Urban Science Research Articles

Analysing trends of computational urban science and data science approaches for sustainable development

AbstractUrban computing with a data science approaches can play a pivotal role in understaning and analyzing the potential of these methods for strategic, short-term, and sustainable planning. The recent development in urban areas have progressed towards the data-driven smart sustainable approaches to resolve the complexities around urban areas. The urban system faces severe challenges and these are complicated to capture, predict, resolve and deliver. The current study advances an unconventional decision-support framework to integrate the complexities of science, urban sustainability theories, and data science, with a data-intensive science to incorporate grassroots initiatives for a top-down policies. This work will influence the urban data analytics to optimize the designs and solutions to enhance sustainability, efficiency, resilience, equity, and quality of life. This work emphasizes the significant trends of data-driven and model-driven decision support systems. This will help to address and create an optimal solution for multifaceted challenges of an urban setup within the analytical framework. The analytical investigations includes the research about land use prediction, environmental monitoring, transportation modelling, and social equity analysis. The fusion of urban computing, intelligence, and sustainability science is expected to resolve and contribute in shaping resilient, equitable, and future environmentally sensible eco-cities. It examines the emerging trends in the domain of computational urban science and data science approaches for sustainable development being utilized to address urban challenges including resource management, environmental impact, and social equity. The analysis of recent improvements and case studies highlights the potential of data-driven insights with computational models for promoting resilient sustainable urban environments, towards more effective and informed policy-making. Thus, this work explores the integration of computational urban science and data science methodologies to advance sustainable development.

Tscluster: A python package for the optimal temporal clustering framework

Temporal clustering extends the conventional task of data clustering by grouping time series data according to shared temporal trends across sociospatial units, with diverse applications in the social sciences, especially urban science. The two dominant methods are as follows: Time Series Clustering (TSC), with dynamic cluster centres but static labels for each entity, and Sequence Label Analysis (SLA), with static cluster centres but dynamic labels. To implement the universe of models spanning the design space between TSC and SLA, we present tscluster, an open-source Python framework. tscluster offers: (1) several innovative techniques, such as Bounded Dynamic Clustering (BDC), that are not available in existing libraries, allowing users to set an upper bound on the number of label changes and identify the most dynamically evolving time series; (2) a user-friendly interface for applying and comparing these methods; (3) globally optimal solutions for the clustering objective by employing a mixed-integer linear programming formulation, enhancing the reproducibility and robustness of the results in contrast to existing methods based on initialization-sensitive local optimization; and (4) a suite of visualization tools for interpretability and comparison of clustering results. We present our framework using a case study of neighbourhood change in Toronto, comparing two methods available in tscluster. Supplemental materials provide an additional case study of local business development in Chicago and a detailed mathematical exposition of our framework. tscluster can be installed via PyPI (pypi.org/project/tscluster), and the source code is accessible on Github (github.com/tscluster-project/tscluster). Documentation is available online at the tscluster website (tscluster.readthedocs.io).

Developing a model for measuring the spatial disability level of cities within the framework of barrier-free pedestrian accessibility: the case of Kırklareli City centre

The problem of disability is directly related to the physical barriers encountered in urban spaces. Pedestrian accessibility performance is a notable urban quality indicator. Universal design principles and barrier-free design standards are important in making urban spaces usable for everyone. However, projects in this context generally cover specific areas of cities. Bringing an entire city into compliance with barrier-free design standards requires high cost and time. Therefore, determining the spatial disability level of the urban texture and planning project stages constitute a critical process for local governments. This study proposes a model to measure the pedestrian accessibility performance of medium-sized cities with relatively walkable distances within the framework of spatial barriers and to identify priority intervention points. In this context, Kırklareli city centre was selected as a case study area. According to the model developed with the support of GIS, the “ideal accessibility network” is determined for the citizens, and the performance level is calculated by identifying the spatial barriers on this network. The model was developed using components that can be applied to any city and tested in a sample urban environment. This conceptual model contributes to urban science and local government policy by providing a monitoring mechanism for spatial disability that can be constantly tracked by citizens, and by supplying an information base for projects to be developed by local governments. It is hoped that this study will popularise pedestrian-oriented spatial design and control in cities where walkability is postponed due to the focus on wheeled vehicle mobility and where spatial barriers are felt only by disabled individuals.

Reparative Urban Science: Challenging the Myth of Neutrality and Crafting Data-Driven Narratives

I offer how urban planning should approach technology within the context of systemic racism, advocating for a reparative approach to address the issues of urban technology perpetuating today’s racial inequality and hindering efforts to redress historical oppression. I identify three mechanisms – formalization, context removal and legitimization, and penalization and extraction – that illustrate how urban technology perpetuates historical inequalities, often penalizing marginalized groups under the pretext of neutrality and fairness. Then, I discuss methodologies of reparative urban science, aiming to use urban technology to challenge race-neutral ideologies and create data-driven narratives for reparations.

Mapping sense of place as a measurable urban identity: Using street view images and machine learning to identify building façade materials

Sense of place, as an intangible perception, is widely recognized as an urban identity and of great value in both cross-cultural studies and contemporary urbanism. Building façade material can effectively capture sense of place due to its combination of physical and social attributes. Nevertheless, there are no widely implementable and high-resolution approaches to identify façade materials on a large scale. As a response, this study proposes a method using street view images (SVIs) and a set of deep Convolutional Neural Networks (CNNs) to identify building façade materials. Specifically, a large cross-cultural training set was built to promote generalizability. Buildings within SVIs were divided into high-resolution rectangular images and classified using a well-trained Residual Network-50 (ResNet-50) model. Sense of place and its spatial patterns were then depicted by measuring façade material and analytical indicators including diversity and continuity. Eight cities worldwide with distinctive urban identities were examined. The findings revealed that compared to Asian cities, New York City, Chicago, and London are similar, while Paris and Tokyo are more distinctive. While challenges persist in comprehensively measuring the sense of place, the analysis of façade materials offers an insightful indicator that can assist in enhancing urban identity for contemporary urbanism. This study not only promotes the fine development of urban science through the empowerment of intelligent algorithms but also introduces a new perspective on exploring unmeasurable qualities based on the objective physical environment.

Spatial inequalities and cities: A review

This special issue of Environment and Planning B focuses on Spatial Inequalities and Cities. As the world progresses to almost a fully urban state, locations, networks, and access shape the everyday lives lived in cities, alongside being the movers and shapers of the future of sustainable and equitable urbanization. This special issue brings together a set of peer-reviewerd papers spanning urban science, urban analytics, geographic information / spatial science, network science, and quantitative socio-economic-spatial analysis, to explore and examine how the morphological, structural and spatial form of cities is linked to the production, maintenance and exacerbation of socio-economic inequalities and injustices. The issue also presents a critical angle on data, methods, and their use, and on how novel data and methods can help shed light on new dimensions of spatial inequalities. This editorial presents a brief critical review of the field of urban spatial inequalities and a summary of the special issue.

Computational urban science needs to go beyond computational

In this paper, I advocate for a radical expansion of computational urban science to encompass a multidisciplinary, human-centered approach, addressing the inadequacies of traditional methodologies in capturing the complexities of urban life. Building on insights from Jane M. Jacobs and others, I argue that integrating computational tools with disciplines like sociology, anthropology, and urban planning can significantly enhance our understanding and management of urban environments. This integration facilitates a deeper analysis of cultural phenomena, improves urban policy design, and promotes more sustainable, inclusive urban development. By embracing qualitative research methods—such as ethnography and participatory observations—alongside computational analysis, I highlight the importance of capturing the nuanced social fabrics and subjective experiences that define urban areas. I also stress the necessity of including community stakeholders in the research process to ensure that urban science not only analyzes but also improves the lived experiences of urban populations. Furthermore, I underscore the need for ethical governance and the mitigation of biases inherent in computational tools, proposing rigorous model auditing and the inclusion of diverse perspectives in model development. Overall, this work champions a holistic approach to urban science, aiming to make cities smarter, more equitable, and responsive to their residents’ needs.

An unsupervised machine learning approach to the spatial analysis of urban systems through neighbourhoods’ dynamics

Urban systems’ dynamics are the result of two intertwined processes that operate at different rhythms: their physical structure and underlying social processes. This paper suggests a novel approach to the spatial analysis of urban systems, using neighborhoods as a basic building block. Neighborhoods are usually the minimal homogeneous geographical unit in urban areas, both regarding their physical, and social characteristics, and the availability of governmental data. Using unsupervised machine learning algorithm, an extensive real-estate transaction dataset, and census data, a multi-scale analysis of neighborhoods’ dynamics in England and Wales is performed. The spatial and temporal dynamics of the resulting clusters of neighborhoods highlights the urban challenges faced by entire urban systems. The results suggest that processes triggered by urban inequalities may affect not only the social structure of cities (for example, through gentrification and displacement), but also the environmental sustainability of the whole urban system at a much larger scale: Suburbanization pressures seem to threaten rural areas at an unprecedented magnitude. The identification of the areas where this pressure is acute allows for the design of appropriate urban policy responses. The main message for the analysis of urban dynamics is that physical transformations and socio-political rearrangements that are intrinsically intertwined: Therefore, the joint management of the both aspects is the key to a sustainable future. The analysis also highlights the potential of machine learning algorithms for the benefit of urban science in general, and the study of urban dynamics in particular.

When geoscience meets generative AI and large language models: Foundations, trends, and future challenges

AbstractGenerative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This article explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi‐criteria decision‐making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics‐informed neural networks (PINNs), and generative pre‐trained transformer (GPT)‐based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super‐resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain, such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data‐driven modelling and uncertainty quantification.

Factories of the Future in Digitization of Industrial Urban Areas

This paper delves into the integration of Factories of the Future (FoF) and digital twin technologies within urban contexts, marking a significant leap in Smart Cities development. We present a thorough exploration of the principles and a scientifically grounded framework designed for seamlessly blending advanced manufacturing systems with the urban environment’s digital and physical aspects. Our detailed analysis has identified core principles crucial for this integration, focusing on interoperability, sustainability, adaptability, stakeholder collaboration, and strong data governance. We propose a structured framework that puts these principles into action, outlining strategic routes for incorporating digital twin and Building Information Modeling (BIM) technologies into FoF, establishing public-private partnerships, enhancing education and workforce development, and setting up mechanisms for ongoing evaluation and enhancement. The potential of this integration to transform urban development is vast, providing a model for boosting operational efficiency, driving economic growth, and enhancing urban livability. Although challenges exist in realizing this vision, our research offers practical insights and strategies for cities and industries to effectively navigate the complexities of the digital era. This contribution enriches the growing field of urban science, advocating for a harmonious integration of industrial production with urban development in the Smart Cities framework.

Human mobility description by physical analogy of electric circuit network based on GPS data

Human mobility in an urban area is complicated; the origins, destinations, and transportation modes of each person differ. The quantitative description of urban human mobility has recently attracted the attention of researchers, and it highly related to urban science problems. Herein, combined with physics inspiration, we introduce a revised electric circuit model (RECM) in which moving people are regarded as charged particles and analogical concepts of electromagnetism such as human conductivity and human potential enable us to capture the characteristics of urban human mobility. We introduce the unit system, ensure the uniqueness of the calculation result, and reduce the computation cost of the algorithm to 1/10,000 compared with the original ECM, making the model more universal and easier to use. We compared features including human conductivity and potential between different major cities in Japan to show our improvement of the universality and the application range of the model. Furthermore, based on inspiration of physics, we propose a route generation model (RGM) to simulate a human flow pattern that automatically determines suitable routes between a given origin and destination as a source and sink, respectively. These discoveries are expected to lead to new approaches to the solution of urban science problems.

New Year Fireworks Influence on Air Quality in Case of Stagnant Foggy Conditions

Urban science plays a pivotal role in understanding the complex interactions between fireworks, air quality, and urban environments. Dense firework smoke worsens air quality and poses a health hazard to the public. In this study, we show a situation where extremely foggy meteorological conditions coincided with intense anthropogenic emissions, including fireworks, in an urban area. For the first time, the chemical composition and sources of non-refractory submicron aerosol (NR-PM1) in outdoor and indoor air were characterized in Vilnius (Lithuania) using an aerosol chemical speciation monitor (ACSM) and Positive Matrix Factorization for the period before the fireworks, on New Year’s Eve, and after the fireworks in 2020/2021; thus, typical changes were assessed. Due to stagnant weather conditions and increased traffic, the highest concentrations of black carbon (BC) (13.8 μg/m3) were observed before the fireworks display. The contribution of organic (Org) fraction to the total NR-PM1 mass concentration, in the comparison of the values of a typical night and New Year’s Eve (from 9 p.m. to 6 a.m.), increased from 43% to 70% and from 47% to 60% in outdoor and indoor air, respectively. Biomass-burning organic aerosol (BBOA, 48% (44%)) and hydrocarbon-like organic aerosol (HOA, 35% (21%)) dominated the organic fraction indoors and outdoors, respectively. HOA was likely linked to increased traffic during the event, while BBOA may have been related to domestic heating and fireworks.

Conditional Diffusion Model for Urban Morphology Prediction

Predicting urban morphology based on local attributes is an important issue in urban science research. The deep generative models represented by generative adversarial network (GAN) models have achieved impressive results in this area. However, in such methods, the urban morphology is assumed to follow a specific probability distribution and be able to directly approximate the distribution via GAN models, which is not a realistic strategy. As demonstrated by the score-based model, a better strategy is to learn the gradient of the probability distribution and implicitly approximate the distribution. Therefore, in this paper, an urban morphology prediction method based on the conditional diffusion model is proposed. Implementing this approach results in the decomposition of the attribute-based urban morphology prediction task into two subproblems: estimating the gradient of the conditional distribution, and gradient-based sampling. During the training stage, the gradient of the conditional distribution is approximated by using a conditional diffusion model to predict the noise added to the original urban morphology. In the generation stage, the corresponding conditional distribution is parameterized based on the noise predicted by the conditional diffusion model, and the final prediction result is generated through iterative sampling. The experimental results showed that compared with GAN-based methods, our method demonstrated improvements of 5.5%, 5.9%, and 13.2% in the metrics of low-level pixel features, shallow structural features, and deep structural features, respectively.

Enhancing Urban Resilience: Strategic Management and Action Plans for Cyclonic Events through Socially Constructed Risk Processes

Cities will face increasing challenges due to the impacts of global climate change, particularly in the form of cyclonic events, necessitating a deeper understanding and the establishment of effective response mechanisms at both institutional and citizen levels. In this research, we tested the efficiency of crowdsourcing in fostering participatory resilience and improving urban management. The main aim was to design novel and accurate proactive response strategies and mitigate the adverse effects of cyclonic wind events through volunteerism, citizen science, and urban science. To achieve this goal, as a case study, the municipality of Soledad, Colombia was used. This research employed a two-phase methodological approach: (i) initially evaluating the spatial distribution of emergency response resources, and (ii) developing a geo-referenced survey to map, systematize, and categorize data and outcomes. A total of three hundred and seventy-eight residents across five neighborhoods in Soledad, which have experienced a high frequency of atmospheric wind phenomena over the past two decades, were surveyed. The results indicate that the crowdsourcing mechanism effectively enhanced the empirical understanding of atmospheric wind events in Soledad, facilitating the establishment of a geo-referenced volunteer network for real-time responses. Additionally, this study shed light on previously undocumented challenges, in terms of reducing the number of people affected, and the actions that would lead to improved urban development to reduce the impacts of cyclonic events, emphasizing the significance of citizen science in the social construction of risk and disaster risk reduction (DDR) efforts.

Towards human-AI collaborative urban science research enabled by pre-trained large language models

Pre-trained large language models (PLMs) have the potential to support urban science research through content creation, information extraction, assisted programming, text classification, and other technical advances. In this research, we explored the opportunities, challenges, and prospects of PLMs in urban science research. Specifically, we discussed potential applications of PLMs to urban institution, urban space, urban information, and citizen behaviors research through seven examples using ChatGPT. We also examined the challenges of PLMs in urban science research from both technical and social perspectives. The prospects of the application of PLMs in urban science research were then proposed. We found that PLMs can effectively aid in understanding complex concepts in urban science, facilitate urban spatial form identification, assist in disaster monitoring, sense public sentiment and so on. They have expanded the breadth of urban research in terms of content, increased the depth and efficiency of the application of multi-source big data in urban research, and enhanced the interaction between urban research and other disciplines. At the same time, however, the applications of PLMs in urban science research face evident threats, such as technical limitations, security, privacy, and social bias. The development of fundamental models based on domain knowledge and human-AI collaboration may help improve PLMs to support urban science research in future.

"Connecting the Dots” for Recruiting Secondary Science Teachers in Urban Schools: An Exploration of Career Choice for Undergraduate Science Majors

Despite critical shortages of secondary science teachers in urban schools, relatively little research has focused on the recruitment of undergraduate science majors to teach in urban contexts. This study utilized a retrospective narrative inquiry methodology to explore the career pathways of 14 undergraduate science majors into secondary science teaching. The primary data source was interviews that were conducted at the beginning of an urban teacher education program. Interviews were analyzed through the framework of social cognitive career theory (SCCT) to identify the characteristics, beliefs, and experiences that featured prominently in their narrativizations of their career pathway into urban science education. Findings highlight a non-linear process of career choice characterized by three themes: rejecting traditional science careers, reevaluating their interests and skills for teaching, and prioritizing socially oriented career goals. Implications for enhancing the recruitment of undergraduate science majors into urban science teaching include transforming undergraduate science majors to value teaching careers and taking a more community-based approach to science teacher recruitment.

Evaluation of the physical-functional resilience of Isfahan City center using spatial statistics methods and ELECTRE*

ABSTRACT Human populations and social wealth are generally concentrated in urban areas-which may be vulnerable to natural or anthropic disasters. In recent decades, increase in the number of natural disasters has caused much economic damage and human loss. Therefore, special attention is needed to reduce vulnerability and increase the capacity and adaptability of urban systems by way of urban resilience in order to deal with natural disasters. The city of Isfahan, located in Iran, is a city with a long history. Isfahan’s location in a low-risk zone has led researchers and urban managers to believe that earthquakes will probably never occur in this city, while recent geological findings have proven the opposite. Therefore, for the first time, this study seeks to determine the physical-functional resilience in the center of Isfahan in terms of enduring a hypothetical earthquake. To achieve this goal, first the required information was collected using library and field methods and then it was analyzed by spatial statistical methods in ArcGIS software, as well as the Analytic Hierarchy Process (AHP) and the ELECTRE Multi Criteria Decision Making method. Based on the results of this study, it seems necessary to propose a special focus on increasing the physical-functional resilience of Naghsh-e Jahan, Khalja, and Shahshahan neighborhoods out of the 25 neighborhoods located in the central districts of Isfahan. Accordingly, the study can help urban science researchers, investors, city managers and local and national organizations to identify resilience districts and focus on addressing their major weaknesses, thus preventing the loss of humn, financial and various limited urban resources as a result of earthquake through increasing the resilience of the physical-functional system of the city.

Scaling law of real traffic jams under varying travel demand

The escalation of urban traffic congestion has reached a critical extent due to rapid urbanization, capturing considerable attention within urban science and transportation research. Although preceding studies have validated the scale-free distributions in spatio-temporal congestion clusters across cities, the influence of travel demand on that distribution has yet to be explored. Using a unique traffic dataset during the COVID-19 pandemic in Shanghai 2022, we present empirical evidence that travel demand plays a pivotal role in shaping the scaling laws of traffic congestion. We uncover a noteworthy negative linear correlation between the travel demand and the traffic resilience represented by scaling exponents of congestion cluster size and recovery duration. Additionally, we reveal that travel demand broadly dominates the scale of congestion in the form of scaling laws, including the aggregated volume of congestion clusters, the number of congestion clusters, and the number of congested roads. Subsequent micro-level analysis of congestion propagation also unveils that cascade diffusion determines the demand sensitivity of congestion, while other intrinsic components, namely spontaneous generation and dissipation, are rather stable. Our findings of traffic congestion under diverse travel demand can profoundly enrich our understanding of the scale-free nature of traffic congestion and provide insights into internal mechanisms of congestion propagation.

Low-Density Urbanisation: Prestate Settlement Growth in a Pacific Society

The recognition of low-density urbanisation has been important in documenting how diverse human settlements generated enduring social and economic change. In tropical regions, the key challenges to studying low-density urbanisation have been the difficulty in acquiring past built environment data and integrating the frameworks that illuminate the social behaviours intrinsic to urbanisation. The introduction of lidar mapping and urban science methods has proven revolutionary in our understanding of low-density urbanisation as demonstrated by emerging research on settlements and states in Mesoamerica and Southeast Asia. These studies draw on urban theory to highlight patterns in the built environment associated with profound societal changes including the rise of social institutions, agglomeration effects, and ongoing settlement growth. Here, we present an approach that combines lidar survey and archaeological fieldwork with recent developments in urban science to understand the built environment of Tongatapu; the location of an archaic state whose influence spread across the southwest Pacific Ocean between the thirteenth and nineteenth centuries a.d. Quantitative results show—for the first time—that settlements on a Pacific island were urbanised in a distinct low-density form and that the processes of urbanisation began prior to state development. This study highlights the potential contribution of Pacific landscapes to urban science and the low-density settlement phenomena given the presence of large populations, hierarchical societies, and vast distributions of archaeological built remains on many island groups.

Urban Landscape Perception Research Based on the ERP Method: A Case Study of Jingdezhen, China

Within the rapidly growing urban tourism industry, the development of urban landscapes plays a crucial role in shaping a city’s image and competitiveness; however, standardized and mismatched landscapes often have a negative impact, highlighting the importance of assessing urban landscape perceptions. Although existing studies have discussed this through subjective questionnaires and physiological methods, the underlying neural mechanisms have not been thoroughly explored. The research focuses on Jingdezhen, a renowned historical and cultural city in China, as its case study. Utilized the event-related potential (ERP) method to explore individuals’ perceptual consistency and neural activity toward different types of urban landscapes. We adopted a 2 (landscape type: historical, modern) X 2 (perceptual match: consistent, inconsistent) within-subject design while recording behavioral data and electrophysiological responses. The results showed that, under any condition, there were no significant differences in people’s behavioral data. Neurophysiological results indicate that consistent perceptions of modern landscapes elicited greater P200 responses, suggesting increased attention driven by visual aesthetics and emotional activation. Under conditions of perceptual inconsistency, historical landscapes elicited higher N400 amplitudes than modern landscapes, revealing cognitive conflict and effort. This study demonstrates that P200 and N400 components are effective indicators for assessing urban perception, proving the viability of the event-related potential method in urban landscape research. Additionally, the research reveals the neural mechanisms of urban environmental perception from the early stages of attention and emotional distribution to the later stages of cognitive decision-making, which involve cognitive processes from “bottom-up” to “top-down”. This study not only provides a reference for efficient design planning for those involved in urban science but also inspires the coordination between the developmental needs of historical and modern urban landscapes. Moreover, it offers a new perspective for an interdisciplinary approach to urban perception assessment.