How should cities invest to improve social welfare when individuals respond strategically to local conditions? We model this question using a game-theoretic version of Schelling's bounded neighbourhood model, where agents choose neighbourhoods based on concave, non-monotonic utility functions reflecting local population. While naive improvements may worsen outcomes - analogous to Braess' paradox - we show that carefully designed, small-scale investments can reliably align individual incentives with societal goals. Specifically, modifying utilities at a total cost of at most $0.81 ε^2 \cdot \texttt{opt}$ guarantees that every resulting Nash equilibrium achieves a social welfare of at least $ε\cdot \texttt{opt}$, where $\texttt{opt}$ is the optimum social welfare. Our results formalise how targeted interventions can transform supra-negative outcomes into supra-positive returns, offering new insights into strategic urban planning and decentralised collective behaviour.
Cities have developed over time alongside advancements in civilization, focusing on efficient travel and reducing costs. Many studies have examined the distinctive features of urban road networks, such as their length, efficiency, connection to population density, and other properties. However, the relationship between car routes and population in city structures remains unclear. In this study, we used the center of mass for each city tract, defined by the US Census, as the origins and destinations for our itineraries. We calculated travel time, and both Euclidean and travel distances for sixty major cities. We discovered that the total sum of all routes adheres to an urban law. The distribution of these car journeys follows Weibull functions, suggesting that the urban center plays a crucial role in optimizing routes across multiple cities. We also developed a simple point pattern model for the population, which aligns with the well-known decreasing exponential density expression. Our findings show that the interplay between population and path optimization influences city structure through its center. This study offers a new perspective on the fundamental principles that shape urban design.
Urban-induced changes in local microclimate, such as urban heat islands and air pollution, are known to vary with city size, leading to distinctive relations between average climate variables and city-scale quantities (e.g., total population). However, these approaches suffer from biases related to the choice of city boundaries and they neglect intra-urban variations of urban characteristics. Here, we use high-resolution data of urban temperatures, air quality, population, and street networks from 142 cities worldwide and show that their marginal and joint probability distributions collapse onto a set of general scaling functions. Using a logarithmic relation between urban spatial features and climate variables, we find that average street network properties are sufficient to characterize the entire variability of the temperature and air pollution fields observed within and across cities. These findings provide a unified statistical framework for characterizing intra-urban climate variability, with important implications for climate modeling and urban planning.
The United Nations’ Sustainable Development Goal 11.7 (SDG 11.7) is primarily used to assess the sustainability of urban public spaces. Urban spatial structure (USS) can profoundly influence the level of SDG 11.7. Existing research has typically focused on the impact of single-dimensional USS indicators on SDG 11.7, often failing to incorporate multiple dimensions into a comprehensive evaluation system. Based on an assessment of SDG 11.7 in 265 Chinese cities, this study selected six-dimensional USS indicators that comprehensively reflect various aspects of USS, namely, urban size, urban sprawl, urban accessibility, urban expansion, urban compactness, and urban shape. It then systematically analyzed the impact of these indicators on SDG 11.7 using panel regression, geographically and temporally weighted regression, quantile regression, and the spatial Durbin model. Furthermore, this study explored the spatial heterogeneity, nonlinear characteristics, and spatial effects present in the influence of USS on SDG 11.7. The findings indicated the following: (1) urban size, accessibility, and compactness significantly impacted SDG 11.7, with urban size and accessibility having positive effects; (2) the impact of USS on SDG 11.7 varied across different spatial locations, and these spatial disparities evolved over time; (3) the impact of USS on SDG 11.7 exhibited nonlinear characteristics. In cities with higher SDG 11.7 levels, the positive effects of urban accessibility and shape became more pronounced; (4) USS affected not only local SDG 11.7 but also that of neighboring cities through spatial effects. These findings elucidate how USS affects SDG 11.7, thereby providing decision support for sustainable urban development.
Urbanization. City and country, Political institutions and public administration (General)
Abstract Mongolia is among the countries undergoing rapid urbanization, and its temporary nomadic dwellings—known as Ger—have expanded into urban areas. Newly formed ger communities in cities are potentially recognized as informal settlements, or slums. The distinctive circular, tent-like shape of gers enables their detection through very-high-resolution satellite imagery. We develop a computer vision algorithm to detect gers in Ulaanbaatar, the capital of Mongolia, utilizing satellite images collected from 2015 to 2025. Results reveal that ger settlements have relocated towards the capital’s peripheral areas. The predicted ger household ratio based on our results exhibits a significant correlation (r = 0.85) with the World Bank’s district-level poverty data. Our nationwide extrapolation suggests that housing improvements in informal settlements have fallen short of official projections by an average of 2.8% since the COVID-19 pandemic. We discuss the potential of machine learning on satellite imagery in providing insights into urbanization patterns and sustainable development.
Purpose: The aim of the study was to analyze the urbanization and its influence on public health in Southeast Asia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Urbanization in Southeast Asia significantly impacts public health through strained healthcare infrastructure, environmental health risks like pollution and inadequate sanitation, and increased prevalence of non-communicable diseases due to urban lifestyles. Infectious disease transmission is amplified in densely populated urban areas, exacerbated by social determinants of health disparities. Effective urban health policies and integrated planning are essential to mitigate these challenges, promoting sustainable development and equitable healthcare access across the region. Unique Contribution to Theory, Practice and Policy: Urbanization theory, social determinants of health theory & ecological systems theory may be used to anchor future studies on urbanization and its influence on public health in Southeast Asia. Initiatives should focus on promoting healthy urban environments through sustainable urban planning and design strategies. Policymakers should prioritize the integration of health considerations into urban planning policies and initiatives.
Sandro M. Reia, Taylor Anderson, Henrique F. Arruda
et al.
The relationship between urban form and function is a complex challenge that can be examined from multiple perspectives. In this study, we propose a method to characterize the urban function of U.S. metropolitan areas by analyzing trip patterns extracted from the 2017 National Household Travel Survey (NHTS). To characterize urban form, we employ measures that capture road network topology. We cluster cities based on both form and function and subsequently compare these clusters. Our analysis of 52 U.S. metropolitan areas identifies 7 distinct clusters of cities that exhibit similar travel behavior, suggesting that diverse mobility patterns can be effectively grouped into a few universal classes. The observed disparity between the urban-function clustering and the urban-form clustering suggests that travel behavior in the U.S. is not strongly influenced by the physical infrastructure of the city.
Abstract Urbanization is occurring at an unprecedented rate in developing countries like Ethiopia, especially with the rapid growth of industrialization. However, this urbanization and industrial development surge puts immense pressure on preparing and implementing city spatial plans. Urban spatial planning plays a crucial role in shaping the development of cities, aiming to create livable and sustainable urban development. The plan-making process and its subsequent implementation often encounter challenges that hinder the realization of planned objectives. Galan and Dukem, cities near Addis Ababa’s capital, have seen substantial industrial investment in recent decades. This article explores the challenges associated with urban spatial planning and industrial development while proposing alternative approaches. The paper relies on information gathered from primary and secondary sources, including expert surveys and key informants’ interviews. The research highlights that industrial developments have led to significant and uncontrolled changes in urban land use and urbanization in the study areas. Due to the lack of clear planning guidelines and institutional capacity, the study cities are experiencing haphazard development without effective urban spatial plans. Proximity to Addis Ababa has driven the urbanization process through increased investments. The article contends that effective management of urbanization and industrialization can create enjoyable living conditions and foster job growth. Macroeconomic policies, including industrial policies, should pay attention to spatial elements and prevent policy-making processes that are “space-blind”. This, however, requires clairvoyance and a high level of expertise, integrating inputs from advocacy planning and community participation to bridge gaps and ensure effective urban spatial plans and industrialization processes.
Land-use changes and urban sprawl have transformed European cities, with a direct impact on both metropolitan structures and socioeconomic functions. However, these processes tend to be relatively different across countries, being influenced by place-specific factors associated to socioeconomic, historical, political and cultural factors that influence decisions on the use of land. Considering 155 metropolitan areas in 6 European macro-regions, the present study investigates spatial patterns of land consumption profiling cities according to a large set of territorial variables, with the final objective to identify relevant socioeconomic dimensions characteristic of recent processes of urban growth. Investigating the socioeconomic background underlying land-use changes in metropolitan regions allows identification of place-specific factors improving the design of effective strategies containing land consumption in different European urban typologies. An exhaustive analysis of land-use changes at regional and local spatial scales contributes to find alternative policies for land-use efficiency and long-term environmental sustainability.
As a major form of administrative division adjustment in China since the reform and opening up, abolishing county and establishing district policy has reshaped the regional administrative power structure, which in turn has had a profound impact on regional economic and social development. Based on data from population census and land survey, this paper uses the general OLS method to estimate the effect of abolishing county and establishing city-administered district reform on the imbalance of urbanization development in prefecture-level cities. The study finds that the reform of abolishing counties and setting up districts softens the constraint of construction land targets and creates favorable conditions for local governments to "seek development through land use", but the larger supply of construction land eventually exacerbates the imbalance between population urbanization and land urbanization, and this finding still holds for cities where the initial relationship between people and land is not tense. By region, the effect of urbanization imbalance exacerbated by abolishing county and establishing district policy is significant in the eastern region but not in the central and western regions; moreover, the effect of urbanization imbalance exacerbated by county revocation is weakened in cities with higher degree of financial deepening. The rapid urbanization with land as the core has supported China's rapid economic growth for a long time in the past, but the role of land as an economic engine is declining. Promoting a new type of people-oriented urbanization and making efforts to improve the quality of urbanization construction is the right way to transform China's economic development mode and promote high-quality development in the future.
Francisco Rowe, Carmen Cabrera-Arnau, Miguel González-Leonardo
et al.
The COVID-19 pandemic has impacted the national systems of population movement around the world. Existing work has focused on countries of the Global North and restricted to the immediate effects of COVID-19 data during 2020. Data have represented a major limitation to monitor change in mobility patterns in countries in the Global South. Drawing on aggregate anonymised mobile phone location data from Meta-Facebook users, we aim to analyse the extent and persistence of changes in the levels (or intensity) and spatial patterns of internal population movement across the rural-urban continuum in Argentina, Chile and Mexico over a 26-month period from March 2020 to May 2022. We reveal an overall systematic decline in the level of short- and long-distance movement during the enactment of nonpharmaceutical interventions in 2020, with the largest reductions occurred in the most dense areas. We also show that these levels bounced back closer to pre-pandemic levels in 2022 following the relaxation of COVID-19 stringency measures. However, the intensity of these movements has remained below pre-pandemic levels in many areas in 2022. Additionally our findings lend some support to the idea of an urban exodus. They reveal a continuing negative net balances of short-distance movements in the most dense areas of capital cities in Argentina and Mexico, reflecting a pattern of suburbanisation. Chile displays limited changes in the net balance of short-distance movements but reports a net loss of long-distance movements. These losses were, however, temporary, moving to neutral and positive balances in 2021 and 2022.
The gravity model of human mobility has successfully described the deterrence of travels with distance in urban mobility patterns. While a broad spectrum of deterrence was found across different cities, yet it is not empirically clear if movement patterns in a single city could also have a spectrum of distance exponents denoting a varying deterrence depending on the origin and destination regions in the city. By analyzing the travel data in the twelve most populated cities of the United States of America, we empirically find that the distance exponent governing the deterrence of travels significantly varies within a city depending on the traffic volumes of the origin and destination regions. Despite the diverse traffic landscape of the cities analyzed, a common pattern is observed for the distance exponents; the exponent value tends to be higher between regions with larger traffic volumes, while it tends to be lower between regions with smaller traffic volumes. This indicates that our method indeed reveals the hidden diversity of gravity laws that would be overlooked otherwise.
Abstract The implementation of the Sustainable Development Goals (SDGs) relies on effective policy integration at all levels of government. However, integration across policy domains remains challenging for local authorities, particularly when it comes to articulating policies that recognise trade-offs and interactions between different SDGs. This study explores how the Voluntary Local Review (VLR) process—a tool to localise the 2030 Agenda—contributes to policy integration by thematically analysing interviews with city officials in 12 frontrunner cities that conducted a VLR between 2019 and 2020. Our results suggest three main ways in which the VLR process affects policy integration: (1) by facilitating cooperation and interdependencies between different policy sectors; (2) by creating new instruments to mainstream SDGs; and (3) by enhancing sustainability competencies. Hence, our study suggests that conducting a VLR has the transformative potential to achieve greater policy integration and further the 2030 Agenda.
Abstract Previous studies have established a significant link between urban form and sustainability. However, the diversity of micro-scale urban forms in cities in the global south has received limited attention, hindered by the lack of neighbourhood-level spatial data and maps, which poses challenges in exploring micro-urban form features. The study addresses this gap using a grid-based k-means clustering algorithm to identify residential built-up form typologies in Delhi and assess their impact on sustainable urbanisation. The algorithm clusters 100×100 metre grid cells based on their attributes of accessibility, built-up density, and street design. The results show six distinct built-up form typologies in Delhi. However, only 19% of residential areas meet the criteria for sustainable urbanisation, highlighting the need for planning interventions in most areas. The study methodology can be applied to analyse micro-scale urban form features in other cities in the global south, providing a fresh perspective on urbanisation research.
Cities have been one of the most important areas of CO2 emissions. It is increasingly important to research the effect of urbanization on CO2 emissions, especially in large emerging and developing economies, due to the indispensable need for understanding the effect of urbanization on CO2 emissions, evaluating carbon reduction tasks and providing the scientific basis for low-carbon urbanization. Utilizing a balanced panel dataset in the Yangtze River Delta (YRD), China, during the period of 2000–2010, this paper employed data envelopment analysis (DEA) window analysis and a spatial lag panel Tobit model to investigate the effect of urbanization on CO2 emissions efficiency (the ratio of the target CO2 emissions to the actual CO2 emissions). The results show that the average CO2 emissions efficiency was 0.959 in 2010, and CO2 emissions efficiency ranged from 0.816 to 1 and exhibited spatial clustering in the region. The larger potential of CO2 emissions reduction appeared in Zhenjiang and Yangzhou, indicating that more CO2 emissions reduction tasks should be allocated to these two cities. Urbanization has negative effects on improving CO2 emissions efficiency, and there is a U-curve relation between CO2 emissions efficiency and urbanization, indicating that CO2 emissions efficiency decreases at the early stage of urbanization, then increases when urbanization reach a high level. There is spatial spillover effect among the prefecture-level cities, suggesting that different prefecture-level governments should coordinate with each other to improve CO2 emissions efficiency in the whole area. Gross domestic product (GDP) per capita also plays a markedly positive role in improving CO2 emissions efficiency. This research highlights the effect of urbanization on CO2 emissions efficiency and the importance of improving CO2 emissions efficiency in developing countries.
Massimiliano Luca, Gian Maria Campedelli, Simone Centellegher
et al.
Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.
An overwhelming majority of the world's human population lives in urban areas and cities. Understanding a city's transportation typology is immensely valuable for planners and policy makers whose decisions can potentially impact millions of city residents. Despite the value of understanding a city's typology, labeled data (city and it's typology) is scarce, and spans at most a few hundred cities in the current transportation literature. To break this barrier, we propose a supervised machine learning approach to predict a city's typology given the information in its Wikipedia page. Our method leverages recent breakthroughs in natural language processing, namely sentence-BERT, and shows how the text-based information from Wikipedia can be effectively used as a data source for city typology prediction tasks that can be applied to over 2000 cities worldwide. We propose a novel method for low-dimensional city representation using a city's Wikipedia page, which makes supervised learning of city typology labels tractable even with a few hundred labeled samples. These features are used with labeled city samples to train binary classifiers (logistic regression) for four different city typologies: (i) congestion, (ii) auto-heavy, (iii) transit-heavy, and (iv) bike-friendly cities resulting in reasonably high AUC scores of 0.87, 0.86, 0.61 and 0.94 respectively. Our approach provides sufficient flexibility for incorporating additional variables in the city typology models and can be applied to study other city typologies as well. Our findings can assist a diverse group of stakeholders in transportation and urban planning fields, and opens up new opportunities for using text-based information from Wikipedia (or similar platforms) as data sources in such fields.
It is likely that Autonomous Vehicles will have significant social, cultural, spatial and environmental implications and that the interaction between humans, automated vehicles and physical environment will provide an array of challenges. This paper aims to explore the use of innovative visualisation approaches, to foster discussion on possible scenarios involving AVs. It is argued that such an approach might be used to help conceptualise human experiences with the potential to enhance understanding of the complex human-machine associations.
Presenting journeys from different perspectives and reconceptualising the context through the eyes of AVs emphasized the nuances of experience between the machines, urban space and human bodies. Unexpected user-technology interactions will emerge as humans are not always passive followers and can be apprehensive when it comes to accepting such a novel technology as self-driving vehicles.
The focus applied in the methodology and data capture was on inclusivity of data, showing not only movement but also noise and human experience of a space. The integration of AVs on public roads will rely on technical innovation to ensure that vehicles can operate safely yet, the study of the perceptual and ethical effects of technology and potential influences on society via engaging the public will help to manage expectations and create platforms for mutual learning.
Transportation engineering, Urbanization. City and country