Urban Built Environment Research Articles

Identification and Causes of Neighborhood Commercial Areas: Focusing on the Development of Daily Life Circles in Urban Built Environments

Urban planning in China is shifting from an administrative unit-based approach to community life circle planning, aiming to align planning units with residents’ actual activity ranges. As the most fundamental life circle, daily life circle (DLC) planning must adopt a bottom-up approach. However, the widely applicable methods for delineating DLCs remain lacking. This study presents a strategy for delineating DLCs centered on neighborhood commercial areas that aggregate essential daily life services. Correspondingly, a method is proposed for identifying neighborhood commercial areas based on residents’ actual usage of facilities. The method was applied in Qinhuai District, Nanjing, where neighborhood commercial areas were identified and the factors influencing their formation and types were quantitatively analyzed. The results indicate the following: (1) the proposed method accurately identifies neighborhood commercial areas that can serve as DLC central areas; (2) commercial diversity, public transportation stops, and parking spots are the three most influential factors in neighborhood commercial area formation, exhibiting non-linear and threshold effects; and (3) the type of neighborhood commercial areas varies by population density, housing prices, and street betweenness, with betweenness being the most significant factor. These findings provide methods and indicators for DLC delineation and planning, contributing to the realization of the DLC construction concept.

Exploring the Complex Association Between Urban Built Environment, Sociodemographic Characteristics and Crime: Evidence from Washington, D.C.

The urban built environment and sociodemographic characteristics have complex relationships with urban crime. However, previous studies have had limitations such as generalizing urban green space types, urban functionality, and sociodemographic characteristics. Given these, this study aimed to explore the relationship between them using more detailed indicators. The study utilized Google Street View and points of interest to depict the built environment. Building on previous work that segmented natural and artificial elements in streetscape images, this study further distinguished trees, bush, and grass. Additionally, it incorporated data from the Data Analysis and Visualization Unit of the DC Office of Planning to reflect a broader range of individual characteristics. Weighted least squares regression and Pearson correlation analysis were used to test the relationship between the built environment, sociodemographic, and crime, respectively. Some of the key findings are as follows. (1) Trees, bushes, and grass all reduce crime. (2) Urban functionality is hard to curb crime by enhancing informal public surveillance. (3) Among the sociodemographic variables, the walking commute rate is the variable most strongly positively correlated with crime. (4) Family relationships play an important role in suppressing crime. This study examined a more comprehensive range of indicators affecting urban crime in favor of safer cities.

Evaluating the Potential of Nature-Based Solutions (NBS) and Solar Energy in Urban Built Environments with Aerial Photogrammetry Dataset

Abstract. The process of urbanisation has a significant impact on climate change, with projections indicating that over 70% of the global population will live in cities by 2050. The challenges associated with climate change impacts on urban areas include urban heat islands, unsustainable water management, floods and air pollution. This study explores the potential of Nature-Based Solutions (NBS) and renewable energy in Torino, Italy, with a particular focus on the exploitation of rooftops for solar panels and green roofs. The data from 159 buildings in San Salvario was collected using aerial photogrammetry. The solar potential was analysed with ArcGIS, taking into account both geographic and building-specific factors. Buildings that were unsuitable for solar panels were identified for green roofs. This research supports the United Nations Sustainable Development Goals 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), 13 (Climate Action), and 15 (Life on Land). The annual solar potential was estimated at 100,300 kWh, with 2,450 m2 of suitable roof area for green roofs. The study demonstrates the feasibility of using rooftops to increase urban sustainability, thereby enhancing the quality of life for residents. The integration of NBS and solar energy can facilitate the creation of more sustainable and resilient urban environments. The study recommends the implementation of spatial-enabled urban planning as a means of overcoming barriers and promoting the development of green, energy-efficient communities.

Data Discovery for Digital Building Logbook (DBL): Directly Implementing and Enabling a Smarter Urban Built Environment

Digital Building Logbooks (DBLs) are the EU repositories for all building-related data. Logbook implementation conveys challenges, but it must be recognised that relevant things already exist. This article bridges the gap at the data discovery level by assessing the existing data and comparing it with EU DBL studies. Action research is the methodology, employing Portugal as an example. A deductive approach and interpretivism are used, supporting the data discovery journey. When evaluating existing datasets with DBL EU guidelines data requirements, the findings demonstrate a match from 90.6% to 82.6%, depending on the level: cadastral parcel, building or building unit. Several additional observed datasets suit the DBL framework, constituting a path for future research. Insights into the dataset landscape from a specific perspective are offered. Given the deliverables’ characteristics, the study results can be generalised. The data discovery journey led to the understanding that duplicates and inconsistencies exist. A strategic approach for data sharing, governance and usage should be established to solve them, increasing digital maturity, integration and interoperability. Revising the legal framework is found to be paramount. Working from the existing elements and aligning them with data space assumptions can make DBL implementation more straightforward.

Urban Built Environment and Flood Ramifications: Evidence from Insurance Claims Data in Miami, Florida

This study examines the role of the urban built environment in mitigating or exacerbating flooding. By analyzing census tract-level insurance claims in Miami, we model such relationships during moderate and extreme flood events. Our findings indicate that lower population density, higher urban compactness, and proximity to coastal and riparian zones are linked to elevated flood insurance claims. The study also highlights the potential of nature-based solutions in alleviating flood impacts during extreme events. These insights provide crucial empirical evidence for urban planners and policymakers to adopt a systemic approach considering density, location, and nature-based solutions to foster climate-resilient cities.

The Heterogeneous Effects of Microscale-Built Environments on Land Surface Temperature Based on Machine Learning and Street View Images

Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being of residents. However, due to limitations in data precision and analytical methods, existing studies often overlook the microscale examination closely related to residents’ daily lives, and lack a deep exploration of the spatial heterogeneity of the influencing factors. This leads to these results being ineffective in guiding the planning and construction of cities. Taking Shenzhen as a case study, our study investigates the effects of various microscale build environment characteristics of LST using street view images and machine learning. A convolutional neural network model adopting the SegNet architecture is used to perform semantic segmentation on street view images, extracting features of the microscale urban-built environment. The LST is inverted through the Google Earth Engine (GEE) platform. By using Multiscale Geographically Weighted Regression (MGWR) models, our study reveals the comprehensive impact of the urban-built environment on LST and its significant spatial heterogeneity. The findings indicate that the proportions of sky, roads, and buildings are positively correlated with LST, while trees have a significant cooling effect. Although earth and water can reduce LST, their overall contribution is minimal due to limitations in their area and distribution patterns. This study not only reveals the key factors affecting urban LST at the microscale but also emphasizes the necessity of considering the spatial heterogeneity of these factors’ impacts. This suggests the need for targeted strategies for different areas to effectively improve the urban thermal environment and achieve sustainable urban development.

An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks

Traditionally, research in the field of traffic safety has predominantly focused on two key areas—the identification of traffic black spots and the analysis of accident causation. However, such research heavily relies on historical accident records obtained from the traffic management department, which often suffer from missing or incomplete information. Moreover, these records typically offer limited insight into the various attributes associated with accidents, thereby posing challenges to comprehensive analyses. Furthermore, the collection and management of such data incur substantial costs. Consequently, there is a pressing need to explore how the features of the urban built environment can effectively facilitate the accurate identification and analysis of traffic black spots, enabling the formulation of effective management strategies to support urban development. In this study, we research the Kowloon Peninsula in Hong Kong, with a specific focus on road intersections as the fundamental unit of our analysis. We propose leveraging street view images as a valuable source of data, enabling us to depict the urban built environment comprehensively. Through the utilization of models such as random forest approaches, we conduct research on traffic black spot identification, attaining an impressive accuracy rate of 87%. To account for the impact of the built environment surrounding adjacent road intersections on traffic black spot identification outcomes, we adopt a node-based approach, treating road intersections as nodes and establishing spatial relationships between them as edges. The features characterizing the built environment at these road intersections serve as node attributes, facilitating the construction of a graph structure representation. By employing a graph-based convolutional neural network, we enhance the traffic black spot identification methodology, resulting in an improved accuracy rate of 90%. Furthermore, based on the distinctive attributes of the urban built environment, we analyze the underlying causes of traffic black spots. Our findings highlight the significant influence of buildings, sky conditions, green spaces, and billboards on the formation of traffic black spots. Remarkably, we observe a clear negative correlation between buildings, sky conditions, and green spaces, while billboards and human presence exhibit a distinct positive correlation.

Expansion of Landscape Architecture Body of Knowledge Based on the Regeneration of Urban Built Environment

Expansion of Landscape Architecture Body of Knowledge Based on the Regeneration of Urban Built Environment

Review of Public Sentiment Experience Research for Urban Built Environment

Review of Public Sentiment Experience Research for Urban Built Environment

Dynamic Impact of Urban Built Environment on Land Surface Temperature Considering Spatio-Temporal Heterogeneity: A Perspective of Local Climate Zone

Thermal environment deterioration has seriously threatened urban habitat quality and urban sustainable development. The evolution of the urban built environment (UBE) is an important cause for urban thermal environment variation. However, the dynamic effect of the UBE on the land surface temperature (LST) is rarely studied by combining the local climate zone (LCZ) theory and spatio-temporal heterogeneity. Based on a case study of Beilin District in Xi’an, China, this paper identified LCZ types of Beilin District in 2010, 2015, and 2020 using the GIS method. It also analyzed the spatial–temporal characteristics of the LST in summer based on the remote sensing retrieval method and explored the effects of the built environment on the LST by Geodetector and geographically weighted regression (GWR). The results showed the following: (1) The area share of dense building zones in Beilin District was greater than that of open building zones and natural surface zones, while the share of mid- and high-rise dense building zones continued to increase and the share of low-rise dense building zones continued to decrease during the study period. (2) The LST of different LCZ types in Beilin District was obviously different, and the LST of dense building zones was generally higher than that of open building zones and natural surface zones. Meanwhile, the LST of mid- and low-rise dense building zones increased gradually, and the LST of high-rise open building zones decreased gradually, but the overall warming area was obviously more than the cooling area. (3) The effects of the UBE factors on the LST varied greatly, with their interaction having an enhancement effect. The direct and interactive influence of the two-dimensional (2D) UBE indicators on the LST were greater than those of the three-dimensional (3D) indicators, but there was a gradual decrease in the force of the 2D indicators and a simultaneous diminution, enhancement, and invariance of the force of the 3D indicators. (4) Vegetation cover (VC) and floor area ratio (FAR) acted negatively, and the building height (BH) was changing from a positive to a negative role, with the average action intensity of VC changing from −0.27 to −0.15, FAR from −0.20 to −0.16, and BH from 0.05 to −0.04. The impervious surface area (ISA), building area (BA), and space congestion (SC) acted positively, with the average action intensity of the ISA changing from 0.12 to 0.20, BA from 0.12 to 0.19, and SC was stable at 0.04. The framework enables a deeper portrayal of LST changes in different LCZs, reflecting the direct and interactive effects of different UBE indicators on LST, as well as local variations in the impact effects and provides a basis for urban managers or planners to improve urban heat resilience.

Bibliometric Insights into the Implications of Urban Built Environment on Travel Behavior

With the rapid pace of global urbanization, understanding the impact of the urban built environment on travel behavior has become increasingly significant for developing sustainable and efficient transportation systems. This study conducts a bibliometric review of related research over the past two decades (1997–2023), utilizing 1745 publications from the Web of Science database through network analysis and content analysis. It provides a comprehensive quantitative analysis encompassing publication trends, national and institutional collaborations, and keyword evolution clustering perspectives. The results reveal that (1) academic interest in exploring the implications of the urban built environment on travel behavior has grown markedly, especially in the past decade, with emerging technological approaches and research perspectives; (2) the USA, P.R.CHINA, and the United Kingdom are major research forces in this field, with notable contributions from research institutions in P.R.CHINA and the USA; (3) the “Transportation Research Part” series journals demonstrate extensive influence both in terms of publication count and citation count; (4) through keyword co-occurrence network analysis, three development stages along with five major clusters were identified: travel behavior modeling and public health, active transportation and sustainable development, urban development and carbon emissions, land use and transportation integration, and urban transportation systems and machine learning. Overall, sustained research remains warranted within this field, particularly focusing on selecting new built environment metrics while integrating emerging technologies into travel behavior modeling frameworks. The insights from this study have implications for urban transportation planning and policy, offering guidance on future research directions and policymaking.

Study on the Relationship between the Spatial Distribution of Shared Bicycle Travel Demand and Urban Built Environment

As a green and sustainable trip mode, shared bicycles play an essential role in completing short-distance trips in cities. This paper proposes a method to analyze the impact of the urban built environment on the distribution of shared bicycles in a small-scale space. First, the Fishnet function of ArcGIS is utilized to divide the study area into grids of 500 m × 500 m. Then, three indicators are proposed to describe the characteristics of the urban built environment, including point of information (POI) comprehensive index, the intensity of public transportation coverage, spatial accessibility, providing them the ways to be assigned to the grids. Finally, the multivariable linear regression model and support vector regression (SVR) models are applied to reveal the impacts of built environment factors on the spatial distribution of shared bicycles. Results show that SVR models based on linear kernel function, Gaussian radial basis kernel function, and Polynomial kernel function can achieve better analysis results. The SVR model based on the Gaussian radial basis function shows higher explanatory power (adjusted R2 = 0.978) than the multivariable linear regression model (adjusted R2 = 0.847). This paper can aid in understanding the demand and supply of shared bicycles and help operators or governments to improve service quality.

Influence of Urban Built Environment on Severity of PM-Pedestrian Accidents in Seoul

Influence of Urban Built Environment on Severity of PM-Pedestrian Accidents in Seoul

Retracted: Study on the Influence of Urban Built Environment Factors on the Social Behavior of ASD Children.

[This retracts the article DOI: 10.1155/2022/8963416.].

Changes in Urban Planning in Response to Pandemics: A Comparative Review from H1N1 to COVID-19 (2009–2022)

The COVID-19 pandemic has brought enormous casualties and huge losses to cities around the world, causing urban planning to reflect on its serious inadequacy in public health crisis management. Looking back at the pandemics of modern history, urban planning has been dedicated to enhancing disease prevention capacity as well as improving the wellness of human beings. By systematically comparing the urban planning response between COVID-19 (2019) and its predecessor H1N1 (2009) in the literature, this paper seeks to explore how urban planning theories evolved through the pandemics and whether COVID-19 has led to possible new implications and directions for urban planning in the future. A total of 3129 related results with overlapping themes of “city”, “pandemic”, and “planning” in the database were narrowed down to 30 articles published between 2009 and 2019 on the topic of H1N1 and 99 articles published between 2020 and 2022 on the topic of COVID-19 after careful extraction and integration. Through bibliographic and detailed analysis, twelve urban theories used to fight against pandemics were identified. In addition, three main changes between urban planning responses to the H1N1 and COVID-19 pandemics were summarized: from focusing on stages of “in-pandemic” and “pre-pandemic” to focusing on stages of “post-pandemic”, from global and national to local, and from the absence of an urban-built environment to a return to ‘healthiness’ in urban planning and design. Such comparisons are useful for examining the current situation and providing suggestions for a possible upcoming outbreak.

Vertical Cities: Emergent Patterns of Movement and Space Use in Dense Vertically Integrated Urban Built Environments

In high-density, land-scarce cities like Singapore, the successful translation of ground-level urban qualities and benefits into vertical living is crucial for social, economic, environmental, and ecological sustainability. This research introduces a Network science-based spatial analysis framework to evaluate the connectivity and relationships of vertically integrated urban open spaces. Kampung Admiralty (KA), a unique development integrating housing for the elderly with various facilities, serves as a case study. The methodology combines static spatial network measures and real-world movement data to predict movement flows, accessibility, and connectivity. Lift lobbies and elevated garden connectors emerged as critical paths, effectively distributing pedestrian flows. Landscape spaces played a key role in visual and physical connectivity, offering high recreational and social value. Strategic placement of “social attractors” improved space utilization. The study highlights the importance of spatial design parameters in user-space interactions and provides insights into socio-spatial networks at both ground and elevated levels. It identifies key connectors that facilitate effective planning and design of vertically integrated public space networks, promoting social and spatial effectiveness.

Geographic Information Systems for Facilitating Audits of the Urban Built Environment

Data collection and large-scale urban audits are challenging and can be time consuming processes. Geographic information systems can extract and combine relevant data that can be used as input to calculation tools that provide results and quantify indicators with sufficient spatial analysis to facilitate the local decision-making process for building renovations and sustainability assessment. This work presents an open-access tool that offers an automated process that can be used to audit an urban area in order to extract relevant information about the characteristics of the built environment, analyze the building characteristics to evaluate energy performance, assess the potential for the installation of photovoltaics on available building rooftops, and quantify ground permeability. A case study is also presented to demonstrate data collection and processing for an urban city block, and the relevant results are elaborated upon. The method is easily replicable and is based on open data and non-commercial tools.

Evidence of Multi-Source Data Fusion on the Relationship between the Specific Urban Built Environment and Urban Vitality in Shenzhen

Urban vitality is the key element of sustainable urban development. This paper aims to explore the relationship between urban vitality and the existing built-up environment of the city of Shenzhen. The regression models with multi-source geographic datasets from 2021–2022 were applied to assess Shenzhen in three dimensions: economic, social, and cultural. The results show that Shenzhen’s vitality originates from multiple popular centers. Dense road networks, abundant transportation, and commercial, recreational, entertainment, sports, and leisure facilities are positive indicators of vitality, while urban villages and residential areas have the opposite effect. The model can explain 59% of vitality changes. This paper proposes a quantifiable and replicable adaptation framework for urban villages that combines urban form with data vitality assessment in order to deepen our understanding of urban villages and offer theoretical justifications for long-term urban regeneration. The findings also suggest that spatial differences should be taken into account when formulating urban regeneration responses to make them more targeted. Overall, this paper provides valuable insights for urban planners, policymaker and researchers interested in promoting sustainable urban development through vitality-based urban regeneration.

Determining behavioural-based risk to SLODs of urban public open spaces: Key performance indicators definition and application on established built environment typological scenarios

A behavioural-based approach can be used to assess how users’ reactions to surrounding environmental conditions can alter the urban Built Environment (BE) risk to Slow Onset Disasters (SLODs). Public Open Spaces (POSs) in the BE are relevant scenarios, due to micro-climate-related stress, users’ vulnerabilities (e.g., age, health frailty) and exposure time. Simulation methods can support behavioural-based risk-assessment, but results are generally site-specific. Performing analysis on BE Typologies (BETs) can improve robustness, since BETs represent archetypes from real-world scenarios. This work adopts a behavioural-based approach to evaluate time-dependant users’ risks of POSs in different BETs due to SLODs-related stress (i.e., heat, air pollution). UTCI and AQI values are mapped within each BET. Users’ distributions are then calculated depending on thermal acceptability correlations. Key Performance Indicators are developed associating users’ distribution to SLODs effects on health (i.e., sweat rate, water loss; health affection rate probability). The approach is applied to Italian BETs, under one relevant climate, rating their heat and air pollution risks. Results suggest critical conditions for toddlers. In detail, about 2-hour high heat exposure could result in dehydration, while 1-hour exposure to low NO2 concentration could result in +1% mortality probability. This approach could potentially support decision-makers on BE risk-assessment.

RivEr/Generation_LAB-Linking Resilience with Inclusiveness in the Urban-Built Environment of Rome

The impact of metropolization processes and climate change effects on natural and anthropic environments, together with energy waste, the excessive consumption of agricultural and natural soils and their progressive waterproofing and a reduction in vegetation cover, highlights the need for sustainable management of existing resources, in terms of equitable and ethical development, towards sustainable and inclusive communities able to adapt to the negative effects of emergency phenomena. This contribution presents the results of the activities conducted in the RivEr/Generation_LAB, a project organized by three CIVIS members (Sapienza University of Rome, Universitè libre de Bruxelles, Universidad Autonoma de Madrid) as a part of the CIVIS Project “RivEr/Generation_LAB. Linking resilience with inclusiveness in the urban built environment of Rome, Brussels, and Madrid”, financed by the Hub4 Cities, Territories & Mobilities’ Call for proposals 2021. The project proposes a methodology of intervention in the Flaminio district, in particular in the Olympic Village and its relationship with the Tiber River, in line with the Sustainable Development Goals, the Millennium Ecosystem Assessment and the New European Bauhaus, to establish new relationships between cities and the natural environment, favoring sustainable and inclusive public spaces.