Multiscale Geographically Weighted Regression Research Articles

Construction of landscape eco-geological risk assessment framework in coal mining area using multi-source remote sensing data

High-intensity and large-scale mining activities have aggravated regional eco-geological risk. Therefore, it is significantly essential to conduct an assessment of the eco-geological risk of mining areas. Although some progress has been achieved in ecological risk assessment studies, existing approaches are not entirely suitable for coal bases with high landscape fragmentation and dense coal mining activities. Here, we developed a novel landscape ecological and geological risk (LEGR) assessment framework based on theories that include landscape ecological risk and eco-geological risk. The framework selected 10 indicators, including slope, fluctuation, lithological hardness, soil type, FVC, RSEI, precipitation, biological abundance, distance to road and subsidence rate, and calculated the weights of indicators by introducing the AHP-CRITIC coupled weighting model. Then, the impact of landscape disturbances on eco-geological risk is quantified by measuring landscape losses. This framework was applied to the Shenfu mining area (SFMA), a typical coal base in northwest China. The results indicated the LEGR was moderate in the SFMA whose spatial distribution exhibited an increasing trend from southwest to northeast. Besides, the high LEGR was mainly in the aggregated mining area with high subsidence. For the eco-geological environment monitoring at the mine scale, a multiscale geographically weighted regression (MGWR) model was utilized for analyzing the relationship between indicators and LEGR within the disturbed range of coal mining. It provided valuable insights for the formulation of environmental protection policies in the mining area.

Variable climatic conditions dominate decreased wetland vulnerability on the Qinghai‒Tibet Plateau: Insights from the ecosystem pattern-process-function framework

Wetlands are of global importance in providing essential ecosystem services but are also sensitive to climate change and human activities. Monitoring and assessing wetland vulnerability are crucial for ecological conservation and management strategies. However, the framework of wetland vulnerability assessment and the underlying mechanisms have not been well studied. In this study, the spatiotemporal variations in wetland vulnerability on the Qinghai‒Tibet Plateau (QTP) between 1990 and 2020 were investigated based on the ecosystem pattern-process-function framework. The key driving factors were identified by partial least squares structural equation modelling (PLS-SEM) and multiscale geographically weighted regression (MGWR) models. Our results showed that the wetland ecosystem pattern index (EPI), ecosystem process index (EPOI), ecosystem function index (EFI), and wetland vulnerability index (WVI) all demonstrated an increasing pattern from northwest to southeast. Between 1990 and 2020, the mean WVI values gradually decreased from 0.616 to 0.588, indicating a steady improvement in the wetland ecosystem on the QTP. Rapid urbanization increased the EPOI, while rugged topography increased both the EPI and EPOI, and the increase in hydrological abundance enhanced the EFI, which in turn contributed to an increase in the WVI. Conversely, climatic conditions led to a reduction in the EPI, which in turn resulted in a significant decrease in the WVI. Therefore, although urbanization and topographical and hydrological factors have somewhat exacerbated the WVI on the QTP, variable climatic conditions have driven the decline in wetland vulnerability in the last three decades. Furthermore, our results indicated that the proposed framework could provide a comprehensive approach for wetland vulnerability assessment and useful implications for wetland conservation and management.

Deciphering spatial heterogeneity of maritime accidents considering impact scale variations

ABSTRACT Ensuring maritime safety has ascended as a preeminent concern within the global maritime sector. Understanding how factors affect maritime accidents’ consequences in different water areas would be of great benefit to preventing the occurrence or reducing the consequences. This study thus employed a multi-scale geographically weighted regression (MGWR) model on the accident dataset from Fujian waters in the East China Sea, to quantify the influences of different factors as well as the spatial heterogeneity in the effects of key factors on maritime accident consequence. The performances of MGWR are compared with multiple linear regression (MLR) and GWR. As expected, MGWR outperforms the other two models in terms of its ability to clearly capture the unobserved spatial heterogeneity in the effects of factors. Results reveal notably distinct influences of some factors on maritime accident consequences in different locations. An intuitive indication by MGWR is that approximately 50% of the accidents present positive coefficients of good visibility while other locations are negative, which are failed to recognize by MLR. The outcomes provide insights for making appropriate safety countermeasures and policies customized for different water areas.

The effect of fine particulate matter (PM2.5) pollution on health inequality: an intergenerational perspective.

Air pollution poses a serious challenge to public health and simultaneously exacerbating regional & intergenerational health inequality. This research introduces PM2.5 pollution into the intergenerational health transmission model, and estimates its impact on health inequality in China using Ordered Logit Regression (OLR) and Multi-scale Geographically Weighted Regression (MGWR) model. The results indicate that PM2.5 pollution exacerbate the intergenerational health inequality, and its impacts show inconsistency across family income levels, parental health insurance status, and area of residence. Specifically, it is more difficult for offspring in low-income families to escape from the influence of unhealthy family to become upwardly mobile. Additionally, this health inequality is more significant in households in which at least one parent does not have health insurance. Moreover, the intergenerational solidification caused by PM2.5 pollution is higher in the east and lower in the west. Both the PM2.5 level and solidification effect are high in Beijing-Tianjin-Hebei region, Yangtze River Delta region and central areas of China, which is the focus of air pollution management. These findings suggest that more emphasis should be placed on family-based health promotion. In areas with high PM2.5 pollution levels, resources, subsidies and air pollution protection should be provided for less healthy families with lower incomes and no health insurance.

GWmodelS: a standalone software to train geographically weighted models

ABSTRACT With the recent increase in studies on spatial heterogeneity, geographically weighted (GW) models have become an essential set of local techniques, attracting a wide range of users from different domains. In this study, we demonstrate a newly developed standalone GW software, GWmodelS using a community-level house price data set for Wuhan, China. In detail, a number of fundamental GW models are illustrated, including GW descriptive statistics, basic and multiscale GW regression, and GW principle component analysis. Additionally, functionality in spatial data management and batch mapping are presented as essential supplementary activities for GW modeling. The software provides significant advantages in terms of a user-friendly graphical user interface, operational efficiency, and accessibility, which facilitate its usage for users from a wide range of domains.

When everyone’s doing it: The relative effects of geographical context and social determinants of health on teen birth rates

Geographic disparities in teen birth rates in the U.S. persist, despite overall reductions over the last two decades. Research suggests these disparities might be driven by spatial variations in social determinants of health (SDOH). An alternative view is that “place” or “geographical context” affects teen birth rates so that they would remain uneven across the U.S. even if all SDOH were constant. We use multiscale geographically weighted regression (MGWR) to quantify the relative effects of geographical context, independent of SDOH, on county-level teen birth rates across the U.S. Findings indicate that even if all counties had identical compositions with respect to SDOH, strong geographic disparities in teen birth rates would still persist. Additionally, local parameter estimates show the relationships between several components of SDOH and teen birth rates vary over space in both direction and magnitude, confirming that global regression techniques commonly employed to examine these relationships likely obscure meaningful contextual differences in these relationships. Findings from this analysis suggest that reducing geographic disparities in teen birth rates will require not only ameliorating differences in SDOH across counties but also combating community norms that contribute to high rates of teen birth, particularly in the southern U.S. Further, the results suggest that if geographical context is not incorporated into models of SDOH, the effects of such determinants may be interpreted incorrectly.

Analysis of Factors Influencing Housing Prices in Mountain Cities Based on Multiscale Geographically Weighted Regression—Demonstrated in the Central Urban Area of Chongqing

By leveraging a multiscale geographically weighted regression (MGWR) model, this paper delves into the intricate factors that influence housing prices in the prototypical mountainous cityscape of Chongqing’s central urban area. The key findings are as follows: Firstly, the distribution of housing prices in the study region exhibits pronounced spatial heterogeneity, with the core area exhibiting a distinct “high-high” clustering pattern and manifesting characteristics of a multicenter group distribution. Secondly, the MGWR model effectively assigns an individual bandwidth to each feature quantity, allowing for a more nuanced portrayal of the varying influence scales exerted by diverse variables. Lastly, the study reveals that factors such as property cost, greening rate, building age, and proximity to rivers have a notable negative impact on housing prices, whereas, educational facilities exert a marked positive influence. Elevation, floor area ratio, and distance from the Central Business District (CBD) exhibit a more complex influence on housing prices.

The Heterogeneous Effects of Microscale-Built Environments on Land Surface Temperature Based on Machine Learning and Street View Images

Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being of residents. However, due to limitations in data precision and analytical methods, existing studies often overlook the microscale examination closely related to residents’ daily lives, and lack a deep exploration of the spatial heterogeneity of the influencing factors. This leads to these results being ineffective in guiding the planning and construction of cities. Taking Shenzhen as a case study, our study investigates the effects of various microscale build environment characteristics of LST using street view images and machine learning. A convolutional neural network model adopting the SegNet architecture is used to perform semantic segmentation on street view images, extracting features of the microscale urban-built environment. The LST is inverted through the Google Earth Engine (GEE) platform. By using Multiscale Geographically Weighted Regression (MGWR) models, our study reveals the comprehensive impact of the urban-built environment on LST and its significant spatial heterogeneity. The findings indicate that the proportions of sky, roads, and buildings are positively correlated with LST, while trees have a significant cooling effect. Although earth and water can reduce LST, their overall contribution is minimal due to limitations in their area and distribution patterns. This study not only reveals the key factors affecting urban LST at the microscale but also emphasizes the necessity of considering the spatial heterogeneity of these factors’ impacts. This suggests the need for targeted strategies for different areas to effectively improve the urban thermal environment and achieve sustainable urban development.

Understanding the Spatiotemporal Dynamics and Influencing Factors of the Rice–Crayfish Field in Jianghan Plain, China

The rice–crayfish co-culture system, a representative of Agri-aqua food systems, has emerged worldwide as an effective strategy for enhancing agricultural land use efficiency and boosting sustainability, particularly in China and Southeast Asia. Despite its widespread adoption in China’s Jianghan Plain, the exact spatiotemporal dynamics and factors influencing this practice in this region are yet to be clarified. Therefore, understanding the spatiotemporal dynamics and influencing factors of the rice–crayfish fields (RCFs) is crucial for promoting the rice–crayfish co-culture system, and optimizing land use policies. In this study, we identified the spatial distribution of RCF using Sentinel-2 images and land use spatiotemporal data to analyze its spatiotemporal dynamics during the period of 2016–2020. Additionally, we used the Multiscale Geographically Weighted Regression model to explore the key factors influencing RCF’s spatiotemporal changes. Our findings reveal that (1). the RCF area in Jianghan Plain expanded from 1216.04 km2 to 2429.76 km2 between 2016 and 2020, marking a 99.81% increase. (2). RCF in Jianghan Plain evolved toward a more contiguous and clustered spatial pattern, suggesting a clear industrial agglomeration in this area. (3). The expansion of the RCFs was majorly influenced by its landscape and local agricultural conditions. Significantly, the Aggregation and Landscape Shape Indexes positively impacted this expansion, whereas proximity to rural areas and towns had a negative impact. This study provides a solid foundation for promoting the rice–crayfish co-culture system and sustainably developing related industries. To ensure the sustainable development of rice–crayfish co-culture industries in Jianghan Plain, we recommend that local governments optimize the spatial layout of rural settlements, improve transportation infrastructure, and enhance regional agricultural water sources and irrigation system construction, all in line with the national strategy of rural revitalization and village planning. Additionally, promoting the concentration and contiguity of RCF through land consolidation can achieve efficient development of these industries.

Spatial–temporal evolution and driving factors of ecosystem services trade‐offs and synergies in karst areas from a geospatial perspective

AbstractNowadays, studies of ecosystem services (ESs) trade‐offs and synergies in karst areas are limited to a single region. In particular, the quantification of the nature and intensity of the relationships between ESs in continuous karst distribution areas (CKDAs) and non‐karst areas (NKAs), and a full understanding of the drivers behind this change remains a challenge. In this study, the province of Guizhou in China was used as study area and a framework was developed to analyze the spatial variation of trade‐offs and synergies between ESs. The framework utilized Spearman, geographical detector, and multi‐scale geographically weighted regression (MGWR) to establish connections between four key ESs: habitat quality (HQ), water yield (WY), soil conservation (SC), and net primary productivity (NPP). Through the examination of these relationships, this article has revealed the influence of the driving forces on this ecological process. The study indicated that: (1) During this period, ESs in study area increased steadily, with the average value of HQ always above 0.6, and WY, SC, and NPP increased by 2.32%, 6.07%, and 15.18%, respectively. (2) WY had a trade‐off relationship with other ESs, and SC, HQ, and NPP were synergistic with each other. (3) In the CKDA, topography and precipitation dominated the relationship between SC and other ESs. In contrast, in the NKA, ecological processes involving HQ were more prominent in human/natural interactions. Therefore, proposing ecological measures at different spatial scales to promote synergistic development between ESs is of great importance for ecological governance and sustainability of karst areas.

Navigating Post-COVID-19 Social–Spatial Inequity: Unravelling the Nexus between Community Conditions, Social Perception, and Spatial Differentiation

The 2023 SDGs report underscores the prolonged disruption of COVID-19 on community living spaces, infrastructure, education, and income equality, exacerbating social and spatial inequality. Against the backdrop of the dual impact of significant events and the emergence of digital technologies, a coherent research trajectory is essential for characterizing social–spatial equity and understanding its influential factors within the urban planning discipline. While prior research emphasized spatial dimensions and mitigated spatial differentiation to ensure urban equity, the complexity of these interconnections necessitates a more comprehensive approach. This study adopts a holistic perspective, focusing on the “social–spatial” dynamics, utilizing social perception (sentiment maps) and spatial differentiation (housing prices index) pre- and post-pandemic to elucidate the interconnected and interactive nature of uneven development at the urban scale. It employs a multi-dimensional methodological framework integrating morphology analysis of housing conditions, GIS analysis of urban amenities, sentiment semantic analysis of public opinion, and multiscale geographically weighted regression (MGWR) analysis of correlation influential factors. Using Suzhou, China, as a pilot study, this research demonstrates how these integrated methods complement each other, exploring how community conditions and resource distribution collectively bolster resilience, thereby maintaining social–spatial equity amidst pandemic disruptions. The findings reveal that uneven resource distribution exacerbates post-pandemic social stratification and spatial differentiation. The proximity of well-maintained ecological environments, such as parks or scenic landmarks, generally exhibits consistency and positive effects on “social–spatial” measurement. Simultaneously, various spatial elements influencing housing prices and social perception show geographic heterogeneity, particularly in areas farther from the central regions of Xiangcheng and Wujiang districts. This study uncovers a bilateral mechanism between social perception and spatial differentiation, aiming to delve into the interdependent relationship between social–spatial equity and built environmental factors. Furthermore, it aspires to provide meaningful references and recommendations for urban planning and regeneration policy formulation in the digital era to sustain social–spatial equity.

Unravelling the influencing hydrogeological factors contributing to land subsidence in the Tianjin Plain of China using a multi-scale geographically weighted regression model and monitoring data

The establishment of a quantitative relationship between land subsidence and its influencing factors is a crucial task in developing prevention strategies of land subsidence in specific areas. In this study we examined the dynamic patterns of land subsidence before and after establishing the South-to-North Water Division Project (SNWDP) in the Tianjin Plain (TJP) that aims to reduce groundwater extractions in certain areas. Statistical analyses were used to determine the key attributing factors contributing to land subsidence that were subsequently used to derive the so-called ‘critical water level of land subsidence’(CWLS), which were used to manage groundwater extractions with the objective of minimizing further land subsidence. The main results obtained were as follows: under the influence of the SNWDP, the groundwater level in the TJP stopped declining and the water level rose in most areas. The area of ‘strong’ subsidence, defined as the area with a subsidence rate greater than 50 mm a −1 , decreased from 16.0% before the SNWDP to 2.5% after. The results of the multi-scale geographically weighted regression (MGWR) model revealed that groundwater drawdown in the second and third confined aquifer was the main contributor to the areas classified as ‘strong’ and ‘medium’ land subsidence areas. Following the reduction in groundwater extractions, the behaviour of surface deformation can be grouped into two categories: first, a rebound in surface elevation; and, second, continuous compression with a lower subsidence rate. The first mode occurs mainly in the area of ‘medium’ and ‘weak’ subsidence, and the second mode occurs mainly in the ‘strong’ subsidence area. Supplementary material : The result of the MGWR model is available at https://doi.org/10.6084/m9.figshare.c.7103611

Investigating the multiscale associations between urban landscape patterns and PM1 pollution in China using a new combined framework

Urban landscape patterns serve as the spatial carriers of emission sources and influencing factors of air contaminants, which inevitably impact submicron particle (PM1) pollution. However, most studies ignore the spatial nonstationary and nonlinear effects of urban landscape patterns on the PM1 concentration. In this study, a novel framework was developed that integrates a multiscale geographically weighted regression (MGWR) model and Shapley Additive exPlanations (SHAP) machine learning method to explore the spatial heterogeneity effect, relative contribution and nonlinear impact of landscape patterns on the PM1 concentration at the national and urban agglomeration scales. The results indicated that urban landscape patterns were closely related to PM1 pollution and exhibited obvious spatial differences. The disorderly expansion of urban built-up areas and an irregular urban morphology could aggravate PM1 pollution, while urban fragmentation and connectivity could impose reducing effects. Dispersed urban landscape patterns could reduce the PM1 concentration in the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Chengdu-Chongqing (CDCQ) regions, whereas compact and continuous landscape patterns positively affected PM1 pollution mitigation in the Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZH) urban agglomerations. The impact of urban landscape patterns on PM1 pollution was greater in urban agglomerations than elsewhere. The ENN_MN landscape index exhibited the highest feature importance and interpretability. The threshold effects between the urban shape indicators and PM1 concentration were more complex than those with the other landscape indices. This critical knowledge provides a scientific basis for further understanding the correlation mechanism between PM1 pollution and the landscape pattern, urban sustainable planning and air pollution control.

Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China

Abstract It is crucial for social sustainability that adolescents have access to social sports services fairly. However, there are few research studies on requirements for sports facilities and spatial accessibility of adolescents at a fine scale. Further, identifying the heterogeneity of the relationship between spatial accessibility and other factors and their scales simultaneously would be conducive to reveal the variations of spatial accessibility effectively under the potential scale effect. This research aims to explore the heterogeneity of spatial accessibility to sports facilities at a fine scale in Changsha, China. The Gaussian-based two-step floating catchment area model is first employed to evaluate spatial accessibility. Then, multiscale geographically weighted regression (MGWR) is applied to examine the relationship between spatial accessibility and its associated factors. The mean accessibility in Yuelu District (0.158) is the highest as well, and the standard deviation (0.236) is also the highest one. Both the accessibility (0.019) and its standard derivation (0.029) in Furong District are the lowest. The results show that there is a lack of balance of spatial accessibility for the clusters with different accessibility levels distributed in the study area. Some socio-economic factors, such as housing price and nighttime light intensity, have significant impacts on spatial accessibility for adolescents with spatial and scale heterogeneity by using MGWR. Based on heterogeneous distribution and association, suggestions for promoting spatial accessibility to sports facilities for adolescents are proposed.

Analyzing the Spatiotemporal Dynamics and Driving Forces of Ecological Environment Quality in the Qinling Mountains, China

The Qinling Mountains serve as a vital ecological barrier for China, needing regular monitoring for sustainable progress. The present study analyzed the Qinling Mountains (sections in Shaanxi province) via the Google Earth Engine (GEE) and Landsat images, constructing a Remote Sensing Ecological Index (RSEI) for quantitatively analyzing the spatiotemporal evolution of the ecological environment quality from 1990 to 2020. Additionally, this study integrated geodetector and multiscale geographically weighted regression (MGWR) to discern the driving factors influencing ecological quality. The results indicated the following: (1) over the 30 years, the RSEI in the study area initially declined (1990–2005) and then began to improve (2005–2020), demonstrating an overall upward trend; (2) RSEI varied with topography: lower values corresponded to lower elevations (<1200 m) and gentle slopes (<10°), whereas higher values were linked to higher elevations (>1600 m) and steeper slopes (>20°); and (3) NDVI, DEM, land use, and the presence of protected areas emerged as the main driving forces of ecological quality. The interaction between ecological conservation policies and socioeconomic factors enhanced the explanatory strength for the differentiation in ecological environment quality. In conclusion, factors like increased vegetation cover and the establishment of protected areas have significantly promoted the improvement of ecological environment quality in the Qinling region, making this study a scientific reference for providing ecological environment protection and promoting high-quality development.

Spatial Relationship of Inter-City Population Movement and Socio-Economic Determinants: A Case Study in China Using Multiscale Geographically Weighted Regression

In the current field of regional studies, there is a growing focus on regional spatial relationships from the perspective of functional linkages between cities. Inter-city population movement serves as an embodiment of the integrated functionality of cities within a region, and this is closely tied to the socio-economic development of urban areas. This study utilized Location-Based Services (LBSs) to collect the scale of inter-city population movement across 355 cities in China. Additionally, socio-economic data published by local governments were incorporated. By establishing a Multiscale Geographically Weighted Regression (MGWR) model, this research explores the spatial relationships between inter-city population movement and socio-economic influencing factors in China. This study aims to elucidate the spatial scales of the relationships between various variables. Our research findings indicate that the relationship between inter-city population movement and potential socio-economic determinants exhibits spatial non-stationarity. It is better to explore this spatial relationship through the MGWR model as there are different determinants operating on inter-city population movement at different spatial scales. The spatial distribution of the coefficient estimates shows significant regional differences and numerical variations. In China’s economically developed coastal regions, there is relatively balanced development among cities, with advanced manufacturing and producer service industries acting as significant drivers of mobility. In inland regions of China, city size is the most influential variable, directing a substantial flow of human and economic resources towards regional socio-economic hubs such as provincial capitals. The main contribution of this study is the re-examination of the relationship between inter-city population movement and socio-economic factors from the perspective of spatial scales. This approach will help China to consider the heterogeneity of different regions more extensively when formulating regional development policies, thereby facilitating the targeted promotion of regional element flow.

Spatial downscaling analysis of GPM IMERG precipitation dataset based on multiscale geographically weighted regression model: a case study of the Inner Mongolia Reach of the Yellow River basin

The Inner Mongolia Reach of the Yellow River Basin is characterized by a relative scarcity of meteorological stations. While satellite precipitation products can complement observations from meteorological stations, their limited spatial resolution restricts their efficacy in regional studies. This study utilizes the GPM IMERG precipitation dataset, considering various factors that influence the spatial distribution of precipitation, such as the Normalized Difference Vegetation Index (NDVI), elevation, slope, aspect, and topographical relief, to construct a multiscale geographically weighted regression (MGWR) model. A spatial downscaling method for the GPM IMERG precipitation dataset is proposed, and its reliability is validated through an accuracy assessment. Moreover, the scale differences in the impact of different factors on the spatial pattern of precipitation in the Inner Mongolia Reach of the Yellow River Basin are scrutinized. The results indicate that: 1) The downscaled GPM IMERG precipitation data (1 km × 1 km) exhibit enhanced accuracy compared to the pre-downscaled data (approximately 11 km × 11 km). The correlation coefficient, Bias, and RMSE of the annual precipitation data after downscaling of GPM IMERG are 0.865, 6.05%, and 68.50 mm/year, respectively. For the monthly downscaled precipitation data, the correlation coefficient, Bias, and RMSE are 0.895, 6.09%, and 16.25 mm/month, respectively. The downscaled GPM IMERG precipitation dataset exhibit high accuracy on both annual and monthly temporal scales. 2) Different factors demonstrate localized effects on precipitation in both dry and wet years. Elevation is the dominant factor influencing the spatial heterogeneity of annual precipitation. The findings from this study can provide technical support for hydrological modeling, drought monitoring, and water resource management in data-scarce areas of the Inner Mongolia Reach of the Yellow River Basin.

Assessing Spatial Heterogeneity in Urban Park Vitality for a Sustainable Built Environment: A Case Study of Changsha

In the realm of sustainable city development, evaluating the spatial vitality of urban green spaces (UGS) has become increasingly pivotal for assessing public space quality. This study delves into the spatial heterogeneity of park vitality across diverse urban landscapes at a city scale, addressing limitations inherent in conventional approaches to understanding the dynamics of park vitality. Leveraging geotagged check-in data from 65 parks in the study case of Changsha City, a quantitative analysis was undertaken to assess spatial vitality. The investigation incorporated data concerning internal and external factors influencing park vitality, employing the Multi-scale Geographically Weighted Regression (MGWR) model to dissect nuanced spatial heterogeneity. The research uncovers notable spatial discrepancies in factors influencing park vitality across diverse urban areas, emphasizing the reliance on adjacent residential communities and internal commercial amenities provision. These dependencies correspond with economic development differences among urban locales, revealing distinct geographic trends. This study has a novel perspective and methodology for investigating urban park vitality, providing significant insights for urban green space planning and management. It emphasizes the necessity of acknowledging spatial diversity in urban park planning and design by incorporating the distinct socio-economic characteristics of each urban zone, which is crucial for both urban planners and policymakers.

A Spatiotemporal Comparative Analysis of Docked and Dockless Shared Micromobility Services

Sustainable urban mobility is an imperative concern in contemporary cities, and shared micromobility systems, such as docked bike-sharing, dockless bike-sharing, and dockless e-scooter-sharing, are recognized as essential contributors to sustainable behaviors in cities, both complementing and enhancing public transport options. Most of the literature on this subject predominantly focuses on individual assessments of these systems, overlooking the comparative analysis necessary for a comprehensive understanding. This study aims to bridge this gap by conducting a spatiotemporal analysis of two different shared micromobility modes of transportation, docked bike-sharing systems and dockless e-scooter-sharing systems operating in the municipality of Lisbon. The analysis is further segmented into arrivals and departures on weekdays and weekends. Additionally, this study explores the impact of sociodemographic factors, the population’s commuting modes, and points of interest (POIs) on the demand for both docked bike-sharing and dockless e-scooter-sharing. Multiscale Geographically Weighted Regression (MGWR) models are employed to estimate the influence of these factors on system usage in different parishes in Lisbon. Comparative analysis reveals that the temporal distribution of trips is similar for both docked bike-sharing and dockless e-scooter-sharing systems on weekdays and weekends. However, differences in spatial distribution between the two systems were observed. The MGWR results indicate that the number of individuals commuting by bike in each parish has a positive effect on docked bike-sharing, while it exerts a negative influence on dockless e-scooter-sharing. Also, the number of commercial points of interest (POIs) for weekday arrivals positively affects the usage of both systems. This study contributes to a deeper understanding of shared micromobility patterns in urban environments and can aid cities in developing effective strategies that not only promote and increase the utilization of these shared micromobility systems but also contribute to sustainable urban mobility.

An indicator model for assessing community resilience to the COVID-19 pandemic and its validation: A case study in Hong Kong

The COVID-19 outbreak had a huge negative impact on the world, and the fifth wave of COVID-19 in Hong Kong brought a considerable shock to Chinese society. There is a growing call for more resilient cities, but empirical evidence and validation of modeling studies of resilience indicators for urban community responses to the COVID-19 pandemic are lacking. In this study, a resilience assessment indicator model comprising 4 subsystems, 7 indicators, and 12 variables was developed to assess the resilience of Hong Kong communities in response to COVID-19 (i.e., Resilience Index). Further, this study utilized regression models such as geographically weighted regression (GWR) and Multiscale GWR (MGWR) to validate the resilience model proposed in this study at the model level and variable level. In the regression model, the Resilience Index and the individual variables in the resilience model are explanatory variables, and the outcomes of the COVID-19 pandemic (confirmed cases, confirmation rate, discharged cases, discharged rate) are dependent variables. The results showed that: (i) the resilience of Hong Kong communities to the COVID-19 pandemic was not strong in general, and showed some clustered distribution characteristics in space; (ii) the validation results at the model level showed that the Resilience Index did not explain the consequences of the COVID-19 pandemic to a high degree; (iii) the validation results at the variable-level showed that the MGWR model was the best at identifying the relationship between explanatory variables and the dependent variable; and (iv) compared with the model-level assessment results, the variable-level assessment explains the consequences of the COVID-19 pandemic better than the model level assessment results. The above analysis and the spatial distribution maps of the resilience variables can provide empirically based and targeted insights for policymakers.