Urban Development Intensity Research Articles

City Logistics Solutions for CO2 Emission Reduction and Energy Efficiency: A Comparative Study of Vitoria-Gasteiz, Tartu, and Sønderborg

Modern cities face the challenge of reconciling dynamic development with environmental protection, as well as ensuring a high quality of life for their residents. In a time of continuous urbanization, with more than half of the world’s population living in cities, city logistics plays a crucial role in managing complex urban environments. As part of the concept of sustainable development, city logistics aims to minimize the negative impact of transport on the environment, while increasing operational efficiency and improving the comfort of life of residents in urban agglomerations. This article presents the results of a comparative analysis of the intensity of sustainable urban logistics development in selected cities of the European Union. It examines various strategies that reduce CO2 emissions, improve air quality, and increase building energy efficiency. The analysis demonstrates that cities investing in sustainable urban logistics not only improve their environmental performance but also increase their attractiveness and competitiveness as desirable places to live and work.

The Effects of Spatial Structure and Development Intensity of the Urban Landscape on Bird Biodiversity in Anhui Province

Exploring the factors that drive changes in biodiversity is a hot and critically important topic in landscape ecology and biogeography. In this study, semi-structured citizen science data and bird distribution maps were employed to gather data from 2015 to 2020 for the calculation of bird species richness, the Shannon index, and the Pielou index in Anhui Province. These metrics were utilized to assess avian biodiversity and to elucidate the spatial patterns of biodiversity distribution across the region. In this research, a structural equation model (SEM) was utilized to investigate the relationships between the three dimensions of landscape spatial structure, urban development intensity, and environmental factors on bird biodiversity, and a conceptual framework was established to identify the key driving factors. The validity, reliability, and fit of the hypothesized model were substantiated through rigorous testing, demonstrating its reasonableness. The results indicate the following: (1) In landscape spatial structure, landscape composition and configuration play crucial roles in influencing bird diversity. An increased proportion of cultivated land negatively impacts bird diversity, whereas the expansion of forested areas promotes it. At the configuration level, the Largest Patch Index (LPI) significantly enhances bird diversity, serving as the primary driving force. Landscape spatial structure affects bird diversity both directly, through its composition, and indirectly, through its configuration. (2) The dimension of urban development intensity generally shows significant negative impacts; among these, GDP has the greatest comprehensive impact and shows a significant negative impact. (3) Topography has the greatest overall impact on bird diversity among the environmental factors, with a predominantly direct positive effect. (4) Overall, urban landscape spatial structure and urban development intensity are the main driving forces of bird diversity in Anhui Province, the greatest of which is the direct effect of the urban development intensity. These results provide an important scientific basis for landscape planning and ecological protection and provide inspiration for assessing the driving factors of animal and plant diversity in other regions.

Impacts of urban block form on carbon and pollutant emissions from urban life in China from the perspective of regional differences

Air pollution emissions (PE) and carbon emissions (CE) pose significant challenges to achieving the Sustainable Development Goals (SDGs) globally. The intensity of urban block development and spatial form can influence the relationship between PE and CE. This study analyzed 11,228 neighborhood samples from various climate zones across China using spatial statistics and an optimized random forest model to examine the impact of block spatial form on PE and CE. The findings reveal that: (1) The PE–CE correlation in non-first-tier city blocks in southern China is stronger than in those in northern China. The correlation is strongest in urban neighborhoods located in temperate climates. Additionally, the PE–CE correlation is weakest in Beijing and Shanghai. (2) The variation in explanatory power of different driving factors is more pronounced for CE than for PE, with PR, NDVI, and AH emerging as the most significant factors. (3) The synergy between PE and CE is strongest when BD is in the 20 %-30 % range. Similarly, the synergy is strongest when PR is in the 2–3 range. (4) BD in the 40 %-60 % range is most effective in reducing PE and CE, with 40 %-50 % range favoring CE reduction and 50 %-60 % range favoring PE reduction.

Creating spatially complete zoning maps using machine learning

Zoning regulates land use and intensity of urban development at the county and municipal level in the United States, promoting economic growth, community health, and environmental preservation. However, limited availability of zoning data at scale hinders regional assessments of regulations and coordinated resilience planning efforts. In this study, we developed an open-source, replicable, and transferable framework to predict spatially complete zoning in areas where zoning information is publicly unavailable. We applied a Hierarchical Random Forest algorithm to predict multilevel zoning districts, including three core districts (residential, non-residential, mixed use) and 13 sub-districts. To mimic real-world data accessibility challenges, we evaluated two models: one filling gaps within a county (within-county) and the other extrapolating for counties with no available data (between-county). We tested our models statewide in North Carolina (NC), USA, and developed the State's first comprehensive zoning map. We found strong predictive performance for our within-county model (∼99% accuracy; macro averaged F1 score of ∼0.97) irrespective of district breakdown (i.e., core and sub). However, our between-county model performance was lower and varied depending on the training counties sampled and the district breakdown considered (19–90% accuracy; macro averaged F1 score of 0.105–0.451). Our framework provides spatially complete zoning maps for previously inaccessible locations, enabling researchers and planners to conduct large-scale comprehensive zoning assessments.

Evaluating inequality divides in urban development intensity between the Global North and South

As the significance of cities worldwide with regard to human activities increases, Urban Development Intensity (UDI) becomes an increasingly critical factor for achieving Sustainable Development Goal (SDG) Target 11.3 and serves as a scientific foundation for establishing urban land development guidelines. However, the existing literature lacks systematic quantification of fine-scale UDI on the global scale. In this study, we propose a comprehensive framework for evaluating UDI globally, utilizing multi-source surveys to compare spatial variations between the Global North and South. We created structured grided UDI layers at a 1-km resolution covering urban areas worldwide, considering various aspects of urban development on land surface characterized by three-dimensional (3D) building metrics, population density, nighttime lights, and transport constructions. By employing composite models and the inequality index, we assessed UDI disparities across 6986 cities and metropolitan areas globally to investigate variations in UDI levels and heterogeneity among the Global North–South regions, inter-city, and intra-city contexts. Furthermore, the bivariate local Moran’s I statistic and k-medoid algorithm were utilized to identify spatial and pattern clusters of UDI among global cities. Our findings revealed diverse spatial distribution of multi-source UDI differences globally, with urban areas in the Global North generally exhibiting higher and more equitable intensity than that exhibited by urban areas in the Global South, where the intensity tends to be lower at the urban peripheries and highly concentrated in city centers. Recognizing the disparities and clustering patterns of UDI levels of global cities underscore the importance of promoting urban equalization to ensure containment and drive improvement in UDI towards diverse models under local urban circumstances for effective implementation of sustainable urbanization development across cities both in the Global North and South.

Unveiling Urban Evolution: Assessing Patterns of Transformation in Guangzhou's Net Production

Urban evolution refers to the continuous changes and adaptation of urban environments to social, economic, technical, and environmental changes, acknowledging that cities are dynamic systems. Urbanization and land use/cover change (LULCC), which are mostly caused by human activity, which have a big impact on the environment. These two variables also affect urban vegetation when combined with climate change. We choose Guangzhou City to examine how urban growth processes and spatial changes in urban areas affect net production (NP). A Machine learning [ML] approach such as Support Vector Machine (SVM) was utilized to examine the relationship between NP and urbanization intensity. This study examined to analyze urban evolution, focusing on the patterns of change in the NP in Guangzhou City, China. The findings demonstrated between 2011 and 2023, the NP in the studied region decreased overall and had distinct geographic variation. The mean Urban Development Index (UDI) in Guangzhou increased significantly between 2011 and 2023. In both years, metropolitan exurbs constituted the majority of the urban geographic category, but the most notable increase in urban fringe areas, which accounted for about 2,320.24 km2 of the urban exurbs. Regarding UDI and NP, there was an opposite relationship, suggesting that the growing intensity of the growth of urban expansion negatively affected NP. Urban development intensity negatively influenced NP, with urban fringes experiencing the most significant losses due to an increase in urbanization and a decrease in agriculture.

Evolution and Built-Up Age Dependency of Urban Thermal Environment

The urban heat island (UHI) represents an anthropogenic modification to the earth’s surface, and its relationship with urban development, built-up age dependency in particular, is poorly understood. We integrated global artificial impervious areas to analyze the impacts of built-up age and urban development intensity (UDI) on land surface temperatures (LSTs) in Hefei, the capital of Anhui Province of China, from 2000 to 2019. A key finding was that the built-up areas with different built-up ages were strongly associated with LST, and this relationship does not change significantly over time, suggesting temporal stability of spatial patterns of LSTs. This finding puts forward a challenge to the application of the classic concept of space-for-time in LST studies because the premise of space-for-time is that spatial and temporal variation are equivalent. This result reveals the vital importance of annual development activities on the urban thermal environment. Another highlighted result was LST sensitivity to UDI, an effective measure of the impact of urbanization on LST, which increased significantly from 0.255 °C per 10% UDI to 0.818 °C per 10% UDI. The more than doubling of LST sensitivity to UDI should be a major concern for city administration. These findings have crucial theoretical and practical significance for the regulation of LSTs and UHI.

Multiclass Compactness Index for Urban Areas

Often urban compactness indexes ignore the variegated intensity of urban development in their focus on the configuration. In this article, I propose an extension to the index of moment of inertia (IMI) as a compactness measure that can account for multiple ordinal classes representing development intensity. I demonstrate this index for developed areas for each county in the contiguous United States. These counties have a range of development configurations and intensity compositions. I show that for this data set, ignoring the multiple class composition and only focusing on the configuration overestimates the urban compactness in almost all instances. I demonstrate the impact of different analytical choices on the index for this data set.

Spatial–Temporal Evolution and Driving Factors of Coupling Coordination between High-Quality Urban Development and Carbon Emissions Intensity in Guangdong Province

Promoting urban green growth necessitates a dual focus on reducing carbon emissions and fostering high-quality development. However, the body of research on the coupling relationship between high-quality urban development and carbon emissions remains remarkably limited. Taking Guangdong province as an example, this study strived to establish a comprehensive evaluation system for high-quality urban development encompassing economic, societal, and ecological dimensions and further conducted an in-depth examination of the spatiotemporal pattern and driving forces of coupling coordination degree between high-quality urban development and carbon emission intensity during 2000–2017. The coupling coordination degree in Guangdong province has shown continuous growth, transitioning from moderate incoordination to moderate coordination. The coupling coordination degree showed the overall spatial distribution characteristics of “high in southeast and low in others”, with Zhuhai, Zhongshan, Foshan, Guangzhou, Dongguan, and Shenzhen as the core. Notably, technological advancement, environmental governance, and economic development emerge as pivotal factors that positively affect carbon emission intensity reduction, environmental quality improvement, and coupling coordination enhancement. This research provides valuable insights for achieving harmonized high-quality development in Guangdong province involving policies of regional differences, industrial competitiveness, and new-type urbanization.

Local carbon emission zone construction in the highly urbanized regions: Application of residential and transport CO2 emissions in Shanghai, China

Optimizing urban morphology can help city managers deploy internal carbon reduction strategies. However, due to the numerous factors that influence urban carbon emissions and the complex paths of their effects, existing research findings are diverse and difficult to compare. This study constructed the local carbon emission zone (LCEZ) which links the highly urbanization morphology profiles and CO2 emission intensity to favor summarizing the form characteristics for the low CO2 emission control in new communities. The approach involved selecting six indicators from the perspectives of urban external morphology, internal characteristics, and development intensity, and using geographically weighted regression (GWR) to identify three key factors. The selected factors were divided into low(1) medium(2) and high(3) categories and were combined with each other to construct the LCEZ. The nonparametric test and GeoDetector model were used to test the difference and similarity of LCEZs, respectively. The results showed that 1) The impacts of functional mixing entropy (FME), building height (BH) and road density (RD) on residential and transport CO2 emissions (RTCE) were relatively large and positive; 2) The low- and medium-FME areas were the major types of LCEZ; 3) The area proportions of the low FME LCEZs in different population areas of the RTCE cold spot regions were the highest (81.84%–88.25%); 4) It is recommended that the morphological feature values of compact-low rise-low network (1-1-1), mid compact-low rise-mid network (1-1-2), compact-mid rise-low network (1-2-1) and mid compact-mid rise-mid network (1-2-2) were taken as the low CO2 emission control of highly urbanized regions.

Collaborative Development and Transportation Volume Regulation Strategy for an Urban Agglomeration

Transportation plays an important role in urban development and the formation of urban agglomerations. Indexes including urban centrality, urban development intensity, and degree of urban development imbalance are defined to measure the level of urban development, and are taken as the basis for transportation regulation within urban agglomerations. Regulation of transportation volume is divided into static and dynamic regulation, and this paper studies static regulation. There are two purposes for static regulation. One is to solve the problems of unbalanced urban development and collaborative development, and the other is to solve the problem of rational utilization of the highway transport network in an urban agglomeration so that the total transportation volume of the urban agglomeration does not exceed the total transportation volume that the transport network can bear, realizing coordinated transport, improving transport efficiency, and reducing traffic congestion and traffic accidents. A distributed intelligent regulation model based on the principle of game control is proposed, which is divided into three layers: macro-regulation (government layer), meso-regulation (urban agglomeration layer), and micro-regulation (individual city game layer). The regulation strategies and methods of the urban agglomeration layer and individual city game layer are given, and are verified and illustrated using as the research object called the Jilin Province urban agglomeration in the northeast of China. The paper contributes to the field by presenting innovative research and provides important theories and methods for collaborative development and transportation within urban agglomerations.

Investigating the relationship between air temperature and the intensity of urban development using on-site measurement, satellite imagery and machine learning

Given the seriousness of the urban heat problem, the relationship between urbanization and air temperature has become a critical concern worldwide. In this study, common urban planning indicators, including the building coverage ratio (BCR), floor area ratio (FAR), and fractional vegetation cover (FVC), were extracted from satellite images to determine the intensity of urban development. On-site measurements and machine learning (ML) were used to observe and analyze the relationship between the intensity of urban development and air temperature. From the on-site measurement results, the air temperature in downtown Taipei decreased by an average of approximately 0.32 °C with every 10 % increase in the FVC. However, it increased by an average of approximately 0.28 °C and 0.03 °C with every 10 % increase in the BCR and FAR, respectively. The results obtained from the ML models demonstrated the same trend, with minor differences from the on-site measurement results, which were regarded as reasonable and acceptable. In this study, a more convenient method was proposed to extract urban planning indicators, describe the intensity of urban development within an area, and help estimate air temperature in areas without measuring instruments. The relationship determined herein may aid in the decision-making process of the balance of urbanization and vegetation.

Factors influencing abundance of 3 tick species across a gradient of urban development intensity in the US Great Plains.

Urbanization alters abiotic conditions, vegetation, and wildlife populations in ways that affect tick abundance and tick-borne disease prevalence. Likely due to such changes, tick abundance has increased in many US urban areas. Despite growing public health importance of tick-borne diseases, little is known about how ticks are influenced by urbanization in North America, especially in the central United States where several pathogens occur at or near their highest incidences. To identify factors influencing tick abundance across a gradient of urbanization intensity, we used CO2 traps and flagging to sample ticks at 16 parks across Oklahoma City, Oklahoma, USA over 2 yr, conducted vegetation surveys, and used trail cameras to estimate a deer abundance index. Our results indicate there is a risk of encountering ticks across the entire urbanization gradient from exurban areas to the urban core, although some species (Dermacentor variabilis (Say)) appear less-common in heavily-urbanized areas. Vegetation variables were also associated with tick abundance. For example, Amblyomma maculatum Koch decreased with increasing woody plant and leaf litter cover, and there was a weak positive relationship between D. variabilis abundance and cover of understory eastern redcedar (Juniperus virginiana L.), indicating this native encroaching tree may increase tick populations in urban areas of the Great Plains. The deer abundance index was positively correlated with A. maculatum and D. variabilis abundance but unrelated to A. americanum (L.) abundance. Public health officials and land managers can use such information about parks/greenspaces and their surroundings to focus public education and land management efforts designed to reduce tick-borne disease prevalence.

Spatial analysis of land use change in a flood-prone watershed: S. Ore Creek, MI, USA

The S. Ore Creek watershed in Hamburg, Michigan is a 93.2 km2 watershed that flows into Ore Lake, whose community is vulnerable to increasing flooding. There have been no studies investigating the influences of upstream inputs on flooding in this watershed, such as urban development and vegetation changes within wetlands. The current study uses the Online ArcGIS software to examine shifts in wetlands and urban development from 2000 to 2019 in the S. Ore Creek watershed. We divided the watershed into four sections from north to south, and identified regions of land use changes within and surrounding wetlands in each section. Using the National Land Cover Database, we identified the area of urban and wetland land use change. We found that there were changes within wetlands (e.g., herbaceous to woody wetlands) in 745,200 m2, increases in intensity of urban development surrounding wetlands in 150,300 m2, and urban development encroaching into wetlands in 62,100 m2. Urban development encroaching into wetlands (and some waterways) occurred in the southern half of the watershed (31,500 m2) with some encroachment in the northernmost section of the watershed (30,600 m2). Relatedly, regions where herbaceous and woody wetlands disappeared occurred in 50,400 m2. Studies have found that increases in urban development and shifts in vegetation within wetlands have been found to increase the potential of flooding. The current study has potential implications for the recent increases in downstream regional flooding.

Research on Spatial–Temporal Characteristics and Driving Factors of Urban Development Intensity for Pearl River Delta Region Based on Geodetector

The Pearl River Delta (PRD) is one of the most dynamic economic regions in the Asia-Pacific region. At present, it still faces many problems, such as the over-exploitation of urban land and unbalanced development. Through the study of the spatial–temporal characteristics of the development intensity of the PRD region and its driving factors, the key points and difficulties of urban development can be intuitively found. In previous studies, geodetector was widely used to determine the impact of driving factors. This paper uses several different research methods, including the Moran index, the semi-variability index, hot and cold spots, etc., based on the land use data of the PRD region in 1990, 2000, 2010, and 2020 to analyze the spatial–temporal characteristics of the development intensity. Combined with the socio-economic data of the statistical yearbook, factor detection and interaction detection of the 10 driving factors of development intensity are carried out based on geodetector, and reasonable optimization suggestions are put forward for the current situation of the region. The main conclusions are as follows: (1) The overall development intensity of the PRD region shows an upward trend, showing a “core periphery” spatial pattern of high center and low periphery centered around the Pearl River estuary. (2) The spatial distribution of cold and hot spots shows agglomeration, mainly in the form of high aggregation and low aggregation. (3) The driving factors for the development intensity for the PRD region in the past 30 years mainly include population agglomeration level, industrial structure level, economic strength level, terrain slope, etc. Among them, any two factors have a stronger interaction than a single factor, and all are enhanced by two factors. The dominant factors of interaction in different periods are different.

Quantifying the effects of urban development intensity on the surface urban heat island across building climate zones

Urban heat island (UHI) has gradually been a critical issue in the context of global environmental change and regional sustainability. The urban development intensity (UDI) increases with rapid urbanization and exacerbates the UHI problem, but the effects and contributions of UDI on UHI remain unclear. Based on Landsat 8 satellite images, this study quantified the effects of UDI on surface UHI intensity (SUHII) in 20 important cities across 5 building climate zones of China by using regression analysis, correlation analysis and boosted regression tree (BRT), and analyzed their potential influence mechanisms. Results indicated that there was apparent spatial heterogeneity of SUHII in different building climate zones (ranging from 3.26 °C to 4.43 °C) and cities (ranging from 1.44 °C to 6.61 °C). The effect of UDI on SHUII in cities of north was stronger than that in south, the average R2 values of the UDI-SUHII relationship in severe cold zone and cold zone were higher than the other climate zones. Building density, as the most critical predictor of SUHII among UDI metrics in all cities, was significantly positively correlated with SUHII. The R2 values showed significant positive correlation with annual average sunshine duration and aridity, and significant negative correlation with annual average precipitation, humidity and soil clay content. Our findings can offer new insights and decision-support in climate-adapted urban planning and heat-related risk management across different building climate zones.

Spatial nonlinear effects of urban vitality under the constraints of development intensity and functional diversity

Urban vitality is the comprehensive characterization of the activities and processes in the city, which is influenced not only by environmental factors but also by the vitality of surrounding areas. Thus, the spatial effect of urban vitality can be understood under the constraint of the built environment. Taking Xiamen Island as a case, multisource big data are used to measure urban vitality, development intensity, and functional diversity. Spatial weight matrices with different distances are constructed. Through the spatial autoregressive threshold model, the threshold values of development intensity and functional diversity are estimated, and the spatial nonlinear effects of urban vitality based on different spatial weight matrices are analyzed. Results show that: (1) There is a significant spatial spillover effect of urban vitality. The spatial spillover effect is influenced by the built environment conditions and shows the characteristics of nonlinear growth with the change in development intensity and functional diversity; (2) the spatial spillover effect of urban vitality is significantly enhanced with the expansion of influence distance; (3) The development intensity has double thresholds. With the improvement of development intensity, the spatial spillover effect of urban vitality becomes stronger and more significant. The functional diversity has a significant single threshold value. The discussion of functional diversity should be carried out on the premise of density. The conclusion provides a theoretical basis for the optimization of urban vitality and built environment.

Understanding archetypal spatial gradient patterns in urban economic, population and air quality nexus: New insights from a geographic-process perspective

The increasing complexity of urban social-ecological dynamics have presented potential challenges on achieving sustainable development goals (SDGs). However, little is known about heterogenous patterns with respect to the multi-dimensional contexts of cities, especially from spatially-explicit and system-integrated perspectives. Here, we developed two spatial trajectory models (exponential and logistic function) from a geographic-process view to quantify spatial gradient patterns of economic-population-air quality nexus along urban development intensity (UDI), and explored their interactions from archetypal analysis that combines static spatial gradient patterns and change magnitude of essential attributes (i.e., background baseline, concentration level) across 32 major cities in China. We found nighttime light intensity and population density experience an accelerated exponential increase, whereas most of PM2.5 concentrations tend to grow in an S-shape manner along a rising UDI gradient. And the evolution dynamics of these three elements are not consistently matched in spatial distribution and aggregation patterns during 2000–2018. Specifically, although the higher economic baseline and more balanced pattern of all the typical 32 cities have been relatively widely achieved during the nearly past two decades, over 80% of sample cities are still currently facing a declining baseline and more concentration level of population density. And nearly half of the cities tend to follow a path of generating PM2.5 pollution that transgressed the Level II concentration threshold of China Ambient Air Quality Standard (GB3095–2012) (>35 μg·m−3) when accompanied by a slightly enhanced spatial aggregated pattern. In terms of archetypal analysis of 32 cities, six regime shifts provide new insights into the prevalence of trade-offs and synergies between urban economic-population-air quality nexus along urbanization levels, basically following the hierarchy and adaptive cycle pathways. Therefore, these new insights into micro- and macro- city analysis allow for spatial refinement of urban internal structure and identifying specific challenges to inform target spatial management for ongoing desirable trajectories towards sustainable urban development.

Study on Coupling Coordination Relationship between Urban Development Intensity and Water Environment Carrying Capacity of Chengdu–Chongqing Economic Circle

The high-level coupling coordination relationship between urban development and a city’s water environment carrying capacity is conducive to urban sustainable development. Taking the Chengdu–Chongqing Economic Circle as the research object, this study developed an index system to determine the urban development intensity and water environment carrying capacity, respectively. Based on the comprehensive evaluation model and the coupling coordination degree model, the spatial distribution characteristics, agglomeration law of urban development intensity, water environment carrying capacity and their coupling coordination relationship were analyzed. The results showed that the areas with a high urban development intensity were distributed in the main urban districts of Chengdu and Chongqing, and the intensity gradually reduced away from both core cities, which formed a “ripple-like” pattern. The areas with a high water environment carrying capacity were mainly in the southwest, southeast, northeast and central parts of the region and were distributed in a “W” pattern along the Yangtze River. The coupling coordination degree between the urban development intensity and water environment carrying capacity was low, as only 11.4% of the cities were coordinated while most cities were slightly uncoordinated. The research results can provide a theoretical basis for sustainable urban development in the Chengdu–Chongqing Economic Circle.

Spatial autocorrelation and spatial heterogeneity of underground parking space development in Chinese megacities based on multisource open data

Underground parking is prevalent in high-density megacities. Understanding the use patterns of underground parking spaces (UPSs) is important for sustainable and resilient urban development. As such, in this study, we employed multisource open data to investigate the spatial autocorrelation and spatial heterogeneity of the UPSs in seven representative Chinese megacities, determining the spatial distribution pattern of and forces driving UPS development. We used the underground parking ratio (UPR) and underground parking density (UPD) at the subdistrict level as critical indicators of UPS use. We found that the UPSs tend to be centrally clustered, whereas the UPR is high in the urban periphery. Both UPR and UPD showed positive univariate spatial autocorrelation, with UPD being much more spatially autocorrelated. The results of univariate spatial autocorrelation revealed the spatial mismatch between UPR and UPD, being high-low in the urban periphery and low-high in the urban centre, respectively. The results of spatial heterogeneity analysis indicated that urban function and land development intensity are common drivers of both UPR and UPD, and socioeconomic conditions are the specific drivers of UPD. Although traffic factors did not have a predominant influence on UPSs, they substantially enhanced the effects when integrated with other factors.