Libraries, laundrettes, and lidos. Pizzerias, plazas, and playgrounds. Sidewalks, swimming pools, and schools. These are just some of the kinds of spaces and facilities that contribute to the public life of cities. Drawing on the arguments of the sociologist Eric Klinenberg, this article develops the concept of “social infrastructure” as a way to research and value these kinds of spaces. Social infrastructure helps in recognising the public dimensions of often overlooked and undervalued spaces. It draws attention to the breadth, depth, and textures of sociality that can be afforded by different urban environments. In developing the concept of social infrastructure, this article pulls together four related strands of social scientific inquiry: work on infrastructure; publicness and public space; sociality and encounter; and the politics of provision. An infrastructural approach to the topic of public space presents geographers with some productive tools for understanding the public life of cities.
Urban Geography studies forms, social fabrics, and economic structures of cities from a geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban Geography seeks new models and research paradigms to explain urban phenomena and address urban issues. Recent years have witnessed significant advances in spatially-explicit geospatial artificial intelligence (GeoAI), which integrates spatial studies and AI, primarily focusing on incorporating spatial thinking and concept into deep learning models for urban studies. This paper provides an overview of techniques and applications of spatially-explicit GeoAI in Urban Geography based on 581 papers identified using a systematic review approach. We examined and screened papers in three scopes of Urban Geography (Urban Dynamics, Social Differentiation of Urban Areas, and Social Sensing) and found that although GeoAI is a trending topic in geography and the applications of deep neural network-based methods are proliferating, the development of spatially-explicit GeoAI models is still at their early phase. We identified three challenges of existing models and advised future research direction towards developing multi-scale explainable spatially-explicit GeoAI. This review paper acquaints beginners with the basics of GeoAI and state-of-the-art and serve as an inspiration to attract more research in exploring the potential of spatially-explicit GeoAI in studying the socio-economic dimension of the city and urban life.
The shift toward high-quality urbanization has brought increased attention to the issue of "urban villages", which has become a prominent social problem in China. However, there is a lack of available geospatial data on urban villages, making it crucial to prioritize urban village mapping. In order to assess the current progress in urban village mapping and identify challenges and future directions, we have conducted a comprehensive review, which to the best of our knowledge is the first of its kind in this field. Our review begins by providing a clear context for urban villages and elaborating the method for literature review, then summarizes the study areas, data sources, and approaches used for urban village mapping in China. We also address the challenges and future directions for further research. Through thorough investigation, we find that current studies only cover very limited study areas and periods and lack sufficient investigation into the scalability, transferability, and interpretability of identification approaches due to the challenges in concept fuzziness and variances, spatial heterogeneity and variances of urban villages, and data availability. Future research can complement and further the current research in the following potential directions in order to achieve large-area mapping across the whole nation...
Urbanization is rapidly increasing, with urban populations expected to grow significantly by 2050, particularly in developing regions. This expansion brings challenges related to chronic stresses and acute shocks, such as the COVID-19 pandemic, which has underscored the critical role of urban form in a city's capacity to manage public health crises. Despite the heightened interest in urban resilience, research examining the relationship between urban morphology and pandemic resilience remains limited, often focusing solely on density and its effect on disease transmission. This work aims to address this gap by evaluating existing frameworks that analyze the relationship between urban resilience and urban form. By critically reviewing these frameworks, with a particular emphasis on theoretical and quantitative approaches, this study seeks to transfer the knowledge gained to better understand the relationship between pandemic resilience and urban morphology. The work also links theoretical ideas with quantitative frameworks, offering a cohesive analysis. The anticipated novelty of this study lies in its comprehensive assessment of urban resilience frameworks and the identification of the current gaps in integrating resilience to pandemic thinking into urban planning and design. The goal is not only to enhance the understanding of urban resilience but also to offer practical guidance for developing more adaptive and effective frameworks for assessing resilience to pandemics in urban environments, thereby preparing cities to better withstand and recover from future crises.
Kunihiko Fujiwara, Ryuta Tsurumi, Tomoki Kiyono
et al.
Three-dimensional urban environment simulation is a powerful tool for informed urban planning. However, the intensive manual effort required to prepare input 3D city models has hindered its widespread adoption. To address this challenge, we present VoxCity, an open-source Python package that provides a one-stop solution for grid-based 3D city model generation and urban environment simulation for cities worldwide. VoxCity's `generator' subpackage automatically downloads building heights, tree canopy heights, land cover, and terrain elevation within a specified target area, and voxelizes buildings, trees, land cover, and terrain to generate an integrated voxel city model. The `simulator' subpackage enables users to conduct environmental simulations, including solar radiation and view index analyses. Users can export the generated models using several file formats compatible with external software, such as ENVI-met (INX), Blender, and Rhino (OBJ). We generated 3D city models for eight global cities, and demonstrated the calculation of solar irradiance, sky view index, and green view index. We also showcased microclimate simulation and 3D rendering visualization through ENVI-met and Rhino, respectively, through the file export function. Additionally, we reviewed openly available geospatial data to create guidelines to help users choose appropriate data sources depending on their target areas and purposes. VoxCity can significantly reduce the effort and time required for 3D city model preparation and promote the utilization of urban environment simulations. This contributes to more informed urban and architectural design that considers environmental impacts, and in turn, fosters sustainable and livable cities. VoxCity is released openly at https://github.com/kunifujiwara/VoxCity.
This article focuses on the challenges that uncertainty poses to countries in global and regional value chains. In this perspective, it focuses specifically on African countries and enriches the results with a comparative approach. Indeed, using a gravity model for 49 African countries and all their trading partners from 1990 to 2019, the paper proposes a comparative analysis of the effects of uncertainty on global trade in value chains and on trade in value chains within Africa. The robustness of the results shows that domestic uncertainty can drive the expansion of intra-African trade in value chains, while uncertainty in the partner country hinders the flourishing of trade relationships within a value chain.
Cities. Urban geography, Urbanization. City and country
Abstract Excess commuting, defined as the inefficiency resulting from spatial mismatches between residential and employment locations, poses significant challenges for urban planning and transportation systems. This study uses big data from individual vehicle trips collected in Tampa, Florida, to quantify excess commuting more accurately than traditional zonal approaches. Through the application of Linear Programming (LP) and Integer Linear Programming (ILP) models, this research measures minimum and actual commuting patterns across different spatial scales—census tract, block group, and individual trip levels. The findings reveal a clear scale effect associated with the Modifiable Areal Unit Problem (MAUP), as smaller spatial units consistently yield shorter minimum commuting distances and times and the ILP model at the individual trip level yields the least. By directly analyzing actual trips rather than simulated data, this approach provides a more precise and realistic assessment of excess commuting. The results underscore the values of methodological improvements and individual-level data in refining our understanding of excess commuting and supporting more efficient urban planning and policymaking.
Covid-19 has dramatically changed life in cities across the globe. What remains uncertain is how national policies and appeals to comply with suggested rules translate to changes in the behaviour of citizens in urban areas. This lack of local knowledge leaves urban policy makers and planners with few clues as to the determinants of social resilience in cities during protracted crises like a pandemic. Methods are required to measure the capacity of people to conduct routine activities without risking exposure to a prevalent disease, particularly for those most vulnerable during a health crisis. By spanning the fields of urban resilience, human geography, mobility studies and the behavioural sciences, this study explores how to measure social resilience in cities during a protracted crisis. Using a public participation GIS online platform, we observe changes in citizen behaviour within urban spaces during the Covid-19 pandemic. Inhabitants from three districts of a Dutch city mapped their activity routines during the lockdown period and during the year before the pandemic. Spatio-temporal analysis reveals changes in the clustering of activities into what we describe as ‘activity bubbles'. We reflect on the influence of the urban space on these changes and assess the contribution of this exploratory research methodology for gaining insights into behavioural change. Implications for urban planning and resilience theory are discussed.
Urban parks are important for public health, but the role of specific spaces, such as playgrounds or lakes, and elements, such as benches or sports equipment, in supporting well-being is not well understood. Based on expert input and a review of the literature, we defined six types of health-related activities: physical, mindfulness, nature appreciation, environmental, social, and cultural. We built a lexicon that links each activity to specific elements and spaces within parks present in OpenStreetMap. Using this data, we scored 23,477 parks across 35 cities worldwide based on their ability to support these activities. We found clear patterns: parks in North America focus more on physical activity, while those in Europe offer more chances to enjoy nature. Parks near city centers support health-promoting activities better than those farther out. Suburban parks in many cities lack the spaces and equipment needed for nature-based, social, and cultural activities. We also found large gaps in park quality between cities. Tokyo and Paris provide more equal access, while Copenhagen and Rio de Janeiro show sharp contrasts. These results can help cities create fairer parks that better support public health.
Entropy is arguably one of the most powerful concepts to understand the world, from the behavior of molecules to the expansion of the universe, from how life emerges to how hybrid complex systems like cities come into being and continue existing. Yet, despite its widespread application, it is also one of the most misunderstood concepts across the sciences. This chapter seeks to demystify entropy and its main interpretations, along with some of its explorations in the context of cities. It first establishes the foundations of the concept by describing its trajectory since its inception in thermodynamics and statistical mechanics in the 19th century, its different incarnations from Boltzmanns pioneering formulation and Shannons information theory to its absorption in biology and the social sciences, until it reaches a nascent urban science in the 1960s. The chapter then identifies some of the main domains in which entropy has been explored to understand cities as complex systems, from entropy-maximizing models of spatial interaction and applications as a measure of urban form, diversity, and complexity to a tool for understanding conditions of self-organization and urban sustainability.
Urban morphology, examining city spatial configurations, links urban design to sustainability. Morphology metrics play a fundamental role in performance-driven computational urban design (CUD) which integrates urban form generation, performance evaluation and optimization. However, a critical gap remains between performance evaluation and complex urban form generation, caused by the disconnection between morphology metrics and urban form, particularly in metric-to-form workflows. It prevents the application of optimized metrics to generate improved urban form with enhanced urban performance. Formulating morphology metrics that not only effectively characterize complex urban forms but also enable the reconstruction of diverse forms is of significant importance. This paper highlights the importance of establishing a bi-directional mapping between morphology metrics and complex urban form to enable the integration of urban form generation with performance evaluation. We present an approach that can 1) formulate morphology metrics to both characterize urban forms and in reverse, retrieve diverse similar 3D urban forms, and 2) evaluate the effectiveness of morphology metrics in representing 3D urban form characteristics of blocks by comparison. We demonstrate the methodology with 3D urban models of New York City, covering 14,248 blocks. We use neural networks and information retrieval for morphology metric encoding, urban form clustering and morphology metric evaluation. We identified an effective set of morphology metrics for characterizing block-scale urban forms through comparison. The proposed methodology tightly couples complex urban forms with morphology metrics, hence it can enable a seamless and bidirectional relationship between urban form generation and optimization in performance-driven urban design towards sustainable urban design and planning.
Understanding how size influences the internal characteristics of a system is a crucial concern across various fields. Concepts like scale invariance, universalities, and fractals are fundamental to this inquiry and find application in biology, physics, and particularly urbanism. Size profoundly impacts how cities develop and function economically and socially. For example, what are the pros and cons of residing in larger cities? Is life really more expensive or less safe in larger cities? Or do they really offer more opportunities and generally higher incomes than smaller ones? To address such inquiries, we utilize theoretical tools from scaling theory, enabling a quantitative description of how a system's behavior changes across different scales, from micro to macro. Drawing parallels with research in biology and spatial economics, this chapter explores recent discoveries, ongoing progress, and unanswered questions regarding urban scaling.
This chapter explores the concept of self-organization in urban planning and design, highlighting its role in shaping the unique characteristics of cities. It examines how various socio-economic, cultural, and political factors contribute to the development of distinct architectural styles, emphasizing the morphological patterns and self-organization principles. The chapter addresses the emergence of scaling laws and fractal geometry in urban forms, using historical and contemporary examples to illustrate these concepts. The discussion also delves into the cognitive aspects of urban design, examining how the physical layout of cities influences cognitive maps and perceptions of urban environments, and how these perceptions, in turn, influence urban design. Through the prism of self-organization, it demonstrates the dynamic interplay between individual and collective actions and the shaping of the urban landscape. This analysis offers insights into the complex, self-organizing systems that define urban spaces, emphasizing the interdependencies among architectural design, urban planning, and human cognition in shaping cityscapes.
Thomas Collins, Diogo Pacheco, Riccardo Di Clemente
et al.
Urban vibrancy is an important measure of the energetic nature of a city that is related to why and how people use urban spaces, and it is inherently connected with our social behaviour. Increasingly, people use a wide range of mobile phone apps in their daily lives to connect socially, search for information, make decisions, and arrange travel, amongst many other reasons. However, the relationship between online app usage and urban vibrancy remains unclear, particularly regarding how sociospatial behaviours interact with urban features. Here, we use app-usage data as a digital signature to investigate this question. To do this, we use a high-resolution data source of mobile service-level traffic volumes across eighteen cities in France. We investigate the social component of cities using socially relevant urban features constructed from OpenStreetMap 'Points of Interest'. We developed a methodology for identifying and classifying multidimensional app usage time series based on similarity. We used these in predictive models to interpret the results for each city and across France. Across cities, there were spatial behavioural archetypes, characterised by multidimensional properties. We found patterns between the week and the weekend, and across cities, and the country. These archetypes correspond to changes in socially relevant urban features that impact urban vibrancy. Our results add further evidence for the importance of using computational approaches to understand urban environments, the use of sociological concepts in computational science, and urban vibrancy in cities.
Abstract The combination of fingerprint positioning and 5G (the 5th Generation Mobile Communication Technology) offers broader application prospects for indoor positioning technology, but also brings challenges in real-time performance. In this paper, we propose a fingerprint positioning method based on a deep convolutional neural network (DCNN) using a classification approach in a single-base station scenario for massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation. The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences. First, the DCNN model is used to determine the sub-area to which the mobile terminal belongs, and then the weighted K-nearest neighbor (WKNN) matching algorithm is used to estimate the position within the sub-area. The positioning performance is simulated in a DeepMIMO indoor environment, showing that the classification DCNN method reduces the positioning time by 77.05% compared to the non-classification method, with only a 1.08% increase in average positioning error.
Sustainable Development Goal 11 (SDG 11) focuses on making cities and human settlements inclusive, safe, resilient, and sustainable. Although the goal primarily addresses urban development, its principles also extend to rural areas, but the extent to which South Africa’s legal and policy frameworks empower traditional authorities to contribute to the development of their communities, particularly towards achieving SDG11, remains insufficiently explored. This study investigates how South Africa’s national legislative frameworks on traditional leadership have been applied to support the advancement of SDG 11. It examines the legal provisions within the 1996 Constitution of the Republic of South Africa, and relevant legislation to determine whether these frameworks provide a strong legal basis for promoting SDG 11 through the empowerment of traditional leadership. This study employs a desktop research methodology involving a comprehensive review of relevant laws, policies, and case law. Secondary data were gathered from case studies, journal articles, books, case laws, and credible internet sources. The findings suggest that the traditional authority system is deeply embedded within the South African Constitution, as well as legislative and policy frameworks, and has been effectively leveraged to advance SDG 11. Key insights emphasise the constitutional and legal recognition of traditional authorities and highlight the enforcement of traditional leadership roles and functions through various legal cases, and SDG 11-aligned programmes in South Africa. The areas where the role and functions of traditional leadership intersect with SDG 11 and rural development include security and safety, community participation, land management and sustainable settlements, cultural heritage and community identity, disaster management, and environmental stewardship. The empowerment of traditional leadership in South Africa has significant implications for achieving SDG 11 and rural development. These implications include enhanced local governance and service delivery, increased accountability and transparency, balanced rural-urban linkages, promotion of environmental stewardship, and the fostering of inclusive development. It also strengthens rural resilience, preserves cultural heritage, promotes sustainable resource management, and improves community engagement. However, challenges related to power dynamics, equity, and the need for policy integration and cohesion must be addressed to ensure that traditional leadership empowerment contributes effectively to sustainable development in South Africa.
Cities. Urban geography, Urban groups. The city. Urban sociology
Cecília Laís Santana da Silva, José Eloízio da Costa
A pecuária leiteira é tradição no semiárido sergipano devido à sua relevância histórica e econômica como fonte de renda e de sobrevivência. Nesse sentido, Poço Redondo é hoje o município que mais produz leite em Sergipe, o que indica um processo de reorganização da bacia leiteira do Alto Sertão. Para entender em qual contexto surge este aumento produtivo, o artigo propõe analisar a inserção do pequeno produtor na cadeia produtiva do leite do povoado Santa Rosa do Ermírio face à subordinação e à assimetria. Portanto, como decurso do método e da análise qualitativa e quantitativa, as nuances da produção leiteira da “terra do leite” podem ser compreendidas como parte de uma macroestrutura do sistema econômico político e em sua relação com Poço Redondo e Sergipe.
The introduction of information and communication technology (ICT) has altered the way society operates things. ICT is used in various sectors, including agriculture. It can be used in the agricultural sector to distribute pricing and encourage agricultural commodity exports. The study aims to investigate the effect of ICT on cashew nut export in Benin using an autoregressive distributed lag (ARDL) approach. Data were collected over the period of 31 years (1990–2020) in Benin. The estimated results showed that mobile cellular telephone subscription is negatively and significantly correlated with cashew nut export in the short-run. However, in the long-run, it exhibits a positive and significant correlation. On the other hand, internet usage had no significant effect on cashew nut export in the short-run, but negatively influenced cashew nut export in the long-run. These results suggest that to increase its cashew nut export, the Beninese government should invest in technological infrastructure to improve internet access by reducing the cost of internet and increasing education that will allow farmers to better understand and use ICT.
Cities. Urban geography, Urbanization. City and country
Federico Cugurullo, R. Acheampong, Ivana Dusparic
et al.
ABSTRACT Autonomous cars controlled by an artificial intelligence are increasingly being integrated in the transport portfolio of cities, with strong repercussions for the design and sustainability of the built environment. This paper sheds light on the urban transition to autonomous transport, in a threefold manner. First, we advance a theoretical framework to understand the diffusion of autonomous cars in cities, on the basis of three interconnected factors: social attitudes, technological innovation and urban politics. Second, we draw upon an in-depth survey conducted in Dublin (1,233 respondents), to provide empirical evidence of (a) the public interest in autonomous cars and the intention to use them once available, (b) the fears and concerns that individuals have regarding autonomous vehicles and (c) how people intend to employ this new form of transport. Third, we use the empirics generated via the survey as a stepping stone to discuss possible urban futures, focusing on the changes in urban design and sustainability that the transition to autonomous transport is likely to trigger. Interpreting the data through the lens of smart and neoliberal urbanism, we picture a complex urban geography characterized by shared and private autonomous vehicles, human drivers and artificial intelligences overlapping and competing for urban spaces.