Urban fragmentation is often seen as a failure of planning. This paper repositions it as a condition for civic infrastructure reuse and reactivation, using Kaohsiung, Taiwan, as a critical case. It reframes shi-jie neighbourhoods – originally school-based units – as morphogenetic seeds for multi-layered education infrastructure. Through arcaded corridors, inverted streets, and sunken railways, it proposes an ‘education belt’ linking institutions, mobility, and civic programmes across territorial scales. Drawing from Cedric Price’s flexible urbanism and the logic of the 15-Minute City, the study envisions a translatable model where learning and public life interweave as distributed civic systems.
فقر روستایی در ایران، مانعی کلیدی برای توسعه پایدار است که از نابرابری در مالکیت زمین، خردشدن اراضی و نابرابری جنسیتی ناشی میشود.این پژوهش باهدف بررسی تأثیر عوامل ساختاری فقر در روستای دهرم، با استفاده از رویکرد ترکیبی (کمی و کیفی) اجرا شده است. در بخش کمی، جامعه آماری شامل 48 خانوار کشاورز روستا بود که به دلیل کوچک بودن جامعه، همه شماری انجام شد. دادههای کمی با استفاده از پرسشنامه ساختاریافته گردآوری و در نرمافزار SPSS تحلیل گردید. دادههای کیفی از طریق مصاحبههای نیمهساختاریافته و مشاهدات میدانی گردآوری شد. در بخش کیفی، با نمونهگیری نظری 15 نفر (کشاورزان، زنان و معتمدان) انتخاب و اشباع نظری حاصل گردید.تحلیل کمی یافتهها نشان داد که 6/66درصد زمینها در اختیار 4/35 درصد خانوارها قرار دارد، خانوارهای طبقات پایینتر با میانگین 84/0 هکتار زمین، درآمدی معادل 43/1 میلیون تومان دارند که زیر خطفقر 8/9 میلیون تومان است. خردشدن اراضی ، بهرهوری را کاهش داده است. یافتههای کیفی نیز محدودیت زمین (7/46 درصد کدها)، تبعیض جنسیتی (3/33 درصد)، و فشارهای اجتماعی مانند مهاجرت و ناامیدی (20 درصد) را بهعنوان عوامل ساختاری فقر شناسایی کرد.نابرابری مالکیت زمین، خردشدن اراضی و تبعیض جنسیتی با کاهش تولید، حذف مشارکت و تداوم پیامدهای اجتماعی آن، فقر را تداوم میبخشند. .پیشنهادها شامل یکپارچهسازی اراضی، ثبت مشترک زمین برای زنان و مردان، و توسعه کشتهای متنوع است. این مطالعه با ارائه مدلی محلی، راهکارهایی برای کاهش فقر در مناطق روستایی مشابه ارائه میدهد.
Improving walking and biking for recreation contributes to enhancing public health. Despite the importance of social and physical variables in promoting walking and biking as recreational activities, few studies have looked at how these domains simultaneously influence these activities. This study aims to investigate the links between walking and cycling as recreational activities and the individual, social, and built environmental factors in the medium-sized southern city of Temuco. The objectives were examined utilising a quantitative method and three types of regression analysis. Despite a finding that people’s attitudes towards cycling are more positive than those towards walking or using a private car, just a small portion of respondents reported using bicycles for recreation. The results show that several factors are related to walking and cycling as recreational activities, such as age, encouragement, companionship, lifestyle, and safety. Urban policymakers may use these findings to promote walking and cycling for recreation.
The old patrimonial souk of Batroun, in northern Lebanon, is undergoing deep change due to overtourism. Once a center for local trade and crafts, the souk has shifted since the early 2000s toward tourism and commercial entertainment. This change has disrupted its multisensory ambiance, altering how long-term residents perceive, use, and emotionally connect with space. While efforts to preserve heritage are underway, they often focus more on visual and economic appeal than on lived experience, causing tension between cultural preservation and tourist development. This study fills a gap in heritage and urban studies by using Jean-Paul Thibaud’s “commented city walks” method to explore how residents describe and sense these changes. Fieldwork shows that residents increasingly feel dislocation and solastalgia as daily social rituals and sensory familiarity fade in favor of overtourism. The research adds to the growing field of urban sensory studies by demonstrating how sensory perception can help identify cultural loss in heritage sites. It also highlights the need for participatory, sensory-based planning approaches that consider the lived experiences of local communities. By viewing sensory co-construction as a potential bridge between tourism and preservation, the study promotes more inclusive urban transformation models. By foregrounding lived and actual urban sensory experiences, this research not only contributes to the field of urban sensory studies but also aligns with the journal’s aim of examining the socio-economic effects of modern urban transformation driven by overtourism, suggesting pathways toward more inclusive and resilient futures in heritage settings.
Urban research aims to understand how cities operate and evolve as complex adaptive systems. With the rapid growth of urban data and analytical methodologies, the central challenge of the field has shifted from data availability to the integration of heterogeneous data into coherent, verifiable urban knowledge through multidisciplinary approaches. Recent advances in AI, particularly the emergence of large language models (LLMs), have enabled the development of AI scientists capable of autonomous reasoning, hypothesis generation, and data-driven experimentation, demonstrating substantial potential for autonomous urban research. However, most general-purpose AI systems remain misaligned with the domain-specific knowledge, methodological conventions, and inferential standards required in urban studies. Here, we introduce the AI Urban Scientist, a knowledge-driven multi-agent framework designed to support autonomous urban research. Grounded in hypotheses, peer-review feedback, datasets, and research methodologies distilled from large-scale prior studies, the system constructs structured domain knowledge that guides LLM-based agents to automatically generate hypotheses, identify and integrate multi-source urban datasets, conduct empirical analyses and simulations, and iteratively refine analytical methods. Through this process, the framework synthesizes new insights in urban science and accelerates the urban research lifecycle.
Urban digital twins are increasingly perceived as a way to pool the growing digital resources of cities for the purpose of a more sustainable and integrated urban planning. Models and simulations are central to this undertaking: They enable "what if?" scenarios, create insights and describe relationships between the vast data that is being collected. However, the process of integrating and subsequently using models in urban digital twins is an inherently complex undertaking. It raises questions about how to represent urban complexity, how to deal with uncertain assumptions and modeling paradigms, and how to capture underlying power relations. Existent approaches in the domain largely focus on monolithic and centralized solutions in the tradition of neoliberal city-making, oftentimes prohibiting pluralistic and open interoperable models. Using a participatory design for participatory systems approach together with the City of Hamburg, Germany, we find that an open Urban Model Platform can function both as a public technological backbone for modeling and simulation in urban digital twins and as a socio-technical framework for a collaborative and pluralistic representation of urban processes. Such a platform builds on open standards, allows for a decentralized integration of models, enables communication between models and supports a multi-model approach to representing urban systems.
Rapid urbanization intensifies the demand for Urban General Intelligence (UGI), referring to AI systems that can understand and reason about complex urban environments. Recent studies have built urban foundation models using supervised fine-tuning (SFT) of LLMs and MLLMs, yet these models exhibit persistent geospatial bias, producing regionally skewed predictions and limited generalization. To this end, we propose Urban-R1, a reinforcement learning-based post-training framework that aligns MLLMs with the objectives of UGI. Urban-R1 adopts Group Relative Policy Optimization (GRPO) to optimize reasoning across geographic groups and employs urban region profiling as a proxy task to provide measurable rewards from multimodal urban data. Extensive experiments across diverse regions and tasks show that Urban-R1 effectively mitigates geo-bias and improves cross-region generalization, outperforming both SFT-trained and closed-source models. Our results highlight reinforcement learning alignment as a promising pathway toward equitable and trustworthy urban intelligence.
Understanding the high-order relationship between urban form and function is essential for modeling the underlying mechanisms of sustainable urban systems. Nevertheless, it is challenging to establish an accurate data representation for complex urban forms that are readily explicable in human terms. This study proposed the concept of core urban morphology representation and developed an explainable deep learning framework for explicably symbolizing complex urban forms into the novel representation, which we call CoMo. By interpretating the well-trained deep learning model with a stable weighted F1-score of 89.14%, CoMo presents a promising approach for revealing links between urban function and urban form in terms of core urban morphology representation. Using Boston as a study area, we analyzed the core urban forms at the individual-building, block, and neighborhood level that are important to corresponding urban functions. The residential core forms follow a gradual morphological pattern along the urban spine, which is consistent with a center-urban-suburban transition. Furthermore, we prove that urban morphology directly affects land use efficiency, which has a significantly strong correlation with the location (R2=0.721, p<0.001). Overall, CoMo can explicably symbolize urban forms, provide evidence for the classic urban location theory, and offer mechanistic insights for digital twins.
Francisco Estrada, Veronica Lupi, Wouter Botzen
et al.
The social cost of carbon (SCC) serves as a concise gauge of climate change's economic impact, often reported at the global and country level. SCC values are disproportionately high for less-developed, populous countries. Assessing the contributions of urban and non-urban areas to the SCC can provide additional insights for climate policy. Cities are essential for defining global emissions, influencing warming levels and associated damages. High exposure and concurrent socioenvironmental problems exacerbate climate change risks in cities. Using a spatially explicit integrated assessment model, the SCC is estimated at USD$137-USD$579/tCO2, rising to USD$262-USD$1,075/tCO2 when including urban heat island (UHI) warming. Urban SCC dominates, with both urban exposure and the UHI contributing significantly. A permanent 1% reduction of the UHI in urban areas yields net present benefits of USD$484-USD$1,562 per urban dweller. Global cities have significant leverage and incentives for a swift transition to a low-carbon economy, and for reducing local warming.
The integration of machine learning techniques has become a cornerstone in the development of intelligent urban services, significantly contributing to the enhancement of urban efficiency, sustainability, and overall livability. Recent advancements in foundational models, such as ChatGPT, have introduced a paradigm shift within the fields of machine learning and artificial intelligence. These models, with their exceptional capacity for contextual comprehension, problem-solving, and task adaptability, present a transformative opportunity to reshape the future of smart cities and drive progress toward Urban General Intelligence (UGI). Despite increasing attention to Urban Foundation Models (UFMs), this rapidly evolving field faces critical challenges, including the lack of clear definitions, systematic reviews, and universalizable solutions. To address these issues, this paper first introduces the definition and concept of UFMs and highlights the distinctive challenges involved in their development. Furthermore, we present a data-centric taxonomy that classifies existing research on UFMs according to the various urban data modalities and types. In addition, we propose a prospective framework designed to facilitate the realization of versatile UFMs, aimed at overcoming the identified challenges and driving further progress in this field. Finally, this paper systematically summarizes and discusses existing benchmarks and datasets related to UFMs, and explores the wide-ranging applications of UFMs within urban contexts, illustrating their potential to significantly impact and transform urban systems. A comprehensive collection of relevant research papers and open-source resources have been collated and are continuously updated at: https://github.com/usail-hkust/Awesome-Urban-Foundation-Models.
AbstractThis chapter aims to analyse the geographic and institutional background, within an authoritarian regime, to understand the changing process of governance over the field of urban redevelopment. A brief introduction of Guangzhou will be displayed. Based on geographic information, relevant institutions are introduced. Institutional resources and power are distributed between different departments in government, and between government, markets, and communities. This distribution constrains and constructs the patterns of collective behaviours in redevelopment process in terms of influencing preference, locating resources and formulating strategies of different entities. Urban redevelopment is a crucial channel for authoritarian local state to pursue economic growth and political performance.
This article explores the connection between Smart Growth and the decolonization of urban growth management in Egypt, examining the impact of former colonial influence on present urban policy and practices. Drawing insights from the urbanization of Egyptian desert areas before and after the New Urban Communities Program (NUCP), it scrutinizes how historical influences adversely affect contemporary approaches, inducing socio-economic impacts. The primary objective is to identify the root causes of misguided urban growth management practices, arguing that mono-institutional and sectoral development is rooted in Egypt's quasi-colonial history preceding the NUCP. The research employs a comprehensive methodological approach, using descriptive qualitative methods to investigate the growth of emerging cities based on Smart Growth principles and quantitative analysis to assess population decongestion resulting from the NUCP. It evaluates the implementation of Smart Growth principles during the NUCP and pre-NUCP, offering insights into adverse management practices. Despite the NUCP's goal to alleviate congestion, only 1.6 percent of the population was decongested by 2017. The research highlights the need for a new municipally guided growth model, emphasizing indigenous and locally validated approaches. This model aims to rectify inefficiencies in current urban management practices, fostering a responsive and sustainable approach aligned with local community needs.
Sidoarjo Regency Government has made many efforts to make the mushroom village more famous and attract tourists to visit. So that efforts are needed to revive Home Based Enterprise (HBE) / household businesses and oyster mushroom cultivation centers, to continue to be the pride of Sidoarjo Regency. The HBE concept supports the economic aspect in realizing Sustainable Development. This can be achieved by realizing environmental integration, socio-cultural interests and maximizing local economic benefits. Of course, the existence of HBE supports the concept of Sustainable Tourism, which can increase people's income in accessing jobs and getting good services in the housing sector. This study uses a mixed method with a combination of quantitative and qualitative. This research begins by collecting information or data in the field. Data collection techniques used in this study include field observations, in-depth interviews and documentation of activities. To support the sustainability of the village, it is necessary to revive marketing facilities in the form of cafes or restaurants that support mushroom village tourism. In addition, the institution that oversees the mushroom cultivation and processed products also needs to be added to control the business management system. The existing environment needs to be improved by adding street furniture elements that support the image of a tourist village. Tourist villages must also be supported by the latest technology to support HBE production. Political relations with government and business CSR can also develop the business of the mushroom village HBE, in helping to invest in capital and equipment.
Details in building design and construction. Including walls, roofs, Urban renewal. Urban redevelopment
The mosque is a place of worship for the Muslim community which has the capacity for a large number of congregations, over time the mosque is not only a place of worship but the mosque can also be a place for socializing in religious matters, in every city and region it has its own characteristics regarding the design and style that is in the mosque. The style of a marker or pattern on a building based on a certain period is usually the style of the mosque found on the exterior and interior of the building such as the shape of the door, the shape of the column, the shape of the roof, the shape of the ceiling and so on. The research method used in this research is descriptive qualitative by searching and collecting data related to research from journals, literature, books and direct surveys to research locations. The results of the research are knowledge of what styles exist in the Al-Mi'raj mosque (Masjid Raya Bogor) and find out which form of mosque design follows.
Details in building design and construction. Including walls, roofs, Urban renewal. Urban redevelopment
Under the current urban renewal background, the subjective attitude of stakeholders directly affects the feasibility of planning projects in the development or protection activities related to brownfield redevelopment. It is key that the public effectively participates in planning and decision-making to explore the suitable expression method of public attitude. In this paper, Jigang, Jinan, Shandong Province is taken as an example. By using participatory mapping and semi-structural interviews, the landscape perceived value of 365 (342 valid) stakeholders in the original site of Jigang is investigated. By using hot spot analysis, correspondence analysis and compatibility index analysis, the spatial composition of public perceived landscape value, the correspondence between landscape value and land use and its compatibility with existing conservation and renewal schemes are revealed. On this basis, three types of brownfield land redevelopment attitude areas are identified. The results show that: 1. The attitude of Jinan Iron and Steel Group’s renewal based on the degree of compatibility is location-dependent, and the spatial difference analysis of this attitude provides more detailed data support for the protection and renewal design, planning management and conflict control; 2. The landscape perceived value of case stakeholders has regularity in spatial distribution and is related to a certain material landscape foundation (land use), which is beneficial in explaining the possible social phenomena caused by landscape change; 3. Participatory cartography combined with landscape value investigation provides an effective method for the study of perceived landscape. Through cartographic visualization, statistical analysis and index model construction, the spatial structure characteristics of perceived landscape value can be revealed. It can provide effective decision support for brownfield urban renewal projects, solve the problem that the current upper-level planning of such renewal is not matched with the actual demand, and improve the vitality of brownfield sites in the development area.
Hari Sharma Neupane, Bikram Acharya, Pradeep Wagle
et al.
Agriculture has been a cornerstone of human civilization for thousands of years, providing food and other essential resources to sustain our societies. However, as we enter the 21st century, we face unprecedented challenges that threaten the very foundations of our agricultural systems. Climate change, resource depletion, and population growth are just a few of the issues that demand urgent attention from policymakers and practitioners alike. Further, the growing population, climate change, the recent COVID-19 pandemic, the Ukraine-Russia war, and the depreciation of national currencies have disrupted the global food supply chain and increased food prices and food insecurity in many countries, including Nepal.
The Nepalese agriculture sector alone contributed employment opportunities for more than 60 % of the population with a 23.9% share in total value added of the national economy (Ministry of Finance, 2022). Though the majority of farmers in Nepal are engaged in the agriculture sector, there is still a dominance of traditional and subsistence agriculture and the country's agricultural production is not enough to feed its population. The continued rise in import bills and volume of food products in recent years has been a major challenge for the country. Addressing these constraints warrants consortia of efforts from the government, nonprofits, and private sectors to promote sustainable and regenerative agricultural concepts and practices that align with local farm attributes and the agroecological environment.
With the above mentioned issue, Policy Research Institute, the publisher of NPPR, collaborated with Association of Nepalese Agricultural Professionals of Americas (NAPA) for the utilization of expert knowledge for public policy making and policy discussion. PRI is open to collaborating with any professional and intellectual society for policy issues.
Thereof, a two-day (January 6-7, 2023) virtual symposium on "Agricultural Policies and Practices in Nepal: Pathways for Transformation" was jointly organized by the PRI and NAPA with the aim to discuss and synthesize structural, policy intervention-related procedural, and local barriers and issues inherent to inadequate agricultural growth in Nepal and recommend transformative and pragmatic policies, programs, and practices feasible at local, regional, and national levels.
The other symposium collaborators were the Ministry of Agriculture and Livestock Development (MoALD), Nepal Agricultural Research Council (NARC), Agriculture and Forestry University (AFU), Institute of Agriculture and Animal Sciences (IAAS, Tribhuvan University), Nepal Agricultural Cooperative Central Federation Ltd. (NACCFL), and Society of Agricultural Scientists-Nepal (SAS-Nepal). The 38 papers presented at the symposium brought together over 500 researchers, policymakers, and practitioners from around the world. The symposium highlighted the importance of innovative policies and practices that can help transform agriculture and ensure its sustainability for future generations.
The symposium was organized and facilitated in four thematic areas. The Agriculture Policy theme highlighted an analysis of current agricultural policies, laws, and regulations that have hindered the production and marketing of farm products, land use policies, transformative agriculture for the viable and circular economy, promoting cooperative farming, farm diversity, and sustainability including internationally successful policy practices suitable for Nepal. The Agricultural Research, Education, and Extension theme included diverse subject matters. These were genetic improvement of crops and livestock for diverse agro-climatic zones; technology innovations and dissemination; science-based knowledge and extension practices; climate-smart and organic agriculture; agri-business and entrepreneurship; commercial agriculture; and integration of agricultural research, education, and extension. Similarly, the Technology and Infrastructure Development theme focused on varied avenues of innovative technology (such as UAV, GIS, and Remote Sensing), farm mechanization, and smart and efficient irrigation practices to optimize costs of production, labor, fertilizer shortages, and monitoring of plant and soil health Finally, the Governance theme underpinned coherence and discordance between the policy frameworks and governing structures/mechanisms of three levels of government and opportunities for realignment for agricultural transformation as well as a local governance framework for agricultural service delivery at a municipality level.
Finally, the symposium highlighted the importance of partnerships and collaborations in driving transformational change. The papers discussed the potential of public-private partnerships, multi-stakeholder platforms, and other forms of collaboration to leverage resources, share knowledge, and scale up innovative solutions.
This special issue received 20 papers for publication consideration, however, after the review process, it is able to manage 12 papers for publication. These papers provide a rich and diverse set of insights into the pathways for transforming agriculture. They offer both practical guidance and theoretical frameworks for policymakers and practitioners seeking to navigate the complex challenges facing agriculture today. We hope this special issue will inspire further research and action towards a more sustainable and equitable agricultural future.
We thank all the authors who contributed to this special issue and the reviewers who provided their valuable feedback. We also extend our appreciation to the symposium organizers and collaborators. Finally, we encourage additional authors/presenters to submit their papers in the NPPR’s Regular Issue, which will be published in September 2023.
Gustavo Moreira, Maryam Hosseini, Md Nafiul Alam Nipu
et al.
While cities around the world are looking for smart ways to use new advances in data collection, management, and analysis to address their problems, the complex nature of urban issues and the overwhelming amount of available data have posed significant challenges in translating these efforts into actionable insights. In the past few years, urban visual analytics tools have significantly helped tackle these challenges. When analyzing a feature of interest, an urban expert must transform, integrate, and visualize different thematic (e.g., sunlight access, demographic) and physical (e.g., buildings, street networks) data layers, oftentimes across multiple spatial and temporal scales. However, integrating and analyzing these layers require expertise in different fields, increasing development time and effort. This makes the entire visual data exploration and system implementation difficult for programmers and also sets a high entry barrier for urban experts outside of computer science. With this in mind, in this paper, we present the Urban Toolkit (UTK), a flexible and extensible visualization framework that enables the easy authoring of web-based visualizations through a new high-level grammar specifically built with common urban use cases in mind. In order to facilitate the integration and visualization of different urban data, we also propose the concept of knots to merge thematic and physical urban layers. We evaluate our approach through use cases and a series of interviews with experts and practitioners from different domains, including urban accessibility, urban planning, architecture, and climate science. UTK is available at urbantk.org.
The urban spatial structure represents the distribution of public and private spaces in cities and how people move within them. While it usually evolves slowly, it can change fast during large-scale emergency events, as well as due to urban renewal in rapidly developing countries. This work presents an approach to delineate such urban dynamics in quasi-real-time through a human mobility metric, the mobility centrality index $ΔKS$. As a case study, we tracked the urban dynamics of eleven Spanish cities during the COVID-19 pandemic. Results revealed that their structures became more monocentric during the lockdown in the first wave, but kept their regular spatial structures during the second wave. To provide a more comprehensive understanding of mobility from home, we also introduce a dimensionless metric, $KS_{HBT}$, which measures the extent of home-based travel and provides statistical insights into the transmission of COVID-19. By utilizing individual mobility data, our metrics enable the detection of changes in the urban spatial structure.
Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to the development and operation of the smart city. As an emerging building block, multi-sourced urban data are usually integrated as urban knowledge graphs (UrbanKGs) to provide critical knowledge for urban spatiotemporal prediction models. However, existing UrbanKGs are often tailored for specific downstream prediction tasks and are not publicly available, which limits the potential advancement. This paper presents UUKG, the unified urban knowledge graph dataset for knowledge-enhanced urban spatiotemporal predictions. Specifically, we first construct UrbanKGs consisting of millions of triplets for two metropolises by connecting heterogeneous urban entities such as administrative boroughs, POIs, and road segments. Moreover, we conduct qualitative and quantitative analysis on constructed UrbanKGs and uncover diverse high-order structural patterns, such as hierarchies and cycles, that can be leveraged to benefit downstream USTP tasks. To validate and facilitate the use of UrbanKGs, we implement and evaluate 15 KG embedding methods on the KG completion task and integrate the learned KG embeddings into 9 spatiotemporal models for five different USTP tasks. The extensive experimental results not only provide benchmarks of knowledge-enhanced USTP models under different task settings but also highlight the potential of state-of-the-art high-order structure-aware UrbanKG embedding methods. We hope the proposed UUKG fosters research on urban knowledge graphs and broad smart city applications. The dataset and source code are available at https://github.com/usail-hkust/UUKG/.
Increasing computational power and improving deep learning methods have made computer vision technologies pervasively common in urban environments. Their applications in policing, traffic management, and documenting public spaces are increasingly common. Despite the often-discussed biases in the algorithms' training and unequally borne benefits, almost all applications similarly reduce urban experiences to simplistic, reductive, and mechanistic measures. There is a lack of context, depth, and specificity in these practices that enables semantic knowledge or analysis within urban contexts, especially within the context of using and occupying urban space. This paper will critique existing uses of artificial intelligence and computer vision in urban practices to propose a new framework for understanding people, action, and public space. This paper revisits Geertz's use of thick descriptions in generating interpretive theories of culture and activity and uses this lens to establish a framework to evaluate the varied uses of computer vision technologies that weigh meaning. We discuss how the framework's positioning may differ (and conflict) between different users of the technology. This paper also discusses the current use and training of deep learning algorithms and how this process limits semantic learning and proposes three potential methodologies for gaining a more contextually specific, urban-semantic, description of urban space relevant to urbanists. This paper contributes to the critical conversations regarding the proliferation of artificial intelligence by challenging the current applications of these technologies in the urban environment by highlighting their failures within this context while also proposing an evolution of these algorithms that may ultimately make them sensitive and useful within this spatial and cultural milieu.