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Vacant land stands as a ubiquitous feature within our urban landscapes, exerting significant influence on local environmental, social, and economic systems, thus affecting neighborhood sustainability. However, existing studies often produce conflicting results regarding the impact of vacant land. This inconsistency largely stems from the assumption that the associations between vacant land and neighborhood sustainability remain stationary across space, overlooking potential spatial variability. To address this gap, this study uses Chicago as the sample and investigates spatial variations in the associations between vacant land and neighborhood sustainability, with a focus on the discrepancies between disadvantaged and non-disadvantaged neighborhoods. The results reveal substantial spatial variations in these associations, primarily influenced by neighborhood socioeconomic patterns, urban structure, and local sustainability levels. Consequently, this study underscores the need for spatially adaptive strategies to address the vacant land issue and foster sustainable neighborhood development. Furthermore, this study advocates repurposing vacant land in disadvantaged neighborhoods to promote social equity and advance citywide sustainable development goals. [ABSTRACT FROM AUTHOR]

This study investigates the roles of the socio-economic, land use, built environment, and weather factors in shaping up the demand for bicycle-sharing trips during the COVID-19 pandemic in Toronto. It uses "Bike Share Toronto" ridership data of 2019 and 2020 and a two-stage methodology. First, multilevel modelling is used to analyze how the factors affect monthly station-level trip generation during the pandemic compared to pre-pandemic period. Then, a geographically weighted regression analysis is performed to better understand how the relationships vary by communities and regions. The study results indicate that the demand of the service for commuting decreased, and the demand for recreational and maintenance trips increased significantly during the pandemic. In addition, higher-income neighborhoods are found to generate fewer weekday trips, whereas neighbourhoods with more immigrants experienced an increase in bike-share ridership during the pandemic. Moreover, the pandemic trip generation rates are more sensitive to the availability of bicycle facilities within station buffers than pre-pandemic rates. The results also suggest significant spatial heterogeneity in terms of the level of influence of the explanatory factors on the demand for bicycle-sharing during the pandemic. Based on the findings, some neighbourhood-specific policy recommendations are made, which inform decisions regarding the locations and capacity of new stations and the management of existing stations so that equity concerns about the usage of the system are adequately accounted for. [ABSTRACT FROM AUTHOR]

Streets are an important component of urban public spaces and also a high-incidence area for urban crime. However, current research mainly involves adult crime, or fails to distinguish between adult and juvenile crime, which poses a severe challenge to the prevention of juvenile delinquency. Juveniles have lower self-control abilities and are more likely to be influenced by external environmental factors to trigger criminal behavior compared to adults. Therefore, this study uses New York's Manhattan district as an example, based on CPTED and social disorganization theories, and utilizes street view data and deep learning techniques to extract street environment indicators. The GWR model is used to explore the influence mechanism of urban street environment on juvenile crime. The results of this study, considering spatial heterogeneity, demonstrate the impact of various physical environmental indicators of urban streets on juvenile delinquency, and reveal that some street indicators have differentiated effects on crime in different areas of the city. Overall, our research helps to uncover the relationship between juvenile delinquency and the built environment of streets in complex urban settings, providing important references for future urban street design and juvenile delinquency prevention. [ABSTRACT FROM AUTHOR]

Determining the factors that influence poverty has been seen as one of the ways of identifying appropriate poverty alleviation strategies in cities. This has been done, in most cases, through the application of ordinary least squares (OLS), which assumes homogeneous influence without considering the spatial heterogeneity that may occur in this influence. This study aims to determine the spatial heterogeneity in the influence of public infrastructure, economic, environmental, and social factors on poverty, by carrying out comparison analysis using OLS and Geographically Weighted Regression (GWR) in ArcGIS Pro 3.0.1, with a view of proffering poverty alleviation strategies for each section of an urban area. A questionnaire in Geographic Open Data Kit (GeoODK) was administered to 366 urban residents across the twenty wards in the selected city in Nigeria. The results reveal that factors that influence income poverty are spatially varying in direction and weight across a city. Spatial heterogeneity of poverty’s determinants should be considered in the formulation and implementation of effective poverty alleviation policies and programs. [ABSTRACT FROM AUTHOR]

In survey sampling, auxiliary information is used to precisely estimate the finite population parameters. There are several approaches available in the literature that provide a practical method for incorporating auxiliary information during the estimation stage. In order to effectively utilize the auxiliary information, a geographically weighted regression (GWR) model-assisted integrated estimator of finite population total under a two-phase sampling design has been proposed in this article. Spatial simulation studies have been conducted to empirically assess the statistical properties of the proposed estimator. In the presence of spatial non-stationarity, empirical findings reveal that the proposed estimator outperforms all existing estimators such as two-phase HT, ratio, and regression estimators, demonstrating the importance of spatial information in survey sampling. [ABSTRACT FROM AUTHOR]

Identifying ecosystem service functions, clarifying the spatiotemporal trade-offs and synergies of terrestrial ecosystem services and their driving mechanisms, and exploring differentiated ecological functional zoning are crucial steps in achieving healthy regional ecosystem management, and are conducive to developing diversified ecological restoration strategies, establishing a robust cross-regional collaborative management mechanism, implementing differentiated ecological management strategies, and contributing to the construction of a Beautiful China. This paper, based on the InVEST model, Pearson correlation, GeoDetector, and Spatiotemporal Geographically Weighted Regression models, constructs a spatial quantification model of the trade-offs and synergies among five key ecosystem service functions -- habitat quality, soil retention, water conservation, food supply, and carbon sequestration -- of China's terrestrial ecosystems from 2000 to 2020. It explores the influencing factors of terrestrial ecosystem services in China and their spatiotemporal heterogeneity, thereby investigating the future strategies for ecological functional zoning and management of China's national land space. The results indicate that: (1) during 2000-2020, China's food supply and soil conservation have increased. However, the habitat quality, water conservation, and carbon sequestration have decreased. (2) Significant spatial and temporal heterogeneities exist in the key ecosystem services of China's terrestrial ecosystems. (3) Natural, economic, and social factors all impact China's terrestrial key ecosystem services. Among them, slope, annual average precipitation, land development intensity, and vegetation coverage are the main influencing factors, and different factors exhibit significant spatial heterogeneity. (4) Significant trade-offs/synergy effects among critical terrestrial ecosystem services exist in China. (5) China's national territory is divided into four ecological protection functional zones: ecological restoration areas, ecological control areas, resilient development areas, and ecological conservation areas, and explores differentiated zoning optimization control paths. [ABSTRACT FROM AUTHOR]

Background: Suicide mortality remains a global health concern, and community characteristics affect regional variations in suicide. This study investigated spatially clustered patterns of suicide mortality rates in South Korea and evaluated the impact of community factors on suicide. Methods: Suicide mortality rates were estimated by sex, age group, and district, using the 2021 Cause of Death Statistics in South Korea from the MicroData Integrated Service. Community-determinant data for 2021 or the nearest year were collected from the Korean Statistical Information Service. The spatial autocorrelation of suicide by sex and age was examined based on Global Moran's I index. Geographically weighted regression (GWR) was used to discern the influence of community determinants on suicide. Results: Suicide mortality rates were significantly higher among men (40.64 per 100,000) and adults over the age of 65 years (43.18 per 100,000). The male suicide mortality rates exhibited strong spatial dependence, as indicated by a high global Moran's I with p < 0.001, highlighting the importance of conducting spatial analysis. In the GWR model calibration, a subset of the community's age structure, single-person household composition, access to mental healthcare centers, and unmet medical needs were selected to explain male suicide mortality. These determinants disproportionately increased the risk of male suicide, varying by region. The GWR coefficients of each variable vary widely across 249 districts: aging index (Q1:0.06–Q3:0.46), single-person households (Q1:0.22–Q3:0.35), psychiatric clinics (Q1:-0.20–Q3:-0.01), and unmet medical needs (Q1:0.09–Q3:0.14). Conclusions: Community cultural and structural factors exacerbate regional disparities in suicide among men. The influencing factors exhibit differential effects and significance depending on the community, highlighting the need for efficient resource allocation for suicide. A regionally tailored approach is crucial for the effective control of the community's mental health management system. [ABSTRACT FROM AUTHOR]

The scientific field of precision agriculture employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impact. However, obtaining a high number of soil samples is challenging in order to make precision agriculture viable. There is a trade-off between the amount of data needed and the time and resources spent to obtain these data compared to the accuracy of the maps produced with more or fewer points. In the present study, the research was based on an exhaustive dataset of apparent electrical conductivity (aEC) containing 3906 points distributed along 26 transects with spacing between each of up to 40 m, measured by the proximal soil sensor EM38-MK2, for a grain-producing area of 72 ha in São Paulo, Brazil. A second sparse dataset was simulated, showing only four transects with a 400 m distance and, in the end, only 162 aEC points. The aEC map via ordinary kriging (OK) from the grid with 26 transects was considered the reference, and two other mapping approaches were used to map aEC via sparse grid: kriging with external drift (KED) and geographically weighted regression (GWR). These last two methods allow the increment of auxiliary variables, such as those obtained by remote sensors that present spatial resolution compatible with the pivot scale, such as data from the Landsat-8, Aster, and Sentinel-2 satellites, as well as ten terrain covariates derived from the Alos Palsar digital elevation model. The KED method, when used with the sparse dataset, showed a relatively good fit to the aEC data (R2 = 0.78), with moderate prediction accuracy (MAE = 1.26, RMSE = 1.62) and reasonable predictability (RPD = 1.76), outperforming the GWR method, which had the weakest performance (R2 = 0.57, MAE = 1.78, RMSE = 2.30, RPD = 0.81). The reference aEC map using the exhaustive dataset and OK showed the highest accuracy with an R2 of 0.97, no systematic bias (ME = 0), and excellent precision (RMSE = 0.56, RPD = 5.86). Management zones (MZs) derived from these maps were validated using soil texture data from clay samples measured at 0–10 cm depth in a grid of 72 points. The KED method demonstrated the highest potential for accurately defining MZs for irrigation, producing a map that closely resembled the reference MZ map, thereby providing reliable guidance for irrigation management. [ABSTRACT FROM AUTHOR]

Water supply continuity is a crucial indicator of service efficiency. Multiple South Asian cities have Intermittent Water Supply (IWS). Water supply equity in cities depends on the spatial patterns of intermittency. The present study used Kernel Density Estimation (KDE) to investigate the spatial pattern of water supply intermittency for 3071 localities of Hyderabad, India. Further, the relationship between population density and supply continuity has been examined in 146 wards of Hyderabad using Spatial Autocorrelation and Geographically Weighted Regression (GWR). Spatial Autocorrelation defines two distinct clusters of central wards for high continuity (12 wards) and population density (19 wards). However, the ward clusters are different for continuity and density, with only three central wards (2.06% of all wards) being common in both the high-high clusters. The results highlight that the water supply continuity in Hyderabad has minimal consideration for population density, resulting in a spatially inequitable water supply. [ABSTRACT FROM AUTHOR]

Although well-designed urban streets are beneficial for sustainability and livability, few studies have considered their role in housing price estimates. To fill this gap, this study conducted in Nanjing, China, aims to examine the contribution of streetscape features to housing prices. Data were collected for 2040 residential blocks within the four municipal districts in July 2021. A semantic segmentation approach was used to identify the percentage of elements in the images from Baidu Street View. Two types of streetscape related variables (Enclosure and Greenery) were calculated and added to a hedonic pricing model based on Geographically Weighted Regression. The results show that the streetscape factors all have positive effects on house prices, and the contribution to house prices from large to small is grass, plants, horizontal buildings, vertical buildings and trees. By comparing the parameters of the models, it can be concluded that the inclusion of streetscape features and consideration of spatial heterogeneity can significantly improve the accuracy of housing price estimation. The findings of the current study contribute to decision-making in housing planning and urban design and to judgments about pricing reasonableness. [ABSTRACT FROM AUTHOR]

Featured Application: (1) The methodological approach based on multiscale geographically weighted regression provides a nuanced understanding of how urban factors influence health disparities. (2) The findings highlight the importance of food accessibility as a determinant of health, reinforcing the need for interdisciplinary approaches that combine public health, urban planning, and nutrition science. (3) Policymakers can use this data to identify priority areas for improving food access, particularly in economically disadvantaged neighborhoods. This could involve incentivizing grocery stores and markets to establish themselves in underserved areas or enhancing public transportation links to existing food resources. (4) The call for sustainable food systems to support public health highlights the importance of long-term strategies that ensure consistent access to healthy, affordable food. This can influence agricultural policies, support for local food producers, and the promotion of urban agriculture initiatives. This study examines the link between food deserts and cardiovascular health in older adults in Santiago's Metropolitan Region, Chile. As the population ages and chronic diseases rise, understanding the impact of food accessibility on health is essential. Using multiscale geographically weighted regression, we analyzed data from the Cardiovascular Health Program, socioeconomic indicators, and food desert maps, sourced from the Chilean Ministry of Health and other databases. Spatial analysis, including Voronoi diagrams, assessed the influence of food deserts on health outcomes. Findings show a significant correlation between limited access to healthy foods and higher cardiovascular disease rates, especially in economically disadvantaged areas. The regression model is significant to contribute to the explanation of disease prevalence, emphasizing the impact of food availability on health. This study highlights the importance of considering spatial factors in urban planning and public health policies. By showcasing the role of food environments in health disparities, it advocates for integrated health interventions. Targeted urban planning to address food deserts can enhance access to healthy foods, improving cardiovascular health and well-being among Santiago's elderly. The findings provide insights for policymakers to create healthier urban environments and stress the need for sustainable food systems to support public health. [ABSTRACT FROM AUTHOR]

The Yellow River Delta (YRD) has the world's highest land formation rate. However, soil salinization has caused severe land degradation in the region. Understanding the distribution of soil salinity and its variation is essential for saline soil management. This study combined soil salinity sampling data, remote sensing imagery, and layers of geographic environmental factors. Three different models, including Ordinary Kriging (OK), Geographically Weighted Regression (GWR), and Bayesian Maximum Entropy (BME), were used and compared to predict the soil salinity of two soil layers in 2006 and 2022. Then the spatial distribution characteristics and development trends of soil salinity in the YRD were analyzed. The results indicated that (1) The BME model is an optimal salinity prediction model that integrates soft data from multiple sources to perform nonlinear estimates. Compared to the OK and GWR models, the RMSE was reduced by up to 25% and 13%, respectively, and the greatest improvement in R2 was increased from 0.0534 and 0.2718 to 0.5569, respectively. (2) Soil salinity in the YRD shows a spatially increasing trend from the southwestern inland to the northeastern coast. Over the past 16 years, the salinization pattern has become more complex: soil salinization has been mitigated in the central and southern regions, with the average salinity decreasing from 1.03% to 0.86% and the salinization rate decreasing from 99% to 88%; and it has significantly intensified in the northern part of the study area near the old Yellow River channel and the Gubei Reservoir, and in some scattered inland areas. Continuous water and sediment regulation in the Yellow River basin and ecological management of natural reserves can generally alleviate soil salinization, but the risk of soil salinization can be increased by seawater erosion, inappropriate land use, and resource exploitation. [ABSTRACT FROM AUTHOR]

AbstractGeographically weighted regression (GWR) and its extensions are important local modelling techniques for exploring spatial heterogeneity in regression relationships. However, when dealing with spatial data of overlapping samples – for example, when precise locational information is aggregated to a shared neighbourhood to avoid revealing the addresses of individual survey respondents – GWR-based models can encounter several problems, including obtaining reliable bandwidths. Because data with this characteristic exhibit spatial hierarchical structures, we propose combining hierarchical linear modelling (HLM) with GWR to give a hierarchical and geographically weighted regression (HGWR) model that divides coefficients into sample-level fixed effects, group-level fixed effects, sample-level random effects, and group-level spatially weighted effects. This paper presents a back-fitting likelihood estimator to fit the model, a simulation experiment that suggests that HGWR is better able to capture these effects and the spatial heterogeneity within them than are traditional HLM or GWR models, and a case study looking at predictors of housing price in Beijing, China. The ability of HGWR to tackle both spatial and group-level heterogeneity simultaneously suggests its potential as a promising data modelling tool for handling spatio-temporal big data with spatially hierarchical structures. [ABSTRACT FROM AUTHOR]

Background: In developing countries, the death probability of a child and mother is more significant than in developed countries; these inequalities in health outcomes are unfair. The present study encompasses a spatial analysis of maternal and child mortalities in Pakistan. The study aims to estimate the District Mortality Index (DMI), measure the inequality ratio and slope, and ascertain the spatial impact of numerous factors on DMI scores across Pakistani districts. Method: This study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were used. This study further utilized spatial autocorrelation tests to determine the magnitude and location of the spatial dependence of the clusters with high and low mortality rates. The Geographically Weighted Regression (GWR) model was also applied to examine the spatial impact of socioeconomic, environmental, health, and housing attributes on DMI. Results: The inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extreme spatial heterogeneity in terms of DMI. The findings of the Local Indicator of Spatial Association (LISA) and Moran's test confirmed spatial homogeneity in all mortalities among the districts in Pakistan. The H–H clusters of maternal mortality and DMI were in Baluchistan, and the H–H clusters of child mortality were seen in Punjab. The results of GWR showed that the wealth index quintile has a significant spatial impact on DMI; however, improved sanitation, handwashing practices, and antenatal care adversely influenced DMI scores. Conclusion: The findings reveal a significant disparity in DMI and spatial relationships among all mortalities in Pakistan's districts. Additionally, socioeconomic, environmental, health, and housing variables have an impact on DMI. Notably, spatial proximity among individuals who are at risk of death occurs in areas with elevated mortality rates. Policymakers may mitigate these mortalities by focusing on vulnerable zones and implementing measures such as raising public awareness, enhancing healthcare services, and improving access to clean drinking water and sanitation facilities. [ABSTRACT FROM AUTHOR]

Urbanization-induced land use transitions (LUTs) result in a decline in ecosystem services, which has implications for regional ecological security. In order to explore the relationship between ecosystem services and land use transition, this paper utilizes the InVEST model, a geographically weighted regression (GWR) model, to examine the impact of land use transition on ecosystem services in the Dongting Lake area (DLA). The results showed that 1) with the change in urbanization development, the average values of land use transition intensity (LUI) in 2000, 2010, and 2020 are 237.99, 235.82, and 238.92, respectively. Land use dynamics (LUD) show a tendency to increase and then decrease, with average values of 5.58 and 5.62 for the periods 2000-2010 and 2010-2020, respectively, and the transformation of land use shows obvious spatio-temporal heterogeneity. 2) Habitat quality and carbon sequestration showed a downward trend. In contrast, food supply followed an upward trend; soil conservation (SC) and water yield (WY) services initially increased and decreased later. The overall spatial changes in habitat quality and carbon sequestration appear to be insignificant. Food supply shows significant differences in the plains compared to other areas, while soil conservation and water yield service show significant changes in places other than the DLA. 3) From 2000 to 2020, land use transition dynamics, population density, GDP density, night lighting, and transition intensity had mainly negative effects on ecosystem services. Only the Normalized Vegetation Index (NDVI) showed a positive effect on ecosystem services. The results of the research will provide valuable references for the development and implementation of spatial ecological restoration planning and land use policies in the national territory. [ABSTRACT FROM AUTHOR]

Spatial data is data that contains the influence of location with non-homogeneous variance at each location, or spatial heterogeneity. To address spatial heterogeneity, the Geographically Weighted Regression (GWR) model is used. However, in the GWR model, there is a phenomenon of multicollinearity, which is a strong relationship between independent variables that will reduce the accuracy of parameter estimation. To overcome multicollinearity in the GWR model, the Least Absolute Shrinkage and Selection Operator (LASSO) method is used. The LASSO method estimates the parameters of the GWR model by minimizing the sum of squared errors subject to a constraint function, which is solved using the Least Angle Regression (LARS) algorithm. This results in the Least Absolute Shrinkage and Selection Operator (LASSO) regression model to address the problem of multicollinearity in spatial data. Based on the research results, the LASSO method can overcome multicollinearity by shrinking the coefficients of parameters that contribute less and have a strong correlation with other independent variables in the GWR model, resulting in 33 final models. One of the models is for Nias Regency, where the factors influencing the poverty rate are the open unemployment rate, life expectancy, average length of schooling, gross participation rate, and per capita income. In Nias Regency, the value of s is 0.288 with an R-squared value of 0.9403. In Nias Regency, 94.03% of the variation in the poverty rate is explained by the independent variables in the model, while the remaining 5.97% is attributed to external factors not covered by the model. Coefficient of the Human Development Index variable shrinks to exactly zero, indicating that it has no effect on the poverty rate in Nias Regency. [ABSTRACT FROM AUTHOR]

Estimating tree height at the national to regional scale is crucial for assessing forest health and forest carbon storage and understanding forest ecosystem processes. It also aids in formulating forest management and restoration policies to mitigate global climate change. Extensive ground-survey data offer a valuable resource for estimating tree height. In tree height estimation modeling, a few comparative studies have examined the effectiveness of global-based versus local-based models, and the spatial heterogeneity of independent variable parameters remains insufficiently explored. This study utilized ~200,000 ground-survey data points covering the entire provincial region to compare the performance of the global-based Ordinary Least Squares (OLS) and Random Forest (RF) model, as well as local-based Geographically Weighted Regression (GWR) model, for predicting the average tree height of Chinese fir forests in Zhejiang Province China. The results showed that the GWR model outperformed both OLS and RF in terms of predictive accuracy, achieving an R-squared (R2) and adjusted R2 of 0.81 and MAE and RMSE of 0.93 and 1.28, respectively. The performance indicated that the local-based GWR held advantages over global-based models, especially in revealing the spatial non-stationarity of forests. Visualization of parameter estimates across independent variables revealed spatial non-stationarity in their impact effects. In mountainous areas with dense forest coverage, the parameter estimates for average age were notably higher, whereas in forests proximate to urban areas, the parameters were comparatively lower. This study demonstrates the effectiveness of large ground-survey data and GWR in tree height estimation modeling at a provincial scale. [ABSTRACT FROM AUTHOR]

This study investigates the factors influencing pricing in Romanian rural tourism using a hedonic pricing model through a hybrid LASSO-OLS regression and geographically weighted regression (GWR). By analyzing data from 5028 unique accommodation units across 1170 local administrative units, we identify some key pricing determinants, including accommodation size, capacity, facilities, and environmental attributes. The results reveal that larger accommodations and those with higher guest capacities command higher prices. Luxurious facilities, such as massage services, pools, and fireplaces, significantly increase pricing, although the impact of such features varies by region, as do accommodation type and natural scenery, with agritouristic boarding houses and proximity to natural attractions like water bodies and forests being more valued in certain regions. These factors can aid rural entrepreneurs in optimizing pricing to enhance competitiveness and profitability. [ABSTRACT FROM AUTHOR]

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

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 Streets are an important component of urban public spaces and also a high-incidence area for urban crime. However, current research mainly involves adult crime, or fails to distinguish between adult and juvenile crime, which poses a severe challenge to the prevention of juvenile delinquency. Juveniles have lower self-control abilities and are more likely to be influenced by external environmental factors to trigger criminal behavior compared to adults. Therefore, this study uses New York’s Manhattan district as an example, based on CPTED and social disorganization theories, and utilizes street view data and deep learning techniques to extract street environment indicators. The GWR model is used to explore the influence mechanism of urban street environment on juvenile crime. The results of this study, considering spatial heterogeneity, demonstrate the impact of various physical environmental indicators of urban streets on juvenile delinquency, and reveal that some street indicators have differentiated effects on crime in different areas of the city. Overall, our research helps to uncover the relationship between juvenile delinquency and the built environment of streets in complex urban settings, providing important references for future urban street design and juvenile delinquency prevention.

Abstract Background Suicide mortality remains a global health concern, and community characteristics affect regional variations in suicide. This study investigated spatially clustered patterns of suicide mortality rates in South Korea and evaluated the impact of community factors on suicide. Methods Suicide mortality rates were estimated by sex, age group, and district, using the 2021 Cause of Death Statistics in South Korea from the MicroData Integrated Service. Community-determinant data for 2021 or the nearest year were collected from the Korean Statistical Information Service. The spatial autocorrelation of suicide by sex and age was examined based on Global Moran’s I index. Geographically weighted regression (GWR) was used to discern the influence of community determinants on suicide. Results Suicide mortality rates were significantly higher among men (40.64 per 100,000) and adults over the age of 65 years (43.18 per 100,000). The male suicide mortality rates exhibited strong spatial dependence, as indicated by a high global Moran’s I with p

Abstract Background In developing countries, the death probability of a child and mother is more significant than in developed countries; these inequalities in health outcomes are unfair. The present study encompasses a spatial analysis of maternal and child mortalities in Pakistan. The study aims to estimate the District Mortality Index (DMI), measure the inequality ratio and slope, and ascertain the spatial impact of numerous factors on DMI scores across Pakistani districts. Method This study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were used. This study further utilized spatial autocorrelation tests to determine the magnitude and location of the spatial dependence of the clusters with high and low mortality rates. The Geographically Weighted Regression (GWR) model was also applied to examine the spatial impact of socioeconomic, environmental, health, and housing attributes on DMI. Results The inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extreme spatial heterogeneity in terms of DMI. The findings of the Local Indicator of Spatial Association (LISA) and Moran's test confirmed spatial homogeneity in all mortalities among the districts in Pakistan. The H–H clusters of maternal mortality and DMI were in Baluchistan, and the H–H clusters of child mortality were seen in Punjab. The results of GWR showed that the wealth index quintile has a significant spatial impact on DMI; however, improved sanitation, handwashing practices, and antenatal care adversely influenced DMI scores. Conclusion The findings reveal a significant disparity in DMI and spatial relationships among all mortalities in Pakistan's districts. Additionally, socioeconomic, environmental, health, and housing variables have an impact on DMI. Notably, spatial proximity among individuals who are at risk of death occurs in areas with elevated mortality rates. Policymakers may mitigate these mortalities by focusing on vulnerable zones and implementing measures such as raising public awareness, enhancing healthcare services, and improving access to clean drinking water and sanitation facilities.

Urban and community forestry is a specialized discipline focused on the meticulous management of trees and forests within urban, suburban, and town environments. This field often entails extensive civic involvement and collaborative partnerships with institutions. Its overarching objectives span a spectrum from preserving water quality, habitat, and biodiversity to mitigating the Urban Heat Island (UHI) effect. The UHI phenomenon, characterized by notably higher temperatures in urban areas compared to rural counterparts due to heat absorption by urban infrastructure and limited urban forest coverage, serves as a focal point in this study. The study focuses on developing a methodological framework that integrates Geographically Weighted Regression (GWR), Random Forest (RF), and Suitability Analysis to assess the Urban Heat Island (UHI) effect across different urban zones, aiming to identify areas with varying levels of UHI impact. The framework is designed to assist urban planners and designers in understanding the spatial distribution of UHI and identifying areas where urban forestry initiatives can be strategically implemented to mitigate its effect. Conducted in various London areas, the research provides a comprehensive analysis of the intricate relationship between urban and community forestry and UHI. By mapping the spatial variability of UHI, the framework offers a novel approach to enhancing urban environmental design and advancing urban forestry studies. The study’s findings are expected to provide valuable insights for urban planners and policymakers, aiding in creating healthier and more livable urban environments through informed decision-making in urban forestry management. [ABSTRACT FROM AUTHOR]

Abstract Background Breastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019. Methods Data from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables. Results The trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section. Conclusions Despite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health.

The long landing distance from 15 m（50 ft）to touchdown is an over-limit event frequently occurring during the landing phase，and also can significantly increase the risk of landing accidents. The study on the impacts of geographical environmental factors of the over-limit event can provide references for airport site selection and flight quality evaluation. Based on the flight quick access record（QAR）data，topographic data and climate data of the airports，global and local correlation analysis are adopted to identify the main factors influencing the over-limit event. The regression model between the over-limit event frequency and elevation，fluctuation，air temperature is established，based on the geographical weighted regression model. According to the regression coefficients，the impact modes of these factors are divided into five categories using K-means method，so as to explore the spatial pattern and mechanism of geographical environmental factors on the over-limit event. The results show that the elevation，fluctuation，air pressure and air temperature have a significant impact on the frequency of the "long landing distance" over-limit event. There is an obvious spatial differentiation of the impact of geographical environment factors. The spatial distribution of airports under the same impact mode shows clustering.

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 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.

Background: Breastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019. Methods: Data from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables. Results: The trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section. Conclusions: Despite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health. [ABSTRACT FROM AUTHOR]

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]

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]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]