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This study investigated the current state of artificial intelligence (AI) in urban planning by analyzing 744 research publications. Utilizing topic modeling analysis with latent Dirichlet allocation (LDA) and ChatGPT, we interpreted and categorized weighted keywords from this analysis, and then generated topic names based on these insights. The analysis identified 16 key themes within the corpus, encompassing a range of topics including urban and transport planning, urban and architectural design methods, as well as algorithms and predictive modeling techniques. The most prevalent topic identified was "Urban Design and Architectural Methods", emphasizing the integration of AI in urban design strategies. Other significant themes included "Smart Urban Development and Social Governance" and "Algorithms and Predictive Modeling in Transportation". The findings demonstrate the diverse applications of AI in urban planning, such as enhancing public services, optimizing transportation systems, and managing urban development. This study underscores the increasing application of AI in addressing urban challenges and provides a comprehensive overview of the current state of research, offering useful insights for future studies and potential implementations in urban planning. The study findings offer researchers and practitioners invaluable insights, uncovering both opportunities and gaps in the literature that can guide and shape future research and practical initiatives. [ABSTRACT FROM AUTHOR]

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We present a data visualisation approach to support the rapid humanitarian response during the 2024 Valparaíso wildfires in Chile. Combining Meta user location data for population displacement, NASA FIRMS satellite imagery for wildfire locations, and census data, we identify key origins and destinations of displaced people during recent wildfires in Valparaíso, Chile. Our choropleth maps reveal spatial patterns of movement and socioeconomic factors, demonstrating the value of integrating diverse data sources for near real-time crisis response.

Abstract This study employs percolation theory to investigate the hierarchical organisation of Australian urban centres through the connectivity of their road networks. The analysis demonstrates how discrete urban clusters have developed into integrated regional entities, delineating the pivotal distance thresholds that regulate these urban transitions. The study reveals the interconnections between disparate urban clusters, shaped by their functional differentiation and historical development. Furthermore, the study identifies a dichotomy of urban agglomeration forces and a persistent spatial disconnection between Australia’s wider urban landscape. This highlights the interplay between urban densification and peripheral growth. It suggests the need for new thinking on potential integrated governance structures that bridge urban development with broader social and economic policies across regional and national scales. Additionally, the study emphasises the growing importance of national coordination in Australian urban development planning to ensure regional consistency, equity, and productivity.

This study employs percolation theory to investigate the hierarchical organisation of Australian urban centres through the connectivity of their road networks. The analysis demonstrates how discrete urban clusters have developed into integrated regional entities, delineating the pivotal distance thresholds that regulate these urban transitions. The study reveals the interconnections between disparate urban clusters, shaped by their functional differentiation and historical development. Furthermore, the study identifies a dichotomy of urban agglomeration forces and a persistent spatial disconnection between Australia's wider urban landscape. This highlights the interplay between urban densification and peripheral growth. It suggests the need for new thinking on potential integrated governance structures that bridge urban development with broader social and economic policies across regional and national scales. Additionally, the study emphasises the growing importance of national coordination in Australian urban development planning to ensure regional consistency, equity, and productivity. [ABSTRACT FROM AUTHOR]

Street view imagery (SVI) is instrumental for sensing urban environments, benefitting numerous domains such as urban morphology, health, greenery, and accessibility. Billions of images worldwide have been made available by commercial services such as Google Street View and crowdsourcing services such as Mapillary and KartaView where anyone from anywhere can upload imagery while moving. However, while the data tend to be plentiful, have high coverage and quality, and are used to derive rich insights, they remain simple and limited in metadata as characteristics such as weather, quality, and lighting conditions remain unknown, making it difficult to evaluate the suitability of the images for specific analyses. We introduce Global Streetscapes — a dataset of 10 million crowdsourced and free-to-use SVIs sampled from 688 cities across 210 countries and territories, enriched with more than 300 camera, geographical, temporal, contextual, semantic, and perceptual attributes. The cities included are well balanced and diverse, and are home to about 10% of the world's population. Deep learning models are trained on a subset of manually labelled images for eight visual-contextual attributes pertaining to the usability of SVI — panoramic status, lighting condition, view direction, weather, platform, quality, presence of glare and reflections, achieving accuracy ranging from 68.3% to 99.9%, and used to automatically label the entire dataset. Thanks to its scale and pre-computed standard semantic information, the data can be readily used to benefit existing use cases and to unlock new applications, including multi-city comparative studies and longitudinal analyses, as affirmed by a couple of use cases in the paper. Moreover, the automated processes and open-source code facilitate the expansion and updates of the dataset and encourage users to create their own datasets. With the rich manual annotations, some of which are provided for the first time, and diverse conditions present in the images, the dataset also facilitates assessing the heterogeneous properties of crowdsourced SVIs and provides a benchmark for evaluating future computer vision models. We make the Global Streetscapes dataset and the code to reproduce and use it publicly available in https://github.com/ualsg/global-streetscapes. [Display omitted] • Largest labelled dataset, with 346 attributes that characterise street photos. • Baseline models and ground truth labels for benchmarking computer vision models. • Reproducible framework to sample and enrich SVIs from cities all around the world. • In-depth discussion of how the dataset could drive novel research questions. • Taking forward the work of Mapillary and KartaView, and their contributors. [ABSTRACT FROM AUTHOR]

With the rapid urbanization in Malaysia, human activities have caused damage to many waterfront areas. Efforts are being made to restore the connection between the community and river waterfronts while creating attractive urban spaces. However, the implementation of modern design development strategies without sufficient consideration of cultural aspects and societal visual preferences has raised a number of questions with regard to the public's acceptance and appreciation of the newly implemented urban facades. This study aims to comprehensively examine the relationship between cultural elements and visual preferences in the context of urban waterfront walkways in Malaysia. This study implemented a photo survey to assess the visual preferences of the study's respondents. The results indicated a significant correlation between subjects' visual preferences and their cultural backgrounds. Additionally, the analysis of the collected data highlights a strong correlation between the presence of green elements and what the respondents perceived as part of the Malaysian culture. Additionally, the historical character of the study area, as represented by historical buildings, significantly influences the preferences of Malaysian respondents. Furthermore, certain elements, such as food carts, high-rise buildings, and water, are among the least preferred compared to other elements. Ultimately, incorporating these elements in the early design stages can contribute to the creation of culturally connected and visually appealing urban waterfront spaces in Malaysia. [ABSTRACT FROM AUTHOR]

The paper delves into the normative Productive City concept as outlined in the New Leipzig Charter, focusing on urban manufacturing's heterogeneity. We define urban manufacturing as manufacturing sectors located in proximity to housing. Urban manufacturing is analyzed based on sectoral (company data classified by NACE codes into 13 types of material industry) and spatial data, considering the distance to priority roads, supply areas, and land-use categories. The methodology is applied to Bochum, Gelsenkirchen, and Herne in the Ruhr area, utilizing 2018 data to identify material industry locations. We highlight differences between central and accessible, and more dispersed sectors in three groups. Group I, including other consumer goods, food products, clothing goods, and repair services, is predominantly located in central and mixed-use locations, suggesting these types could be preserved or developed by urban planners under the Productive City framework. However, there is a clear need for commercial and industrial spaces for emitting industries (Group II) to fully realize this concept. Construction industries (Group III) are often found in residential areas due to off-site activities. Challenges arise in historical industrial cities especially in integrated areas where existing zoning regulations and grandfathering rights restrict changes in manufacturing types or expansions near residential zones. [ABSTRACT FROM AUTHOR]

The core promise of land use and zoning reforms is to metamorphose the car-dominated urban spatial structure—which is the legacy of use-based, modernist land use and transportation planning of the past century—into human-centered forms of urbanism characterized by walkable, accessible, transit-friendly, ecologically sustainable, equitable and resilient urban fabrics. This empirical study aims to measure the effectiveness of a reformed city planning framework, known as the form-based code (FBC), in terms of optimizing journey-to-work trips. To this end, the study integrates geographic information systems (GIS) and spatial analysis techniques with linear programming, including a variant of the transportation problem, to evaluate aggregated and disaggregated commuting efficiency metrics. Utilizing the zonal data (ZDATA) for the Orlando metropolitan region, the proposed models account for the commuting terrains associated with three major workforce cohorts, segmented along key industry sectors, within the context of three urban growth scenarios. The findings suggest that the FBC system holds the potential to enhance commuting patterns through various place-based strategies, including juxtaposing, densifying, and diversifying employment and residential activities at the local level. At the regional level, however, the resultant urban form falls short of an ideal jobs–housing arrangement across major industry sectors. [ABSTRACT FROM AUTHOR]

We present a data visualisation approach to support the rapid humanitarian response during the 2024 Valparaíso wildfires in Chile. Combining Meta user location data for population displacement, NASA FIRMS satellite imagery for wildfire locations, and census data, we identify key origins and destinations of displaced people during recent wildfires in Valparaíso, Chile. Our choropleth maps reveal spatial patterns of movement and socioeconomic factors, demonstrating the value of integrating diverse data sources for near real-time crisis response. [ABSTRACT FROM AUTHOR]

Street view imagery (SVI) is an increasingly important data source for urban analytics and environmental researchers studying the visual quality of the built environment. Compared to remote sensing imagery, SVI can provide a different plane of perspective at ground level and better determine the interplay between urban physical settings and socio-ecological factors that enhance well-being and sustainability. Mapillary, a platform for volunteered street view imagery, has emerged as a promising alternative to Google Street View, offering greater accessibility. Nonetheless, the utility of this open-source database can be limited by the current Mapillary Application Programming Interface (API), which only partially meets the needs of urban analytics research. To address this, we introduce ''streetscape,'' an R package designed to provide user-friendly functions for collecting and analyzing street view imagery data from Mapillary. In addition, the package supports the generation of surveys for the qualitative study of urban landscapes.

The implications of digital technologies for planning practice are receiving renewed interest in the wake of ever-improving capabilities in Big Data and artificial intelligence, as well as the rapid uptake of new technologies that allowed planners to work remotely during the COVID-19 pandemic. Despite this interest, there has been little cross-country comparative research regarding the adoption of technology within the planning profession and even less that addresses planners' expectations and desires for future digital tools. We undertook a multinational online survey of planners in the United States, Canada, the United Kingdom, Australia, and New Zealand to gain a comprehensive understanding of current and expected future use of data and software in planning practice. Although the current use of data-intensive digital tools was limited, we found widespread expectations of change across the planning profession. Remarkable similarities were observed across the countries surveyed. The biggest differences in tech use were among planners undertaking strategic, specialist, and regulatory roles. Planning organizations around the world should prepare for a new wave of digital change as many technical obstacles that previously hindered the rapid exchange and analysis of vast amounts of data have now been overcome. Continued development of digital skills among planners is important but should be paired with career pathways for digital specialists within the profession. Planners should not complacently assume that adopting digital technologies will automatically lead to more effective and equitable planning outcomes. They should use digital processes to actively address biases in the underlying planning system. [ABSTRACT FROM AUTHOR]

Urban environments are undergoing significant transformations, with pedestrian areas emerging as complex hubs of diverse mobility modes. This shift demands a more nuanced approach to urban planning and navigation technologies, highlighting the limitations of traditional, road-centric datasets in capturing the detailed dynamics of pedestrian spaces. In response, we introduce the DELTA dataset, designed to improve the analysis and mapping of pedestrian zones, thereby filling the critical need for sidewalk-centric multimodal datasets. The DELTA dataset was collected in a single urban setting using a custom-designed modular multi-sensing e-scooter platform encompassing high-resolution and synchronized audio, visual, LiDAR, and GNSS/IMU data. This assembly provides a detailed, contextually varied view of urban pedestrian environments. We developed three distinct pedestrian route segmentation models for various sensors—the 4K camera, stereocamera, and LiDAR—each optimized to capitalize on the unique strengths and characteristics of the respective sensor. These models have demonstrated strong performance, with Mean Intersection over Union (IoU) values of 0.84 for the reflectivity channel, 0.96 for the 4K camera, and 0.92 for the stereocamera, underscoring their effectiveness in ensuring precise pedestrian route identification across different resolutions and sensor types. Further, we explored audio event-based classification to connect unique soundscapes with specific geolocations, enriching the spatial understanding of urban environments by associating distinctive auditory signatures with their precise geographical origins. We also discuss potential use cases for the DELTA dataset and the limitations and future possibilities of our research, aiming to expand our understanding of pedestrian environments. [ABSTRACT FROM AUTHOR]

The dynamic nature of urbanization calls for more frequently updated and more reliable datasets than conventional methods, in order to comprehend it for planning purposes. The current widely used methods to study urbanization heavily depend on shifts in residential populations and building densities, the data of which are static and do not necessarily capture the dynamic nature of urbanization. This is a particularly the case with low- and middle-income nations, where, according to the United Nations, urbanization is mostly being experienced in this century. This study aims to develop a more effective approach to comprehending urbanization patterns through big data fusion, using multiple data sources that provide more reliable information on urban activities. The study uses five open data sources: national polar-orbiting partnership/visible infrared imaging radiometer suite night-time light images; point of interest data; mobile network coverage data; road network coverage data; normalized difference vegetation index data; and the Python programming language. The findings challenge the currently dominant census data and statistics-based understanding of Sri Lanka's urbanization patterns that are either underestimated or overestimated. The proposed approach offers a more reliable and accurate alternative for authorities and planners in determining urbanization patterns and urban footprints. [ABSTRACT FROM AUTHOR]

Vehicular air pollution has created an ongoing air quality and public health crisis. Despite growing knowledge of racial injustice in exposure levels, less is known about the relationship between the production of and exposure to such pollution. This study assesses pollution burden by testing whether local populations' vehicular air pollution exposure is proportional to how much they drive. Through a Los Angeles, California case study we examine how this relates to race, ethnicity, and socioeconomic status---and how these relationships vary across the region. We find that, all else equal, tracts whose residents drive less are exposed to more air pollution, as are tracts with a less-White population. Commuters from majority-White tracts disproportionately drive through non-White tracts, compared to the inverse. Decades of racially-motivated freeway infrastructure planning and residential segregation shape today's disparities in who produces vehicular air pollution and who is exposed to it, but opportunities exist for urban planning and transport policy to mitigate this injustice.

This study investigated the current state of artificial intelligence (AI) in urban planning by analyzing 744 research publications. Utilizing topic modeling analysis with latent Dirichlet allocation (LDA) and ChatGPT, we interpreted and categorized weighted keywords from this analysis, and then generated topic names based on these insights. The analysis identified 16 key themes within the corpus, encompassing a range of topics including urban and transport planning, urban and architectural design methods, as well as algorithms and predictive modeling techniques. The most prevalent topic identified was “Urban Design and Architectural Methods”, emphasizing the integration of AI in urban design strategies. Other significant themes included “Smart Urban Development and Social Governance” and “Algorithms and Predictive Modeling in Transportation”. The findings demonstrate the diverse applications of AI in urban planning, such as enhancing public services, optimizing transportation systems, and managing urban development. This study underscores the increasing application of AI in addressing urban challenges and provides a comprehensive overview of the current state of research, offering useful insights for future studies and potential implementations in urban planning. The study findings offer researchers and practitioners invaluable insights, uncovering both opportunities and gaps in the literature that can guide and shape future research and practical initiatives.

In this third and final report on quantitative methods, I focus on academic community: what we do, what we call ourselves, and why this is a matter of importance for the entire discipline of geography, but especially quantitative human geographers. I first highlight the increasingly diverse ways in which quantitative methods community is produced and manifested, before turning to the shifting, ever-expanding, and overlapping names and labels used to define this group. I argue that, although there is ample evidence that the quantitative methods community is thriving, there is also a growing disconnect from the sub-discipline of quantitative human geography. [ABSTRACT FROM AUTHOR]

With the rapid urbanization in Malaysia, human activities have caused damage to many waterfront areas. Efforts are being made to restore the connection between the community and river waterfronts while creating attractive urban spaces. However, the implementation of modern design development strategies without sufficient consideration of cultural aspects and societal visual preferences has raised a number of questions with regard to the public’s acceptance and appreciation of the newly implemented urban facades. This study aims to comprehensively examine the relationship between cultural elements and visual preferences in the context of urban waterfront walkways in Malaysia. This study implemented a photo survey to assess the visual preferences of the study’s respondents. The results indicated a significant correlation between subjects’ visual preferences and their cultural backgrounds. Additionally, the analysis of the collected data highlights a strong correlation between the presence of green elements and what the respondents perceived as part of the Malaysian culture. Additionally, the historical character of the study area, as represented by historical buildings, significantly influences the preferences of Malaysian respondents. Furthermore, certain elements, such as food carts, high-rise buildings, and water, are among the least preferred compared to other elements. Ultimately, incorporating these elements in the early design stages can contribute to the creation of culturally connected and visually appealing urban waterfront spaces in Malaysia.

How do we quantify the levels of greenness within urban street networks? Numerous attempts to quantify this factor have been made through survey methodologies, remote sensing data, and street view imagery. The results are promising, but are often limited by scalability constraints, including limited data availability, high requirements of computational power, and validation challenges. This study introduces a comprehensive framework for urban greenness assessment, leveraging open-source data to overcome these limitations. Central to this framework is the development of the greenR R package and an accompanying Shiny app, designed to compute, visualize, and analyze a novel proximity-based green index for individual street segments. Beyond this, the framework innovatively extends its analytical capability by integrating accessibility analysis, Green View Index quantification as well as introducing the Green Space Similarity Index, a novel metric that evaluates and compares the characteristics of urban green spaces across different regions. This extension enriches the proposed framework, providing not just a measure of proximity to green spaces, but also insights into their spatial connectivity and distribution. The efficacy of greenR is demonstrated by studying urban greenness patterns across several cities, highlighting the potential impact of such an open-source framework for citizens, urban planners, and policy-makers. This study not only advances our methodological approach to quantifying urban greenness but also provides practical tools and metrics that can inform sustainable urban planning and policy decisions.

The core promise of land use and zoning reforms is to metamorphose the car-dominated urban spatial structure—which is the legacy of use-based, modernist land use and transportation planning of the past century—into human-centered forms of urbanism characterized by walkable, accessible, transit-friendly, ecologically sustainable, equitable and resilient urban fabrics. This empirical study aims to measure the effectiveness of a reformed city planning framework, known as the form-based code (FBC), in terms of optimizing journey-to-work trips. To this end, the study integrates geographic information systems (GIS) and spatial analysis techniques with linear programming, including a variant of the transportation problem, to evaluate aggregated and disaggregated commuting efficiency metrics. Utilizing the zonal data (ZDATA) for the Orlando metropolitan region, the proposed models account for the commuting terrains associated with three major workforce cohorts, segmented along key industry sectors, within the context of three urban growth scenarios. The findings suggest that the FBC system holds the potential to enhance commuting patterns through various place-based strategies, including juxtaposing, densifying, and diversifying employment and residential activities at the local level. At the regional level, however, the resultant urban form falls short of an ideal jobs–housing arrangement across major industry sectors.

The dynamic nature of urbanization calls for more frequently updated and more reliable datasets than conventional methods, in order to comprehend it for planning purposes. The current widely used methods to study urbanization heavily depend on shifts in residential populations and building densities, the data of which are static and do not necessarily capture the dynamic nature of urbanization. This is a particularly the case with low- and middle-income nations, where, according to the United Nations, urbanization is mostly being experienced in this century. This study aims to develop a more effective approach to comprehending urbanization patterns through big data fusion, using multiple data sources that provide more reliable information on urban activities. The study uses five open data sources: national polar-orbiting partnership/visible infrared imaging radiometer suite night-time light images; point of interest data; mobile network coverage data; road network coverage data; normalized difference vegetation index data; and the Python programming language. The findings challenge the currently dominant census data and statistics-based understanding of Sri Lanka’s urbanization patterns that are either underestimated or overestimated. The proposed approach offers a more reliable and accurate alternative for authorities and planners in determining urbanization patterns and urban footprints.

Accurate estimation of the change in crime over time is a critical first step toward better understanding of public safety in large urban environments. Bayesian hierarchical modeling is a natural way to study spatial variation in urban crime dynamics at the neighborhood level, since it facilitates principled "sharing of information" between spatially adjacent neighborhoods. Typically, however, cities contain many physical and social boundaries that may manifest as spatial discontinuities in crime patterns. In this situation, standard prior choices often yield overly smooth parameter estimates, which can ultimately produce mis-calibrated forecasts. To prevent potential over-smoothing, we introduce a prior that partitions the set of neighborhoods into several clusters and encourages spatial smoothness within each cluster. In terms of model implementation, conventional stochastic search techniques are computationally prohibitive, as they must traverse a combinatorially vast space of partitions. We introduce an ensemble optimization procedure that simultaneously identifies several high probability partitions by solving one optimization problem using a new local search strategy. We then use the identified partitions to estimate crime trends in Philadelphia between 2006 and 2017. On simulated and real data, our proposed method demonstrates good estimation and partition selection performance. for this article are available online. [ABSTRACT FROM AUTHOR]

Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively conduct analytics and disseminate reproducible workflows that weave together code, visuals, and narratives. This article explores the potential of computational notebooks in urban analytics and planning, demonstrating their utility through a case study of OSMnx and its tutorials repository. OSMnx is a Python package for working with OpenStreetMap data and modeling, analyzing, and visualizing street networks anywhere in the world. Its official demos and tutorials are distributed as open-source Jupyter notebooks on GitHub. This article showcases this resource by documenting the repository and demonstrating OSMnx interactively through a synoptic tutorial adapted from the repository. It illustrates how to download urban data and model street networks for various study sites, compute network indicators, visualize street centrality, calculate routes, and work with other spatial data such as building footprints and points of interest. Computational notebooks help introduce methods to new users and help researchers reach broader audiences interested in learning from, adapting, and remixing their work. Due to their utility and versatility, the ongoing adoption of computational notebooks in urban planning, analytics, and related geocomputation disciplines should continue into the future.

The field of urban analytics and city science has seen significant growth and development in the past 20 years. The rise of data science, both in industry and academia, has put new pressures on urban research, but has also allowed for new analytical possibilities. Because of the rapid growth and change in the field, terminology in urban analytics can be vague and unclear. This paper, an abridged synthesis of a panel discussion among scholars in Urban Data Science held at the 2019 American Association of Geographers Conference in Washington, D.C., outlines one discussion seeking a better sense of the conceptual, terminological, social, and ethical challenges faced by researchers in this emergent field. The panel outlines the difficulties of defining what is or is not urban data science, finding that good urban data science must have an expansive role in a successful discipline of “city science.” It suggests that “data science” has value as a “signaling” term in industrial or popular science applications, but which may not necessarily be well-understood within purely academic circles. The panel also discusses the normative value of doing urban data science, linking successful practice back to urban life. Overall, this panel report contributes to the wider discussion around urban analytics and city science and about the role of data science in this domain.

Abstract Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities.

Abstract Cities have been extensively studied as complex adaptive systems over the last 50 years. Recently, several empirical studies and emerging theory provided support for the fact that many different urban indicators follow general consistent statistical patterns across countries, cultures and times. In particular, total personal income, measures of innovation, crime rates, characteristics of the built environment and other indicators have been shown to exhibit non-linear power-law scaling with the population size of functional cities. Here, we show how to apply this type of analysis inside cities to establish universal patterns in the quantity and distribution of urban amenities such as restaurants, parks, and universities. Using a unique data set containing millions of amenities in the 50 largest US metropolitan areas, we establish general non-linear scaling patterns between each city’s population and many different amenities types, the small-area statistics of their spatial abundance, and the characteristics of their mean distance to each other. We use these size-specific statistical findings to produce generative models for the expected amenity abundances of any US city. We then compute the deviations observed in given cities from this statistical many-amenity model to build a characteristic signature for each urban area. Finally, we show how urban planning can be guided by these systemic quantitative expectations in the context of new city design or the identification of local deficits in service provision in existing cities.

Despite the unprecedentedly growing discussion of big data generated in urban environments and the widespread use of so-called urban big data (UBD) in recent years, there has been no consensus or widely accepted definition of UBD. Existing UBD studies have been either case-specific or applied in specific planning domains, such as transportation or tourism planning. A comprehensive exploration of the definitions of UBD in urban planning and related fields is timely and vital. This study is a systematic review of recent literature, consolidating 49 UBD definitions from 48 published articles in 39 journals, and classifying them into four themes: characteristics, sources, analytics, and impact. We found that most definitions are not given in an urban context and do not differentiate UBD from big data in a general sense. It is difficult to arrive at a one-size-fits-all definition of what constitutes UBD. Instead, the fourfold classification of UBD definitions allows us to identify three essential qualities of UBD that differentiate UBD from general big data and benefit urban studies: refinement of both spatiotemporal features and individual attributes at the microlevel, and the capacity and impact to depict, predict, and manage cities. We also identified three categories of challenges imposed on urban planning. This study serves as a starting point for a comprehensive understanding of UBD and contributes to expanding the discussion of UBD definitions and opportunities that UBD opens up in urban planning, facilitating better city management in the future. [ABSTRACT FROM AUTHOR]

Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities. [ABSTRACT FROM AUTHOR]

Urban agglomeration is a continuous urban spread and generally comprises a main city at the core and its adjoining growth areas. These agglomerations are studied using different concepts, theories, models, criteria, indices, and approaches, where population distribution and its associated characteristics are mainly used as the main parameters. Given the difficulties in accurately demarcating these agglomerations, novel methods and approaches have emerged in recent years. The use of geospatial big data sources to demarcate urban agglomeration is one of them. This promising method, however, has not yet been studied widely and hence remains an understudied area of research. This study explores using a multisource open geospatial big data fusion approach to demarcate urban agglomeration footprint. The paper uses the Southern Coastal Belt of Sri Lanka as the testbed to demonstrate the capabilities of this novel approach. The methodological approach considers both the urban form and functions related to the parameters of cities in defining urban agglomeration footprint. It employs near-real-time data in defining the urban function-related parameters. The results disclosed that employing urban form and function-related parameters delivers more accurate demarcation outcomes than single parameter use. Hence, the utilization of a multisource geospatial big data fusion approach for the demarcation of urban agglomeration footprint informs urban authorities in developing appropriate policies for managing urban growth. [ABSTRACT FROM AUTHOR]

With the emergence of new forms of geospatial/urban big data and advanced spatial analytics and machine learning methods, new patterns and phenomena can be explored and discovered in our cities and societies. In this special issue, we presented an overview of nine studies to understand how to use urban data science and GIS in healthcare services, hospitality and safety, transportation and mobility, economy, urban planning, higher education, and natural disasters, spreading across developed countries in North America and Europe, as well as Global South areas in Asia and the Middle East. The embrace of diverse geo-computational methods in this special issue brings forward an outlook to future GIS and Urban Data Science towards more advanced computational capability, global vision and urban-focused research.

Urban lighting reflects nocturnal activities and it is traditionally observed using Nighttime Lights (NTL) satellite imagery. Few studies systematically measure the nightscape from a human perspective. This study brings a new paradigm — urban lighting sensing via Nighttime Street View Imagery (SVI). The paradigm draws on the accomplishments of (daytime) SVI and gives attention to its ignored nighttime counterpart. We put forward this idea by manually collecting 2,831 nighttime SVIs across various urban functional areas in Singapore. We investigated their values by developing a use case for clustering nighttime lighting patterns. To mitigate the scarcity of nighttime SVI, deep learning regression models were trained to predict nighttime brightness based on corresponding daytime SVIs obtained from widely available sources. The results were compared with brightness data derived from satellite imagery, to affirm the novelty and uniqueness of nighttime SVI. As a result, there are 7 lighting patterns within the collected nighttime SVI, distinct in lighted spot features and total brightness. The identified patterns effectively characterize different urban function scenarios. The best trained brightness prediction model performs well in revealing the city-scale lighting landscape. The SVI-predicted brightness shows a distribution similar to the brightness from satellite imagery and complements it in urban areas with complex vertical lighting structures. This study demonstrates the potential of nighttime SVI as a valuable data source for mapping urban lighting and activities, offering advantages over satellite data. The proposed paradigm contributes significantly to cross-modal information mining in urban studies and has potential applications in scenarios such as light pollution mitigation and crime prevention. [Display omitted] • Street View Imagery (SVI) is taken almost exclusively during daytime, ignoring the urban nightscape. • Elucidation of data collection and usability of such imagery during night. • Exploring the profound correspondence between daytime SVI and nighttime SVI. • Identifying nighttime SVI as viable complement for Nighttime Lights satellite imagery. • Position that nighttime SVI is a latent but valuable urban dataset. [ABSTRACT FROM AUTHOR]

With the increasing stock of ageing infrastructure and resource constraints in Singapore, related risks and carbon emissions can be mitigated through long-term resilience planning, automated building inspection, and effective maintenance. Sustainable actions are needed to maintain Singapore's ageing infrastructure. Hence, a state-of-the-art control and management system is required in the form of smart city digital tools. We introduce an Urban Digital Twin (UDT)—GHG App for decision-makers in Singapore's operational building greenhouse gas (GHG) emission mitigation and decarbonisation initiatives. Based on multiple-criteria decision analysis (MCDA), a Potential for Intervention (PFI) map was created to rejuvenate the building system. Decision-makers can use this map to prioritise the rejuvenation of low-carbon building systems in the built environment. A heat map of the PFI results highlights which buildings need urgent rejuvenation based on critical parameters. The GHG App utilises this method to generate maps and enables users to modify parameter weights based on their priorities, automatically updating the map. Users can plan an intervention for buildings with higher PFI values once the map is generated. The GHG App provides interactive data visualisation of 119,872 features representing Singapore's built environment, including the context size of 6,785 existing residential buildings modelled and used to demonstrate the analysis results. Our research findings can contribute to the development of standards for accounting for operational GHG emissions, setting emission limits, and planning decarbonisation in the built environment sector. [ABSTRACT FROM AUTHOR]

The convergence of flooding and environmental contamination heightens the potential for mobility and transfer of toxic substances. Spatial analytic platforms can help identify the risks of toxic substance release during flooding, but these platforms are not integrated with one another, making data sharing difficult across platforms. Using the Toxics Mobility Inventory for the State of Rhode Island, this paper presents a method which integrates Toxicological Prioritization Index outputs across multiple data visualization platforms. The workflow presented creates an accessible interface for residents and policy makers to monitor the spatial conditions related to toxic substances during floods to better target solutions. [ABSTRACT FROM AUTHOR]

The image of a city represents the sum of beliefs, ideas, and impressions that people have of that city. Mostly, city images are assessed through direct or indirect interviews and cognitive mapping exercises. Such methods consume more time and effort and are limited to a small number of people. However, recently, people tend to use social media to express their thoughts and experiences of a place. Taking this into consideration, this paper attempts to explore city images through social media big data, considering Colombo, Sri Lanka, as the testbed. The aim of the study is to examine the image of a city through Lynchian elements—i.e., landmarks, paths, nodes, edges, and districts—by using community sentiments expressed and images posted on social media platforms. For that, this study conducted various analyses—i.e., descriptive, image processing, sentiment, popularity, and geo-coded social media analyses. The study findings revealed that: (a) the community sentiments toward the same landmarks, paths, nodes, edges, and districts change over time; (b) decisions related to locating landmarks, paths, nodes, edges, and districts have a significant impact on community cognition in perceiving cities; and (c) geo-coded social media data analytics is an invaluable approach to capture the image of a city. The study informs urban authorities in their placemaking efforts by introducing a novel methodological approach to capture an image of a city. [ABSTRACT FROM AUTHOR]

Smart city aims at amassed connectivity at various levels in the midst of citizens, as well as amid the administration and the daily growing population. India is one of the developing countries where population growth is one of the significant areas to be noted seriously. A city is a large and permanent human environment that provides its citizens with many services and opportunities. The rapid economic growth and population growth have put a huge amount of strain on urban infrastructure and service provision. India is an under developing nation to modernize urban life; the current urbanization needs good tactics and creative planning. India's government has launched 100 smart cities where it is expected that citizens will use new innovations and resolve the issues. Smart cities are intended for finest usage of space and resources along with an effectual and optimum dissemination of benefits. This study aims to investigate and analyse Chennai smart city mission (SCM) development. This work has been undertaken to learn about the aspects of smart development and the factors governing smart city. The analysis has been split up into 4 portions as questionaries' survey in Chennai city, frequency and percentage analysis, descriptive analysis and using structural equation modelling (SCM). Using the Statistical Package for Social Sciences (SPSS) version 21.0, conversational interviewing and questionnaire survey and also journal study are conducted to find factors influencing the implementation of smart city and reviewed. Using the structural equation model (SEM) AMOS 21.0 software, confirmatory factor assessment had been done. This study gives in-depth knowledge in the implementation of the smart city scheme aspects and also suggests solution for the most affecting factor in a city. [ABSTRACT FROM AUTHOR]

Cities have been extensively studied as complex adaptive systems over the last 50 years. Recently, several empirical studies and emerging theory provided support for the fact that many different urban indicators follow general consistent statistical patterns across countries, cultures and times. In particular, total personal income, measures of innovation, crime rates, characteristics of the built environment and other indicators have been shown to exhibit non-linear power-law scaling with the population size of functional cities. Here, we show how to apply this type of analysis inside cities to establish universal patterns in the quantity and distribution of urban amenities such as restaurants, parks, and universities. Using a unique data set containing millions of amenities in the 50 largest US metropolitan areas, we establish general non-linear scaling patterns between each city's population and many different amenities types, the small-area statistics of their spatial abundance, and the characteristics of their mean distance to each other. We use these size-specific statistical findings to produce generative models for the expected amenity abundances of any US city. We then compute the deviations observed in given cities from this statistical many-amenity model to build a characteristic signature for each urban area. Finally, we show how urban planning can be guided by these systemic quantitative expectations in the context of new city design or the identification of local deficits in service provision in existing cities. [ABSTRACT FROM AUTHOR]