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Mixed land use is widely practiced yet needs to be understood regarding its spatially-differentiated patterns and implications over time. This study builds a framework considering both the degree and dominant type, incorporates them into the traditional and geographically weighted hedonic price models, and conducts an empirical analysis in Qingdao, China. We found that: (1) In a global sense, the degree of mixed land use has a significant positive impact on housing prices. The influence of the "commercial-service-dominated" (Bdominate) is negative, and that of the "public-commercial-balanced" (ABdominate) type is worse. (2) When considering spatial autocorrelation, the degree shows a negative effect on average, with the effect of dominant types varying spatially. (3) Following the direction of development sequences, the districts and counties can be categorized into four types. The overall degree of mixed land use decreases while its effect on housing prices increases and decreases, and the negative effects of Bdominate and ABdominate types on housing prices also increase and decrease. The framework for studying mixed land use can be applied in other areas, and the insights on different dominant types and spatial variances are helpful in guiding planning practices. [ABSTRACT FROM AUTHOR]

Identifying the changes in landscape pattern and ecosystem service value (ESV) and clarifying their relationship in temporal changes and spatial variations can provide insight into regional landscape features and scientific support for regional landscape planning. Leveraging land use data from the Yihe River Basin, we quantitatively assessed the landscape pattern and ESV shifts spanning from 2000 to 2018 using the landscape pattern indexes and the equivalence factor method. We employed Pearson correlation metrics and the geographically weighted regression model to explore the interrelation of their spatiotemporal variations. Our results show the following: (1) Forestland represents the most expansive land cover category. Apart from construction land, all other types experienced a decline in area. The most notable change occurred in the area of construction land. (2) The aggregation of the overall landscape shows a downward trend. The levels of fragmentation, landscape diversity, and richness increased. (3) Throughout the entire study period, the overall ESV gradually decreased, and the land cover type with the greatest contribution to the ESV was forestland. (4) In terms of temporal changes, the patch density and edge density of the overall area are significantly negatively correlated with total ESVs. The largest values for the patch index, perimeter–area fractal dimension (PAFRAC), and aggregation are significantly positively correlated with total ESVs. (5) In terms of spatial variation, the contagion index (CONTAG), PAFRAC, and the Shannon diversity index (SHDI) were noticeably correlated with ESVs. The CONTAG is positively correlated with ESVs upstream, but negatively midstream and downstream. The SHDI is negatively correlated with ESVs upstream, but positively midstream and downstream. The PAFRAC exhibits a positive correlation with ESVs for the most part. The association between the landscape pattern indexes and ESVs exhibits temporal and spatial inconsistencies in most instances, suggesting a spatiotemporal scale effect in their relationship. This study recommends that the local government devises a long-term strategy for urban development and exercises stringent control over the unregulated expansion of construction land. Through reasonable territorial spatial planning, government departments could enhance the connectivity of the overall landscape pattern of the Yihe River Basin to achieve the reasonable allocation and sustainable development of regional resources. [ABSTRACT FROM AUTHOR]

Household energy consumption (HEC) varies across neighbourhoods and gender groups. This database provides raw and analysed data on HEC determinants and their estimated influence on HEC in 2707 residential neighbourhoods (Wijk) in the Netherlands in 2018. The raw data consists of 17 indicators on energy demand, socioeconomic characteristics, microclimate and buildings. The indicators are retrieved from and calculated based on open national and international datasets. The analysed data presents the local coefficients of the HEC determinants, the outcome of the geographically weighted regression model (GWR) presented in the related article [1].

Exploring the spatial non-stationary relationships between land surface temperature (LST) and their driving environmental factors is important for selecting appropriate strategies to mitigate and regulate the thermal environment of watersheds. To examine the influence of various biophysical factors on LST in the Upper Awash Basin (UAB) of Ethiopia, the study used two models: Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. As a global model, the OLS model was initially used to capture the overall relationship between LST and some biophysical factors. And then the GWR, a local spatial modeling approach, was used to examine the spatial non-stationary relationships between LST and its influencing biophysical factors. Landsat 8 OLI/TIRS image and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) were used to generate the LST and its influencing biophysical factors. Biophysical parameters such as enhanced vegetation index (EVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), albedo and elevation were used as potential driving environmental factors of LST. The result showed that the GWR model, with a higher coefficient of determination (R2) (GWR: 0.98; OLS: 0.52) and a smaller Akaike Information Criterion (AIC) (GWR: 12354; OLS: 65412), provides a better prediction than the traditional OLS model, reflecting the spatial non-stationarity relationships. The results also showed that increased LST was significantly affected by NDBI, NDBaI, and albedo, with NDBI having the greatest effect. Conversely, EVI, MNDVI, and DEM showed a negative correlation with LST, with EVI having the greatest impact. These findings highlighted the importance of considering the spatial non-stationarity relationships between LST and pertinent driving factors, and they also offer recommendations for mitigating measures to control the thermal environment of a river basin.

Regression analysis is a statistical method used to investigate and model the relationship between variables. Furthermore, a regression analysis was developed that involved spatial aspects, namely Geographically Weighted Regression (GWR). GWR modeling consists of various types, one of which is Geographically Weighted Logistic Regression Semiparametric (GWLRS), an extension of the Logistic GWR model that produces local and global parameter estimators. In this study, it is proposed to combine the GWLRS model using panel data or Geographically Weighted Panel Logistic Regression Semiparametric (GWPLRS). The case study used in this research is the problem of poverty in 38 regions/cities in East Java, Indonesia, in 2018 – 2022 as seen from the Poverty Gap Index. The weights used in this research are the adaptive gaussian kernel weighting functions. The results of the parameter significance test show that the Human Development Index as global variable has a significant effect on each region/city.

How to enhance community resilience to natural disasters is a major question for researchers and policymakers. Although researchers agree that sport generates community benefits, few scholarly efforts in sport management have been invested in understanding the sport–resilience association. This paper attempted to address whether and how sport clusters—the clustering of sport industries—are associated with community resilience across locations. To achieve this, geographically weighted regression and visualization techniques were applied to macro-level data regarding community resilience and the clustering of 13 separate sport industries across 3,108 counties in the contiguous United States. The results indicate that, overall, the clustering of eight sport industries was significantly associated with community resilience and demonstrates the existence of spatially heterogeneous associations in magnitudes and signs of community resilience in sport clusters. The findings of this paper have the potential to help community sport scholars and policymakers implement location-specific resilience policies through sport industry development. [ABSTRACT FROM AUTHOR]

The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap in understanding whether Airbnb financially burdens local renters within different income groups. The authors also differentiated the effect of Airbnb accommodations with different levels of commercialization by categorizing Airbnb listings based on their level of commercialization. Using the multiscale geographically weighted regression technique, this study also considered spatial variations in the relationship between short- and long-term rental markets. The findings indicate that the density of Airbnb only affects the relative rent of renters with a yearly household income between USD 50,000 and USD 75,000. Furthermore, the density of Airbnb listings from more commercialized hosts that own between three and eleven showed a positive relationship with the relative rent cost. This study highlighted the variability in the impact of Airbnb on the local community by income group, listing characteristic, and geographic region. This finding underscores the need for differentiated regulation toward peer-to-peer accommodations, as the impact on rent affordability varies by host commercialization level and renter income group.

Autism spectrum disorder (ASD) has become an emerging public health problem. The impact of multiple environmental factors on the prevalence of ASD remains unclear. This study examined the association between the prevalence of ASD and the environmental quality index (EQI), an indicator of cumulative environmental quality in five major domains, including air, water, land, built and sociodemographic variables in the United States. The results from Poisson regression models show that the prevalence of ASD has a positive association with the overall EQI with a risk ratio (RR) of 1.03 and 95% confidence intervals (CI) of 1.01–1.06, indicating that children in counties with poor environmental quality might have a higher risk of ASD. Additionally, the prevalence of ASD has a positive association with the air index (RR = 1.04, 95% CI: 1.01–1.06). These associations varied in different rural–urban groups and different climate regions. This study provided evidence for adverse effects of poor environmental quality, particularly air pollutants, on children’s neurodevelopment.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 Background In Sarawak, 252 300 coronavirus disease 2019 (COVID-19) cases have been recorded with 1 619 fatalities in 2021, compared to only 1 117 cases in 2020. Since Sarawak is geographically separated from Peninsular Malaysia and half of its population resides in rural districts where medical resources are limited, the analysis of spatiotemporal heterogeneity of disease incidence rates and their relationship with socio-demographic factors are crucial in understanding the spread of the disease in Sarawak. Methods The spatial dependence of district-wise incidence rates is investigated using spatial autocorrelation analysis with two orders of contiguity weights for various pandemic waves. Nine determinants are chosen from 14 covariates of socio-demographic factors via elastic net regression and recursive partitioning. The relationships between incidence rates and socio-demographic factors are examined using ordinary least squares, spatial lag and spatial error models, and geographically weighted regression. Results In the first 8 months of 2021, COVID-19 severely affected Sarawak’s central region, which was followed by the southern region in the next 2 months. In the third wave, based on second-order spatial weights, the incidence rate in a district is most strongly influenced by its neighboring districts’ rate, although the variance of incidence rates is best explained by local regression coefficient estimates of socio-demographic factors in the first wave. It is discovered that the percentage of households with garbage collection facilities, population density and the proportion of male in the population are positively associated with the increase in COVID-19 incidence rates. Conclusion This research provides useful insights for the State Government and public health authorities to critically incorporate socio-demographic characteristics of local communities into evidence-based decision-making for altering disease monitoring and response plans. Policymakers can make well-informed judgments and implement targeted interventions by having an in-depth understanding of the spatial patterns and relationships between COVID-19 incidence rates and socio-demographic characteristics. This will effectively help in mitigating the spread of the disease.

Geographically weighted regression (GWR) takes a prominent role in spatial regression analysis, providing a nuanced perspective on the intricate interplay of variables within geographical landscapes (Brunsdon et al., 1998). However, it is essential to have a strong rationale for employing GWR, either as an addition to, or a complementary analysis alongside, non-spatial (global) regression models (Kiani, Mamiya et al., 2023). Moreover, the proper selection of bandwidth, weighting function or kernel types, and variable choices constitute the most critical configurations in GWR analysis (Wheeler, 2021). [...]

The replacement of people by machines is not a recent concern. Common occupations may cease to exist due to recent advances in artificial intelligence, robotics, algorithms, and mechanization. This article aims to identify the regional impacts of replacing humans with machines, i.e., which municipalities will be most vulnerable. Using data from the research by Frey and Osborne (2017), the study analyzed and identified through geographically weighted regression that, in Paraná State, municipalities with a larger rural population should be the most affected by automation.

Tourism in urban forests is rapidly becoming an increasing trend; however, rather few studies have used quantitative measurement to describe the relationship between tourism intensity and ecological functions. This study provides a practical framework that integrates ecosystem service value (ESV) assessment, Internet big data mining and spatial regression analysis to identify the spatial response of ESV and land use/land cover change to tourism activities from 2009 to 2019 in the Siming Mountain Region (SMR), a famous tourist resort located in the eastern coastal China. Results showed that between 2009 and 2019 total ESV increased by 7.1%. Nevertheless, there have been drastic transitions in land use types with function adjustments from traditional agricultural production to diversified tourism-oriented services. Significant spatial autocorrelation was identified for the patterns of ESV changes. GWR further highlighted that the relationship between ESV change and rural tourism indicators varied in space. ESV change in the core zone was negatively correlated with changes in catering service spots and recreational venues, whereas it was positively correlated with local lodgings. Ultimately, targeted recommendations and countermeasures for spatial planning and sustainable tourism development of urban forests under new circumstances were discussed.

Tourism development is one of the main means of productive cultural protection, and culture is an important factor in the attractiveness of tourist attractions. With the development of the concept of regional tourism, China’s tourism and culture have moved to a new stage. In this paper, we screen the indicators from the perspective of industrial income, the number of institutions, service teams, etc., and use the entropy weight method with relevant data to measure the level of tourism and cultural development and tourism priority. Then, the influence factors of tourism and culture development are discussed by combining the least squares regression model and geographically weighted regression model. Finally, to ensure the reliability of the conclusions, the sample reconstruction method and the supplementary variable method are used to conduct the robustness test and endogeneity test. According to the regression results, the regression coefficients for market demand and government support are positive and pass the 5% significance level test. Government support and market demand are currently the main influencing factors for the development of national tourism and culture.

Given the climate change challenges, Uttarakhand has become crucial for examining land dynamics and regional climate interactions. This study employed a Support Vector Machine (SVM) for land use and land cover mapping for 2024, achieving 94% accuracy and a Kappa coefficient of 0.90, indicating robust mapping. Key land indices such as NDVI, NDWI, NDBI, NDSI, and NDBaI were calculated, along with Land Surface Temperature (LST) from Landsat 8 imagery. These indices were selected for their relevance in representing vegetation health (NDVI), measuring water content (NDWI), assessing urban areas (NDBI), identifying snow cover (NDSI), and highlighting the barren land (NDBaI), which all influence LST. Hotspot analysis with Getis-Ord Gi∗ revealed spatial distribution patterns of LST. Regression analysis showed significant relationships: a strong positive correlation between LST and NDBI (R2 = 0.78) and a substantial negative correlation between LST and NDSI (R2 = −0.80). The strong positive correlation highlights how urbanization contributes to rising surface temperatures, while the substantial negative correlation underscores the cooling effect of snow cover, which is particularly relevant as reduced snow cover could lead to higher LST in the context of climate change. These correlations offer deeper insights into how land cover changes can exacerbate or mitigate climate impacts in Uttarakhand. Two regression models were used for statistical modeling and spatial analysis: Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). In OLS, the results reveal non-stationarity (p = 0.000) with an R2 value of 0.79 while GWR significantly enhanced performance, achieving an R2 value of 0.94. The improved performance of GWR (R2 = 0.94) compared to OLS (R2 = 0.79) can be attributed to GWR’s ability to account for spatial non-stationarity. This method allows for variations in relationships between LST and explanatory variables across different locations, effectively capturing local patterns that OLS may overlook. Spatial autocorrelation analysis, utilizing Moran’s I, exhibited a decrease from 0.606 (OLS) to 0.02 (GWR), This reduction indicates that GWR effectively captures spatial non-stationarity, minimizing residual autocorrelation by modeling local relationships between LST and its predictors that often remain in global models like OLS, thereby demonstrating its advantages in heterogeneous regions. The findings underscore the importance of employing GWR to better elucidate the connection between LST and its predictors, specifically in regions characterized by spatial non-stationarity, thereby offering insights crucial for informed decision-making amidst changing climatic conditions.

Epidemics are dangerous and difficult to prevent and control, especially in urban areas. Clarifying the correlation between the COVID-19 Outbreak Frequency and the urban spatial environment may help improve cities’ ability to respond to such public health emergencies. In this study, we firstly analyzed the spatial distribution characteristics of COVID-19 Outbreak Frequency by correlating the geographic locations of COVID-19 epidemic-affected neighborhoods in the city of Beijing with the time point of onset. Secondly, we created a geographically weighted regression model combining the COVID-19 Outbreak Frequency with the external spatial environmental elements of the city. Thirdly, different grades of epidemic-affected neighborhoods in the study area were classified according to the clustering analysis results. Finally, the correlation between the COVID-19 Outbreak Frequency and the internal spatial environmental elements of different grades of neighborhoods was investigated using a binomial logistic regression model. The study yielded the following results. (i) Epidemic outbreak frequency was evidently correlated with the urban external spatial environment, among building density, volume ratio, density of commercial facilities, density of service facilities, and density of transportation facilities were positively correlated with COVID-19 Outbreak Frequency, while water and greenery coverage was negatively correlated with it. (ii) The correlation between COVID-19 Outbreak Frequency and the internal spatial environmental elements of neighborhoods of different grades differed. House price and the number of households were positively correlated with the COVID-19 Outbreak Frequency in low-end neighborhoods, while the number of households was positively correlated with the COVID-19 Outbreak Frequency in mid-end neighborhoods. In order to achieve spatial justice, society should strive to address the inequality phenomena of income gaps and residential differentiation, and promote fair distribution of spatial environments.

High-resolution soil moisture (SM) products are crucial for managing water in agricultural regions, irrigation scheduling, and land–atmosphere model simulations. Most satellite-based SM products have spatial resolutions in the tens of kilometers, which cannot satisfy the aforementioned applications, and there is an urgent need to disaggregate them to fine spatial resolutions. Currently, disaggregation method based on multi-source data fusion tend to be robust, but the following difficulties remain: 1) satellite products (microwave, optical/infrared) are somewhat missing in space and time which hinder the applications; 2) only one model-based product is used in the fusion process and different model-based products were not sufficiently evaluated. To overcome these problems, this work developed an SM disaggregation methodology. The best model-based SM product was first assessed using the triple collocation (TC) method, which allows us to obtain the weight of individual product. The model-based and microwave SM products were combined based on weight to produce daily seamless SM data at 25 km. Second, the iterative multi-temporal reconstruction and the ESTARFM method were combined to solve the invalid grids problem of optical/infrared product, thus obtaining 1 km daily seamless auxiliary data. Finally, daily seamless SM data at 1 km were obtained by geographically weighted regression (GWR) method. The methodology proposed in this study had a well-defined mechanism, and the resulting SM product achieved a satisfied accuracy (ubRMSE = 0.044 m3/m3), with characteristics of spatiotemporal continuity as well as fine spatial detail. The disaggregated SM product makes full use of model-based, microwave, and optical/infrared products, which is valuable in areas such as agricultural management policy, high-resolution hydro-meteorological simulations, and disaster monitoring.

The rapid shifts in landuse and the frequency of flooding events brought by urbanization significantly impact the urban basins, which means that optimizing landscape patterns is crucial for improving urban hydrology. Therefore, the Liuxi River Basin (LRB), an important river in the Pearl River Delta region, was utilized as an example in the study to examine how the landscape configuration of greenspaces and water bodies influences the hydrological components. However, there are few studies on landscape hydrology in the Pearl River Delta (PRD) and the spatial linkages between landscape metrics and hydrological components are not clear. Based on this, this paper aims to explore the mathematical and spatial associations between landscape configuration and hydrological variables in the PRD region. The SWAT model was constructed to simulate hydrological components based on landuse changes from 1980 to 2018, and regressions were established for landscape pattern indices, water morphology parameters, and hydrological components using stepwise multiple linear regression and geographically weighted regression (GWR). According to the results, the LRB continued to develop, although the greenspaces spread out and changed shape considerably. The influence of water bodies on the hydrological component is mainly in evapotranspiration, and some three-dimensional parameters such as relief ratio favor surface runoff. In the case of greenspace, the runoff is less in connected and wide farmland areas. Irregular forest land will promote water yield but suppress evapotranspiration, and the effect of forest’s shape on hydrological components is more obvious in upstream areas because the regression coefficients in this range are higher, such as 90.27. Methodologically, we found that for water yield, the accuracy of GWR was significantly higher (R2 = 0.814) than that of multiple linear regression (R2 = 0.243). In addition, understanding land use changes before conducting landscape hydrology studies can help make the results more interpretable. We supplemented the relevant research gaps in the PRD region and revealed the spatial correlation between landscape metrics and the hydrological cycle. These findings can aid urban planners in improving their comprehension of hydrological processes and landscape design, leading to more sustainable management of the urban basins.

Population density is important spatial information for addressing the use and access to land resources in cities under the Sustainable Development Goals. This is because the spatial data support appropriate spatial policies at the spatial scale and predicts how much land will be consumed in the future. The study aims to compare and evaluate the regression tools in the context of estimating the population density difference. The three analysis tools used are Random Forest-Based Classification, Multiple Linear Regression, and Geographically Weighted Regression. The sampling area covers cities around Türkiye. Comparative results showed that the two most important descriptive variables in the Random Forest-Based Classification model are the density difference of the new developed area and the connectivity. The three main explanatory variables of the Multiple Linear Regression model are centrality, vehicle ownership, and accessibility. The results of the Multiple Linear Regression model (a non-spatial model) and the Geographically Weighted Regression model (a spatial model), were found to be quite similar. The importance of accessibility and connectivity is more evident in the Multiple Linear Regression model when the Random Forest-Based Classification model highlights the density values in the new development areas.

The rate of transformation of natural land use land cover (LULC) to the built-up areas is very high in the peri-urban areas of Indian metropolitan cities. Delhi National Capital Region (Delhi NCR) is an inter-state planning region, located in the central part of India. The region has attracted a larger chunk of population by providing better economic opportunities during last few decades. This has resulted in large-scale transformation of the LULC pattern in the region. Thus, this study is intended to analyze and quantify the LULC change and its drivers in the peri-urban areas of Delhi NCR using Landsat datasets. Based on an extensive literature survey, several potential drivers of the LULC change have been analyzed using ordinary least squares (OLS) and geographical weighted regression (GWR) for the Delhi NCR. The results from LULC classification showed that the built-up area has increased from 1.67 to 7.12% of the total area of Delhi NCR during 1990–2018 while other LULC types have declined significantly. The OLS results showed that migration and employment in the tertiary sector are the most important drivers of built-up expansion in the study area. The standard residuals and local R2 results from GWR showed spatial heterogeneity among the coefficients of the explanatory variables throughout the study area. This study can be helpful for the urban policy makers and planners for making better master plan of Delhi NCR and other cities of developing countries. [ABSTRACT FROM AUTHOR]

With the development of the Chinese Fengyun satellite series, Fengyun-2G (FY-2G) quantitative precipitation estimates (QPE) can provide real-time and high-quality precipitation data over East Asia. However, FY-2G QPE cannot offer precipitation information beyond the latitude band of 50°N due to the limitation of the observation coverage of the FY-2G-based satellite-borne sensor. To this end, a precipitation space reconstruction using the geographically weighted regression (GWR) coupled with a geographical differential analysis (GDA) (PSR2G) algorithm was developed, based on the land surface variables related to precipitation, including vegetational cover, land surface temperature, geographical location, and topographic characteristics. This study used the PSR2G-based reconstructed model to estimate the FY-2G QPE over Northeast China (the latitude band beyond 50°N) from December 2015 to November 2019 with a spatiotemporal resolution of 0.1°/month. The PSR2G-based reconstructed results were validated with the ground observations of 80 rain gauges, and also compared to the reconstructed results using random forest (RF) and GWR. The results show that the spatio-temporal pattern of PSR2G QPE is closer to ground observations than those of RF and GWR, which indicates that the PSR2G QPE is more competent to capture the spatio-temporal variation of rainfall over Northeast China than other two reconstruction methods. In addition, the reconstructed precipitation dataset using PSR2G has higher accuracy over study area than the FY-2G QPE below the band of 50°N. It suggested that PSR2G reconstruction precipitation strategies do not lose the precision of the original satellite precipitation data. [ABSTRACT FROM AUTHOR]

Land use change is an important factor affecting the performance of ecosystem services (ESs). Therefore, understanding the impact of land use change on ESs is of great significance for promoting the coordination of regional human–land relationships. In this study, random forest and cellular automata were used to simulate and predict the characteristics of land use change in the Yangtze River Economic Belt, and diversified land use evolution patterns were formed in combination with China's strategic development needs. The effects of habitat suitability on ESs were analyzed by using a multiscenario land use change model. The results demonstrated that the driving factors selected in this article had a good induction effect on the law of land use evolution, and the simulated land use change had high credibility. Under the mode of ecological protection and cultivated land protection, the expansion of construction land was greatly affected and was not conducive to social and economic development. Under the natural evolution mode, farmland was greatly encroached upon, and food security was greatly threatened. The regional coordination model had relative advantages, and all kinds of land use needs were met to a certain extent. The water production function of ESs was strong, but the carbon storage function was weak. The relationship between the habitat suitability index and ES changes under land use change revealed that there were significant differences in ES changes caused by ecological quality changes in mountainous and plain areas. This study provides a reference for promoting social and economic development and ecosystem integrity. Integr Environ Assess Manag 2023;19:1473–1484. © 2023 SETAC Key Points: Random forest and cellular automata were used to simulate and predict the characteristics of land use change in the Yangtze River Economic Belt, and diversified land use evolution patterns were formed in combination with China's strategic development needs.The application of national strategies to ecosystem services (ESs) analysis is described.The effects of habitat suitability on ESs were analyzed by using a multiscenario land use change model. [ABSTRACT FROM AUTHOR]

Rapid urbanization threatens the ecological environment and quality of life by significantly altering land use and land cover (LULC) and heat distribution. One of the most significant environmental consequences of urbanization is the urban heat island effect (UHI). This study investigated the spatiotemporal characteristics of the SUHI and its relationship with land use types from 2000 to 2020 in Urumqi City, located in an arid and semi-arid region of northwestern China. Additionally, the ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the relationship between the land surface temperature (LST) and influencing factors. The results showed that the area of the lower surface temperature classes has decreased significantly. In comparison, the area of the higher surface temperature classes has experienced a steady rise over the last two decades. From 2000 to 2020, the share of the area occupied by the temperature range <30 °C decreased by 67.09%. In addition, the LST varied significantly from one category of land use to another. The average LST of built-up land and unused land was higher than the average LST of other land use types in all years, while the average LST of grassland, forest land, and water bodies was significantly lower. Finally, the results of the GWR model showed that R2 and adjusted R2 of the GWR were 0.75 and 0.73, obviously larger than the 0.58 of the OLS models. The GWR model's higher R2 and adjusted R2 compared to the OLS model indicates that the relationship between LST and the influencing factors underlying the model may exhibit spatial non-stationarity, and the GWR model performs better than the OLS model. The results of both OLS and GWR models show that the normalized difference vegetation index (NDVI) and slope were negatively correlated with LST, while the urban index (UI) and normalized difference built-up index (NDBI) were positively correlated with LST. The findings of the study indicate that increasing green spaces and limiting the unplanned expansion of urban areas are effective measures to mitigate the UHIs in the study area. The results of the study may provide valuable insights into the spatiotemporal characteristics of the UHI and its drivers. Understanding the spatiotemporal characteristics of the UHI can help urban planners, policymakers, and scientists develop more effective urban cooling strategies and improve the urban thermal environment. [ABSTRACT FROM AUTHOR]

The forest ecosystem of Indian Himalayan Region offers various ecosystem services (ESs) that are crucial for the sustenance of human beings. However, the rapid expansion of human activities (HA) poses a significant threat to the provision of the forest ecosystem services (FES). For simple and definitive assessments of FES and HA, the use of indicators has become an indispensable approach. In the present study, we performed: (i) indicator-based mapping of FES and HA, and (ii) evaluated the impact of HA on FES with the aid of geospatial techniques. Village-level analysis was conducted for FES and HA in the Aglar watershed of Uttarakhand, India for 2015. Four dominant forest types in the watershed—Quercus mixed, Pinus roxburghii, Cedrus deodara, and mixed forest were considered. For spatial characterization of FES, indicators such as forest carbon stock, net primary productivity, total water retention, and sediment yield were assessed, whereas human activity index (HAI) was evaluated using indicators of HAs, namely population density, road density, farmland, and habitation ratio. The integration of normalized values of FES indicators generated multiple ecosystem services indicator (MESI), and HAI was contructed using analytical hierarchical process based assignment of weights to HA indicators. Spatial analysis techniques such as ordinary least-square regression (OLS) and geographically weighted regression (GWR) models were used to derive the spatial relationship between them. The adjusted R2 and AIC were utilized to evaluate the effectiveness of the model. The GWR model had a better fit with an adjusted R2 of 0.68 and a lower AIC of 42.940, compared to the OLS model with an adjusted R2 of 0.21 and an AIC of 60.52. The statistics showed that GWR performed better than OLS and ably captured the heteroscedasticity of the phenomena. An inverse relation was observed between MESI and HAI. The findings of the study highlight the close link between the supply of FES and the impact of human-induced disturbances over the provision of FES, which has the potential to increase over time. The study provides a scientific basis for structuring policy dialogues to coordinate the long-term regional sustainability of FES provisioned from the Himalayan regions. [ABSTRACT FROM AUTHOR]

The Three Gorges Dam project and other human activities, including regional urbanization and industrialization, have had a substantial influence on the biological environment of the Three Gorges Reservoir Area (TGRA). They have changed the surface land use pattern, disrupted ecosystem structure and function, and influenced changes in the value of ecosystem services. The human activity intensity (HAI) assessment model, the ecosystem services value (ESV) assessment model, and the bivariate spatial autocorrelation model were used based on the spatiotemporal evolution data of towns along the Yangtze River in the TGRA in 1995, 2000, 2005, 2010, 2015, and 2020. At the same time, the spatiotemporal impact of the HAI on land use patterns was evaluated and the magnitude of the spatiotemporal influences on the ESV was investigated. The findings demonstrate the following: (1) The TGRA's higher reaches are occupied by forested land, while the middle and lower portions are characterized by agricultural land. Land change in the reservoir region has mostly featured transitions between wooded land, agricultural land, grassland, and building land during the last 25 years. Because of differences in natural geography and administrative divisions, the intensity of human activity in the TGRA changes throughout the Yangtze River, with higher intensity in Chongqing and lower intensity in Hubei. By comparing the ESV and the HAI and validating with Moran scatter plots, it was determined that there is a negative relationship between the value of ecosystem services and the intensity of human activities. (2) The ESV rose from CNY 1017.16 × 108 in 1995 to CNY 1052.73 × 108 in 2020, suggesting that the policies of converting farmland back into forests, eliminating outdated production capacity, and developing green industries, among other ecological conservation measures, are effective. (3) In the research area, the effect coefficient of HAI on ESV ranges from −0.02 to −0.032 to −0.031. This coefficient represents the correlation between the HAI and ESV and can preliminarily judge the change in the degree of correlation between the HAI and ESV. The increase in HAI leads to a decrease in the value of ecosystem services, and there is a clear negative spatial correlation between the two. The low human activity area and low ecosystem service value area in the Chongqing section have been transformed into a high ecosystem service value area through years of returning farmland to forest and ecological management measures for sustainable development. [ABSTRACT FROM AUTHOR]

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Background: This study aimed at examining the prevalence and variations in undernutrition among children below five years of age in Uganda while considering the influence of space and time factors. Various studies conducted in Uganda analyzed the undernutrition phenomenon among children below five years of age with a focus on the risk factors and spatial variations; however, no study has ever integrated the elements of time in examining the problem of undernutrition in Uganda. The approach of spatial and spatiotemporal analysis is essential in identifying cluster patterns, hotspots, trends, and emerging hotspots, which is crucial in making timely and location-specific interventions. Methods: Data from the six Uganda Demographic and Health Surveys spanning from 1990 to 2016 were used, with the main outcome variable being undernutrition among children below five years of age. A Composite Index of Anthropometric Failure was derived from the three undernutrition outcomes and subsequently used as a proxy of undernutrition in this study. All data that were relevant to this study were retrieved from the survey datasets and combined with the 2014 shape files of Uganda to enable spatial and spatiotemporal analysis. Spatial maps with the spatial distribution of the prevalence of undernutrition, both in space and time, were generated using ArcGIS Pro version 2.8. Moran's I, an index of spatial autocorrelation, was used to test the hypothesis of no spatial autocorrelation, while the Getis–Ord (Gi*) statistic was used to examine hot and cold spot areas. Furthermore, space-time cubes were generated to establish the trend in undernutrition as well as to mirror its variations over time and across the country. Moreover, emerging hot spot analysis was done to help in identifying the patterns of undernutrition over time. Results: The national prevalence of undernutrition among children below five years of age was 31.96 percent, with significant spatial variations both in space across Uganda and in the time since 1989. The index of spatial autocorrelation (Moran's I) confirmed spatial clustered patterns as opposed to random distributions of undernutrition prevalence. Four hot spot areas, namely, the Karamoja, the Sebei, the West Nile, and the Toro regions, were significantly evident. Most of the central parts of Uganda were identified as cold spot clusters, while most of Western Uganda, the Acholi, and the Lango regions had no statistically significant spatial patterns by the year 2016. The spatio-temporal analysis identified the Karamoja and Sebei regions as clusters of persistent, consecutive, and intensifying hot spots, West Nile region was identified as a sporadic hotspot area, while the Toro region was identified with both sporadic and emerging hotspots. In conclusions, undernutrition is a silent pandemic that calls for immediate and stringent measures. At 31.96 percent, the prevalence is still very high and unpleasant. To reduce the prevalence of undernutrition and to achieve SDG goal 2, policymakers, as well as implementers, should consider the spatial effects and spatial and spatiotemporal variations across the country and prioritize interventions to hot spot areas. This would ensure efficient, timely, and region-specific interventions. [ABSTRACT FROM AUTHOR]

This paper examines different geographically weighted (GW) approaches for calculating spatially distributed measures of accuracy / uncertainty, consolidating current approaches and proposing 2 new ones. GW frameworks use a moving window or kernel to extract and weight data subsets, from which local (ie spatially distributed) statistics or metrics are calculated. A validation dataset with hard and soft classifications is used to illustrate the approaches. It contains observed field survey data (also commonly derived from higher resolution imagery), and predicted data from a fuzzy c-means classification. The hard classes were used to estimate spatially distributed measures of overall, user's and producer's accuracies in two ways. First, by conceptualising them as probabilities to be estimated from generalised linear regression models (GLMs), extended into Geographically Weighted GLMs. Second, by constructing local GW correspondence matrices and then calculating local accuracy measures from these. The soft classes were used to calculate per-class measures of fuzzy certainty from the absolute difference between predicted and observed fuzzy memberships. Then, a novel fuzzy certainty logic is proposed and used to create fuzzy confusion matrices and per-class measures of fuzzy omission and commission error, supporting measures of fuzzy user's and producer's certainties. These were extended to the GW case to generate spatially distributed measures. Finally, the soft classifications were conceptualised as compositional data and measures of difference were estimated using Aitchison distances. In each case, the local hard and soft accuracy and certainty measures were interpolated over a 1 km grid to estimate accuracy surfaces. The context for this review is the increasing operational use of training and validation data, often with high numbers of records, containing both hard and soft classes. The data and R code used to undertake all the analyses in this paper are provided, supporting more nuanced analyses of such data. [ABSTRACT FROM AUTHOR]

Previous research on beach access typically uses socioeconomic variables such as race or ethnicity and different levels of wealth to identify marginalized groups. Such an additive approach, however, fails to consider the intercategorical intersectionality between variables when defining marginalized groups. Moreover, there is little research that assesses the spatial variability of intersectional groups in relation to public beach access. This study addressed these gaps by empirically examining the spatially heterogeneous intercategorical intersectionality of race or ethnicity and poverty in terms of public beach access. A geographically weighted regression was employed via a case study of 784 census tracts in the Metro Detroit area. The results showed that economically marginalized White neighborhoods, overall, had greater access to public beaches than economically marginalized African American and Asian neighborhoods had. Furthermore, there exists spatially heterogeneous (in)equitable access to public beaches depending on the type of intersectional composition of the neighborhood. These findings are useful for beach managers to allocate resources to neighborhoods in need of more access to public beaches. [ABSTRACT FROM AUTHOR]

Wheat is an important food crop in China. The quality of wheat affects the development of the agricultural economy. However, the high-quality wheat produced in China cannot meet the demand, so it would be an important direction for research to develop high-quality wheat. Grain protein content (GPC) is an important criterion for the quality of winter wheat and its content directly affects the quality of wheat. Studying the spatial heterogeneity of wheat grain proteins is beneficial to the prediction of wheat quality, and it plays a guiding role in the identification, grading, and processing of wheat quality. Due to the complexity and variability of wheat quality, conventional evaluation methods have shortcomings such as low accuracy and poor applicability. To better predict the GPC, geographically weighted regression (GWR) models, multiple linear regression, random forest (RF), BP neural networks, support vector machine, and long-and-short-term memory algorithms were used to analyze the meteorological data and soil data of Jiangsu Province from March to May in 2019–2022. It was found that the winter wheat GPC rises by 0.17% with every 0.1° increase in north latitude at the county level in Jiangsu. Comparison of the prediction accuracy of the coefficient of determination, mean deviation error, root mean square error, and mean absolute error by analyzing multiple algorithms showed that the GWR model was the most accurate, followed by the RF model. The regression coefficient of precipitation in April showed the smallest range of variation among all factors, indicating that precipitation in April had a more stable effect on GPC in the study area than the other meteorological factors. Therefore, consideration of spatial information might be beneficial in predicting county-level winter wheat GPC. GWR models based on meteorological and soil factors enrich the studies regarding the prediction of wheat GPC based on environmental data. It might be applied to predict winter wheat GPC and improve wheat quality to better guide large-scale production and processing. [ABSTRACT FROM AUTHOR]

[Objective] The aim of this study is to explore the change characteristics of supply and demand pattern of ecosystem services and its comprehensive influencing factors in Jiangsu Province, which can provide scientific basis and theoretical support for the formulation and sustainable management of ecological protection policies in this region. [Methods] Based on the land use data of the four periods from 2005 to 2020, the supply, demand and their matching ratio in Jiangsu Province was explored, and the key factors influencing the supply and demand of ecosystem services in Jiangsu Province were explored from the aspects of natural and socio-economic factors by using geographic probe. Furthermore, geographical regression model (GWR) was used to reveal the spatiotemporal differentiation of the relationship between supply and demand. [Results] That the matching ratio of ecosystem services supply-demand was higher in central Jiangsu Province and lower in northern and southern Jiangsu Province. The high-high clusters of the matching ratio of supply-demand distributed in central Jiangsu Province, and the number decreased over time. Low-low clusters were located in developed counties/districts in southern Jiangsu. The GWR analysis indicated that their effects on supply-demand relationship varied spatiotemporally. Population density, human impact index, urbanization rate and economic density had significant negative effect. While average annual temperature had both positive and negative effects on supply-demand relationship. [Conclusion] The spatial difference of ecosystem service supply and demand pattern in Jiangsu Province was significant, and the imbalance pressure of ecosystem service supply and demand increased gradually during the study period. The core influencing factors of ecosystem service supply and demand relationship in the study area were population density, human impact index, urbanization rate and economic density. [ABSTRACT FROM AUTHOR]

The development and functional perfection of urban areas have led to increasingly severe fire risks in recent decades. Previous urban fire risk assessment methods relied on subjective judgment, rough data collection, simple linear statistical methods, etc. These drawbacks can lead to low robustness of evaluation and inadequate generalization ability. To resolve these problems, this paper selects the indicator and regression models based on the high-resolution data of the spatial distribution characteristics of Longquanyi distinct in Chengdu, China. and proposes an integrated machine learning algorithm for fire risk assessment. Firstly, the kernel density analysis is used to map the fourteen urban characteristics related to fire risks. The contributions of these indicators (characteristics) to fire risk and its corresponding index are determined by Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost). Then, the spatial correlation of fire risks is determined through Moran's I, and the spatial distribution pattern of indicator weights is clarified through the raster coefficient space analysis. Finally, with these selected indicators, we test the regression performance with a backpropagation neural network (BPNN) algorithm and a geographically weighted regression (GWR) model. The results indicate that numerical variables are more suitable than dummy variables for estimating micro-scale fire risks. The main factors with a high contribution are all numerical variables, including roads, gas pipelines, GDP, hazardous chemical enterprises, petrol and charging stations, cultural heritage protection units, assembly occupancies, and high-rise buildings. The machine learning algorithm integrating RF and BPNN shows the best performance (R2 = 0.97), followed by the RF-GWR integrated algorithm (R2 = 0.87). Compared with previous methods, this algorithm reduces the subjectivity of the traditional assessment models and shows the ability to automatically obtain the key indicators of urban fire risks. Hence, this new approach provides us with a more robust tool for assessing the future fire safety level in urban areas. [ABSTRACT FROM AUTHOR]

As an important component of national integrated transport system, civil aviation transportation is able to effectively promote regional connectivity and social economic development. Therefore, it is of great practical significance to study the driving factors of civil aviation airline network connectivity. First, the airline network connectivity was evaluated by social network analysis method in Inner Mongolia Autonomous Region. Then, the least square method and the geographically weighted regression method were applied to demonstrate the driving factors of airline network connectivity. Results show that factors including the additional value of the tertiary industry, the disposable income of residents, the number of tourists and the government planning level have significant promotion on the connectivity of regional airline network. Meanwhile, these driving factors also have notable spatial distribution characteristics. The research results are of practical significance for strengthening the connectivity of the airline network within the Inner Mongolia Autonomous Region. [ABSTRACT FROM AUTHOR]