and perpetuate the traditional patterns and historical features of rural heritage. However, these rural heritage listings exhibit significant macro-level distributional imbalances and generally lack consideration of regional characteristics. This study uses the Taihang Baxing (THBX) in China as a case study, initially employing historical local documents and maps as base data. It utilizes the stability in the evolution of Chinese characters to compare historical and contemporary village names, filtering out existing historical rural settlements (HRS) to construct a dataset. Further, a multi-scale geographically weighted regression (MGWR) model is applied to analysis the impacts of natural, economic, and social factors on the concentration of HRS (CHRS). The study reveals that the CHRS in the region is markedly characterized by clustering, with evident spatial heterogeneity in the impacts of various factors. Slope (SLP), total annual precipitation (TP), and potential crop yield (PCY) significantly influence the CHRS globally, while elevation (ELV), distance to ancient path (DAP) and surface runoff (DSR), gross domestic product (GDP), and road density (RD) have significant local impacts. This research offers a methodological framework for HRS data compilation and provides theoretical and methodological references for national and local levels of rural heritage protection, optimization of rural spatial patterns, prediction of rural evolution pathways, and implementation of China’s rural revitalization strategy.

An Improved Machine Learning Framework Considering Spatiotemporal Heterogeneity for Analyzing the Relationship Between Subway Station-Level Passenger Flow Resilience and Land Use-Related Built Environment

The COVID-19 pandemic and similar public health emergencies have significantly impacted global travel patterns. Analyzing the recovery characteristics of subway station-level passenger flow during the pandemic recovery phase can offer unique insights into public transportation operations and guide practical planning efforts. This pioneering study constructs a station-level passenger flow recovery resilience (PFRR) index during the rapid recovery phase using subway AFC system swipe data. Additionally, it develops an analytical framework based on a multiscale geographically weighted regression (MGWR) model, the improved gray wolf optimization with Levy flight (LGWO), and light gradient boosting machine (LightGBM) regression to analyze passenger flow resilience on weekdays and weekends in relation to land use-related built environment types. Finally, SHAP attribution analysis is used to study the nonlinear relationships between built environment variables and PFRR index. The results show significant spatial heterogeneity in the impact of commercial, recreational, and residential land, as well as POI (points of interest) of leisure and shopping on PFRR. On weekdays, the most relevant built environment variables for PFRR are POI of enterprises and shopping numbers. In contrast, the contribution of built environment variables affecting PFRR of weekend is more balanced, reflecting the recovery of non-essential travel on weekends. Most land use-related built environment variables exhibit nonlinear associations with PFRR values. The proposed analytical framework shows significant performance advantages over other baseline models. This study provides unique insights into subway passenger flow characteristics and surrounding land use-related development layouts under the impact of public health emergencies.

Revealing the multiscale impact of external factors on dockless bike sharing and metro integration: Evidence from Shenzhen

Dockless bike sharing (DBS) and metro integration offer innovative solutions for the first-mile and last-mile challenges of urban transportation. However, accurately identifying transfer passenger volume, analyzing DBS and metro integration characteristics, and understanding the spatial heterogeneity of factors affecting this volume remain areas to explore. This study, using Shenzhen as a case, constructs a framework to identify transfer passenger volume by considering built environment elements around metro stations and applying a Thiessen polygon. Transfer passenger volumes are classified into six categories, and their spatio-temporal characteristics are analyzed. A multiscale geographically weighted regression (MGWR) model investigates the impacts of socio-economic attributes, built environment, and transportation-related factors on transfer passenger volume, revealing spatial heterogeneity. Results show that the demand for DBS and metro integration is higher in the morning peak than in the evening peak, with access transfer passenger volume peaks occurring earlier than egress peaks. Spatially, transfer passenger volume shows heterogeneity, with higher demand in the urban core than in the suburbs. Commercial and residential and enterprises have the most significant impact on various types of transfer passenger volume, showing the highest spatial heterogeneity in enterprises. In contrast, science education and culture positively influence evening peak access transfer volume and exhibit the weakest spatial heterogeneity. Population density, sports and leisure, shopping and entertainment, and tourist attractions display localized positive or negative impacts. These findings can guide operators and planners in optimizing public transportation resources to enhance DBS and metro integration.

Multi-Scale Effects of Supply–Demand Changes in Water-Related Ecosystem Services Across Different Landscapes in River Basin

To promote sustainable hierarchical management, it is essential to understand the complex relationships within and underlying causes of supply–demand changes in water-related ecosystem services (WESs) across different spatial scales and landscape patterns. Consequently, the Optimal Parameters-based Geographical Detector (OPGD) and Multi-Scale Geographically Weighted Regression (MGWR) are used to analyze the factors influencing changes in WESs supply–demand. The findings indicate that (1) at the macroscale, population size, and economic activity are the main driving factors, while at the microscale, precipitation becomes the primary factor influencing fluctuations in WESs supply–demand. (2) Furthermore, over time, the influence of social factors becomes increasingly significant. (3) The explanatory power of a single factor typically increases as it interacts with other factors. (4) Abundant precipitation helps in the generation and maintenance of WESs, but intense human activities may have negative impacts on them. Therefore, we have made significant progress in identifying and analyzing the natural and human-induced driving forces affecting changes in WESs by deeply integrating long-term multi-source remote sensing data with the OPGD and MGWR models.

Towards Resilient Communities: Evaluating the Nonlinear Impact of the Built Environment on COVID-19 Transmission Risk in Residential Areas

Reflections on urban epidemics often drive improvements in the resilience of the built environment. However, the assessment regarding the nonlinear influence of the community environment on the spread of Corona Virus Disease 2019 (COVID-19) is inadequate. This study analyzed the influential mechanism of built environment factors on the epidemic risk in residential areas, using Shanghai as a case study. During the lockdown in April 2022, Shanghai reported daily data on COVID-19 outbreaks in residential areas, amounting to a total of 90,323 entries. Based on a GIS-based grid analysis approach, we employed a Random Forest (RF) model and a Multiscale Geographically Weighted Regression (MGWR) model to investigate the marginal effects and spatial heterogeneity of environmental factors on the mean count of COVID-19 outbreak days (MC) in residential areas within each grid zone. The results show that the value of MC forms a ring-mountain distribution surrounding the city's outer ring road. The RF model (R² = 0.57) demonstrates that the house price, population density, family number, and the standard deviation of building height (BH_SD) significantly correlated with MC, with the relative importance of 25%, 13%, 11%, and 6%, respectively. The MGWR model (R² = 0.63) highlights the spatial heterogeneity of family number, house age, house price, property fee, and delivery density. We also found that property fee and green rate were negatively correlated with the MC. These findings help improve responses to public health emergencies and create more resilient communities to cope with pandemics.

Exploring the Spatiotemporal Heterogeneities in Urban Vitality Through Scalable Proxies from Mobile Data

Jane Jacob’s concepts of urban vitality and diversity have become prevailing urban planning philosophies in most countries for making cities more livable. Recent changes in demographics and the impacts of COVID-19 have exacerbated the economic and social challenges that cities commonly face, particularly spatiotemporal heterogeneities. Being able to understand these heterogeneities in scalable approaches is fundamental to tackling these challenges in cities. Therefore, this article aims to provide a new form of scalable estimation of urban vitality by using the de facto population. Instead of merely adopting static statistical information such as morphological characteristics in areas, we leverage dynamic factors such as internal mobility flows and energy use intensity as proxies for the spatiotemporal dynamics of indoor and outdoor behaviors of crowds. In this way, we combine dynamic attributes and static features to describe the patterns of urban vitality, which are directly related to spatiotemporal dynamics in urban places. We utilize GNSS-based mobile data and building energy usage intensity as dynamic proxies along with static data such as land use mix and age distribution. To better capture spatial heterogeneity, we use a Multiscale Geographically Weighted Regression (MGWR) model to identify the relationships between the de facto population and the dynamic and static factors. Drawing from the factors determining urban vitality, this article provides policy implications for alleviating spatiotemporal urban imbalances. These data-driven implications can fill the technical knowledge gaps in establishing planning strategies for achieving urban sustainability while enhancing overall subjective livability.

A Study on the Influence Mechanisms of Neighborhood Vitality and the Characteristics of Spatial and Temporal Differentiation in the Urban Fringe Areas of Wuhan City

Achieving effective integration of urban–rural relationships and promoting the flow of resources between urban and rural areas in megacities are a key priority in the development of China’s new urbanization efforts. As a transitional zone between urban and rural areas, the urban fringe is the frontier of urban–rural integration. The specific research object of this paper is the urban fringe areas of Wuhan City. This paper quantifies the neighborhood vitality of the fringe areas by the short-stay visitors in the fringe areas and selects the 5D elements of the built environment and social media data from multiple sources to construct the indicator system assessing the neighborhood vitality of the urban fringe areas. This paper analyzes the spatial distribution characteristics of neighborhood vitality and its influencing factors in urban fringe areas and investigates the connection between neighborhood vitality and its influencing factors through the application of the multi-scale geographically weighted regression (MGWR) model. Based on the regression results, relevant planning recommendations are made on how to enhance the vitality of neighborhoods in urban fringe areas. The results show that the index system constructed by “5D” elements of built environment and social media data can well explain the spatial distribution of neighborhood vitality in urban fringe areas. Among the influencing factors, the absolute value of the correlation coefficient of network exposure is the largest, followed by road density and functional density. Thanks to the different bandwidths given by MGWR to the influencing factors, the global influencing factors are only two indicators—development intensity and functional mixing degree—while the other influencing factors are all local, and the influence degree of different regions is different, so it is necessary to analyze and put forward different planning suggestions accordingly.

Study on the spatiotemporal pattern evolution of surface urban heat island in shrinking cities: Fushun and Tieling

Under rapid urbanization, the urban heat island (UHI) problem impacts not only large cities, but also poses severe challenges to shrinking cities with rapidly declining population. In China, most shrinking cities are characterized by population loss alongside the expansion of built-up areas due to policy. Urban warming exacerbates the human settlement environment, with UHI intensifying due to urban expansion, while population loss simultaneously alleviates it. This raises a question: will the UHI problem be mitigated in shrinking cities? In this study, we analyze the spatiotemporal pattern evolution of surface urban heat island (SUHI) in Fushun and Tieling from 2000 to 2020 using Landsat series products. We combine landscape pattern indices and SUHI indicators, and perform correlation analyses of the factors influencing SUHI with multiscale geographically weighted regression (MGWR). The findings reveal that in Fushun, mining activities significantly impact SUHI, while in Tieling, extremely Land Surface Temperature (LST) zones are expanding and dispersing. SUHI patterns are notably shaped by subsurface conditions, and spatial configurations play key roles in regulating SUHI. However, population loss has not significantly influenced SUHI, even in shrinking cities. This study offers a new perspective for SUHI research and provides further insights into urban planning strategies.

Spatial and temporal evolution patterns and driving mechanisms of rural settlements: a case study of Xunwu County, Jiangxi Province, China

Urbanization and industrialization have driven rapid socio-economic development, leading to a significant population shift from rural areas to cities. This demographic transition has resulted in substantial changes in rural settlements, presenting considerable challenges for rural revitalization. Given the spatial differentiation, it is crucial to analyze the spatiotemporal evolution patterns and driving mechanisms of rural settlements under various development scenarios and at multiple scales within a county. Such analysis is essential for rational planning and sustainable development. This study analyzes the spatial and temporal evolution patterns and driving mechanisms of rural settlements in Xunwu County, Jiangxi Province, China, using land use dynamics, hot and cold spot analysis, and center of gravity migration models. The Multiscale Geographically Weighted Regression Model (MGWR) is employed to explore their transformation driving mechanisms. The results show that during 2010–2020 in the study area, the total change rate of rural settlements was 39.82%, with a dynamic degree of 2.87%. The average centroid of the settlements is located in Wenfeng Township, gradually migrating southeastward by 3.7 km. Cold spots are shrinking northward, while hotspots are gathering in Chengjiang Town. Socio-economic conditions dominate the evolution of rural clusters in this region, showing obvious spatial heterogeneity. This is followed by locational conditions that are weakening in influence, while the impact of natural geographical conditions exists in the northeast region. The research findings offer a scientifically rigorous and well-founded analysis of the evolution and driving mechanisms of rural settlements. This analysis provides a solid theoretical basis for policy planning in urban-rural integrated development and supports the sustainable growth of rural communities.

Heterogeneous Interaction Effects of Environmental and Economic Factors on Green Efficiency of Water Resources in China

Identifying the green efficiency of water resources and its driving factors is paramount for promoting sustainable development in China. The existing research has primarily focused on the spatial heterogeneity of individual factors that impact green efficiency of water resources. However, it has often overlooked the heterogeneity in the interactions between these factors. In this study, we utilized a multiscale geographically weighted regression (MGWR) model to discern the spatial heterogeneity of the individual factors influencing the green efficiency of water resources in China between 2002 and 2016. Subsequently, we demarcated several subregions based on the coefficients derived from the MGWR model. Employing a geographical detector (GD), we quantified the interactive impacts of different factors within these subregions. Our findings unveiled, for the first time, the diverse patterns in the temporal and spatial fluctuations in the factors impacting the eco-friendliness of water resources. The findings underscored that disregarding the spatial heterogeneity of these interactive effects may result in an underestimation of the interactions among factors. Significantly, in 2016, the impact of tertiary industry proportion and completed investment in pollution treatment displayed an enhanced non-linear effect across the entire sample and concurrently demonstrated a bivariate enhanced effect within subregions. These discoveries contribute to a deeper comprehension of the mechanisms influencing these factors, providing valuable insights for policymakers in crafting region-specific water resource policies tailored to the unique developmental requirements of different areas.

Multi-scale analysis of urban forests and socioeconomic patterns in a desert city, Phoenix, Arizona

Understanding the relationship between various socioeconomic factors and urban forest structure is essential for directing resources to ensure equitable distribution of green space. Through a case study of a desert city, i.e., Phoenix, AZ, this study provides a novel application of Multiscale Geographically Weighted Regression (MGWR) in which we explore the spatially variable relationships between a wide array of socioeconomic indicators and urban forest attributes. Through the computation of various scales of influence for different explanatory variables, MGWR enhances our analysis’s precision and stresses the association between socioeconomic status and urban forest structure at local and regional scales. Our results indicate that although there has been a pattern of green inequality where minority and low-income communities have less access to urban forests, education levels were mostly insignificant based on the MGWR results. In some instances, higher incomes are negatively correlated with tree canopy coverage. Additionally, the stem density model outperformed the canopy coverage model in terms of prediction accuracy. This research adds a new dimension to urban forestry literature and emphasizes the value of customized urban planning strategies and the environmental justice implications of urban forestry, particularly in arid environments.

Geospatial analysis of diarrhoea determinants among children under five in Pakistan using Multiscale Geographically Weighted Regression (MGWR)

Diarrhoea is the second most common cause of death among children under five worldwide. About 90 percent of diarrhoeal diseases occur in South Asia and sub-Saharan countries. In Pakistan, it causes almost 53,000 children to die every year. Against this background, this study aimed to analyze the geographical variations in the socio-economic and climatic determinants of under-five diarrhoea cases in districts of Pakistan. We used Generalized Linear Regression (GLR) and Multiscale Geographically Weighted Regression (MGWR) to explore the spatial variation of diarrhoea and its associated determinants. The MGWR model with 10 independent variables outperforms the GLR model (R2 = 0.72) with an adjusted R2 of 0.86. The local accuracy of the MGWR model increases from southwest to northeast. Furthermore among ten selected regressors, significant geographic diversity was found in the determinants of diarrhoea, including altitude, temperature, fever, ARI, and sanitation practices. These insights demand the need for targeted public health interventions, such as immunization campaigns, improved sanitation, and access to clean water and nutritional supplements, poverty eradication, especially in rural and high-risk regions. Future research should employ longitudinal designs and advanced spatial modeling to evaluate the impact of strategies and inform evidence-based policies to reduce the burden of childhood diarrhoea in Pakistan.

Analysis of Surface Urban Heat Island in the Guangzhou-Foshan Metropolitan Area Based on Local Climate Zones

Understanding the driving mechanisms behind surface urban heat island (SUHI) effects is essential for mitigating the degradation of urban thermal environments and enhancing urban livability. However, previous studies have primarily concentrated on central urban areas, lacking a comprehensive analysis of the entire metropolitan area over distinct time periods. Additionally, most studies have relied on regression analysis models such as ordinary least squares (OLS) or logistic regression, without adequately analyzing the spatial heterogeneity of factors influencing the surface urban heat island (SUHI) effects. Therefore, this study aims to explore the spatial heterogeneity and driving mechanisms of surface urban heat island (SUHI) effects in the Guangzhou-Foshan metropolitan area across different time periods. The Local Climate Zones (LCZs) method was employed to analyze the landscape characteristics and spatial structure of the Guangzhou-Foshan metropolis for the years 2013, 2018, and 2023. Furthermore, Geographically Weighted Regression (GWR), Multi-scale Geographically Weighted Regression (MGWR), and Geographical Detector (GD) models were utilized to investigate the interactions between influencing factors (land cover factors, urban environmental factors, socio-economic factors) and Surface Urban Heat Island Intensity (SUHII), maximizing the explanation of SUHII across all time periods. Three main findings emerged: First, the Local Climate Zones (LCZs) in the Guangzhou-Foshan metropolitan area exhibited significant spatial heterogeneity, with a non-linear relationship to SUHII. Second, the SUHI effects displayed a distinct core-periphery pattern, with Large lowrise (LCZ 8) and compact lowrise (LCZ 3) areas showing the highest SUHII levels in urban core zones. Third, land cover factors emerged as the most influential factors on SUHI effects in the Guangzhou-Foshan metropolis. These results indicate that SUHI effects exhibit notable spatial heterogeneity, and varying negative influencing factors can be leveraged to mitigate SUHI effects in different metropolitan locations. Such findings offer crucial insights for future urban policy-making.

Analysis of the Evolution of Spatio Temporal Pattern of Rural Industrial Integration and its Influencing Factors

Regional development has a significant impact on Rural Industrial Integration (RII), which substantially boosts economic growth in rural regions and decreases the economic disparity between rural regions and urban areas. Addressing the Spatio-Temporal Patterns (STP) of RII and the factors that impact these developments is essential for today's economies to attempt balanced regional development successfully. The objective of the present study is to investigate the STP of RII during time considering Zhejiang Province, China, as a case study. The present research examines the primary social, economic, and environmental variables that result in RII applying spatial economic frameworks like Adaptive Geographically Weighted Regression (AGWR) and Multiscale Geographically Weighted Regression (MGWR). The research study evaluated how they relate and impact these factors to integration across multiple spatial scales. With AGWR and MGWR values achieving 0.0083 and 0.0085, respectively, the study indicated that the most significant variable determining RII is the development of urban infrastructure. Significant grouping impacts have been shown by the spatial autocorrelation (Moran's I) for this metric, which attained values that were as high as 0.4205. Significant variables comprised the cost of investment and the urban-rural per capita disposable income (PCDI) proportion, with PCDI ratio ratios of 0.0053 (AGWR) and 0.0056 (MGWR), respectively.

Multiscale cooperative optimization in multiscale geographically weighted regression models

Scale in multiscale geographically weighted regression (MGWR) directly impacts the accuracy of coefficient estimates and shapes the comprehensive evaluation of the intensity of spatially non-stationary relationships. Presently, MGWR primarily utilizes back-fitting for sequentially optimizing multiple scales (MGWR-BF). However, the set of individual optima obtained through sequential optimization may not necessarily represent the global optimum. To address this issue, this paper proposes a multi-scale cooperative optimization within MGWR (MGWR-GA) model. Specifically, MGWR-GA employs a genetic algorithm to simultaneously input potential scale combinations, each comprising P scales. Subsequently, it introduces a dedicated overall estimation algorithm designed for these P scales, ultimately determining the optimal scale combinations based on the AICc. Simulation experiments have shown that, at least for global stationarity, the scales obtained by MGWR-GA approximate the true values across twelve different test environments. Additionally, the coefficient estimation bias of MGWR-GA is lower than that of MGWR-BF, especially in low signal-to-noise ratio settings. Empirical experiments further confirm the effectiveness of MGWR-GA in identifying both globally stationary and locally non-stationary scales. Furthermore, MGWR-GA outperforms MGWR-BF in terms of goodness-of-fit, adjusted goodness-of-fit, AICc and spatial autocorrelation of residuals. These findings indicate that MGWR-GA can serve as a valuable tool for modeling spatially non-stationary relationships.

Analyzing the spatial scale effects of urban elements on urban flooding based on multiscale geographically weighted regression

In the context of rapid urbanization and the frequent occurrence of extreme rainfall in cities, the risk of flooding in the future will further increase, and the problem of urban flooding cannot be ignored. Urban elements exhibit significant spatial heterogeneity, which largely determines the spatial distribution differences in urban flooding. Therefore, it is important to clarify the scale of influence of different urban elements and explore their scale effects on urban flooding to accurately assess the risk of urban flooding. Taking Zhengzhou City, China, as the study area, this study analyzed the urban elements associated with urban flooding, quantified the scale of the influence of urban elements on flooding using multiscale geographically weighted regression (MGWR), and further explored the spatial scale effects of urban elements on urban flooding. The results showed that MGWR can better fit the spatially non-uniform distribution of urban flooding and that the scale of the influence of urban elements on urban flooding can be reflected by the bandwidth of MGWR. The results of MGWR indicated that the bandwidths of elevation, number of drainage outfalls (NDO1Abbreviations: RRP, Rainfall return period; DDR, Distance to river; NDO, Number of drainage outfalls; ILMFCP, Input level of materials for flood control projects; PRL, Proportion of residential land; PCL, Proportion of commercial land; PIL, Proportion of industrial land; PPSL, Proportion of public service land; PRA, Proportion of road area; PGL, Proportion of green land; PD, Population density; MI, Manpower input; GDP, Gross domestic product; ILER, Investment level in education and research.1), distance to river (DR), Gross domestic product (GDP), proportion of residential land (PRL), and proportion of commercial land (PCL) were small, and their influence scales were localized. In contrast, the influence of rainfall return period (RRP), slope, proportion of industrial land (PIL), proportion of public service land (PPSL), proportion of road area (PRA), proportion of green land (PGL), input level of materials for flood control projects (ILMFCP), population density (PD), manpower input (MI), and investment level in education and research (ILER) were at global scales. The most influential factors for urban flooding were RRP, PD, and MI. Slope, DR, and PRA had less influence on urban flooding. This study helps improve the effectiveness of urban flood prevention and mitigation efforts.

Urban Perception Evaluation and Street Refinement Governance Supported by Street View Visual Elements Analysis

As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analyzed street view images from Nanshan District, Shenzhen, identifying critical factors that shape residents’ spatial perceptions, such as urban greenery, road quality, and infrastructure. The findings indicate that robust vegetation, well-maintained roads, and well-designed buildings significantly enhance positive perceptions, whereas detractors like fences reduce quality. Furthermore, Moran’s I statistical analysis and multi-scale geographically weighted regression (MGWR) models highlight spatial heterogeneity and the clustering of perceptions, underscoring the need for location-specific planning. The study also points out that complex street networks in accessible areas enhance living convenience and environmental satisfaction. This research shows that integrating street view data with deep learning provides valuable tools for urban planners and policymakers, aiding in the development of more precise and effective urban governance strategies to foster more livable, resilient, and responsive urban environments.

Spatial non-stationarity effects of the driving factors on landscape ecological risk: A case of the Xiamen-Zhangzhou-Quanzhou Urban Agglomeration, China

Increasing construction land demands and population growth within urban agglomerations have led to increasingly prominent landscape ecological risk (LER) issues. However, the multi-scale nature and spatial non-stationarity characteristics of the driving patterns have not been fully investigated. In this study, taking the Xiamen-Zhangzhou-Quanzhou Urban Agglomeration (XZQUA) as a case, we analyzed the spatio-temporal evolution characteristics of LER utilizing the long time series land use data from 1980 to 2020; then, the Exploratory Regression Analysis (ERA) was employed to investigate the optimal combination of key influential elements affecting LER. Finally, the Multiscale Geographically Weighted regression (MGWR) model was applied to explore the spatial non-stationarity effects of the key driving elements. The results reveal that: (1) The LER of the XZQUA has shown a declining trend from 1980 to 2020, characterized with significantly and increasingly spatial autocorrelation as well. (2) The optimal variable combination is the temperature, distance from roads, elevation, and construction land area. (3) The MGWR model results reveal that the impact of various factors on LER demonstrates significant spatial non-stationarity. The mechanisms and intensity of these influences change depending on geographical location. The negative proportion of regression coefficients for construction land area (−0.203 to −0.002) and elevation (−0.046 to −0.001) all exceeded 80%, indicating a high negative effect on LER; The positive proportion of the regression coefficient for distance from roads (−0.026 to 0.042) exceeded 60%, indicating a predominantly positive effect on LER. Our findings provide guidance for regional policymakers to adopt targeted ecological risk intervention measures, thus reducing LER in the urban agglomeration.

Community identification and carbon storage monitoring of Heritiera littoralis with UAV hyperspectral imaging

The Heritiera littoralis, an important rare semi-mangrove species found in coastal protective forests, plays a crucial role in carbon storage and cycling within mangrove wetland ecosystems. Despite its importance, previous studies have overlooked remote monitoring of its carbon storage. Taking the world’s oldest and largest natural community of H. littoralis in Shenzhen, China, as the study area, this research pioneers the use of UAV hyperspectral imaging to identify the H. littoralis community and estimate its above-ground, below-ground, and total carbon storage. The impacts of five geo-environmental factors (elevation, slope, aspect, vegetation communities, and inland distance from the coastline) on the spatial variability of carbon storage using SHapley Additive exPlanations (SHAP) and multiscale geographically weighted regression (MGWR) methods were also investigated. The results demonstrate that the first derivative bands within the red edge and near-infrared regions, the anthocyanin reflectance index 2 (ARI2) and newly-developed three-band VIs, were sensitive features for community identification and carbon storage estimation within the H. littoralis community. Among the four machine learning models (eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR)), the best identification accuracy for the H. littoralis community was achieved by XGBoost. Among the four machine learning models (XGBoost, RF, SVM and kernel ridge regression (KRR)), RF model achieved the best performance in estimating above-ground (R2 = 0.749, RMSE=1.723 kg/m2, EV (explained variance) = 0.704), below-ground (R2 = 0.636, RMSE=0.6 kg/m2, EV=0.606) and total carbon storage (R2 = 0.613, RMSE=2.592 kg/m2, EV=0.597). SHAP and MGWR analysis showed that elevation and inland distance from the coastline were key factors influencing the spatial variability of carbon storage. In conclusion, this study showcases significant advantages in community identification and carbon storage monitoring of rare mangrove communities, providing valuable insights for biodiversity conservation efforts and management within the H. littoralis ecosystem.

Spatial heterogeneity and interacting intensity of drivers for trade-offs and synergies between carbon sequestration and biodiversity

Global crises of biodiversity loss and climate change highlighted the urgent need to understand the trade-off and synergy between carbon sequestration and biodiversity and the driving mechanisms behind them. However, existing research on driving mechanisms primarily focused on the influence of independent variables on dependent variables, neglecting the interaction between independent variables. The InVEST and PLUS models were utilized in this study to evaluate the dynamics of carbon sequestration and biodiversity in the Chishui River Basin, Southwest of China. By integrating the factor detection of the Geographic Detector and the Multi-scale Geographically Weighted Regression (MGWR) model, this study identified the key drivers affecting trade-off and synergy and revealed their spatial heterogeneity. Furthermore, utilizing the interaction detection of the Geographic Detector and the Geographic Cross-Convergence Mapping (GCCM) model, this study analyzed the interaction direction and interaction intensity among key drivers. The results showed that: (1) From 2012–2035, the total values of carbon sequestration and biodiversity exhibited an overall increasing trend, with 58.49 % of the area showing trade-off effects, 23.86 % showing positive synergy, and 17.65 % showing negative synergy. (2) Elevation, slope, topographic relief, temperature, and land use intensity were identified as key factors influencing trade-off and synergy, and these factors displayed significant spatial heterogeneity. (3) When multiple drivers interacted, their explanatory power for trade-off and synergy increased significantly. The interaction patterns among drivers presented a “topography➙climate↔human activity↔distance➙soil” structure, where human activity interacted bidirectionally with other factors, with the strongest interaction occurring between climate factors and human activity. This study advanced the understanding of the driving mechanisms behind trade-off and synergy through MWGR and GCCM model. Based on the supply-demand theory, a synergistic management framework was proposed to emphasize the “pattern-process-service-feedback” nexus. The hotspot areas identified through scenario simulation could be used for synergistic enhancement of carbon sequestration and biodiversity.