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The number of electric vehicles (EVs) continues to increase in the automobile market, driven by public policies since they contribute to the global decarbonization of the transportation sector. Still, the main challenge to increasing EV adoption is charging infrastructure. Therefore, the site selection of public EV charging stations should be made very carefully to maximize EV usage and address the population's range anxiety. Since electricity demand for charging EVs introduces new load shapes, the interrelationship between the location of charging stations and long-term electrical grid planning must be addressed. The selection of the most suitable site involves conflicting criteria, requiring the application of multi-criteria analysis. Thus, a geographic information system-based Multicriteria Decision Analysis (MCDA) approach is applied in this work to address the charging station site selection, where the demographic criteria and energy density are taken into account to formulate an EV increase model. Several methods, including Fuzzy TOPSIS, are applied to validate the selection of suitable sites. In this evaluation, the impact of the EV charging station on the substation capacity is assessed through a high EV penetration scenario. The proposed method is applied in Cuenca, Ecuador. Results show the effectiveness of MCDA in assessing the impact of charging stations on power distribution systems ensuring suitable system operation under substation capacity reserves. [ABSTRACT FROM AUTHOR]

Urban green and recreational spaces are important for residents and one of the foundations for the functioning of cities. They offer numerous benefits, including the influence on the price of real estate as an economic benefit. In this paper, the influence of green and recreational areas in Zagreb on the price of apartments in residential buildings is analysed using the hedonic pricing model. The data for the study was processed and analysed using a geographic information system. A local hedonic price model was developed. The results show that some of the observed green and recreational areas increase the price of housing due to their proximity, while others decrease it. Some areas have no influence on the price of housing. The influence of the observed areas on housing prices was heterogeneous. Spatial differences were also found in the influence of the observed areas on housing prices. The most attractive factors when choosing a place to live are Jarun and the parks in the city centre. The price of apartments that near the Jarun increases by 41 EUR/m2 if the apartment is 100 m closer to the Jarun. The price of the apartment increases by 91 EUR/m2 the closer the apartment is to the parks in the city centre (per 100 m). Apartments near Jarun and the parks in the city centre are not available for residents with lower socio-economic status. The forest areas, Maksimir Park and the banks of the Sava river are not favoured when choosing a place to live. Neighbourhoods near these areas are available to the lower socioeconomic status population. Bundek raises the price of one part of the apartments and lowers the price of the other. The results of this study can contribute to the study of green gentrification in Zagreb, but further research is needed on socio-economic indicators and other changes in the region. [ABSTRACT FROM AUTHOR]

It is necessary to explore the spatial pattern and influencing factors of ecosystem services (ESs) to ensure the sustainability of ecosystems. However, research on ESs and their influencing factors across various spatial scales is scarce and is often restricted to a single scale. Water yield, soil conservation, carbon storage, and habitat quality were simulated with the InVEST model at multiple scales. The complex relationship between ES pairs was analyzed. Based on this, the Geographical Detector Model was used to reveal the influencing factors of the ESs, and the Geographically Weighted Regression was used to study the characteristics of local spatial distributions of the main influencing factors. The results indicated that: First, ESs showed prominent spatial differentiation characteristics at different scales. With the increase in spatial scale, the distribution of multiple ESs tended to become uniform and clustered. Then, the relationships between ESs were sensitive to alterations in the spatial scale of the evaluation unit, and the relationships of ESs changed with increasing scale. Finally, individual ES and total ecosystem service were subject to the combined effects of natural and social factors, and the level of influence of each influencing factor on ESs varies considerably at different spatial scales. The study can give guidance for policy decision-makers to conduct differentiated regional management. [ABSTRACT FROM AUTHOR]

Scientific analysis of the spatial heterogeneity of intensive cultivated land use, and exploration of the influencing factors and their mechanism is of great practical significance for promoting intensive potential exploitation and efficient utilization of cultivated land resources. This study constructed a comprehensive evaluation framework based on two aspects: natural conditions and human factors. Principal component analysis, the Dagum Gini coefficient, and a geographically weighted regression model were used to empirically demonstrate the intensive use of cultivated land in the Beibu Gulf urban agglomeration in 2021. The index of cultivated land use intensification was 0.334, and the Gini coefficient was 0.183. The comprehensive level showed low intensity and no distinct spatial nonequilibrium characteristics, with the main sources being hypervariable density (50.33%) and the intra-provincial gap (45.60%). The geographically weighted regression coefficients of temperature (0.491), proportion of agricultural sown area (0.273), elevation (0.165), and urban land intensification (0.146)were relatively large and positive, whereas the regression coefficients of precipitation (-0.440), urbanization rate (-0.207), and construction input intensity (-0.170) were relatively small and negative. All factors exhibited spatial non-stationarity. This study shows that the spatial heterogeneity of intensive cultivated land use in the Beibu Gulf urban agglomeration is manifested by spatial non-equilibrium of pattern and spatial non-stationarity of factors. The mechanism of action among multiple factors reflects that natural factors are the basis, with agricultural land intensification being positively promoted and non-agricultural land intensification negatively inhibited. Improving the intensity of urban land use, coordinating the scale of agricultural sowing, and quantifying the remuneration of cultivated land are the key points to improve the level of intensive use of cultivated land. [ABSTRACT FROM AUTHOR]

Exploring the relationship and driving forces between supply–demand of ecosystem services (ESs) and urbanization can help solve the environmental problems and promote regional sustainable development. This study analyzed the spatio-temporal distribution characteristics of supply–demand of ESs and comprehensive urbanization level (CUL) in Shaanxi Province from 2010 to 2019 and assessed the coupling relationship between ecosystem service supply–demand ratio (ESSDR) and CUL using the coupling coordination degree (CCD) model. Random forests and geographically weighted regression methods were utilized to characterize the contribution and spatial distribution of the drivers of CCD. The results showed that: (1) except for habitat quality, the ESSDR of the other three types of services as well as the comprehensive services showed a decreasing trend, CUL exhibited increasing trend; (2) Although CCD was generally increasing, a significant portion (78.51%) of regions still remained uncoordinated, with relatively better coordination shown around the Guanzhong urban agglomeration, which has a higher urbanization level; (3) The CCD in Shaanxi Province was primarily influenced by local financial income, the secondary industry, and temperature forces. In regions with high and increasing CCD, the tertiary industry was the decisive force. In other areas, there were significant spatial variations in the driving forces. These findings provide a coupled and coordinated perspective for urban ecological management, which can provide scientific reference and practical guidance for cities with different development modes. [ABSTRACT FROM AUTHOR]

Strategies to address the decline and marginalization of rural areas are emerging as a central issue of European and national policies. Yet, the conceptualization of decline and the recognition of forms of decline specific to distinct rural areas, resulting in conditions of intra-rural divide, are fundamental aspects for developing place-based and targeted strategies. The article thus focuses on the concept of intra-rural divide. It introduces the categories of declining, adapting, stabilizing, and developing regions as spatial clusters that present specific population trends and require specific place-based policies. These policies are defined as adaptation, mitigation, and development strategies. The aim of the study is to develop a procedure, based on a set of indicators of population trends and population structure, to investigate the impact of active decline resulting from out-migration and legacy decline resulting from demographic imbalances produced by past migration. The objective is to understand the intra-rural divide and identify clusters of declining transition and prosperous rural areas. Six Italian regions were selected as study areas, including Abruzzo, Basilicata, Calabria, Campania, Molise, and Puglia. The Municipalities are selected as the unit of analysis. The study's findings provide relevant information for defining targeted policies and balancing adaptation, mitigation, and development strategies. [ABSTRACT FROM AUTHOR]

Spatially varying coefficient models, such as GWR (Brunsdon et al. in Geogr Anal 28:281–298, 1996 and McMillen in J Urban Econ 40:100–124, 1996), find extensive applications across various fields, including housing markets, land use, population ecology, seismology, and mining research. These models are valuable for capturing the spatial heterogeneity of coefficient values. In many application areas, the continuous expansion of spatial data sample sizes, in terms of both volume and richness of explanatory variables, has given rise to new methodological challenges. The primary issues revolve around the time required to calculate each local coefficients and the memory requirements imposed for storing the large hat matrix (of size n × n ) for parameter variance estimation. Researchers have explored various approaches to address these challenges (Harris et al. in Trans GIS 14:43–61, 2010, Pozdnoukhov and Kaiser in: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2011; Tran et al. in: 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE), IEEE, 2016; Geniaux and Martinetti in Reg Sci Urban Econ 72:74–85, 2018; Li et al. in Int J Geogr Inf Sci 33:155–175, 2019; Murakami et al. in Ann Am Assoc Geogr 111:459–480, 2020). While the use of a subset of target points for local regressions has been extensively studied in nonparametric econometrics, its application within the context of GWR has been relatively unexplored. In this paper, we propose an original two-stage method designed to accelerate GWR computations. We select a subset of target points based on the spatial smoothing of residuals from a first-stage regression, conducting GWR solely on this subsample. Additionally, we propose an original approach for extrapolating coefficients to non-target points. In addition to using an effective sample of target points, we explore the computational gain provided by using truncated Gaussian kernel to create sparser matrices during computation. Our Monte Carlo experiments demonstrate that this method of target point selection outperforms methods based on point density or random selection. The results also reveal that using target points can reduce bias and root mean square error (RMSE) in estimating β coefficients compared to traditional GWR, as it enables the selection of a more accurate bandwidth size. We demonstrate that our estimator is scalable and exhibits superior properties in this regard compared to the (Murakami et al. in Ann Am Assoc Geogr 111:459–480, 2020) estimator under two conditions: the use of a ratio of target points that provides satisfactory approximation of coefficients (10–20 % of locations) and an optimal bandwidth that remains within a reasonable neighborhood ( < 5000 neighbors). All the estimator of GWR with target pointsare now accessible in the R package mgwrsar for GWR and Mixed GWR with and without spatial autocorrelation, available on CRAN depository at https://CRAN.R-project.org/package=mgwrsar. [ABSTRACT FROM AUTHOR]

Shared bikes, shared e-scooters, and public transit make up most public transportation modes in big cities. Their combination can provide a convenient, efficient, and flexible multi-modal transportation service. Despite the obvious similarity among them, differences exist in the roles that they play in a multi-modal transportation system. A case study in the City of Austin, where shared bikes, shared e-scooters, and public transit coexist, is used to explore their unique characteristics and how they spatially complement or compete with each other. The results show that public transit has more pronounced characteristics related to commuting than shared micromobility modes do, and that shared bikes are more likely to be used for commuting compared to shared e-scooters. Interestingly, the results suggest that there is spatial segregation between where shared bikes complement public transit and shared e-scooters complement public transit, i.e., only one shared mode complements public transit at a given area. [ABSTRACT FROM AUTHOR]

With the energy crisis, environmental pollution and recession becoming increasingly serious, regional sustainable development has become an important global research issue. As a special form of economic development, industrial agglomeration plays an important role in regional sustainable development. However, while driving economic development, industrial agglomeration can have a negative external effect. How to balance the two sides of industrial agglomeration to promote sustainable regional development is an important challenge facing contemporary society. From the perspective of industrial factor agglomeration, this study examines the impacts of the spatial agglomeration of patent technology and practitioners in new energy industries on regional sustainable development capability. The results show that the spatial agglomeration of new energy technology and of practitioners has negative and positive effects on regional sustainable development capability, respectively, and this effect is more obvious in economically underdeveloped regions. Based on the results, countermeasures and suggestions are proposed, including adjusting the structure of regional industrial agglomeration, shifting the pattern of new energy technology diffusion, optimizing the regional staffing structure and implementing institutional reforms in regional sustainable development governance. [ABSTRACT FROM AUTHOR]

The traditional settlements are of paramount significance as indispensable elements of China's cultural heritage, simultaneously functioning as prime assets for the enhancement of rural economic and social dynamics. Nestled within the comprehensive framework of China's rural revitalization endeavor and Shandong Province's proactive initiatives toward the amalgamation of cultural and tourism sectors, a meticulous exploration of the spatiotemporal evolution and connectivity of traditional villages in Shandong Province is indispensable for their preservation and forward-thinking evolution. For this study, 557 traditional villages across Shandong Province are identified as pivotal points, with the application of geographic information system (GIS) techniques to scrutinize their spatiotemporal transformation patterns and spatial characteristics. Additionally, a suite of analytical instruments, encompassing metrics for accessibility assessment, ordinary least squares (OLS) linear regression, and geographically weighted regression (GWR) models, are deployed to evaluate the accessibility levels and influential factors shaping traditional villages within the region. The analytical outcomes reveal the following: (1) Chronologically, approximately 80% of the traditional villages in the province of Shandong were established during the Ming and Qing epochs, and they demonstrate a migratory pattern that is spatially and temporally oriented from "southwest to northeast"; geographically, these traditional villages are characterized by pronounced clustering, predominantly situated at the confluence of Jinan and Zibo Cities, the Shantou District of Zaozhuang City, Zhaoyuan City of Yantai City, and Rongcheng City of Weihai City, forming a coherent "four-core" spatial distribution configuration. (2) Considering the criteria for village location, traditional villages in Shandong are predominantly found in areas with a predominantly flat landscape and a certain proximity to water bodies. (3) On the whole, the accessibility of traditional villages in Shandong is relatively high, with the average accessibility assessed at 199.92 min, a range spanning from 175 min, and approximately 57.99% of the villages falling within the 100 to 200 min accessibility bracket, indicating a systematic decline in accessibility from the central areas to the periphery. (4) The pivotal factors influencing the accessibility of traditional villages in Shandong are primarily altitude, slope, and road network density, with altitude and slope showing a negative correlation with accessibility, whereas road network density exhibits a positive correlation, and the proximity to water bodies has a relatively minor impact on accessibility. [ABSTRACT FROM AUTHOR]

The listed price of real estate is a subjective reflection of its value by sellers, usually related to structural, neighborhood, and environmental attributes. Although previous studies have proposed the hedonic pricing model, factors related to perception are rarely seen in explanatory variables. This study aims to explore the impact of street-level perceived qualities on the listed price per square meter of plot set by the seller of the real estate using the Geographically Weighted Regression (GWR)-based hedonic pricing model and analyzes the spatially heterogeneous effects of the coefficients. In the city of Eindhoven, the Netherlands, Google Street View photos collected at 200 m intervals were employed to calculate representative variables of perceptual quality via a validated convolutional neural network, alongside structural and neighborhood attributes. The final model includes eight explanatory variables, and the results indicate that, apart from the plot area and the number of rooms, the influencing mechanisms of other factors are different. The impact of perceived beautiful quality on listed real estate prices demonstrates obvious distinctions between the north and the south. Perceived livability (positive) and depressing (negative) qualities show similar heterogeneous characteristics. This study offers a comprehensive approach to promote diverse strategies for real estate development across urban areas and recommends a heightened emphasis on the design quality of residential streets. [ABSTRACT FROM AUTHOR]

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AbstractIn many agricultural, forestry, environmental, and ecological surveys, data are often spatial in nature and exhibit spatial nonstationarity. A well-known method for addressing spatial nonstationarity and capturing the spatially varying relationships between different variables is Geographic Weighted Regression (GWR). The calibration approach is one of the most widely used techniques in sample surveys for incorporating the known population characteristics of auxiliary variables by changing the original sampling design weights. The model-calibration approach is an improvement on the conventional calibration approach that can handle a variety of assisting working models. Two-stage sampling is one of the most frequently used sampling strategies in large-scale sample surveys. In the present study, a couple of GWR model-calibration estimators were proposed under two-stage sampling, assuming the availability of population-level complete auxiliary information. Under a set of regularity assumptions, the asymptotic properties of the developed estimators have been evaluated such as design unbiasedness, model unbiasedness, approximate variance, and estimators of variances. The performance of the developed estimators has been compared with the existing estimators through a spatial simulation study and a design-based simulation based on real data. The performance of the proposed estimator was found to be more precise than the existing estimators under two-stage sampling. [ABSTRACT FROM AUTHOR]

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

Identifying the relationship between forest roads and wildfires in forest ecosystems is a crucial priority to integrate suppression and prevention within wildfire management. In various investigations, the interaction of these elements has been studied by using road density as one of the anthropogenic dependent variables. This study focused on the use of a broader set of metrics associated with forest road networks, such as road density, the number of links (edges), and access percentage based on two effect zones (road buffers of 75 m and 97 m). These metrics were employed as response variables to assess forest road network suitability in relation to wildfires, specifically the number and size of fires (2000–2021), using the Apulia region (Italy) as a case study. In addition, to enhance the comprehensive understanding of road networks in forest ecosystems in relation to wildfires, this study considered various affecting factors, including land-cover data (forest, maquis, natural grassland), geomorphology (slope, aspect), vegetation (Normalized Difference Vegetation Index (NDVI)), and morphometric indexes (Topographic Position Index (TPI), Terrain Ruggedness Index (TRI), Topographic Wetness Index (TWI)). We used geographically weighted regression (GWR) and ordinary least squares (OLS) to analyze the interaction between forest road metrics and dependent variables. Results showed that the GWR models outperformed the OLS models in term of statistical results such as R2 and the Akaike Information Criterion (AICc). We found that among road metrics, road density and number of links do not effectively demonstrate the correlation between roads and wildfires as a singular criterion. However, they prove to be a beneficial supplementary variable when considered alongside access percentage, particularly within the 75-m buffer zone. Our findings are used to discuss implications for forest road network planning in a broader wildfire management analysis. Our findings demonstrate that forest roads are not one-dimensional and static infrastructure; rather, they are a multi-dimensional and dynamic structure. Hence, they need to be analyzed from various perspectives, including accessibility and ecological approaches, in order to obtain an integrated understating of their interaction with wildfire. [ABSTRACT FROM AUTHOR]

Our study examines how dengue fever incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales in the city of Ibagué, located in the Andean region of Colombia. We used the dengue incidence in Ibagué from 2013 to 2018 to examine the associations with climate, socioeconomic, and demographic factors from the national census and satellite imagery at four levels of local spatial aggregation. We used geographically weighted regression (GWR) to identify the relevant socioeconomic and demographic predictors, and we then integrated them with environmental variables into hierarchical models using integrated nested Laplace approximation (INLA) to analyze the spatio-temporal interactions. Our findings show a significant effect of spatial variables across the different levels of aggregation, including human population density, gas and sewage connection, percentage of woman and children, and percentage of population with a higher education degree. Lagged temporal variables displayed consistent patterns across all levels of spatial aggregation, with higher temperatures and lower precipitation at short lags showing an increase in the relative risk (RR). A comparative evaluation of the models at different levels of aggregation revealed that, while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling, and they highlight the potential for targeted public health interventions based on localized risk factor analyses. Notably, the intermediate levels emerged as the most informative, thereby balancing spatial heterogeneity and case distribution density, as well as providing a robust framework for understanding the spatial determinants of dengue. [ABSTRACT FROM AUTHOR]

Objectives: As the main form of urbanization, the urban agglomeration can greatly affect the urban spatial pattern in China. Methods: Based on the impervious area data of Chang-Zhu-Tan urban agglomeration (CZT-UA), the spatial organization structure, dynamic development pattern and spatially heterogeneous driving mechanism of the expansion of built-up areas in CZT-UA are quantitatively revealed with a collection of measurement methods, e.g., fractal dimension, expansion intensity index, Moran's I, GetisOrd Gi*, principal component analysis and geographically weighted regression. Results: From 2003 to 2018, the spatial structure of CZT-UA shows obvious axial distribution, which generally follows Xiang-jiang River and the transportation network consisting of five vertical and five horizontal trunk lines. The built-up area and expansion speed of Changsha, Zhuzhou and Xiangtan show an upward trend. Compared with Changsha, Zhuzhou and Xiangtan expanded more slowly and the gap is gradually widened. The overall spatial differences in expansion and distribution of the built-up area in CZT-UA are gradually narrowed. The hotspot regions of urban expansion form a kernel in central CZT-UA, which provides driving force to the peripheral areas. Conclusions: The geographically weighted regression model demonstrates that the flow of residents and economy between cities, clear policy guidance from government, convenient transportation network as well as the radiation effect of the central city can jointly attribute to the expansion of built-up areas. However, the importance and effects of each factor varied in different regions of CZT-UA. [ABSTRACT FROM AUTHOR]

Park tourist facility managers need to understand guests' preferences for elements of the accommodation experience to provide an ideal mix of attributes. Hedonic pricing models were used earlier to estimate the implicit effect of facilities' characteristics on room rates. But these models do not account for possible spatial variations in the relationships. This study assessed the global and spatial influence of facilities' attributes on room rates in Amboseli, Tsavo East, and Tsavo West National Parks, the three contiguous most visited parks in Kenya, an important wildlife tourism destination, using data from on–line and geographical information systems. Results of the global model indicate that the availability of buffet breakfast, bathrobes, plunge pools, and ecolodge designs positively influences room rates, while the distance to aesthetic appreciation hotpots inversely correlates to rates. The spatial model uncovering non–stationarity in estimating the effects of facilities' attributes on room rates, predicts that the causal effects are better in particular places. It also revealed that particular facility attributes influenced room rates more strongly than others. The results of the global model can help park managers design destination marketing programmes while the facilities may use the spatial model to derive indices for product design and marketing decisions. [ABSTRACT FROM AUTHOR]

With or without education services of English or Montessori, the Japanese government and many municipalities provide subsidies to protectors for the childcare charges of unauthorized nursery schools. To examine the adequacy of the subsidies, this paper looks into the factors that influence variations in the childcare charges. Geographically weighted regression shows that childcare charges are positively influenced by the dummy variables based on whether each nursery school offer English teaching or Montessori education as well as by the number of passengers at the nearest railway station in a wide area of Tokyo. [ABSTRACT FROM AUTHOR]

Enhancing soil organic carbon (SOC) content in farmland is crucial for soil quality maintenance and food security. However, the relationship between crop rotation and SOC sequestration remains unclear. We used sample data on SOC, collected in September of every year, from cultivated land for quality monitoring from 2017 to 2021, combined with spatially extracted planting system information, and focused on the effects of wheat–maize crop rotation on SOC in the Sushui River Basin. An analysis of variance (ANOVA) indicated that the SOC content was only significantly different between wheat monoculture and maize monoculture. Among the three cropping systems, wheat–maize rotation did not show absolute superiority. The Geodetector analysis showed that the planting system dominated the spatial distribution of soil organic carbon (p = 0.05), but its explanatory factor was only 5%, and the explanatory power was significantly improved after interaction with other factors. Geographically weighted regression showed that wheat–maize rotation had a trade-off effect with elevation and synergistic effects with rainfall and pH. It displayed a synergistic effect with temperature in the southwest and a trade-off effect in the northeast. The degrees of trade-offs and synergy varied spatially among all interacting factors. We focused on the spatial heterogeneity of soil organic carbon in a small watershed, and the results had scientific significance for the layout of planting systems according to local conditions and the improvement in soil organic carbon levels. [ABSTRACT FROM AUTHOR]

The classical linear regression model allows for a continuous quantitative variable to be modeled simply from other variables. However, this model assumes independence between observations, which, if ignored, can lead to methodological issues. Additionally, not all data follow a normal distribution, prompting the need for alternative modeling methods. In this context, geographically weighted beta regression (GWBR) incorporates spatial dependence into the modeling process and analyzes rates or proportions using the beta distribution. In this study, GWBR was applied to the traffic injury (fatal and non-fatal) crash proportions in Fortaleza, Ceará, Brazil, from 2009 to 2011. The results demonstrated that the local approach using the beta distribution is a viable model for explaining the traffic injury crash proportions, due to its flexibility in handling both symmetric and skewed distributions. Therefore, when analyzing rates or proportions, the use of the GWBR model is recommended. [ABSTRACT FROM AUTHOR]

Context: A new dengue fever (DF) epidemic in Hangzhou, China has placed a serious burden on the urban sustainable development. However, the key drivers of DF epidemic are still unclear, especially the effects of urban landscape patterns. Objectives: In the present study, the spatial relationships between DF epidemic and urban landscape attributes in Hangzhou were investigated based on the framework and approach of landscape epidemiology. Methods: The landscape indices of green space, waterbody, and built-up area were calculated based on land cover and land use (LULC) data. Population density, road density, GDP, and property price were used to represent socioeconomic conditions. The densities of social gathering places (SGPs) such as restaurants, malls, services, entertainment, traditional markets, and parks were recorded. A geographically weighted zero-inflated poisson regression (GWZIPR) model was applied to analyze the effects of LULC patterns, socioeconomic conditions, and SGPs on the risk of DF epidemics. An additional ZIPR-LASSO algorithm was used to explore the dominant drivers of spatial patterns of DF risk to construct the optimal model. Results: DF cases were mainly clustered in central Hangzhou. Landscape patterns involving LULC and SGPs played a more important role in DF epidemic than the socioeconomic conditions. Among these factors, the total area of built-up area had the greatest effect to DF risk. Furthermore, property price, density of entertainment and services could better explain the spatial variation of DF risk in Hangzhou. Conclusions: Overall, the contribution of landscape patterns to the DF epidemic was more important than socioeconomic conditions within the urban region, especially in the new epidemic region. These findings revealed the key role of landscape epidemiology in public health management and landscape governance to improve the urban sustainable development and human well-being. [ABSTRACT FROM AUTHOR]

Geographically weighted regression (GWR) models handle geographical dependence through a spatially varying coefficient model and have been widely used in applied science, but its general Bayesian extension is unclear because it involves a weighted log-likelihood which does not imply a probability distribution on data. We present a Bayesian GWR model and show that its essence is dealing with partial misspecification of the model. Current modularized Bayesian inference models accommodate partial misspecification from a single component of the model. We extend these models to handle partial misspecification in more than one component of the model, as required for our Bayesian GWR model. Information from the various spatial locations is manipulated via a geographically weighted kernel and the optimal manipulation is chosen according to a Kullback-Leibler (KL) divergence. We justify the model via an information risk minimization approach and show the consistency of the proposed estimator in terms of a geographically weighted KL divergence. [ABSTRACT FROM AUTHOR]

As an essential part of daily life, commuting produces considerable carbon emissions and is currently receiving increased amounts of attention. Comprehensive explorations of carbon emissions and the spatial distribution of their effects based on previous studies are lacking. First, we adopt stepwise regression and geographically weighted regression (GWR) to explore the diverse impacts of carbon emissions on the different layers of metropolitan areas, employing factors from the perspectives of socioeconomics, transportation services, and road networks. Our findings show that optimizing the road network structure could be an effective approach to reducing carbon emissions from commuting, especially in the periphery of metropolitan areas. In addition, the mixed use of land contributes to reducing carbon emissions from commuting, especially in the central areas. Thus, the coverage of public transport should be improved, especially in peripheral regions. Policymakers should monitor the spatial heterogeneity of variables and develop suitable policies to adapt to the conditions of the different layers of metropolitan areas. [ABSTRACT FROM AUTHOR]

Urban eco-efficiency (EE) refers to the ability of a city to balance economic growth with environmental protection through the optimal use of resources and environmental management, reflecting the city’s performance in sustainable development. Quantitative EE analysis is a crucial evaluation criterion for measuring the process of sustainable development (SD). To assess the current state of sustainable development in China, this study employs the slacks-based super efficiency model (Super-SBM) to measure the the urban eco-efficiency in China (CUEE) of 284 cities. The study utilizes Kernel Density Estimation (KDE) and Spatial Autocorrelation Analysis (SAA) to investigate the fundamental patterns of CUEE in terms of inter-city differences and relationships. Complex Network Analysis (CNA) and Link Prediction methods are applied to examine the characteristics and future trends of the CUEE network. Additionally, Geodetector and Geographically Weighted Regression (GWR) are used to analyze the influencing factors of CUEE. The findings are as follows: Overall CUEE is relatively low but shows a steady increase. The spatial association network of CUEE is becoming more complex, with increasing association strengths. The Chengdu-Chongqing Economic Circle (CCEC) is expected to further enhance its spatial association strength and may emerge as a new core area. The effectiveness of the interactions among selected factors is greater than that of individual factors. Degree centrality (X1) is identified as a core explanatory factor influencing CUEE, showing a positive correlation with CUEE. Scientific expenditure (X5) is also positively correlated with CUEE. Local fiscal budget expenditure (X3), industrial output value of foreign-invested enterprises (X7), and total cargo transportation volume (X9) are negatively correlated with CUEE. The analytical framework and research conclusions of this study offer valuable insights into the development of urban ecological efficiency in China and provide constructive recommendations for promoting sustainable urban development.

Mining is one of the most pervasive of the various anthropogenic disturbances to landscape ecology. Currently, more attention is paid to the landscape ecological impacts caused by large-scale opencast mining, while small- to medium-scale mines (SMMs) are often neglected due to their small individual size. However, for SMMs, which account for up to 80 % of total mines in China, the impacts of their different agglomeration scales on regional landscapes, especially in ecologically fragile regions, are not yet clear. Therefore, a typical watershed with SMMs aggregated in the semi-arid ecologically fragile area of northwestern Liaoning Province was selected to explore the spatiotemporal dynamics of landscape patterns and landscape ecological risk (LER) under the influence of long-time mining by SMMs. Based on the geographically weighted regression model, the differences in the impact of SMMs on LER at different agglomeration scales were identified. The results revealed that (1) from 1989 to 2022, cropland as the dominant landscape decreased by 1504.82 hm2, while the area of mining land increased the largest, by 668.51 hm2. (2) SMMs were an important factor affecting the landscape pattern, and their multi-point dispersed mining destroyed the original large and intact landscape patches (e.g., cropland), leading to an increase in landscape fragmentation and heterogeneity, and a decrease in connectivity. (3) From 1989 to 2022, the total proportion of very high-risk and high-risk areas increased from 8.18 % to 38.29 %, which are primarily concentrated in the northeast of SHCRB where SMMs were aggregated. (4) The impact on LER was negligible when SMMs did not form an agglomeration scale, while the impact had a diffusion effect when SMMs formed a certain or larger agglomeration scale (the total number of mines was more than 100 and the total area was more than 500 hm2 in a kernel density region with a bandwidth of 1 km), i.e., the overall impact of aggregated SMMs on LER was much greater than the sum of the individual impacts of these mines. Additionally, LER was positively correlated with mine kernel density. The findings can provide a reference for ecological protection and systematic restoration of many similar watersheds with SMMs aggregated, as well as for SMMs around the world in future mining and planning.

Urban heat islands (UHI) are important environmental issue in cities where urban spatial structure has been proven to play an important role in alleviating UHI effects. The relationship between land surface temperature and urban spatial structures has been explored, providing strong support for their cooling effects. Urban roads are the skeleton of urban spatial structures, with obvious spatial structure characteristics; however, research on the relationship between roads and the thermal environment has been mostly focused at the micro and meso level, lacking exploration at the macro spatial structure scale. Xuzhou—a typical average-sized city in China—was selected as the research object and the road system as the carrier. The thermal environmental effects of road elements such as their structural attributes, geometric attributes and unique construction attributes were quantitatively analyzed using geographically weighted regression analysis. The results revealed that 1) the contribution of roads in the study area to the UHI effect is relatively stable; therefore, this area should become an important cooling space to decompose UHI patch connectivity and thus decrease the UHI effect. 2) the self-organizing structural characteristics of urban roads affect their thermal environments where in the straightness of the road structure and road thermal environment showed a clear overall negative correlation And 3) the length and width of the road segments had negative and positive effects on the thermal environment, respectively. The green coverage of the roads has a global negative effect on the thermal environment, but shows obvious spatial non-stationarity. Therefore, green measures must be implemented in different regions. The results here provide a quantitative basis for urban road system planning and urban form management and control that incorporates thermal environment improvements, as well as a reference for the study of urban thermal environments under different spatial forms and planning control systems in other countries and regions.

Recently, rapid economic development of the Yangtze River Economic Belt region in China and the growing problem of regional habitat quality have seriously threatened the process of biodiversity conservation in the Yangtze River Basin. Studying the impact of changes in landscape pattern indexes on habitat quality in the Yangtze River Economic Belt can provide policy insights for biodiversity conservation in the Yangtze River Basin. However, existing studies are still lacking in exploring the effects of changing landscape pattern indices on habitat quality under future scenarios. Based on the land use data of the Yangtze River Economic Belt from 2000 to 2020, this study simulates the land use situation in 2030, and analyzes the landscape pattern indexes and habitat quality during the 30 years, to investigate the impact of landscape pattern indexes on habitat quality at global and local scales through various methods. The results show that landscape patterns were significantly affected by human activities and land use types, with the Yangtze River Economic Belt experiencing increased levels of land use and structural stabilization during the three decades (Shannon Diversity Index increased from 0.90 in 2000 to 0.94 in 2030), fragmentation of the landscape (Landscape fragmentation index increase from 0.094 in 2000 to 0.102 in 2030), and weakened connectivity (contagion index decrease from 48.213 in 2000 to 45.437 in 2030). The Habitat Quality has a large variation in spatial distribution at the county scale, with an average change in habitat quality of 1.04 % from 2000 to 2030, and an overall downward trend. There is a strong localized spatial autocorrelation between landscape pattern indices and habitat quality, with significant spatial heterogeneity and some form of variability at time scales. The results of this study help to further understand the eco-environmental problems of the Yangtze River Economic Belt, and provide theoretical references for the formulation of ecological environmental protection policies and ecological functional area planning.

Hills and mountains cover approximately 25 % of Earth’s landmass; therefore, investigating landscape ecological risk (LER) in these regions is imperative for ecosystem management. Hilly and mountainous regions susceptible to influences such as desertification and erosion include the Southern Hill and Mountainous Belt (SHMB) in southern China. Over the past 30 years, the SHMB has undergone significant changes, offering an excellent research case. A comprehensive assessment of the ecological status of this region is crucial for the protection and management of the ecosystems in the SHMB. This study assessed the LER of the SHMB, identified influencing factors over the past 30 years, and utilized a patch-generating land-use simulation model (PLUS) to project future land use and LER. Accounting for more than 90 % of the total area, cropland and forestland were the two most common land use types in the SHMB. From 1990 to 2020, impervious surface and water area saw obvious increases, rising by 154 % (from 1209.37 km2 to 3073.59 km2) and 21 % (from 1584.78 km2 to 1924.8 km2), respectively. Grasslands and shrublands decreased by 54 % and 34 %. It is anticipated that there will be little change in land-use types by 2030 compared to 2020. Additionally, the overall LER in the SHMB was low, with low- and medium–low-risk representing around 70 % of the overall risk. The general state of LER improved during the research period but did not converge. Specifically, the LER intensity did not decrease although the total LER showed a declining trend. In the western portion of the research region, the karst area displayed an increasing LER, and the severity of the high-risk area increased. Furthermore, natural factors promote the restoration of LER. For example, the abundance of hydrothermal conditions (i.e., increased temperature and precipitation) promotes the recovery of vegetation growth and reduces the LER. Human activities, such as the construction of stations, highways, and an increasing population, exacerbate LER, contributing to an increase in LER. This study offers helpful guidance for formulating ecological conservation strategies to maintain the ecological balance and sustainability of hilly and mountainous regions.

Abstract Urban areas worldwide are experiencing escalating temperatures due to the combined effects of climate change and urbanization, leading to a phenomenon known as urban overheating. Understanding the spatial distribution of land surface temperature (LST) and its driving factors is crucial for mitigation and adaptation of urban overheating. So far, there has been an absence of investigations into spatiotemporal patterns and explanatory factors of LST in the city of Addis Ababa. The study aims to determine the spatial patterns of land surface temperature, analyze how the relationships between LST and its factors vary across space, and compare the effectiveness of using ordinary least squares and geographically weighted regression to model these connections. The findings showed that the spatial patterns of LST show statistically significant hot spot zones in the north-central parts of the study area (Moran’s I = 0.172). The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic.The result revealed non-stationarity (p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic. The result revealed non-stationarity ((p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. Consequently, Localized understanding of the spatial patterns and the driving factors of LST has been formulated.

Heat-related environmental justice has gained increasing attention, with many studies focusing on social inequalities in exposure to extreme surface temperatures. However, changes in land surface temperature (LST) also contribute to residents’ thermal discomfort, underscoring the need to explore inequalities in exposure to these changes. This study addresses this gap by analyzing spatial and social disparities in exposure to both LST changes and summer LST in Shanghai. We employed a boosted regression tree model to quantify LST changes, offering better predictive performance than traditional linear regression, and used geographically weighted regression with data from China's sixth population census to assess the city-wide and local scale inequality differences in exposure to LST changes and summer LST. Our findings reveal that 57.83 % of Shanghai's subdistricts experienced above-average LST changes and summer LST. Specific groups—males (22.17 %), children (22.61 %), the elderly (13.04 %), and residents in older housing (60.87 %)—were disproportionately exposed to local-scale LST changes. In contrast, those employed in agriculture, forestry, and fisheries faced consistently lower exposure. Furthermore, exposure to LST changes was 3.47 % higher among the elderly and 28.26 % higher for residents in older housing compared to exposure based on summer LST alone. To mitigate these inequalities, we recommend increasing water coverage and green roofs, enhancing green infrastructure in aging neighborhoods, and providing financial subsidies for outdoor workers. These findings emphasize the importance of addressing social inequalities in exposure to temperature changes to enhance urban resilience and promote sustainable urban planning in the face of climate change.

Negative externalities stemming from overtourism have been widely reported in the literature. However, a consequence less explored in scientific research is the increase in crime rates derived from high levels of tourism. This article analyses the relationship between the concentration of Airbnb accommodation and crime density in the city of Malaga (Spain); both the social disorganization and the routine activities theories are used as the theoretical basis. The region under study has been defined by removing the non-urbanized part of the city and the unit of spatial analysis is the census tract. The relationship between Airbnb and crime has been measured by controlling for other factors traditionally related to crime. Two models that take into account the spatial nature of the data have been employed: a two-stage spatially weighted spatial least squares model and a geographically weighted regression model. Results show that Airbnb is significantly related to an increase in crime density in the census tract, as is the proportion of people born in countries with a Human Development Index below .825. Nightlife likewise shows a significant relationship with crime in the city, but this relationship is inverse. Nevertheless, the variable that shows the strongest link is crime itself in neighboring census tracts. An increase in crime in a given region leads to an increase in crime in the surrounding census tracts. However, the local model reveals that the impact of crime in adjacent units is not related in the same way to local crime in region analyzed as a whole. The policy implications of these results and other findings contrary to the original criminological theories are discussed.

ABSTRACTThis research investigated the spatial variations of heavy metals and nutrients in the sediment of the Great Chao Phraya River network of Thailand using geographically weighted regression (GWR). The performance of GWR in explaining the spatiality of selected contaminants using single and multiple predictors was compared and discussed, and a suitable approach was recommended for sediment study. Extensive explanatory variables, including land use type and topographic, socio-economic, and meteorological indicators were utilized in the geospatial regressions of river sediment contaminants at the sub-catchment scale. In the case of simple regression, GWR outperformed the ordinary least square (OLS) regressions with global R2 (range 0.34–0.72 for GWR and 0–0.24 for OLS, respectively), over 2-fold of which was derived by OLS. Similar observations were obtained in multiple regressions, where GWR offered global R2 values between 0.27 and 0.6, higher than those that were provided by OLS (i.e. 0.12–0.44). Furthermore, multiple GWR model presented higher local explanatory power and performance with significantly increased local R2 (up to 51%) compared with simple GWRs (p ≤ 0.05). It is recommended to combine the applications of simple and multiple GWR models to understand the effects of selected explanatory predictors on sediment contaminations from different perspectives.

Vehicular air pollution has created an ongoing air quality and public health crisis. Despite growing knowledge of racial injustice in exposure levels, less is known about the relationship between the production of and exposure to such pollution. This study assesses pollution burden by testing whether local populations' vehicular air pollution exposure is proportional to how much they drive. Through a Los Angeles, California case study we examine how this relates to race, ethnicity, and socioeconomic status---and how these relationships vary across the region. We find that, all else equal, tracts whose residents drive less are exposed to more air pollution, as are tracts with a less-White population. Commuters from majority-White tracts disproportionately drive through non-White tracts, compared to the inverse. Decades of racially-motivated freeway infrastructure planning and residential segregation shape today's disparities in who produces vehicular air pollution and who is exposed to it, but opportunities exist for urban planning and transport policy to mitigate this injustice.

One of the measures of housing wealth inequality is the property's market value. In existing analyses, this figure is often a subjective value determined by homeowners. Little is known about the validity of using this type of data as a substitute for market value in inequality studies. Therefore, this paper aims to examine whether self-reported home values can be applied to evaluate housing wealth inequality. In order to achieve this goal, first, a theoretical framework on the irrelevance of valuation bias for the assessment of housing wealth inequality was developed, followed by an empirical analysis. The latter included gathering information on subjective flat values and their characteristics in Warsaw. Next, a geographically weighted regression was calibrated to calculate the market value of these dwellings. Then, using the Gini coefficient, housing wealth inequality levels were estimated separately for subjective and objective home values. The results revealed that the former could serve as a very good proxy for the latter in housing wealth inequality analysis. The findings hold across almost all identified subgroups based on respondents' gender, age, income, wealth, education and employment status. Finally, recommendations were formulated for public institutions and housing research. [ABSTRACT FROM AUTHOR]

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While the land use‐street network nexus is well acknowledged, evidence for the one‐way impacts of land‐use patterns on street accessibility is still inadequate. The measurements of land‐use patterns and street accessibility lack systematic knowledge. Their empirical correlations also lack geographical variability, constraining site‐specific land‐use practices. Therefore, this study overcame the aforementioned limitations by examining the two‐level spatial models to formulate accessibility‐oriented land plans, using a well‐developed Chinese city as an example. Firstly, two landscape metrics—Euclidean Nearest‐Neighbor Distance (ENN) and Similarity Index (SIMI)—were used to quantify the intra‐ and inter‐land‐use configurations, respectively. Both city‐level and local accessibility were measured using spatial design network analysis. Performing both ordinary least squares (OLS) and geographically weighted regression (GWR) models, results identified the statistically significant effects of inter‐land‐use patterns on two‐level street accessibility. An exception was that land‐use configurations within residential and industrial regions were irrelevant to street accessibility. We also found GWR was a better‐fitting model than OLS when estimating locally‐varied accessibility, suggesting hierarchical multiscale land‐use planning. Overall, locally heterogeneous evidence in this study can substantialize land use‐street network interactions and support the decision‐making and implementation of place‐specific accessibility‐oriented land use. [ABSTRACT FROM AUTHOR]

Shoho Castle Maps are maps of castle towns throughout Japan drawn by Kano School painters on the order of the shogun in 1644. The Shoho Castle Map of Takada, Joetsu City, Niigata Prefecture was used to visualize local distortions in historical maps and to scrutinize the mapmaking process. A novel method, geographically weighted bidimensional regression, was developed and applied to visualize the local distortions of the map. Exaggerated expressions by mapmakers that have not been identified in previous studies were revealed. That is, in addition to the castle being drawn enlarged, the town where the merchants and artisans lived was drawn larger than the castle. Therefore, the Takada Shoho Castle Map reflects mapmakers' intentions, besides enlarging military facilities, which appear to have emphasized the pictorial composition of the map by placing the main gate to the castle at the center and drawing the map area evenly from the center in a well-balanced layout. [ABSTRACT FROM AUTHOR]

This study aims to explore the factors affecting variation in ozone concentration, using ozone concentration monitoring data from the Beijing–Tianjin–Hebei (Jing–Jin–Ji) region from 2015 to 2022. The characteristics of the spatial and temporal variation in ozone concentration, as well as the impact of meteorological and land-use types on this variation, were investigated. Seasonally, ozone concentrations in the Jing–Jin–Ji region were highest in summer, followed by spring and autumn, and were lowest in winter. Spatially, a characteristic trend of lower concentrations in urban centers and higher concentrations in suburban areas was found. From the results of correlation analysis and a stepwise regression model, temperature and solar radiation were found to be the primary factors affecting seasonal variations in ozone concentration, explaining 67.9% of the variance. Additionally, the results of geographically weighted regression models confirmed the spatial heterogeneity of the impact of temperature and solar radiation on ozone concentration. In urban centers, such as Beijing and Tianjin, temperature and solar radiation more strongly influenced ozone concentration. To determine the factors driving this spatial heterogeneity, a geodetector was employed to analyze the impact of various geographical factors on the spatial distribution of ozone concentration. In winter, the impact of various factors of urbanization, such as lighting index, population density, and construction land, as well as air quality factors (PM2.5 and NO2), on ozone concentration was stronger, with q values greater than 0.2. However, in autumn, the influence of these factors on ozone concentration weakened. Furthermore, the results of the two-factor interaction detection analysis showed that the q values for the interaction between urban and air quality factors ranged between 0.40 and 0.47, which were substantially higher than those for other interactions. This further confirmed the strong influence of urban activities on ozone concentration. [ABSTRACT FROM AUTHOR]

In response to the inherent requirements of low-carbon land spatial planning in Jiangxi Province and the lack of existing research, this paper explored the mechanism of spatial form elements of Poyang Lake urban agglomeration on urban carbon emissions. Based on generalized linear regression and geographically weighted regression models, this paper analyzed the spatiotemporal distribution characteristics of carbon emissions, the spatiotemporal relationship between urban form index and carbon emissions, and the spatial differentiation of the intensity of dominant factors from 63 county-level administrative units in the Poyang Lake city group from 2005 to 2020. The results showed that: ① The carbon emissions of urban agglomerations around Poyang Lake are generally increasing, and the spatial distribution of carbon emissions is characterized by high-value concentration in the middle and low-value agglomeration in pieces; ② The main driving factor for the spatial heterogeneity of carbon emissions was the expansion of built-up area; ③ Improving urban compactness and optimizing urban form could effectively reduce urban carbon emissions. The results showcased the correlation between urban spatial landscape pattern and the spatiotemporal distribution of carbon emissions, which could make the lowcarbon land spatial planning in the Poyang Lake city group more reasonable and practical. [ABSTRACT FROM AUTHOR]

This research investigates the spatial correlation among per capita electricity consumption, per capita industrial electricity consumption, and economic growth by employing various regression models, including linear regression, geographically weighted regression, and multi-stage geographically weighted regression. The primary goal is to illustrate the presence of spatial effects in the connection between electricity consumption and economic growth. In this context, this study made for Turkey distinguishes itself from previous research by utilizing the multi-stage spatially weighted regression model to examine this relationship. The findings reveal that the multi-scale spatial regression model is the most effective in explaining the relation between economic growth at the provincial level and per capita electricity consumption and per capita industrial electricity consumption. Moreover, the study emphasizes that per capita Gross Domestic Product emerges as the most influential regional economic indicator when assessing its impact on per capita electricity consumption and per capita industrial electricity consumption. [ABSTRACT FROM AUTHOR]

With the advancement of urban modernization, more and more residents are flocking to large cities, leading to problems such as severe traffic congestion, uneven distribution of spatial resources, and deterioration of the urban environment. These challenges pose a serious threat to the coordinated development of cities. In order to better understand the travel behavior of metropolitan residents and provide valuable insights for urban planning, this study utilizes taxi trajectory data from the central areas of Beijing, Shanghai, Shenzhen, and Wuhan. First, the relationship between daytime taxi drop-off points and urban amenities is explored using Ordinary Least Squares (OLS). Subsequently, Geographically Weighted Regression (GWR) techniques were applied to identify spatial differences in these urban drivers. The results show that commonalities emerge across the four cities in the interaction between external transport stops and commercial areas. In addition, the average daily travel patterns of residents in these four cities show a trend of "three peaks and three valleys", indicating the commonality of travel behavior. In summary, this study explores the travel characteristics of urban residents, which can help urban planners understand travel patterns more effectively. This is crucial for the strategic allocation of transport resources across regions, the promotion of sustainable urban transport, and the reduction in carbon emissions. [ABSTRACT FROM AUTHOR]

Commuting is an important part of primary school students' travel behavior, which has been concerned for a long time. We found that the commute-mode choice behavior of primary school students in the context of regional segmentation shows strong characteristics in space, but has not yet been considered in traditional research. To fill this gap, this study summarizes the commute-mode choices of primary school students with different characteristics based on the Beijing School Commute Survey. And the geographically weighted regression (GWR) model is built to analyze the zonal heterogeneity of the impact of personal characteristics, family factors and school factors on the primary school students' commute-mode choices from a low-carbon perspective. The results demonstrate that the possibility of primary school students choosing low-carbon commuting modes is positively correlated with the grade, commuting time, commuting escort type and housing category, but is inversely related with the commuting distance and the distance from the school to the city center. The coefficient estimates of explanatory variables vary across regions. Finally, we put forward policy suggestions regarding promoting the low-carbon commuting structure, such as developing the urban slow traffic system, which serve as a reference for policymakers. [ABSTRACT FROM AUTHOR]

Urban areas worldwide are experiencing escalating temperatures due to the combined effects of climate change and urbanization, leading to a phenomenon known as urban overheating. Understanding the spatial distribution of land surface temperature (LST) and its driving factors is crucial for mitigation and adaptation of urban overheating. So far, there has been an absence of investigations into spatiotemporal patterns and explanatory factors of LST in the city of Addis Ababa. The study aims to determine the spatial patterns of land surface temperature, analyze how the relationships between LST and its factors vary across space, and compare the effectiveness of using ordinary least squares and geographically weighted regression to model these connections. The findings showed that the spatial patterns of LST show statistically significant hot spot zones in the north-central parts of the study area (Moran's I = 0.172). The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic.The result revealed non-stationarity (p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic. The result revealed non-stationarity ((p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. Consequently, Localized understanding of the spatial patterns and the driving factors of LST has been formulated. [ABSTRACT FROM AUTHOR]

As an affordable form of public transit, the metro still faces first- and last-mile problems. Integration of dockless bike sharing (DBS) and the metro offers a reasonable solution to this problem. However, an in-depth exploration of the spatio-temporal characteristics and determinants of DBS–metro integration, especially meteorological indicators, is lacking. The spatiotemporal characteristics of the transfer volume on weekdays and weekends in Beijing were analyzed. Geographically weighted regression was utilized to investigate the effects of the significant determinants on the spatial variation of the transfer volume. The results indicate that the transfer volume is concentrated within the Fifth Ring Road in Beijing. The effects of education services and metro station density were negatively correlated with transfer volume. As expected, population density and enterprises positively affect transfer volume, whereas wind speed and relative humidity have negative effects. These findings provide new insights into the promotion of DBS–metro integration. [ABSTRACT FROM AUTHOR]