Zhicheng Deng, Zhaoya Gong, Jean-Claude Thill
et al.
Current studies on activity space are limited by the conceptualization of absolute physical space that fails to consider the heterogeneity of relational spaces reconstructed from spatial interactions of human movements between locations and falls short in incorporating the inherent hierarchical property of human mobility. Consequently, these approaches cannot faithfully reflect how people interact with urban spaces through travels. From the lens of relational space, this study proposes the new Hierarchical Activity Region Model (HARM) to derive the space and hierarchical properties of activity spaces perceived by various urban groups. We demonstrate the enhanced validity of our model on travel behavior in Manhattan, New York City, before, during, and after Hurricane Sandy on the basis of taxi data. Empirical results show that intra-urban travel retains clear hierarchical organization, even under disruption of a major weather event. Yet, travel undergoes a compression effect in travel hierarchies, characterized by fewer hierarchical levels and enlarged characteristic scales, followed by a rebound. Clustering the derived hierarchies reveals pronounced heterogeneity that stems from differences in population profiles; some groups sustain deeper structures or recover quickly, while others experience a persistent loss of levels. This study provides valuable insights into the functional hierarchies of urban mobility, which could inform more sustainable, resilient and equitable urban planning. The proposed methodological framework is generic for studying human mobility in broader contexts.
Roberta Mota, Julio D. Silva, Fabio Miranda
et al.
The visualization of temporal data on urban buildings, such as shadows, noise, and solar potential, plays a critical role in the analysis of dynamic urban phenomena. However, in dense and geographically constrained 3D urban environments, visual representations of time-varying building data often suffer from occlusion and visual clutter. To address these two challenges, we introduce an immersive lens visualization that integrates a view-dependent cutaway de-occlusion technique and a temporal display derived from a conformal mapping algorithm. The mapping process first partitions irregular building footprints into smaller, sufficiently regular subregions that serve as structural primitives. These subregions are then seamlessly recombined to form a conformal, layered layout for our temporal lens visualization. The view-responsive cutaway is inspired by traditional architectural illustrations, preserving the overall layout of the building and its surroundings to maintain users' sense of spatial orientation. This lens design enables the occlusion-free embedding of shape-adaptive temporal displays across building facades on demand, supporting rapid time-space association for the discovery, access and interpretation of spatiotemporal urban patterns. Guided by domain and design goals, we outline the rationale behind the lens visual and interaction design choices, such as the encoding of time progression and temporal values in the conforming lens image. A user study compares our approach against conventional juxtaposition and x-ray spatiotemporal designs. Results validate the usage and utility of our lens, showing that it improves task accuracy and completion time, reduces navigation effort, and increases user confidence. From these findings, we distill design recommendations and promising directions for future research on spatially-embedded lenses in 3D visualization and urban analytics.
شورش های شهری، اعتراضاتی خشونت بار، ناگهانی و مبتنی بر انگیزه های اقتصادی هستند که عمدتا با اشغال فضاهای شهری به عنوان بستر اصلی اعتراضات همراه هستند. فضای شهری نیز خودشان متشکل از فضاهای مصرف، فضاهای قدرت و فضاهای شبکه ای هستند که هرسه، ابزار و هدفی برای شورش های شهری هستند. پژوهش حاضر با تأکید بر مهم ترین ترین شورش هایی که پس از انقلاب اسلامی رخ داده اند، به دنبال تحلیل سویه های فضایی شورش های شهری است. داده های تحقیق، منابع تاریخی ثانویه هستند و برای تحلیل داده ها از تکنیک بولی استفاده شده است. نتایج ماتریس بولی نشان میدهد، زمانی که علت اعتراضات، سکونتگاه های شهری و کنشگری شورشیان صرفا مبنی بر وندالیسم باشد، شورش های شهری در سطح محلی و اگر نارضایتی ها فارغ از موضوع از طریق شبکه های تعاملی تشدید شوند و کنشگری کارناوالی در کنار کنشگری وندالیستی در بین معترضان متداول شود، شورش های شهری در سطح ملی رخ میدهند.
Abstract The Italian school of morphology remarks on the importance of the historical background of a settlement as a guide for the urban design process in addition to the French school that focuses on history as it gives the main character that shapes the current physical structure of a settlement area. Thus, it is aimed to investigate how an urban design can be guided concerning the history of a settlement, regarding the knowledge of these two schools of urban morphology. This paper considers how new ideas may be integrated into the urban form of a city that has been powerfully shaped by a long history. Since the typo-morphological interpretation of settlements; understanding physical form, formation, and transformation using types and typologies; can represent an essential reference point for urban planning and design, this study also aims to investigate past traces and to find clues that can be a reference for the future, and determine the design principles. Haydarpaşa in the Kadıköy district, a focal point welcoming visitor to Istanbul in the past but is an inactive space with potential based on the old Train Station and archaeological excavations of Chalcedon, is selected as the study area. In this scope, typo-morphological analyses in the neighboring Yeldeğirmeni settlement area guided the urban design process in Kadıköy to preserve the historical identity as a transportation hub and a commercial area; on the other hand, assuming a new role of being a recreational area that responds to the requirements of Yeldeğirmeni neighborhood.
The article straddles the intersection of legislation, planning guidelines, and housing policy studies in the neoliberal era. Its objective is to examine the right to a home within urban renewal projects. It addresses the gap between residents’ experience of housing as “home” and private developers’ view of housing as strictly an investment. This raises the question: how do laws, planning guidelines, and scholarly studies reflect the meaning of home? This question is examined through the Israeli case study. The method is parallel and interpretive content analysis of laws, guidelines, and research spanning more than a decade. The results indicate that in response to rapid population growth, urban renewal in Israel relies heavily on demolition and rebuilding. Low-rise buildings accommodating mainly disadvantaged populations are replaced by high-rises, to which these populations are expected to return. The conclusion is that the neoliberal perspective dominates the discourse. Despite the financial and human costs associated with high-rise living, the relevant literature pays insufficient attention to the loss of the right to a home. Accordingly, financial compensation for disadvantaged populations is recommended by legislation and research, along with limiting residents’ responsibility to their apartment as a planning solution for the eroded right to a home.
Understanding human movement and city dynamics has always been challenging. From traditional methods of manually observing the city's inhabitant, to using cameras, to now using sensors and more complex technology, the field of urban monitoring has evolved greatly. Still, there are more that can be done to unlock better practices for understanding city dynamics. This paper surveys how the landscape of urban dynamics studying has evolved with a particular focus on event-based cameras. Event-based cameras capture changes in light intensity instead of the RGB values that traditional cameras do. They offer unique abilities, like the ability to work in low-light, that can make them advantageous compared to other sensors. Through an analysis of event-based cameras, their applications, their advantages and challenges, and machine learning applications, we propose event-based cameras as a medium for capturing information to study urban dynamics. They offer the ability to capture important information while maintaining privacy. We also suggest multi-sensor fusion of event-based cameras and other sensors in the study of urban dynamics. Combining event-based cameras and infrared, event-LiDAR, or vibration has to potential to enhance the ability of event-based cameras and overcome the challenges that event-based cameras have.
Realistic traffic simulation is critical for ensuring the safety and reliability of autonomous vehicles (AVs), especially in complex and diverse urban traffic environments. However, existing data-driven simulators face two key challenges: a limited focus on modeling dense, heterogeneous interactions at urban intersections - which are prevalent, crucial, and practically significant in countries like China, featuring diverse agents including motorized vehicles (MVs), non-motorized vehicles (NMVs), and pedestrians - and the inherent difficulty in robustly learning high-dimensional joint distributions for such high-density scenes, often leading to mode collapse and long-term simulation instability. We introduce City Crossings Dataset (CiCross), a large-scale dataset collected from a real-world urban intersection, uniquely capturing dense, heterogeneous multi-agent interactions, particularly with a substantial proportion of MVs, NMVs and pedestrians. Based on this dataset, we propose IntersectioNDE (Intersection Naturalistic Driving Environment), a data-driven simulator tailored for complex urban intersection scenarios. Its core component is the Interaction Decoupling Strategy (IDS), a training paradigm that learns compositional dynamics from agent subsets, enabling the marginal-to-joint simulation. Integrated into a scene-aware Transformer network with specialized training techniques, IDS significantly enhances simulation robustness and long-term stability for modeling heterogeneous interactions. Experiments on CiCross show that IntersectioNDE outperforms baseline methods in simulation fidelity, stability, and its ability to replicate complex, distribution-level urban traffic dynamics.
This paper presents a novel computational approach for evaluating urban metrics through density gradient analysis using multi-modal satellite imagery, with applications including public transport and other urban systems. By combining optical and Synthetic Aperture Radar (SAR) data, we develop a method to segment urban areas, identify urban centers, and quantify density gradients. Our approach calculates two key metrics: the density gradient coefficient ($α$) and the minimum effective distance (LD) at which density reaches a target threshold. We further employ machine learning techniques, specifically K-means clustering, to objectively identify uniform and high-variability regions within density gradient plots. We demonstrate that these metrics provide an effective screening tool for public transport analyses by revealing the underlying urban structure. Through comparative analysis of two representative cities with contrasting urban morphologies (monocentric vs polycentric), we establish relationships between density gradient characteristics and public transport network topologies. Cities with clear density peaks in their gradient plots indicate distinct urban centers requiring different transport strategies than those with more uniform density distributions. This methodology offers urban planners a cost-effective, globally applicable approach to preliminary public transport assessment using freely available satellite data. The complete implementation, with additional examples and documentation, is available in an open-source repository under the MIT license at https://github.com/nexri/Satellite-Imagery-Urban-Analysis.
Background: Medina Al-Munawara, Saudi Arabia is one of the oldest holy cities for Muslims prominent for its historical significance as the site of the twelfth Islamic battle. The city had faced challenges of poor building quality and urban planning, prompting Governor Prince Faisal bin Salman to initiate a transformative project to enhance living conditions. Focusing on Hamra’a Al-Assad neighborhood, this research examines the urban improvement project under that initiative. Unlike most common urban improvement research that mostly focuses on design aspects, this study uniquely prioritizes residents' perceptions to comprehensively assess the success of the project. Methods: A quantitative research approach, employing a semi-structured online questionnaire with both closed and open-ended questions, delves into aspects such as daily life impact, community empowerment, economic opportunities, historical awareness, and overall satisfaction. Targeting Hamra’a Al-Assad residents, the primary users of the neighborhood, the study surpassed expectations with 102 collected responses, facilitated by collaboration with local authorities for widespread outreach. The collected data undergoes careful analysis using patterns and connections, empowering residents and providing valuable insights for decision-making in current and future urban redevelopment projects in Medina Al-Munawara. Findings: The study showed that 38.5% of residents felt safer and 50% noted increased property values, but only 28.4% saw better economic opportunities, 35.5% felt stronger community ties, 45% perceived a positive cultural impact, and just 28.4% participated in planning, revealing key gaps in engagement and inclusivity. Conclusion: This research emphasizes residents' perspectives and informs practical decisions that prioritize community well-being and inclusiveness in urban redevelopment. Novelty/Originality of this article: The novelty of this research lies in its emphasis on a resident-centric framework for evaluating urban redevelopment in a culturally and historically significant city, addressing a critical gap in the existing literature that often overlooks the socio-cultural dimensions of urban renewal in Islamic contexts.
Sustainable urban renewal is an important approach to achieving high-quality urban development. The elements of megacities are diverse, and their structures are complex. It is critical to carry out the scientific classification of grassroots governance units based on the concept and needs of urban renewal to promote targeted sustainability evaluation and achieve the precise application of renewal design and planning. This study takes the jurisdiction of Chengdu City as an example and constructs a hierarchical dimension composite classification. For this classification, 128 grassroots governance units are divided into nine types, according to their obvious spatial differences. Based on the properties of these types, suggestions for evaluating and implementing urban renewal are proposed: (1) high-density central areas generally face the dilemma of complex and rigid needs and administrative weaknesses, so the development of public participatory governance is an urgent issue; (2) in transitional suburban zones, areas on and between the development axes are significantly different, indicating that extra attention should be paid to the fairness of the renewal of semi-urbanized areas; (3) outer areas are generally marginalized in urban renewal processes and destructive redevelopment behaviors should be avoided.
Tackling Urban Physical Disorder (e.g., abandoned buildings, litter, messy vegetation, graffiti) is essential, as it negatively impacts the safety, well-being, and psychological state of communities. Urban Renewal is the process of revitalizing these neglected and decayed areas within a city to improve the physical environment and quality of life for residents. Effective urban renewal efforts can transform these environments, enhancing their appeal and livability. However, current research lacks simulation tools that can quantitatively assess and visualize the impacts of renewal efforts, often relying on subjective judgments. Such tools are crucial for planning and implementing effective strategies by providing a clear visualization of potential changes and their impacts. This paper presents a novel framework addressing this gap by using human perception feedback to simulate street environment enhancement. We develop a prompt tuning approach that integrates text-driven Stable Diffusion with human perception feedback, iteratively editing local areas of street view images to better align with perceptions of beauty, liveliness, and safety. Our experiments show that this framework significantly improves perceptions of urban environments, with increases of 17.60% in safety, 31.15% in beauty, and 28.82% in liveliness. In contrast, advanced methods like DiffEdit achieve only 2.31%, 11.87%, and 15.84% improvements, respectively. We applied this framework across various virtual scenarios, including neighborhood improvement, building redevelopment, green space expansion, and community garden creation. The results demonstrate its effectiveness in simulating urban renewal, offering valuable insights for urban planning and policy-making.
The rapid urbanization of Mostaganem, Algeria, has led to significant challenges in the accessibility of urban green spaces (UGS), crucial for promoting environmental sustainability and public health. This study uses a space syntax approach, specifically angular segment analysis (ASA), to assess UGS accessibility at city-wide and local scales. By integrating quantitative measures like "Choice" and "Integration" with Geographic Information Systems (GIS), the research identifies spatial disparities in green space distribution and accessibility, with global integration values ranging from 0.469 to 0.801. Results reveal unequal distribution, infrastructure inadequacies, and safety issues affecting accessibility. The study highlights areas like Boudjemaa and Emir Abd El Kader, which offer high connectivity, while others like Jannat El Aarif suffer from limited accessibility. Recommendations include enhancing transportation infrastructure, prioritizing green spaces in urban planning, and diversifying facilities to improve accessibility and promote social inclusion. This research provides a comprehensive framework for policymakers and urban planners, aiming to optimize urban green space accessibility and contribute to sustainable urban development in Mostaganem, aligning with global efforts towards equitable urban environments.
پژوهش حاضر با اتخاذ رویکرد مردمنگاری انتقادی در پی فهم وضعیت اجتماع حاشیهنشین شهر خرمآباد است که از این طریق روایتهای غالب افراد از تجربه زندگی در اجتماع حاشیهنشین را ارائه نماید و درنهایت با در نظر گرفتن شرایط و اقتضائات بومی منطقه، تحلیلی واقعبینانه متناسب با بافت محلی ارائه دهد. در این راستا از نمونهگیری هدفمند بهره گرفته شده است و دادهها با استفاده از ابزارهای مشاهده مشارکتی و مصاحبههای نیمهساخت یافته گردآوری و با تکنیک تحلیل مضمون تجزیهوتحلیل شدهاند. در این فرآیند تعداد 21 مصاحبه در هفت محله حاشیهنشین شهر خرمآباد انجام شده است. پس از کدگذاری مصاحبهها، 109 کد اولیه و 11 مضمون سازماندهی استخراج شده است تا درنهایت مضمون فراگیر تحقیق تحت عنوان «بیقدرتی و تله محرومیت اجتماع حاشیهنشین» شناسایی و انتخاب شود. استدلال مقاله این است که بیقدرتی و تله محرومیت اجتماع حاشیهنشین در شهر خرمآباد ناشی از سلسلهمراتب نابرابر در نظام برنامهریزی و اجرا و سازماندهی ناصحیح اجتماعات است. ازاینرو برای بهبود وضعیت اجتماع حاشیهنشین، اتخاذ رویکرد حکمرانی شهری عدالتبنیان و اصلاح نحوه سازماندهی جامعه شهری لازم و ضروری است.
در پی سیاست مسکن مهر در ایران، از سال ۱۳۸۶ مجتمعهای مسکونی مختلفی در سرتاسر کشور ساخته شد. مسکن علاوه بر تأمین سرپناه، باید نیازهای اجتماعی ساکنان را نیز برآورده نماید که حاصل آن را میتوان بهصورت احساس رضایت، احساس امنیت و احساس تعلق در ساکنان آنها مشاهده کرد. با توجه به اینکه برخی از مجتمعهای مسکن مهر، دولتی و برخی دیگر خودمالکی بودند؛ برخی در داخل محدوده و برخی دیگر در خارج محدوده شهر بودند، در این مطالعه به مقایسه احساس رضایت، احساس امنیت و احساس تعلق ساکنان در مجتمعهای مسکن مهر مختلف (داخل/خارج محدوده و دولتی/خودمالکی) پرداخته شده است. بر این اساس، تمام مجتمعهای مسکن مهر بزرگ در دو شهر بابل و بابلسر (درمجموع ۱۳ مجتمع) انتخاب شدند که بر اساس روش نمونهگیری طبقهبندیشده، تعداد ۳۴۶ پرسشنامه به روش سیستماتیک در این مجتمعها تکمیل شد. به دلیل اینکه توزیع دادهها نرمال نبود، برای مقایسه متغیرها در مجتمعهای مختلف، از آزمونهای ناپارامتریک کراسکال- والیس و من- ویتنی و برای بررسی رابطه آنها از آزمون همبستگی اسپیرمن استفاده شد. نتیجه تحقیق نشان داد که تفاوت احساس امنیت/رضایت/تعلق میان مجتمعهای داخل و خارج محدوده معنادار است و هر چه از داخل به سمت خارج محدوده برویم از میزان احساس امنیت/رضایت/تعلق کاسته میشود؛ اما تفاوت معناداری میان مجتمعهای دولتی و خودمالکی وجود ندارد.
Eliza Femi Sherley S, Sanjay T, Shri Kaanth P
et al.
This article includes a comprehensive collection of over 800 high-resolution streetlight images taken systematically from India's major streets, primarily in the Chennai region. The images were methodically collected following standardized methods to assure uniformity and quality. Each image has been labelled and grouped into directories based on binary class labels, which indicate whether each streetlight is functional or not. This organized dataset is intended to make it easier to train and evaluate deep neural networks, allowing for the creation of pre-trained models that have robust feature representations. Such models have several potential uses, such as improving smart city surveillance systems, automating street infrastructure monitoring, and increasing urban management efficiency. The availability of this dataset is intended to inspire future research and development in computer vision and smart city technologies, supporting innovation and practical solutions to urban infrastructure concerns. The dataset can be accessed at https://github.com/Team16Project/Street-Light-Dataset/.
Thermal comfort is essential for well-being in urban spaces, especially as cities face increasing heat from urbanization and climate change. Existing thermal comfort models usually overlook temporal dynamics alongside spatial dependencies. We address this problem by introducing a spatio-temporal jump model that clusters data with persistence across both spatial and temporal dimensions. This framework enhances interpretability, minimizes abrupt state changes, and easily handles missing data. We validate our approach through extensive simulations, demonstrating its accuracy in recovering the true underlying partition. When applied to hourly environmental data gathered from a set of weather stations located across the city of Singapore, our proposal identifies meaningful thermal comfort regimes, demonstrating its effectiveness in dynamic urban settings and suitability for real-world monitoring. The comparison of these regimes with feedback on thermal preference indicates the potential of an unsupervised approach to avoid extensive surveys.
This chapter investigates the concept of living structure - which is defined as a structural hierarchy that has a recurring pattern of an abundance of small substructures compared to larger ones - and the application of such structures in creating livable cities within urban informatics. By integrating practical, scientific, and artistic innovations, living structures provide a theoretical framework for designing healing environments and understanding urban complexity. We conceptualize spaces through hierarchical transformations, calculating livingness (L) as L = S * H, where S is the number of substructures and H is the inherent hierarchy of those substructures. Living structure is governed by the scaling law and Tobler's law, and guided by differentiation and adaptation principles, and it fosters vibrant and engaging spaces that enhance human well-being and a sense of place. Urban informatics, urban planning, and architecture must evolve beyond just understanding and prediction to include purposeful design. This new kind of science integrates the theory of living structure and emphasizes creation and design, thus transforming those disciplines. This chapter looks at environments that have high structural beauty, as defined by the 15 properties that Christopher Alexander proposed, and discusses the application of those properties in architecture, urban informatics, and emerging technologies such as artificial intelligence, with the aim of making built environments more vibrant and conducive to human well-being. Keywords: Livable cities, structural beauty, differentiation, adaptation, architectural design, urban complexity
Agent-based models (ABMs) have long been employed to explore how individual behaviors aggregate into complex societal phenomena in urban space. Unlike black-box predictive models, ABMs excel at explaining the micro-macro linkages that drive such emergent behaviors. The recent rise of Large Language Models (LLMs) has led to the development of LLM agents capable of simulating urban activities with unprecedented realism. However, the extreme high computational cost of LLMs presents significant challenges for scaling up the simulations of LLM agents. To address this problem, we propose OpenCity, a scalable simulation platform optimized for both system and prompt efficiencies. Specifically, we propose a LLM request scheduler to reduce communication overhead by parallelizing requests through IO multiplexing. Besides, we deisgn a "group-and-distill" prompt optimization strategy minimizes redundancy by clustering agents with similar static attributes. Through experiments on six global cities, OpenCity achieves a 600-fold acceleration in simulation time per agent, a 70% reduction in LLM requests, and a 50% reduction in token usage. These improvements enable the simulation of 10,000 agents' daily activities in 1 hour on commodity hardware. Besides, the substantial speedup of OpenCity allows us to establish a urban simulation benchmark for LLM agents for the first time, comparing simulated urban activities with real-world data in 6 major cities around the globe. We believe our OpenCity platform provides a critical infrastructure to harness the power of LLMs for interdisciplinary studies in urban space, fostering the collective efforts of broader research communities. Code repo is available at https://anonymous.4open.science/r/Anonymous-OpenCity-42BD.