Search

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

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

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]

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]

A scientific foundation for the sustainable development of ecosystems and the improvement of the ecological spatial security pattern in the area is provided by carrying out a scientific assessment of ecological risk levels in coastal counties. An ecological risk assessment model was established in Pulandian district, Dalian, based on four periods of land use data from 1990 to 2020 combined with the PSR model. The spatial and temporal evolution of ecological risk in Pulandian was analyzed on this basis, and an exploratory regression analysis and a geographically weighted regression model were then used to explore the driving role of natural and social factors on comprehensive ecological risk in coastal counties. The findings demonstrate that there is an obvious ecological landscape type of conversion, with the majority of arable land being converted to forest land in northcentral and southwest areas, reflecting an improvement in the ecological environment and air quality, and the majority of coastal beach land being converted to construction land among the volumes transferred out and in. The area of high risk increased by 73.17% during the course of 30 years, with the majority of it concentrated in the research areas southern Fengrong, Tiexi, and Taiping regions in 1990 before expanding to the northeast, southeast, and central regions. The status index and response index both show a decline followed by an increase in change, while the pressure index shows a rising tendency with socioeconomic progress. The comprehensive ecological risk in the study area is significantly influenced by the urbanization rate, the ratio of environmental protection investment to GDP, the ecosystem service index, and the ecological space–land use ratio, with the urbanization rate displaying more overt negative correlation-driving characteristics, the ratio of environmental protection investment to GDP displaying significant spatial division characteristics, and the ecological space–land use ratio being an important factor. The findings serve as a foundation for decisions on ecological risk avoidance, control, and construction in Pulandian. [ABSTRACT FROM AUTHOR]

The industrial transfer of heavy industries such as non-metallic mineral manufacturing, metal smelting and manufacturing from the eastern coast of China to the central region is beneficial to the economic development of the central region on the one hand, but increases carbon emissions in the central region on the other hand. In February 2022, the National Development and Reform Commission approved the "14th Five-Year Plan for the Development of the Urban Agglomeration in the Middle Reaches of the Yangtze River". This indicates that the urban agglomeration of the middle reaches of the Yangtze River is an important region for implementing green development in the central area. The spatial and temporal evolution of carbon emissions and influencing factors in this region are the foundation for achieving carbon peaking and the carbon neutrality goal. This paper calculates the total carbon emissions of the cities in the urban agglomeration of the middle reaches of the Yangtze River and uses models such as spatial autocorrelation, geographically weighted regression, and Geodetector to explore the spatial–temporal pattern of carbon emissions. The results show the following: (1) The total carbon emissions of the middle reaches of the Yangtze River urban agglomeration showed fluctuations during 2010–2020, and the carbon emission reduction effect is unstable. Additionally, the carbon emissions of the middle reaches of the Yangtze River city cluster show obvious spatial variability, but the high carbon emission area is always concentrated in Wuhan, and this remains unchanged. (2) In 2010, 2014 and 2017, population size was the most important factor affecting carbon emission divergence, and in terms of interaction, the interaction between energy intensity and GDP and urbanization is the reason for the increasing carbon emissions. (3) The influence of population size on carbon emissions decreases from north to south, the influence of energy intensity on carbon emissions shows a spread from the most influential region in the northwest to the centre and then to the northeast, and the GDP per capita has little influence on the difference of carbon emissions spatial distribution. [ABSTRACT FROM AUTHOR]

China's pursuit of sustainable and healthy economic growth requires the promotion of high-quality development. While many scholars have studied high-quality development, few have examined the coupling and coordination among its internal systems. The study aims to analyze the influencing factors of high-quality coordinated development, identify problem areas based on the five development statuses, and provide practical recommendations for optimizing the spatial layout of high-quality development in these areas. By applying a coupling coordination model and a geographically weighted regression model, the comprehensive level of high-quality coordinated development in the eastern coastal areas was evaluated. The results revealed that the majority of the eastern coastal region exhibited weak coordination and significant spatial differences in their comprehensive level. The problem cities were predominantly located in the southern and northern coastal areas. An economic foundation and innovation potential have a positive and stable impact on high-quality coordinated development. [ABSTRACT FROM AUTHOR]

Cycling is encouraged as a means of sustainable urban transport, yet its uptake rate is uneven between different ethnic groups. The ethnic minority population in England is underrepresented as cyclists, but the reasons for this are unclear. Through linear regression and Geographically Weighted Regression (GWR), this research investigates the spatial distribution of the propensity to cycling among the ethnic minority population and the white population across England with the aim to identify the contributing factors toward the discrepancy of cycling rates between both groups and how these factors vary geographically. Results from OLS regression suggest that cycle rates are generally affected most by hilliness, the presence of school-age children, and income, with the presence of school-age children affecting the ethnic minority group and hilliness affecting the white group the most. The use of GWR revealed that income generally reduces cycle rates but has a positive impact in London for both groups. The length of cycleways and the length of 20 mph speed limit roads per unit area were statistically insignificant, but their local coefficients in GWR showed strong regional variations for both groups. The study also found that, with the exception of the level of income, ethnic minority cyclists are less sensitive to contributing factors than the white cyclists. [ABSTRACT FROM AUTHOR]

Green infrastructure refers to connected corridors of greenspaces within and beyond urban areas. It provides sustainable ecosystem goods and services for people and wildlife, enhancing their wellbeing and protecting them against climatic extremes. However, the exact contributing factors to the betterment of green infrastructure are not systematically examined at a national level. This study aims to identify what helps improve biodiversity and the recreational value of green infrastructure. The study uses hotspot analysis, ordinary least squares (OLS) regression and geographically weighted regression (GWR) to understand the spatial patterns of the relevant variables and outcomes. Findings suggest that high wildlife species richness was reported in Forestry Commission woodlands and country parks, whilst doorstep greens and village greens returned poor species richness. The recreational value of greenspace was affected the most by certain types of greenspace (e.g., woodlands) as well as the percentage of urban cover. They indicate that biodiversity is generally high in areas away from urban centres, while access to greenspace in an urban space brings us high recreational value. These results indicate that green infrastructure is a complex system that requires the right balance between different priorities and services. [ABSTRACT FROM AUTHOR]

As a new industry in modern agriculture, leisure agriculture has a strong correlation with rural tourism, and provides rural areas with positive prospects for sustainable development. However, leisure agriculture tends to include a number of bottlenecks. In this study, we investigated the spatial distribution of leisure agriculture in Xinjiang, and the factors that affect it. Kernel density analysis, the nearest-neighbor index, and the geographic concentration index were used to analyze the distribution characteristics of leisure agriculture. Following the conclusion of the ordinary least squares tests, geographically weighted regression (GWR) was conducted to explore the factors affecting spatial distribution. The findings were as follows: (1) The spatial distribution of leisure agriculture in Xinjiang is uneven, and is concentrated in the northern and southern parts of the Tianshan Mountains in western Xinjiang. (2) In terms of the distribution density, there are four high-concentration centers (Bosten Lake, Hami, and the east and west sides of the Ili River Valley) and one subconcentration center (spreading outward from Urumqi). (3) Population, transportation, tourism resources, urban factors, and rainfall, all had significant effects on the distribution of leisure agriculture. These factors had positive and negative effects on the distribution of leisure agriculture, forming high- and low-value areas in space. (4) The leisure agricultural sector responded in varying degrees to the different factors, with large internal variability. Rainfall and population had greater differential effects on the spatial distribution of leisure agriculture compared to transportation, tourism resources, and urban factors, and there were significant two-way effects. Transportation, urban factors, and tourism resources all had consistent, predominantly positive, effects on the distribution of leisure agriculture. [ABSTRACT FROM AUTHOR]

The public transit system is often developed in tandem with urban growth, bringing improved accessibility and raising housing prices around stations. The impact of public transport accessibility on housing prices demand in-depth empirical studies to understand the underlying factors. The confounding influence of polycentric cities, contrasting with monocentric ones, deserves more attention. We studied the effects of metro and bus accessibility on housing prices along metro lines 6, 7, 9 and 14 in Beijing under the polycentric scenario. Other property attributes, including building features, location characteristics and neighborhood amenities, served as explanatory variables. Ordinary least squares (OLS) and geographically weighted regression (GWR) were used to build the global (whole-study area) and local hedonic price models, respectively. The results indicated that GWR performed better than OLS in predicting the effects of public transport accessibility on housing prices. Residential properties with access to more metro lines and stations and bus stops were associated with higher housing prices, with metro stations exerting more effects. The premiums of monocentric and polycentric models showed similar spatial patterns. In polycentric Beijing, the premiums of metro accessibility were higher in the eastern part, and the effect of bus accessibility showed circular distribution. Beijing could be regarded as a monocentric city on a global scale, but the influence of subcenters should be considered in a local polycentric regression model. The findings can inform urban planning concerning land use and public transport provision in Beijing and selecting research models in cognate studies. [ABSTRACT FROM AUTHOR]

Urban greening has been widely regarded as the most effective, lasting, and economical strategy for alleviating the effects of urban heat islands (UHIs). Previous studies on the cooling effect of urban green spaces (UGSs) tend to analyze the correlation between landscape metrics and land-surface temperature (LST) based on a global parameter estimation, while ignoring urban heterogeneity and autocorrelation. This study focuses on the sixth ring road of Beijing and uses Landsat 8 imagery to retrieve the LST and extract the position of UGSs. We propose a new approach to optimize the selection of landscape metrics, to identify the least and most effective metrics to establish a geographically weighted regression (GWR) model, and to plot the distribution of local regression coefficients to investigate the spatially heterogeneous cooling effects of greenspaces. The effect of UGS landscape metrics on the LST differs according to spatial location; the method enhances our understanding of the effects of UGS spatial configuration on UHIs and better guides the planning and construction of future UGSs. [ABSTRACT FROM AUTHOR]

Recent years have seen an increased interest in demand-side mitigation of greenhouse gas emissions. Despite the oftentimes spatial nature of emissions research, links to social factors and infrastructure are often not analysed geographically. To reach substantial and lasting emission reductions without further disadvantaging vulnerable populations, the design of effective mitigation policies on the local level requires considerations of spatial and social inequalities as well as the context of well-being. Consequently, we explore spatial variations in the links between consumption-based transport emissions with infrastructural factors, such as workplace distance and public transport density, and with risk-factors of transport poverty, including income, age, ethnicity, mobility constraints in London. We find that linear models report significant spatial autocorrelation at p ≤ 0.01 in their model residuals, indicating spatial dependency. Using geographically weighted regression models improves model fits by an adjusted R2 value of 9–70% compared to linear models. Here, modelling flight emissions generally sees the lowest improvements, while those models modelling emissions from cars and vans see the highest improvements in model fit. We conclude that using geographically weighted regression to assess the links between social factors and emissions offers insights which global, linear models overlook. Moreover, this type of analysis enables an assessment of where, spatially, different types of policy interventions may be most effective in reducing not only emissions, but transport poverty risks. Patterns of spatial heterogeneity and policy implications of this research are discussed. [ABSTRACT FROM AUTHOR]

To investigate the spatial evolution process of economic development in the urban agglomeration and its influencing factors, the network construction method, modified gravity model, geographic detector and Geographically Weighted Regression (GWR) model are used to analyze the intensity of urban association; then, the evolution of economic, spatial pattern and its influencing factors are further discussed, and the Chengdu-Chongqing urban agglomeration of China from 2005 to 2020 is studied as an example. The results show that: (1) the economically developed zones of the Chengdu-Chongqing urban agglomeration mainly concentrated in the core cities of Chengdu and the central city of Chongqing, and the region shows an uneven spatial pattern of economic development distribution. (2) The share of economic linkages with the central city of Chengdu and Chongqing as the twin cities is significant, the intensity of Chengdu and its neighboring cities is gradually decreasing, while the central city of Chongqing has increased, but it still has an insufficient influence on the peripheral areas. (3) The intensities and directions of the factors influencing economic development in the Chengdu-Chongqing urban agglomeration are different. The total output value of the secondary industry, total social fixed asset investment, the number of beds in health institutions, and road freight turnover are significant factors with consistently strong explanatory ability for economic development. The promotion effect of these four significant factors on economic development is mainly concentrated in the eastern and western regions of Chengdu-Chongqing urban agglomeration, while the inhibiting effect is mainly on the cities in the south and north. Based on this study, relevant recommendations are made to promote the coordinated development of the Chengdu-Chongqing urban agglomeration. [ABSTRACT FROM AUTHOR]

Taken as a classical issue in applied economics, the notion of 'convergence' is based on the concept of path dependence, i.e., from the previous trajectory undertaken by the system during its recent history. Going beyond social science, a 'convergence' perspective has been more recently adopted in environmental studies. Spatial convergence in non-linear processes, such as desertification risk, is a meaningful notion since desertification represents a (possibly unsustainable) development trajectory of socio-ecological systems towards land degradation on a regional or local scale. In this study, we test—in line with the classical convergence approach—long-term equilibrium conditions in the evolution of desertification processes in Italy, a European country with significant socioeconomic and environmental disparities. Assuming a path-dependent development of desertification risk in Italy, we provided a diachronic analysis of the Environmental Sensitive Area Index (ESAI), estimated at a disaggregated spatial resolution at three times (1960s, 1990s, and 2010s) in the recent history of Italy, using a spatially explicit approach based on geographically weighted regressions (GWRs). The results of local regressions show a significant path dependence in the first time interval (1960–1990). A less significant evidence for path-dependence was observed for the second period (1990–2010); in both cases, the models' goodness-of-fit (global adjusted R2) was satisfactory. A strong polarization along the latitudinal gradient characterized the first observation period: Southern Italian land experienced worse conditions (e.g., climate aridity, urbanization) and the level of land vulnerability in Northern Italy remained quite stable, alimenting the traditional divergence in desertification risk characteristic of the country. The empirical analysis delineated a more complex picture for the second period. Convergence (leading to stability, or even improvement, of desertification risk) in some areas of Southern Italy, and a more evident divergence (leading to worse environmental conditions because of urban sprawl and crop intensification) in some of the land of Northern Italy, were observed, leading to an undesired spatial homogenization toward higher vulnerability levels. Finally, this work suggests the importance of spatially explicit approaches providing relevant information to design more effective policy strategies. In the case of land vulnerability to degradation in Italy, local regression models oriented toward a 'convergence' perspective, may be adopted to uncover the genesis of desertification hotspots at both the regional and local scale. [ABSTRACT FROM AUTHOR]

Lake ecosystems play an important role in regional ecological security and the sustainable development of the economy and society. In order to study the evolution of landscape patterns and the main driving forces in the Chagan Lake Nature Reserve in recent years, we used landscape type data from 2005, 2010, 2015, and 2019 to study the characteristics of the regional landscape's structural changes. At the same time, the spatial heterogeneity of the driving factors of landscape change was analyzed using the spatial analysis method, and the driving mechanism of landscape change was quantitatively analyzed. The results showed that: (1) from 2005 to 2019, the area of cultivated land, marshland, and water bodies increased, while the area of grassland and the area of bare land decreased. (2) The dominant patch types in the study area formed good connectivity, and the degree of landscape fragmentation increased. (3) In the past 15 years, there has been spatial heterogeneity in the regression coefficients of different driving factors of landscape change: the area with a greater influence of the elevation factor was in the south; the regression coefficient of precipitation showed the spatial distribution characteristics of highs in the west and lows in the east; the gross domestic product had a greater impact on the east and the south; the spatial variation of grain yield was mainly reflected in the southeast and northwest regions; the fishery yield gradually changed from high in the southeast and low in the northwest to the distribution characteristic of decreasing from the east to the southwest; the lake fluorine content showed a distribution pattern that gradually changed from high in the southeast and low in the northwest to high in the middle and low in the north and south; the distribution pattern of the distance to oil production changed from north to southeast to south to north; the distance to the road changed from high in the east and low in the west to the opposite spatial distribution pattern. (4) The interaction of precipitation and lake fluoride content with other factors showed a strong driving effect, which had a significant impact on the landscape change of Chagan Lake Nature Reserve. Since the study area is located in a typical fluorine-rich geochemical environment, human activities, such as the expansion of irrigation areas around Chagan Lake and groundwater exploitation, have accelerated the dissolution of fluorine-containing minerals, promoted the enrichment process of fluorine in Chagan Lake, and enhanced the explanatory power of lake fluorine content in terms of landscape changes. At the same time, the increase in precipitation during the study period is beneficial to the growth of vegetation and the storage of water in lakes, which promotes changes in landscape types such as grasslands and areas of water. [ABSTRACT FROM AUTHOR]

Ecological risks (ERs) are external threats to ecosystem structure and function, and the evaluation of ER is valuable for understanding the extent of the disturbance of ecosystems by human activities. An objective investigation of the spatio-temporal characteristics of ER and its affecting elements can contribute to the improvement of the ecological environment and the sustainable development of regional resources. The Funiu Mountains were chosen as the study area, the ecological risk index was built using landscape interference and landscape separation, and the spatial–temporal properties and scale effect of the ER were assessed. The findings of ER may be separated into four distinct zones to study the variations in ER levels from 2000 to 2015: modest, mild, moderate, and high. Furthermore, geographically weighted regression (GWR) was used to ascertain the degree to which environmental factors impact ER and regional disparities. The results show: (1) Ecological risk assessment was sensitive to scale transformation, and there are variations in the spatial pattern of ER on diverse scale units. With the scale following a 1 km grid to the watershed, Global Moran's I values dropped from 0.53 to 0.04, and 1 km × 1 km was the most suitable unit. (2) The ecological and environmental quality of the Funiu Mountains improved from 2000 to 2015. The extent of the high ecological risk zone dropped by 398.07 km2, but other types of ecological risk regions rose, owing to the execution of policies aimed at restoring cropland to forests. The ER presented a transitional distribution from a high risk in the surrounding area to low risk in the central area. (3) From the perspective of correlations among the influencing factors, altitude and slope had a negative correlation with other influencing factors, while others were positively correlated. The correlation coefficient between land-use degree and human activities intensity and ER was the highest. Each environmental factor had significant effects on the ERs and spatial variation existed, where the land-use degree and human activities intensity were the main obstacle factors limiting ER improvement. Population density, altitude, slope, and maximum temperature all displayed effects on ER with different directions and intensities. [ABSTRACT FROM AUTHOR]

Given the rapidly developing processes in the housing market of China, the significant regional difference in housing prices has become a serious issue that requires a further understanding of the underlying mechanisms. Most of the extant regression models are standard global modeling techniques that do not take spatial non-stationarity into consideration, thereby making them unable to reflect the spatial nature of the data and introducing significant bias into the prediction results. In this study, the geographically weighted regression model (GWR) was applied to examine the local association between housing price and its potential determinants, which were selected in view of the housing supply and demand in 338 cities across mainland China. Non-stationary relationships were obtained, and such observation could be summarized as follows: (1) the associations between land price and housing price are all significant and positive yet having different magnitudes; (2) the relationship between supplied amount of residential land and housing price is not statistically significant for 272 of the 338 cities, thereby indicating that the adjustment of supplied land has a slight effect on housing price for most cities; and (3) the significance, direction, and magnitude of the relationships between the other three factors (i.e., urbanization rate, average wage of urban employees, proportion of renters) and housing price vary across the 338 cities. Based on these findings, this paper discusses some key issues relating to the spatial variations, combined with local economic conditions and suggests housing regulation policies that could facilitate the sustainable development of the Chinese housing market. [ABSTRACT FROM AUTHOR]

The healthy development of the ecosystem and tourism in destinations plays an essential role in sustainable development. Taking Shennongjia as an example, we analyzed the spatial–temporal variation in the ecosystem services value (ESV) and investigated the impacts of tourism on ESV and their spatial heterogeneity using the geographically weighted regression (GWR) and boosting regression tree (BRT) models. The results showed that (1) the types of ecosystem services (ESs) were dominated by climate regulation and biodiversity. The ESV increased from 3.358 billion yuan to 8.910 billion yuan from 2005 to 2018 and showed significant spatial divergence, maintaining a long-term distribution pattern of high in the center and low at the border. (2) The GWR and BRT results showed that the Distance to Scenic Spots (DSS) and the Distance to Residential Areas (DRA) are important factors influencing ESV, with the Distance to Hotels (DH) and the Distance to Roads (DR) having a relatively weak influence on ESV. (3) The influencing factors presented positive and negative effects, and the degree of influence has spatial heterogeneity. The DRA and DH inhibited the increase in ESV in nearby areas, while DR was the driving factor for increasing ESV. The assessment results of DSS vary according to the models. [ABSTRACT FROM AUTHOR]

The assessment of the 'quality' of built heritage is a complex transdisciplinary issue, which both public administrations and real estate developers need to carefully consider when making any interventions. Recent international climate regulations underline that currently around 75% of buildings in the EU are not energy efficient. In Italy, those inefficient buildings are more than 50 years old and, if subjected to retrofit interventions, risk being totally transformed and losing their historical value in favor of a more contemporary use. This work aimed to study the residential heritage of the second half of the 20th century in the real estate market and to understand if, how, and in what measure the building and architectonical qualities are recognized and monetized by buyers. The city of Turin was chosen as a study area, and residential building qualities were analyzed using two quality indicators to perform a GWR on market POIs. The results highlighted that housing historical qualities are not homogeneously recognized by the real estate market, in favor of green ones. This work can help both public and private bodies to identify which 'invisible' quality residential buildings are immediately exploitable for enhancement strategies, with more respectful retrofitting interventions and a modern protection policy. [ABSTRACT FROM AUTHOR]

Metals and metalloids accumulate in soil, which not only leads to soil degradation and crop yield reduction but also poses hazards to human health. Commonly, source apportionment methods generate an overall relationship between sources and elements and, thus, lack the ability to capture important geographical variations of pollution sources. The present work uses a dataset collected by intensive sampling (1848 topsoil samples containing the metals Cd, Hg, Cr, Pb, and a metalloid of As) in the Shanghai study area and proposes a synthetic approach to source apportionment in the condition of spatial heterogeneity (non-stationarity) through the integration of absolute principal component scores with geographically weighted regression (APCA-GWR). The results showed that three main sources were detected by the APCA, i.e., natural sources, such as alluvial soil materials; agricultural activities, especially the overuse of phosphate fertilizer; and atmospheric deposition pollution from industry coal combustion and transportation activities. APCA-GWR provided more accurate and site-specific pollution source information than the mainstream APCA-MLR, which was verified by higher R2, lower AIC values, and non-spatial autocorrelation of residuals. According to APCA-GWR, natural sources were responsible for As and Cr accumulation in the northern mainland and Pb accumulation in the southern and northern mainland. Atmospheric deposition was the main source of Hg in the entire study area and Pb in the eastern mainland and Chongming Island. Agricultural activities, especially the overuse of phosphate fertilizer, were the main source of Cd across the study area and of As and Cr in the southern regions of the mainland and the middle of Chongming Island. In summary, this study highlights the use of a synthetic APCA-GWR model to efficiently handle source apportionment issues with spatial heterogeneity, which can provide more accurate and specific pollution source information and better references for pollution prevention and human health protection. [ABSTRACT FROM AUTHOR]

Bangkok suffered from the world's worst traffic congestion in the 1990s due to rapidly increasing car ownership, reflecting the economic growth and road-dependent transport policy beginning in the 1960s. Due to its monocentric but scattered urban structure, traffic congestion is severe, causing tremendous economic loss, deteriorating air quality, and badly affecting the quality of life. A historical review reveals that the urban and transport plan and development were not efficiently coordinated, resulting in unorganized suburbanization and progressively more severe traffic congestion. It is important to reveal the impact of the transportation project on the housing market in order to incorporate the policies for transportation and urban development. To define the impact, the OLS hedonic price model and the local multiscale geographically weighted regression (MGWR) model were estimated, along with the condominium sales data. The results revealed that the impact of rail transit on a rise in property value significantly varied across the study area. It was estimated that, for the area along the major rail transit corridor in the city center, a premium of a location 100-m closer to the station would be more than 200 USD per square meter. At the same time, the value would be less than 80 USD for the area along the rail corridor in the suburb. These findings provide policy insights for future urban and railway development, including the proper coordination of rail transit development and urban development with subcenters, transit-oriented development, and improved pedestrian flow around transit stations. [ABSTRACT FROM AUTHOR]

Pile foundations of long-span bridges are often deeply buried in soil, and their structural condition is difficult to accurately diagnose by conventional methods. To address this issue, a method for diagnosing the structural condition of bridge pile foundations based on the spatial correlation of high-density strain measurement points is proposed. The strain data of the high-density measurement points of a bridge pile foundation are obtained by using distributed optical fiber sensing technology based on Brillouin scattering, and then an algorithm for diagnosing the structural condition of the pile foundation based on geographically weighted regression analysis is presented. On this basis, aiming at the scour of the pile foundation of long-span bridges, an algorithm for estimating the scour depth of the pile foundation based on sliding plane clustering is proposed. Finally, the effectiveness of the proposed method is verified by numerical simulation and actual bridge data. [ABSTRACT FROM AUTHOR]

The increase in income among Chinese residents has been accompanied by dramatic changes in dietary structure, promoting a growth in carbon emissions. Therefore, in the context of building a beautiful countryside, it is of great significance to study the carbon emissions of rural residents' food consumption to realize the goal of low-carbon food consumption. In this paper, the calculation of food consumption carbon emissions of Chinese rural residents is based on the carbon conversion coefficient method, and the spatial heterogeneity of influencing factors is analyzed with the aid of the ESDA-GWR model. The results indicate that the per capita food consumption carbon emissions of rural residents have increased by 1.68% annually, reaching 336.73 kg CO2-eq in 2020, which is 1.32 times that of 2002. Carbon emissions generated from rural residents' food consumption have significant spatial agglomeration characteristics, showing the spatial distribution characteristics of a north–south confrontation, with a central area collapse. The influencing factors of food consumption carbon emissions have significant spatial heterogeneity, among which, as the main force to restrain the growth of food consumption carbon emissions, the price factor has a regression coefficient between −0.1 and −0.3, and its influence has weakened from northwest to southeast in 2020. The education–social factor is the main driving force for the growth of food consumption carbon emissions, with a regression coefficient between 0.58 and 0.99, and its influence has increased from east to west. In the future, formulating food consumption optimization policies should be based on the actual situation of food consumption carbon emissions in various regions to promote the realization of low-carbon food consumption. [ABSTRACT FROM AUTHOR]

This manuscript analyzes an inter-parish housing rents gradient with respect to surrounding parishes. Using data on housing rents for 4049 Portuguese parishes in 278 municipalities, the paper explores the spatial patterns of housing rents using the geographically weighted regression (GWR) methodology. The housing rents can be explained by socio-economic factors comprising the effects of unemployment, sustainability, social diversity, elderly dependency, and population density. The proportion of overcrowded dwellings reflecting how poor living conditions affect housing rents was also included in the spatial analysis. On the structural side, characteristics of the dwellings were also included such as the area of the home and the number of other homes available in the parishes. Locational factors reflect households' valuation for access to other parishes. In order to capture location characteristics, besides considering mobility within municipalities, the GWR allowed using distances to nearby parishes, i.e., parish hierarchy distance effect. The results suggest that the Portuguese rental housing market exhibits a heterogeneous pattern across the territory, displaying spatial variability and a hierarchical space pattern as a consequence of its locational attributes. [ABSTRACT FROM AUTHOR]

Ecological degradation caused by rapid urbanisation has presented great challenges in southern China. Fractional vegetation cover (FVC) has long been the most common and sensitive index to describe vegetation growth and to monitor vegetation degradation. However, most of the studies have failed to adequately explore the complexity of the relationship between fractional vegetation cover (FVC) and impact factors. In this research, we first constructed a Semi-parametric Geographically Weighted Regression (SGWR) model to analyse both the stationary and nonstationary spatial relationships between FVC and driving factors in Guangdong province in southern China on a county level. Then, climate, topographic, land cover, and socio-economic factors were introduced into the model to distinguish impacts on FVC from 2000–2015. Results suggest that the positive and negative effects of rainfall and elevation coefficients alternated, and local urban land and population estimates indicated a negative association between FVC and the modelled factors in each period. The SGWR FVC make significantly improves performance of the geographically weighted regression and ordinary least squares models, with adjusted R2 higher than 0.78. The findings of this research demonstrated that, although urbanisation in the Pearl River Delta in Guangdong has encroached on the regional vegetation cover, the total vegetation area remained unchanged with the implementation of protection policies and regulations. [ABSTRACT FROM AUTHOR]

The financial transmission of the USA's housing price bubble has highlighted the inadequacy of the valuation methods adopted by the credit institutions, due to their static nature and inability to understand complex socio-economic dynamics and their related effects on the real estate market. The present research deals with the current issue of using Automated Valuation Methods for expeditious assessments in order to monitor and forecast market evolutions in the short and medium term. The paper aims to propose an evaluative model for the corporate market segment, in order to support the investors', the credit institutions' and the public entities' decision processes. The application of the proposed model to the corporate real estate segment market of the cities of Rome and Milan (Italy) outlines the potentialities of this approach in property big data management. The elaboration of input and output data in the GIS (Geographic Information System) environment allowed the development of an intuitive platform for the immediate representation of the results and their easy interpretation, even to non-expert users. [ABSTRACT FROM AUTHOR]

Evaluation of urban planning and development is becoming more and more important due to the large-scale urbanization of the world. With the application of mobile phone data, people can analyze the development status of cities from more perspectives. By using the mobile phone data of Beijing, the working population density in different regions was identified. Taking the working population density in Beijing as the research object and combining the land use of the city, the development status of Beijing was evaluated. A geographically weighted regression model (GWR) was used to analyze the difference in the impact of land use on the working population between different regions. By establishing a correlation model between the working population and land use, not only can the city's development status be evaluated, but it can also help city managers and planners to make decisions to promote better development of Beijing. [ABSTRACT FROM AUTHOR]

There are multiple types of regression, the essential task of which is the obtaining of models which, starting from a set of regressive values, are capable of finding explanations for the variability of a dependent. However, in many cases, the territorial criterion is not considered to be a noteworthy factor of analysis, owing to which this deficiency has encouraged the arising of spatial statistics. Nevertheless, given the variety of regressions, it is not clear which can best be adapted to the analysis of tourism. In this sector, when the supply of accommodation is analysed, it is understood that it must be strongly related to the presence of resources, owing to which it has been taken as an example of an application between two differentiated regression techniques: ordinary least squares (OLS) and geographically weighted regression (GWR), with the objective of determining which of the two is best adapted to this type of analysis. The model has been drawn up based on various methods, although it has been shown that it is more efficient to resort to the declared preferences of the rural tourist, with the starting point being a survey made of the tourists. These aspects have been taken as independent variables with the aim of explaining the distribution of accommodation establishments. The results obtained show that the configuration of the spatial relations between the variable included in the model encourages the explanation of the latter, owing to which GWR is much more suitable than OLS, even when a system as complex as the distribution of accommodation establishments is analysed. Likewise, it is noteworthy that the distribution of accommodation does not also follow the guidelines marked by demand; far from it, it appears that in some areas, it is of a random nature. [ABSTRACT FROM AUTHOR]

The ridership of a metro station during a city's peak hour is not always the same as that during the station's own peak hour. To investigate this inconsistency, this study introduces the peak deviation coefficient to describe this phenomenon. Data from 88 metro stations in Xi'an, China, are used to analyze the peak deviation coefficient based on the geographically weighted regression model. The results demonstrate that when the land around a metro station is mainly land for work, primary and middle schools, and residences, its station's peak hour is consistent with the city's peak hour. Additionally, the station's peak hour is more likely to deviate from the city's peak hour for suburban stations. There are two ridership options when designing stations, namely the extra peak hour ridership during a city's peak hour and that during a station's peak hour, and the larger of the two is used to design metro stations. The mixed land use ratio must be considered in urban land use planning, because although non-commuting land can mitigate the traffic pressure of a city's peak hour, it may cause the deviation of the station's peak hours from that of the city. [ABSTRACT FROM AUTHOR]

Globalization has driven the promotion of international vocational qualification and certification (IVQC) to unify certification systems and standards. We explore IVQC promotion paths through the introduction and development of China's International Project Manager Professional (IPMP) certification and identify and analyse IVQC processes' influencing factors. Four factors (economic level, education level, employment level, sex ratio) are proposed; their impacts are hypothesized. Geographically weighted regression (GWR) is employed to identify factor impact relationships and validate assumptions. The results show that the four factors are positive for the promotion of IVQC. Economic level, employment level, and sex ratio contribute to the promotion of IVQC; employment level contributes most. Education level has relatively small impact. Therefore, IVQC is more likely to enter areas with developed economies, high employment rates, and more males. The promotion of IVQC can be facilitated by continuous social progress and international development. However, areas where salient factor levels are too low still present challenges. [ABSTRACT FROM AUTHOR]

In order to analyze the coordination relationship between investment potential and economic development and its driving mechanisms, this study integrated the entropy weight method, coupling coordination degree model, exploratory spatial data analysis, geographic detector, and geographically weighted regression model. The developed approach was applied using data from 51 African countries from 2008 to 2016. The results showed that: (1) While the level of economic development in the African continent has increased steadily, the overall investment potential needs to be improved. The mean economic development index rose from 0.116 to 0.151, but the economic gap among countries was still highly evident. (2) Uncoordinated development and barely coordinated development level were the dominant types of relationship between investment potential and economic development in African countries. The spatial distribution showed significant agglomeration characteristics; the sub-hot spot and sub-cold point regions maintained strong dependence with their hot spot and cold point counterparts. The hot spot areas gradually formed an agglomeration in Southern Africa and highly fragmented distribution in other areas. The cold spot areas formed a spatial distribution pattern of "one core and one belt" with some countries in Western Africa forming the core, while some Central and East African countries constituting the belt. (3) The coordination relationship between investment potential and economic development was influenced mainly by factors including economic base, residents' living standard, industrial construction level, information support level, and business friendliness. Using geographically weighted regression coefficient distribution of indicators, the driving mechanisms of spatial distribution could be divided into five types: economic base driven, industry-driven, information application-driven, business convenience-driven, and consumer market-driven. [ABSTRACT FROM AUTHOR]

In the context of economic globalization, the manufacturing production space in the global city-regions of developing countries have presented significant spatial characteristics, attracting attention to the problems of intensive and sustainable development of production space. Taking global city-region in the Pearl River Delta (PRD) as an example, manufacturing production space based on remote sensing (RS) technology and point of interest (POI) data extraction was more precise and continuous, which had more advantages for further analysis of spatial characteristics and influencing factors in multi-scale, and precise policy recommendations. The results show that: (1) under different scales, the distribution characteristics of manufacturing production space and the agglomeration characteristics of spatial form are different. It is not simply extensive agglomeration or diffusion that can accurately explain its diversified spatial characteristics. Meanwhile, for the local manufacturing production space optimization control, the local government should apply advanced experience according to local conditions instead of simply and roughly promotion or containment. (2) Influencing factors show a strong positive correlation with the urbanization rate, the number of foreign direct investment (FDI) enterprises and gross industrial production, and which shows a weak negative correlation with fixed asset investment and the employment population. In conclusion, the spatial characteristics of manufacturing production space in global city-regions in developing countries is significantly different from that in Western countries, and its influencing factors have similarities and differences. Therefore, when conducting multi-scale space optimization and sustainable regulation, the government should consider more about the actual multi-scale spatial characteristics of manufacturing production space and its influencing factors instead of copy the Western experience. [ABSTRACT FROM AUTHOR]

Taxis are an important part of the urban public transit system. Understanding the spatio-temporal variations of taxi travel demand is essential for exploring urban mobility and patterns. The purpose of this study is to use the taxi Global Positioning System (GPS) trajectories collected in New York City to investigate the spatio-temporal characteristic of travel demand and the underlying affecting variables. We analyze the spatial distribution of travel demand in different areas by extracting the locations of pick-ups. The geographically weighted regression (GWR) method is used to capture the spatial heterogeneity in travel demand in different zones, and the generalized linear model (GLM) is applied to further identify key factors affecting travel demand. The results suggest that most taxi trips are concentrated in a fraction of the geographical area. Variables including road density, subway accessibility, Uber vehicle, point of interests (POIs), commercial area, taxi-related accident and commuting time have significant effects on travel demand, but the effects vary from positive to negative across the different zones of the city on weekdays and the weekend. The findings will be helpful to analyze the patterns of urban travel demand, improve efficiency of taxi companies and provide valuable strategies for related polices and managements. [ABSTRACT FROM AUTHOR]

Land carrying capacity is an important factor for urban sustainable development. It provides essential insights into land resource allocation and management. In this article, we propose a framework to evaluate land carrying capacity with multiple data sources from the first geographical census and socioeconomic statistics. In particular, an index, Land Resource Pressure (LRP), is proposed to evaluate the land carrying capacity, and a case study was carried out in Wuhan. The LRP of Wuhan was calculated on 250 m * 250 m grids, and showed a circularly declining pattern from central to outer areas. We collected its influencing factors in terms of nature resources, economy, transportation and urban construction, and then analyzed its causes via geographically weighted (GW) models. Firstly, pair-wise correlations between LRP and each influencing factor were explored via the GW correlation coefficients. These local estimates provide an important precursor for the following quantitative analysis via the GW regression (GWR) technique. The GWR coefficient estimates interpret the influences on LRP in a localized view. Results show that per capita gross domestic product (PerGDP) showed a higher absolute estimate among all factors, which proves that PerGDP has a relieving effect on LRP, especially in the southwestern areas. Overall, this study provides a technical framework to evaluate land carrying capacity with multi-source data sets and explore its localized influences via GW models, which could provide practical guidance for similar studies in other cities. [ABSTRACT FROM AUTHOR]

The pursuit of sustainability has been at the forefront of contemporary planning initiatives. However, most recent research has focused on the environmental and economic aspects of developing sustainable urban environment, whilst largely neglecting the social aspects. Contemporary political thinking in the UK often disregards the potential of the urban infrastructure to improve social equity. The aim of this study was to analyse the impact of transport infrastructure on a variety of social measures, in an empirical and ideologically unbiased fashion, using both quantitative and qualitative methods. We selected "Tramlink" as a case study: a light-rail system in the London Borough of Croydon which began operation in 2000. We used quantitative methods, including advanced spatial statistics, to produce a more detailed analysis of social equity than has been previously published. This acknowledges that determining localised issues can produce more informed and effective policy interventions. Our results demonstrate that the physical properties of transport infrastructure and the non-physical attributes of society, in combination, help to create opportunities for individuals to succeed. We also find that in order to reduce the negative effects of austerity, public money could be more effectively spent if diverted to areas that are most in need which can be highlighted through localised investigations. [ABSTRACT FROM AUTHOR]

Ecosystems provide services that contribute to the well-being of people living within a city's borders and on the urban–rural fringe. While benefits from green areas in urban settings are well investigated, peri-urban areas are significantly less addressed by researchers than cities. This study aims to evaluate the importance of environmental amenities in peri-urban areas using the hedonic pricing method to assess people's willingness to pay for environmental goods and services. A local regression model (geographically weighted regression) and two global regression models (generalized spatial two-stage least squares and ordinary least square) are used to identify the spatial patterns and level of influence of protected areas, forests, rivers, trees, and landscape diversity. This paper includes the Central European case study example of a peri-urban area of the city of Wroclaw, Poland. The results from the three models show that out of all of the environmental amenities included in this study, proximity to protected areas—such as Natura 2000 sites and landscape parks—and the diversity of land-use patches within the 500-m radius around the sites exert the strongest influence on plot prices. The overall impact of environmental amenities on vacant plot prices in peri-urban areas is low or, as in the case of river and streams, not significant. The results of the analysis reveal the preferences of the new peri-urban inhabitants concerning green spaces that have an effect on the real estate market in Poland. [ABSTRACT FROM AUTHOR]

With regard to important scientific and policy issues in the Wei River Catchment, much emphasis has been put on the objective assessment of the effectiveness of ecological restoration measures and the analysis of effective ways to promote the efficiency of ecological management. Based on an interdisciplinary approach, the present study investigates the measurement of the control efficiency for soil and water loss induced by the Sloping Land Conversion Program and terrace fields, a part of the Water and Soil Conservation Project, in an attempt to detect and quantify indicators of different fields to do so. The applied methods included a Bootstrap Data Envelopment Analysis model which covers 39 counties over the period of 2000–2015. Then, an exploratory spatial data analysis was conducted to capture the spatial characteristics for the control efficiency of each county. Finally, the geographically weighted regression model was employed to identify the spatial heterogeneity and evolutionary characteristics in the relationship between control efficiency and natural conditions and socioeconomic development in each sample county. Results show that the control efficiency increased from 0.263 to 0.365 during the study period. In addition, the Moran'I indicates that efficiency presented a stable spatial agglomeration but still at a low level, from 0.192 to 0.151. The difference in control efficiency is the result of a combination of multiple factors, of which the most relevant factors often vary among different counties, indicating that regional governments should consider full-scale initiatives. This analytical perspective helps to provide experience and reference for countries facing the same environmental challenges in the world. [ABSTRACT FROM AUTHOR]

Long-distance school commuting is a key aspect of students' choice of car travel. For cities lacking school buses, the metro and car are the main travel modes used by students who have a long travel distance between home and school. Therefore, encouraging students to commute using the metro can effectively reduce household car use caused by long-distance commuting to school. This paper explores metro ridership at the station level for trips to school and return trips to home in Nanjing, China by using smart card data. In particular, a global Poisson regression model and geographically weighted Poisson regression (GWPR) models were used to examine the effects of the built environment on students' metro ridership. The results indicate that the GWPR models provide superior performance for both trips to school and return trips to home. Spatial variations exist in the relationship between the built environment and students' metro ridership across metro stations. Built environments around metro stations, including commercial-oriented land use; the density of roads, parking lots, and bus stations; the number of docks at bikeshare stations; and the shortest distance between bike stations and metro stations have different impacts on students' metro ridership. The results have important implications for proposing relevant policies to guide students who are being driven to school to travel by metro instead. [ABSTRACT FROM AUTHOR]

This paper investigates local-scale social vulnerability to flood hazards in Romania, aiming to identify the most vulnerable social and demographic groups across a wide range of geographical locations by considering three dimensions: demographic, socioeconomic, and the built environment. The purpose of the paper is threefold: first, it strives to improve the Social Vulnerability model (SoVI®) by applying a different weighting method adapted to the Romanian context, taking into consideration the municipalities exposed to flood movements. Second, it aims to develop an assessment model for the most vulnerable communities by measuring the heterogeneity according to local indicators related to disaster risks. Third, it aims to facilitate emergency managers to identify community sub-groups that are more susceptible to loss and to increase the resilience of local communities. To perform local-level vulnerability mapping, 28 variables were selected and three aggregated indexes were constructed with the help of the ArcGIS software. Moreover, a model of Geographically Weighted Regression (GWR) between communities directly affected by floods and localities with high- and very high values of the Local Social Vulnerability Index (LoSoVI) was used to explore the spatial relationship among them and to compare the appropriateness of Ordinary Least Square (OLS) and GWR for such modelling. The established GWR model has revealed that the negative effects of flood hazards are often associated with communities with a high degree of social vulnerability. Thus, the analysis is able to provide a more comprehensive picture on communities in desperate need of financial resources in order to have the ability to diminish the negative impacts of flood hazards and to provide a more sustainable society. [ABSTRACT FROM AUTHOR]

Urban rainstorm waterlogging has become a typical "city disease" in China. It can result in a huge loss of social economy and personal property, accordingly hindering the sustainable development of a city. Impervious surface expansion, especially the irregular spatial pattern of impervious surfaces, derived from rapid urbanization processes has been proven to be one of the main influential factors behind urban waterlogging. Therefore, optimizing the spatial pattern of impervious surfaces through urban renewal is an effective channel through which to attenuate urban waterlogging risk for developed urban areas. However, the most important step for the optimization of the spatial pattern of impervious surfaces is to understand the mechanism of the impact of urbanization processes, especially the spatiotemporal pattern of impervious surfaces, on urban waterlogging. This research aims to elucidate the mechanism of urbanization's impact on waterlogging by analysing the spatiotemporal characteristics and variance of urban waterlogging affected by urban impervious surfaces in a case study of Guangzhou in China. First, the study area was divided into runoff plots by means of the hydrologic analysis method, based on which the analysis of spatiotemporal variance was carried out. Then, due to the heterogeneity of urban impervious surface effects on waterlogging, a geographically weighted regression (GWR) model was utilized to assess the spatiotemporal variance of the impact of impervious surface expansion on urban rainstorm waterlogging during the period from the 1990s to the 2010s. The results reveal that urban rainstorm waterlogging significantly expanded in a dense and circular layer surrounding the city centre, similar to the impervious surface expansion affected by urbanization policies. Taking the urban runoff plot as the research unit, GWR has achieved a good modelling effect for urban storm waterlogging. The results show that the impervious surfaces in the runoff plots of the southeastern part of Yuexiu, the southern part of Tianhe and the western part of Haizhu, which have experienced major urban engineering construction, have the strongest correlation with urban rainstorm waterlogging. However, for different runoff plots, the impact of impervious surfaces on urban waterlogging is quite different, as there exist other influence factors in the various runoff plots, although the impervious surface is one of the main factors. This result means that urban renewal strategy to optimize the spatial pattern of impervious surfaces for urban rainstorm waterlogging prevention and control should be different for different runoff plots. The results of the GWR model analysis can provide useful information for urban renewal strategy-making. [ABSTRACT FROM AUTHOR]

This paper proposes a new non-radial biennial Luenberger energy and environmental performance index (EEPI) to measure the energy and environmental performance (EEP) change in various Chinese cities. The sources of EEP change, in terms of technical efficiency change and technological change, are examined by Luenberger EEPI. The contributions from specific undesirable outputs and energy inputs to the EEP change are identified by means of the non-radial efficiency measure. The proposed approach is applied to evaluate the EEP of the industrial sector in 283 cities in China over 2010–2014. Factors influencing the emission abatement potential are investigated by employing geographically weighted regression (GWR) model. We find that (1) changes in EEP can be attributed to technological progress but that technological progress slows down across the study period; (2) the soot emission performance experiences a downtrend among four specific sub-performances (i.e., energy, wastewater, SO2 and soot performances) in line with the truth that severe haze happened frequently in China; (3) the best performers begin to move from the coastal to inland cities with the less resource consumption and higher ecological quality; (4) cities with the strongest positive effect in regards to pollution intensity on emission abatement potential are located in the areas around the Bohai Gulf, where air pollution is particularly severe. [ABSTRACT FROM AUTHOR]

With the commercialization of housing and the deepening of urbanization in China, housing prices are having increasing influence on the land market, and thus indirectly affecting urban development. As various spatial features of an urban housing property directly affect its price, the study of this connection has significance for urban planning. The present study uses mainly open internet data of housing prices, supplemented by other data sources, to identify the spatial features of housing prices and the influence factors in a case study city, Wuhan. Methods employed in the study include the hedonic linear regression model, the geographically weighted regression (GWR) model and the artificial neural network (ANN) model, etc. Progress is made in the following two aspects: first, when calculating the influence factors, hierarchical values for accessibility variables of certain public facilities are used instead of simple Euclidean distance and the results shows a better model fit; second, the ANN model shows the best fit in the study, and while the three models all show respective strengths, the combined use of all models offers the possibility of a more comprehensive analysis of the influence factors of housing prices. [ABSTRACT FROM AUTHOR]

The U.S. household (HH) energy consumption is responsible for approximately 20% of annual global GHG emissions. Identifying the key factors influencing HH energy consumption is a major goal of policy makers to achieve energy sustainability. Although various explanatory factors have been examined, empirical evidence is inconclusive. Most studies are either aspatial in nature or neglect the spatial non-stationarity in data. Our study examines spatial variation of the key factors associated with HH energy expenditures at census tract level by utilizing geographically weighted regression (GWR) for the 14 metropolitan statistical areas (MSAs) in North Carolina (NC). A range of explanatory variables including socioeconomic and demographic characteristics of households, local urban form, housing characteristics, and temperature are analyzed. While GWR model for HH transportation expenditures has a better performance compared to the utility model, the results indicate that the GWR model for both utility and transportation has a slightly better prediction power compared to the traditional ordinary least square (OLS) model. HH median income, median age of householders, urban compactness, and distance from the primary city center explain spatial variability of HH transportation expenditures in the study area. HH median income, median age of householders, and percent of one-unit detached housing are identified as the main influencing factors on HH utility expenditures in the GWR model. This analysis also provides the spatial variability of the relationship between HH energy expenditures and the associated factors suggesting the need for location-specific evaluation and suitable guidelines to reduce the energy consumption. [ABSTRACT FROM AUTHOR]