Road Network Density Research Articles

Multidimensional Evaluation of Traditional Villages in Jiangnan Region, China: Spatial Pattern, Accessibility and Driving Factors

Assessment of the spatial distribution and accessibility of traditional villages is closely related to their development. However, the impacts of spatial heterogeneity on the accessibility of traditional villages remain largely unknown. A total of 644 national-level traditional villages in the Jiangnan region were used to explore the spatial heterogeneity impact with a geographically weighted regression (GWR) model. We found: (1) spatially, the villages exhibit a predominant aggregation distribution pattern with significant local spatial disparities (R < 1, p < 0.01), predominantly originating from the Song and Ming dynasties (59.21%). Their clustering shifts from northeast to southwest, with over 70% of the villages located on slopes ranging from 0° to 20° and within 1 km of water. (2) The accessibility of these villages is generally low, with 85.66% being accessible within 200 ~ 300 min; it decreases concentrically outwards from Changzhou and exhibits clustering autocorrelation characteristics (Moran’s I > 0, Z > 2.58). (3) Road network density, elevation, and slope are significantly correlated with accessibility (p < 0.05), demonstrating pronounced spatial heterogeneity in their effects, with these factors collectively explaining approximately 85% of the accessibility levels. These findings provide a basis for comprehensive planning and categorized strategies for traditional villages.

Evaluating the impact of urban morphology on urban vitality: an exploratory study using big geo-data

ABSTRACT The United Nations has proposed Sustainable Development Goals (SDGs), of which SDG11 aims to “make cities and human settlements inclusive, safe, resilient and sustainable”. This is in line with Urban Vitality's objectives. This study proposes a quantitative framework to evaluate the impact of urban morphology on urban vitality. In this framework, a proxy that is more reflective of economic and human activities is proposed for depicting urban vitality based on geographic big data; then we use a Multi–scale Geographically Weighted Regression (MGWR) regression considering spatial heterogeneity to analyze the different effects of urban form on urban vitality. Taking the Beijing Sixth Ring Road area as the study region, the results indicate that tall, large–area, multi–functional buildings have a significantly positive impact on urban vitality. These areas exhibit single–function buildings and high levels of greenery, leading to inefficient space utilization. Increasing road network density, as well as the density of transportation and public facilities, also positively influences urban vitality. However, commercial density has a negative impact on workday vitality. Furthermore, excessive green space density, a high proportion of the largest green patches, and complex green space borders all contribute to a reduction in urban vitality.

Research on charging demands of commercial electric vehicles based on Voronoi diagram and spatial econometrics model: An empirical study in Chongqing China

In order to address the mismatch between the current charging demand of electric vehicles and the layout of charging stations, this paper collects multi-source heterogeneous data that may affect charging demand, and explores their spatiotemporal distributions in depth. Then, this paper proposes a Voronoi polygon spatial partitioning method based on charging demand clustering and two spatial econometric models, spatial lag model (SLM) and spatial error model (SEM), to quantitatively analyze the influences of various elements on charging demands. Empirical research in Chongqing, China reveals that the spatiotemporal distribution of charging demand is uneven, with peak areas and periods for charging, and the division of Voronoi polygon can better reflect the spatial heterogeneity of charging demands than regular grid division. The positive factors include the low SOC, parking lot density, population density, and road network density, especially the low SOC and parking lot density, and the negative items contain consumption related Point Of Interest (POI), tourism related POI, and transportation hub related POI. Among them, population density and road network density, as static data, have similar influences on charging demands at different intervals, while the impact of other factors on charging demand varies with periods, with obvious peak-valley characteristics. There is spatial dependence and lag effect between charging demands within polygons, causing SLM outperforms SEM. The research results are beneficial for the rational siting and optimization of charging stations.

COVID-19 impact on taxi-metro integrated usage: Changes and role of built environment, taking Lanzhou, China as an example

The COVID-19 pandemic has had a profound impact on how and how often people travel. The extent of changes in taxi-metro integrated usage (cooperative trips and competitive trips) before and after the pandemic is still unclear. Therefore, this paper aims to explore the impacts of COVID-19 on taxi-metro integrated usage and the relationship with built environment. This paper develops a novel method to infer the impact of COVID-19 on taxi-metro integrated usage and measures the resilience of cooperative and competitive ridership using a series of Bayesian structural time series models. The bootstrap aggregating regression tree model is applied to investigate the contribution of built environment on the relative impacts and resilience. Partial Dependence Plots (PDP) analysis is employed to explore the nonlinear effects of built environment elements. Results suggests: (1) The travel restrictions have a more significant impact than the number of COVID-19 cases on taxi-metro integrated usage. (2) Built environment factors contribute similarly to resisting the impact of COVID-19 on cooperative and competitive ridership during short-term and long-term period. (3) The recovery of the ridership shows a significant spatial heterogeneity. (4) Variables such as residential ratio, road network density, and bus stop density show nonlinear and threshold effects on the resilience of the ridership. Our findings have implications for travel policies during recovery from the pandemic.

Abandoned Farmland Extraction and Feature Analysis Based on Multi-Sensor Fused Normalized Difference Vegetation Index Time Series—A Case Study in Western Mianchi County

Farmland abandonment monitoring is one of the key aspects of land use and land cover research, as well as being an important prerequisite for ecological environmental protection and food security. A Normalized Difference Vegetation Index (NDVI) time series analysis is a common method used for farmland abandonment data extraction; however, extracting this information using high-resolution data is still difficult due to the limitations caused by cloud influence and data of low temporal resolution. To address this problem, this study used STARFM for GF-6 and Landsat 8 data fusion to enhance the continuity of high-resolution and cloudless images. A dataset was constructed by combining the phenological cycle of crops in the study area and then extracting abandoned farmland data based on an NDVI time series analysis. The overall accuracy of the results based on the NDVI time series analysis using the STARFM-fused dataset was 93.42%, which was 15.5% higher than the accuracy of the results obtained using only GF-6 data and 28.52% higher than those obtained using only Landsat data. Improvements in accuracy were also achieved when using SVM for time series analysis based on the fused dataset, indicating that the method used in this study can effectively improve the accuracy of the results. Then, we analyzed the spatial distribution pattern of abandoned farmland by extracting the results and concluded that the abandonment rate increased with the increase in the road network density and decreased with the increase in the distance to residential areas. This study can provide decision-making guidance and scientific and technological support for the monitoring of farmland abandonment and can facilitate the analysis of abandonment mechanisms in the study area, which is conducive to the sustainable development of farmland.

The Relationship between Emotional Perception and High-Density Built Environment Based on Social Media Data: Evidence from Spatial Analyses in Wuhan

The utilization of Social Media Data (SMD) from location-based services offers a wealth of information to analyze changes in human emotional perception influenced by high-density built environments. This study aimed to examine the impact of high-density built environment factors on human emotion perception. First, a set of indicators for high-density built environments was established. Subsequently, Natural Language Processing (NLP) was employed to analyze SMD for sentiment identification and classification. Finally, the Multi-scale Geographically Weighted Regression (MGWR) model was utilized to investigate the spatial differentiation of human emotional perception in high-density built environments. The findings revealed that positive emotions display spatial variations in high-density built environments. Additionally, positive emotions were found to be influenced by multiple variables, with different variables simultaneously affecting individuals’ positive emotions. Specific built environment indicators showed positive correlations with Open Space Ratio (OSR), Green Space Ratio (GSR), POI Functional Density (PFD), and Road Network Density (RND), while negative correlations with Floor Space Index (FSI), Ground Space Index (GSI), Building Average Layer (BAL), Water Index (WI), and Space Syntax Integration (SSI) were observed. Normalized Difference Vegetation Index (NDVI), POI Functional Mixture (PFM), Space Syntax Choice (SSC), and Population Density (PD) exhibited mixed results in different spatial contexts. This research on human perception provides insights for refined urban design and governance, addressing the limitations of top-down approaches in dense urban renewal.

Mental health in China: exploring the impacts of built environment, work environment, and subjective perception.

The shifting living and working conditions have profound impacts on the residents' mental health. However, current research in this field has not remarkable investigated. This study used the China Labor-force Dynamic Survey (CLDS) dataset from 2018 and relied on a regression model to examine the effects of the built environment, work environment, and subjective perception on the mental health of Chinese citizens. It also considers the circumstances of both migrants and local residents. This study revealed significant correlations between mental health and greening space rate, road network density, commuting time, work feelings, community trust, economic satisfaction, and other factors. Additionally, the mental health of local residents was shown to be significantly affected by community security, while it shows no significance in migrants. Furthermore, a significant spatial autocorrelation was found in terms of mental health within the central and eastern regions of China. The findings of this study offer valuable insights that can be used to facilitate measures aimed at improving the mental health of residents and promoting the development of healthy cities.

Exploring the spatiotemporal relationships between built environment and the public transport competitiveness: A case study from Beijing

Public transport (PT) competitiveness refers to the advantage and attraction of PT compared to other modes of transport from a specific perspective. In recent years, research on PT competitiveness based on accessibility has made significant progress, but the research on impact mechanisms of external influencing factors on PT competitiveness is lagging behind. This study addressed this gap by using a Space Barrier model (SBM) to evaluate PT competitiveness relative to private cars based on real traffic data from Beijing. Additionally, a Spatial Durbin model (SDM) was used to analyze the spatiotemporal relationship between PT competitiveness and six built environment (BE) variables, including the detour coefficient of PT, bus stop density, land use mixture, metro station density, residential density, and road network density during peak and off-peak hours. This study has identified that the influence of BE variables varies across different time periods. Specifically, land use mixture demonstrates notably stronger effects during peak hours as opposed to off-peak hours. During evening peak hours, the influence of land use mixture on PT competitiveness at the origin location (CO) is 27.4% higher than the effect during off-peak hours. Moreover, this investigation has uncovered the spatial dependence characterizing the relationship between each BE and PT competitiveness. It has indicated that BE exert both direct and indirect impacts on PT competitiveness. For instance, metro station density extends the PT competitiveness within both the area it is located and its surrounding areas. During the evening peak hours, when metro density increases by 1%, the CO in the area it is located and in its surrounding areas will respectively decrease 0.045%, and 0.143%. Overall, this research offers valuable insights for bus transit operators and policymakers seeking to enhance PT efficiency and competitiveness by devising strategies such as skipping stops during off-peak hours, balancing regional PT facility supply, optimizing the connectivity of PT, and increasing the density of PT stations.

Study on spatiotemporal dynamic characteristics of precipitation and causes of waterlogging based on a data-driven framework

The discernible alterations in regional precipitation patterns, influenced by the intersecting factors of urbanization and climate change, exert a substantial impact on urban flood disasters. Based on multi-source precipitation data, a data-driven model fusion framework was constructed to analyze the spatial and temporal dynamic distribution characteristics of precipitation in Beijing. Wavelet analysis method was used to reveal the periodic variation characteristics and multi-scale effects of precipitation, and the machine learning method was used to characterize the spatiotemporal dynamic change pattern of precipitation. Finally, geographical detector was used to explore the causes of waterlogging in Beijing. The research outcomes reveal a disparate distribution of precipitation across the year, with 78 % of the total precipitation occurring during the flood season. The principal periodic cycles observed in annual cumulative precipitation (ACP) were identified at 21, 13, and 9-year intervals. Spatially, while a decreasing trend in precipitation was observed in most areas of Beijing, 63.4 % of the region exhibited an escalating concentration trend, thereby heightening the risk of urban waterlogging. Machine learning model clustering elucidated three predominant spatial dynamic distribution patterns of precipitation in Beijing. The utilization of web crawler technology to acquire water accumulation data addressed challenges in obtaining urban waterlogging data, and validation through Landsat8 images enhanced data reliability and authenticity. Factor detection shows that road network density, topography, and precipitation were the main factors affecting urban waterlogging. These findings hold significant implications for informing flood control strategies and emergency management protocols in urban areas across China.

Research on the Spatiotemporal Characteristics of Block Vitality based on LBS Data and the Impact Mechanisms of the Built Environment -Taking the Third Ring Road in Fuzhou City as an example

Using location-based services (LBS) data from the third ring area in Fuzhou city, this study analyzes the spatiotemporal characteristics of block vitality by measuring the density of active population gatherings. It explores the spatiotemporal heterogeneity of block vitality based on a geographically and temporally weighted autoregressive model (GTWAR), considering built environment features such as POI density, functional mix, and road network density. Additionally, it investigates their interactions using a geographic detector and compares differences between weekdays and weekends across five daily periods. Key findings include:(1) The overall urban vitality in the region exhibits a spatial pattern of “one core, two centers, multiple sub-centers, and multiple clusters” and undergoes spatiotemporal dynamic changes across five periods, characterized by ‘dispersion, agglomeration, deep agglomeration, and dispersion.” (2) The marginal effects of POI density, functional mix, and road network density on block vitality exhibit instability in both spatial and temporal dimensions.(3)Interaction effects between two factors exert a more significant impact on block vitality than the effects of individual factors, with interaction types including dual-factor enhancement and nonlinear enhancement.(4)Differences exist in the spatiotemporal characteristics of block vitality, built environment influences, and their interactions between weekdays and weekends.

Analysis of logistics capacity, influencing factors and spatial spillover effect in Yangtze River Economic Belt.

The logistics industry plays a crucial role in facilitating regional economic development. Serving as a vital link connecting producers, consumers, and various components of the supply chain, it has a direct and profound impact on the prosperity and advancement of regional economies. Based on the panel data of 11 provinces in the Yangtze River Economic Belt from 2016 to 2020, this paper constructs the logistics capability evaluation index system from four aspects: regional economic base, logistics infrastructure, logistics development scale, information technology and talent support, and uses the entropy weight TOPSIS method to measure the logistics capability of each province. The adjacency space weight matrix, geographical distance weight matrix and economic distance weight matrix are selected to build a spatial econometric model to analyze the influencing factors and spatial spillover effects of regional logistics capability. The following conclusions can be drawn from the analysis. From 2016 to 2020, the regional logistics capacity of the Yangtze River Economic Belt shows a trend of increasing year by year, but the logistics capacity of different provinces within the region has a large room for improvement. From the perspective of spatial dimension, the logistics capacity of the Yangtze River Economic Belt is "high in the east and low in the west". The results of spatial econometric analysis based on the spatial Durbin model show that there are significant spatial spillover effects on the logistics capacity of provinces in the Yangtze River Economic Belt. Factors such as road network density, port throughput, water freight turnover, transportation, warehousing and postal employment will not only affect the logistics capacity of the region, but also have a spillover effect on the material capacity of neighboring provinces in the Yangtze River Economic Belt. This study improves the level of regional logistics capacity and promotes the regional economic development of the Yangtze River Economic Belt. It can be used as a reference for other regions or countries in terms of enhancing regional logistics capacity and promoting regional economic development.

Climate match is key to predict range expansion of the world's worst invasive terrestrial vertebrates.

Understanding the determinants of the range expansion of invasive alien species is crucial for developing effective prevention and control strategies. Nevertheless, we still lack a global picture of the potential factors influencing the invaded range expansion across taxonomic groups, especially for the world's worst invaders with high ecological and economic impacts. Here, by extensively collecting data on 363 distributional ranges of 19 of world's worst invasive terrestrial vertebrates across 135 invaded administrative jurisdictions, we observed remarkable variations in the range expansion across species and taxonomic groups. After controlling for taxonomic and geographic pseudoreplicates, model averaging analyses based on generalized additive mixed-effect models showed that species in invaded regions having climates more similar to those of their native ranges tended to undergo a larger range expansion. In addition, as proxies of propagule pressure and human-assisted transportation, the number of introduction events and the road network density were also important predictors facilitating the range expansion. Further variance partitioning analyses validated the predominant role of climate match in explaining the range expansion. Our study demonstrated that regions with similar climates to their native ranges could still be prioritized to prevent the spread of invasive species under the sustained global change.

The Relationship between Built Environment Characteristics and Metro Stations Pedestrian Catchment Areas

The study of pedestrian catchment areas for metro stations serves as the foundation for addressing issues such as‐passenger flow forecasting, land use planning around stations, and transportation link facility planning. This study aims to investigate how various factors related to pedestrians, the environment, and the characteristics of metro stations affect the areas where pedestrians are attracted to. The goal is to gain insights into design strategies and policy interventions that can enhance the appeal of metro stations. The present study considers the metro stations in the Chengdu area as the primary research objects. A framework for examining the connection between the built environment and the pedestrian catchment area of a subway station through the use of a hybrid gradient decision tree approach has been used. The study examines the pedestrian catchment areas of these stations and confirms the presence of a nonlinear relationship between pedestrian catchment areas and variables at these stations. There exists a negative correlation, within specific thresholds, between the number of companies, bus stops, and road network density with the catchment area of passengers. Concurrently, there is a positive correlation between the distances to the city center and the catchment area. Furthermore, this framework, which accounts for spatial heterogeneity, showed significant goodness‐of‐fit and predictive capability, aspects that were overlooked in earlier research. As a result, these research findings can serve as a key foundation for advancing the theoretical framework and statistical proof to facilitate land planning and development near metro stations, predicting passenger flow, and designing essential connecting amenities in the vicinity of such facilities.

A cellular automata model coupled with partitioning CNN-LSTM and PLUS models for urban land change simulation

Urbanisation is a key aspect of land use change (LUC), and accurately modelling of urban LUC is crucial for sustainable development. Cellular automata (CA) are widely used in LUC research. However, previous studies have overlooked the significant temporal dependence and spatial heterogeneity associated with LUC. To address these gaps, this study proposes a novel model called KCLP-CA, which integrates k-means, a convolutional neural network (CNN), a long and short-term memory neural network (LSTM), and the popular patch-generation land use model (PLUS). Initially, k-means and CNN are utilised to address spatial heterogeneity, while LSTM tackles temporal dependence. The LSTM and land expansion analysis strategy (LEAS) models of PLUS are employed to obtain land use conversion probability maps. Finally, a simulation of land use dynamic change was conducted using a linear weighted fusion conversion probability map that accounts for random factors. To validate the KCLP-CA model, land use data collected from Hangzhou between 1995 and 2000 were employed. The results showed that the KCLP-CA model outperformed traditional methods, including artificial neural networks and random forest model, with the figure of merit (FoM) index increasing from 2.12% to 4.19%. Random forest analysis of drivers impacting LUC revealed that distance to water and road network density exerted the greatest influence on urban land development in Hangzhou. Incorporation of various policy planning factors affecting urban development yielded simulation results aligning more closely with reality, resulting in a FoM index increase of 1.64–1.76%. In summary, the model developed in this study combines the strengths of two sub models to deliver an accurate and effective simulation of future land use.

A novel index for assessing the rural population hollowing at fine spatial resolutions based on Tencent social media big data: A case study in Guangdong Province, China

Evaluating the rural population hollowing (RPH) caused by the massive out-migration of rural laborers is essential for China’s rural land use policy formulation and rural revitalization. Existing studies mainly assessed the RPH based on survey data or statistical data. The former is mainly based on selected local villages and towns, and cannot be replicated on a large scale, while the latter is usually at the county or city scale and cannot reveal the cyclical migration of rural labors at fine spatial resolutions such as townships. To fill this gap, this paper proposed a social media data based-rural population hollowing (SM-RPH) index, which was calculated by Tencent User Density big data on holidays and non-holidays and was used to characterize the "migratory bird" process of rural laborers. Taking Guangdong Province, China as the case, this study employed the proposed novel index to evaluate the RPH at the county and township scales. This was integrated with a GWR model to explore how the important contributing factors of RPH vary across the province. The results showed a trend of transition from the cold spot areas in the core regions of PRD to the hot spot areas around the PRD. Additionally, this study revealed that distance to the nearest prefecture-level city, total nighttime lighting per capita, fiscal expenditure per unit of GDP and road network density were dominant factors in affecting rural population hollowing, and the influence of these four variables vary considerably throughout the province. Further, k-means cluster was used to group the hollowing towns according to the local coefficients of the influencing factors, and policy zoning was suggested to provide differentiated countermeasures and suggestions for rural revitalization. This methodological framework can be easily applied to other regions, and is expected to be used to evaluate the return characteristics of migrant workers in real time to help monitor the progress of rural revitalization in China and provide decision support for policy makers.

THE RATIONALITY OF URBAN FINANCIAL NETWORK LAYOUT BASED ON POI DATA

Abstract. Financial outlets are branches or business outlets of financial institutions such as banks, investment and finance companies and insurance companies in cities, which are used to provide a variety of financial services and businesses. The location of financial outlets is usually determined on the basis of factors such as population density, commercial areas, residential areas and transportation convenience. As an important carrier of urban financial services, the rationality of the layout of financial outlets can reflect the financial needs and behavioral patterns of people in the city. Urban functional area is a product after urbanization, different from urban land use type which focuses on the classification of urban land use through natural conditions, urban functional area mainly analyzes the internal spatial structure of the city, focusing on the discernment of the dominant functions within the built-up area of the city. By categorizing urban functions such as residence, commercial service and industry, the spatial distribution of urban functions can be grasped to provide support for urban planning and development. Urban functional areas can reveal human behavior patterns, needs and preferences in different areas. The current research on financial outlets mainly focuses on the accessibility and coverage of financial outlets, and researchers treat financial outlets as spatially undifferentiated points, ignoring the problem of spatial heterogeneity of financial outlets within different functional zones. When studying the rationality of the layout of urban financial outlets, considering different functional zones within the city can better meet the financial needs of residents, enterprises and institutions in the region and improve the accessibility and convenience of financial services. Analyzing the comprehensive service capacity of financial outlets within different functional areas is obviously insufficient in the current study, which is not conducive to the configuration planning of financial outlets and the promotion of the accessibility of urban financial services. The wide application of Point of Interest (POI) data in spatial analysis provides a new perspective for the study of urban financial outlets, for this reason, this paper analyzes and researches the current situation and rationality of the spatial layout of financial outlets based on POI data, under the perspective of the functional areas of the city, and combined with the population travel vitality index. The Term Frequency-Inverse Document Frequency (TF-IDF) algorithm is used to process the POI data to generate the urban functional area, on the basis of which the travel vitality index in the city is calculated by combining the population coverage and road network density, so as to judge the reasonableness of the layout of urban financial outlets. This helps us to better understand the behavioral decision-making process and social interaction of human beings about financial life in the city, and provides guidance and decision-making support for urban financial layout planning, financial public services and financial policies.

Prediction of transportation index for urban patterns in small and medium-sized Indian cities using hybrid RidgeGAN model

The rapid urbanization trend in most developing countries including India is creating a plethora of civic concerns such as loss of green space, degradation of environmental health, scarcity of clean water, rise in air pollution, and exacerbated traffic congestion resulting in significant delays in vehicular transportation. To address the intricate nature of transportation issues, many researchers and planners have analyzed the complexities of urban and regional road systems using transportation models by employing transportation indices such as road length, network density, accessibility, and connectivity metrics. This study addresses the complexities of predicting road network density for small and medium-sized Indian cities that come under the Integrated Development of Small and Medium Towns (IDSMT) project at a national level. A hybrid framework based on Kernel Ridge Regression (KRR) and the CityGAN model is introduced to predict network density using spatial indicators of human settlements. The major goal of this study is to generate hyper-realistic urban patterns of small and medium-sized Indian cities using an unsupervised CityGAN model and to study the causal relationship between human settlement indices (HSIs) and transportation index (network density) using supervised KRR for the real cities. The synthetic urban universes mimic Indian urban patterns and evaluating their landscape structures through the settlement indices can aid in comprehending urban landscape, thereby enhancing sustainable urban planning. We analyzed 503 real cities to find the actual relationship between the urban settlements and their road density. The nonlinear KRR model may help urban planners in deriving the network density for GAN-generated futuristic urban patterns through the settlement indicators. The proposed hybrid process, termed as RidgeGAN model, can gauge the sustainability of urban sprawl tied to infrastructure and transportation systems in sprawling cities. Analysis results clearly demonstrate the utility of RidgeGAN in predicting network density for different kinds of human settlements, particularly for small and medium Indian cities. By predicting future urban patterns, this study can help in the creation of more livable and sustainable areas, particularly by improving transportation infrastructure in developing cities.

Estimating population density using open-access satellite images and geographic information system: Case of Al Ain city, UAE

Efficient and real-time estimates of population density are crucial for monitoring urban systems and disaster response. To develop this capability, this study estimates population density using covariates derived from remote-sensing images [e.g., building density, vegetation temperature light population index (VTLPI), and day–night band (DNB)]. Data were obtained from open-access satellite images, specifically Sentinel 2A, Landsat 8, and NPP-VIIRS images. Building density, VTLPI, and DNB were extracted at the district level and compared with population density. Furthermore, vector data from geographic information systems, such as human points of interest and road networks, were used to develop a geographically weighted regression model for estimating population density. Building density and DNB correlated positively with population density (r = 0.89), and population density correlated positively with the VTLPI (r = 0.75). Although all covariates correlated positively with estimates of population density, building density and the DNB showed the best correlation. Furthermore, both parameters were considerably related to human points of interest and road network density. Finally, using geographically weighted regression (GWR) and multiple linear regression (MLR) with the building density, DNB, and human points of interest as input, we predicted the population density. The GWR model built from independent variables correlates well with the population density (AICc = 526; R2 adj. = 0.89). Overall, the GWR and MLR produce similar results. To assess the transferability of the GWR model used for Al Ain, it was tested on Abu Dhabi, and the results produce an accuracy of 36%. This low accuracy may be attributed to the acquisition dates of the census data being different from those of the human points of interest. Further research is required to address such limitations in the proposed method and to provide additional support for using open-access remote-sensing images to estimate population density.

A review of factors influencing sensitive skin: an emphasis on built environment characteristics.

Sensitive skin (SS) is a condition characterized by hyperreactivity. Impacting around 37 percent of the worldwide population and exerting an influence on the quality of life for affected individuals. Its prevalence rate has increased due to factors such as elevating stress levels and deteriorating environmental conditions. The exposome factors influencing SS have extended from demographic, biological attributes, and lifestyle to external environments. Built environments (BEs) have demonstrated as root drivers for changes in behaviors and environmental exposure which have the potential to trigger SS, but the review of the associations between BEs and SS is currently lacking. This review aims to achieve two primary objectives: (1) Examine exposome factors that exert influence on SS at the individual and environmental levels. (2) Develop a theoretical framework that establishes a connection between BEs and SS, thereby offering valuable insights into the impact of the built environment on this condition. An extensive literature search was carried out across multiple fields, including sociology, epidemiology, basic medicine, clinical medicine, and environmental research, with a focus on SS. To identify pertinent references, renowned databases such as PubMed, Web of Science, and CNKI were utilized. SS is the outcome of interactions between individual attributes and environmental factors. These influencing factors can be categorized into five distinct classes: (1) demographic and socioeconomic characteristics including age, gender, and race; (2) physiological and biological attributes such as emotional changes, skin types, sleep disorders, and menstrual cycles in women; (3) behavioral factors, such as spicy diet, cosmetic use, alcohol consumption, and physical exercise; (4) natural environmental features, including climate conditions and air pollution; (5) built environmental features such as population density, green space availability, road network density, and access to public transportation, also have the potential to affect the condition. The importance of interdisciplinary integration lies in its ability to ascertain whether and how BEs are impacting SS. By elucidating the role of BEs in conjunction with other factors in the onset of SS, we can provide guidance for future research endeavors and the formulation of interventions aimed at mitigating the prevalence of SS.

Research on Risk Assessment Method for Land Subsidence in Tangshan Based on Vulnerability Zoning

The vulnerability zoning of the urban ground subsidence in Tangshan City was investigated in this study, taking into account factors such as the geological lithology, rainfall, population density, road network density, seismic acceleration, and river network density. The main research of this paper is slow land subsidence affecting large territories. A single-factor analysis was conducted using ArcGIS, followed by a grid-weighted overlay analysis. Values were assigned to each factor to generate a vulnerability zoning map. The analytic hierarchy process (AHP) and information value evaluation model were then employed to assess the ground subsidence risk. An evaluation index system was established, comprising the evaluation objective layer, criterion layer, and indicator layer. An evaluation judgment matrix for the ground subsidence risk was constructed, with the calculation of the weight and information value of each risk evaluation indicator, and the total information value of the study area. Four risk levels were determined: high, moderate, relatively low, and low. The findings demonstrate the scientific validity, rationality, and practical applicability of the vulnerability zoning research and risk assessment method for addressing the ground subsidence in Tangshan City. This study contributes to the understanding and management of the ground subsidence risk, providing a robust framework for urban planning and disaster mitigation.