Daniel Villa-Castro, Juan Carlos García-Montoya, Santiago Cabrera-García
et al.
The necessity to develop greener, more efficient and context-adaptable methods to address global cities problems is more urgent than never as urbanisation rises and so do environmental, social and economic related problematics. This study proposed an innovative multidisciplinary Least-Cost Path method (LCP) application for modelling Green Infrastructure (GI) networks in urban areas. The model simultaneously considered different character variables including ground-level CO2 distribution. Performance of three LCP variants were compared, traditional (LCP), LCP plus a Principal Component Analysis (LCP+PCA) and LCP plus an Analytic Hierarchy Process (LCP+AHP). Final accuracy scores demonstrated that LCP+AHP model was the most effective for predicting GI routes in an urban context with 83.56% of precision. Findings suggested the proposed method application is a robust planning tool capable of effectively addressing modern challenges faced by cities worldwide. It can be incorporated at various scales into land use planning processes and other related initiatives.
Urban parks play a vital role in delivering various essential ecosystem services that significantly contribute to the well-being of urban populations. However, there is quite a limited understanding of how people value these ecosystem services differently. Here, we investigated the relationships among nine ecosystem service demands in urban parks across China using a large-scale survey with 20,075 responses and a point-allotment experiment. We found particularly high preferences for air purification and recreation services at the expense of other services among urban residents in China. These preferences were further reflected in three distinct demand bundles: air purification-dominated, recreation-dominated, and balanced demands. Each bundle delineated a typical group of people with different representative characteristics. Socio-economic and environmental factors, such as environmental interest and vegetation coverage, were found to significantly influence the trade-off intensity among service demands. These results underscore the necessity for tailored urban park designs that address diverse service demands with the aim of enhancing the quality of urban life in China and beyond sustainably.
Job F. Rosier, Elizabeth Wamuchiru, Vita Bakker
et al.
While spatial analysis has provided great insights into the expansion of built-up areas, changes within built-up areas have mostly been ignored. As a result, the amount of change within existing urban areas and the change processes they represent remain unknown. Even so, such changes can have important implications for urban sustainability. Here, we use very-high-resolution imagery to analyse different types of building-level changes in Nairobi, Kenya, between 2010 and 2021. Specifically, we manually map whether buildings appeared, persisted, changed, were replaced, or removed, and link these changes to urban development processes. We find that removal, replaced, and renewal combined make up as much as 29% of the total mapped building area in 2010. These changes highlight intricate patterns of urban change, driven by processes such as slum formalisation, rezoning and urban renewal. Our approach provides a generalisable framework for analysing urban change within built-up areas.
مکانهای سوم شهری، فضاهایی فراتر از خانه و محل کار هستند که نقش مهمی در ارتقای تعاملات اجتماعی، هویت مکانی و کیفیت زندگی دارند. این پژوهش با هدف بررسی عوامل مؤثر بر حس تعلق به مکان سوم، با مطالعهی موردی تفرجگاه عینالی تبریز انجام شد. روش تحقیق توصیفی–تحلیلی و با رویکرد کمّی بود. دادهها با پرسشنامهای محققساخته در مقیاس پنجدرجهای لیکرت از میان ۳۸۴ بازدیدکننده بهصورت تصادفی گردآوری شد و ۳۵۵ پرسشنامه معتبر تحلیل شد. متغیرهای اصلی شامل دلبستگی مکانی، مشارکت در فعالیتها و وفاداری کاربران بودند. پایایی ابزار با آلفای کرونباخ ۰٫۹۲۸ تأیید شد. یافتهها نشان داد بین حس تعلق به مکان سوم شهری و متغیرهای پژوهش رابطه مثبت و معناداری وجود دارد. همچنین تفاوت معناداری در میانگین نمرات مؤلفهها بر اساس الگوی بازدید و سطح تحصیلات مشاهده شد. مدل معادلات ساختاری نشان داد که دلبستگی مکانی از طریق پیوند اجتماعی و مشارکت در فعالیتها، به شکل غیرمستقیم موجب تقویت وفاداری میشود. این مدل توانست سهم بالایی از واریانس متغیرهای وابسته را تبیین کند. نتایج بر اهمیت توجه همزمان به ابعاد هویتی، اجتماعی و رفتاری در ارتقای حس تعلق به مکانهای سوم تأکید دارد.
ازهمگسیختگی و کاهش عملکرد بومشناختی در مناطق شهری یکی از تهدیدهای مهم برای حفاظت از منابع ارزشمند اکولوژیک است و راهبردهای مؤثر برای کنترل تکه تکه شدن و نابودی سرزمین در این مناطق مستلزم شناسایی منابع اکولوژیک و عملکردهای زیستی میباشد. براین اساس در مطالعه حاضر به شناسایی منابع حساس و اکولوژیک و تهیه نقشه شبکه اکولوژیک در مجموعه شهری تهران-البرز پرداخته شد و در ادامه پیوستگی عملکرد زیستی در این مجموعه مورد بررسی قرار گرفت. عمدهترین منابع حساس و اکولوژیک در این مجموعه شهری، شامل مناطق تحت حفاظت از جمله پارک ملی، اثر طبیعی ملی، منطقه حفاظت شده، پناهگاه حیات وحش، منطقه شکارممنوع و همچنین ذخایر حفاظتی جنگلی میباشد. همچنین نواحی شمال و شمال شرق منطقه که از پراکندگی مناطق تحت حفاظت و ذخایرزیستی بیشتری برخوردارند، دارای اهمیت اکولوژیک بالاتر و همچنین حساسیت زیستی بیشتری نسبت به روند شهرنشینی و توسعه فعالیتهای انسانی هستند. همچنین تحلیل عملکرد و پیوستگی اکولوژیک در مجموعه شهری تهران-البرز حاکی از آن است که در بین سالهای 1379 تا 1402 پیوستگی اکولوژیک در این منطقه کاهش یافته است. با توجه به اینکه شبکه مناطق حساس زیستی در مجموعه شهری تهران-البرز از اهمیت بالایی برخوردارند، بنابراین انجام برنامهریزی و فراهم کردن ابزارهای مناسب برای کاهش اثرات توسعه شهری و تهدیدات ناشی از آن امری ضروری است. همچنین نتایج این تحلیل میتواند بهعنوان یک راهکار مدیریتی به حفاظت و برنامهریزی صحیح در راستای توسعه فعالیتهای انسانی و به دنبال آن مخاطرات و پیامدهای محیطی کمک کند.
Claus Hedegaard Sørensen, Fredrik Pettersson-Löfstedt
Avoiding exceeding planetary boundaries, while still achieving a decent standard of living for all is a global challenge represented in the so-called doughnut model. Policies for local mobility in line with the doughnut model, will lead to less and slower mobility for many people. This paper contributes to doughnut literature with insights regarding people’s visions of life with doughnut-inspired mobility and accessibility patterns as well as the preconditions required for visions of less and slower mobility to gain legitimacy. This matter was studied in workshops attended by Swedish citizens. Two preconditions are crucial to gain legitimacy. First, visions have to address issues of proximity, freedom, flexibility and spontaneity and a sense of community and second, visions must be tangible and more broadly focused on preferable visions of life and not just mobility.
Accurate assessment of urban canopy coverage is crucial for informed urban planning, effective environmental monitoring, and mitigating the impacts of climate change. Traditional practices often face limitations due to inadequate technical requirements, difficulties in scaling and data processing, and the lack of specialized expertise. This study presents an efficient approach for estimating green canopy coverage using artificial intelligence, specifically computer vision techniques, applied to aerial imageries. Our proposed methodology utilizes object-based image analysis, based on deep learning algorithms to accurately identify and segment green canopies from high-resolution drone images. This approach allows the user for detailed analysis of urban vegetation, capturing variations in canopy density and understanding spatial distribution. To overcome the computational challenges associated with processing large datasets, it was implemented over a cloud platform utilizing high-performance processors. This infrastructure efficiently manages space complexity and ensures affordable latency, enabling the rapid analysis of vast amounts of drone imageries. Our results demonstrate the effectiveness of this approach in accurately estimating canopy coverage at the city scale, providing valuable insights for urban forestry management of an industrial township. The resultant data generated by this method can be used to optimize tree plantation and assess the carbon sequestration potential of urban forests. By integrating these insights into sustainable urban planning, we can foster more resilient urban environments, contributing to a greener and healthier future.
Delineating areas within metropolitan regions stands as an important focus among urban researchers, shedding light on the urban perimeters shaped by evolving population dynamics. Applications to urban science are numerous, from facilitating comparisons between delineated districts and administrative divisions to informing policymakers of the shifting economic and labor landscapes. In this study, we propose using commute networks sourced from the census for the purpose of urban delineation, by modeling them with a Graph Neural Network (GNN) architecture. We derive low-dimensional representations of granular urban areas (nodes) using GNNs. Subsequently, nodes' embeddings are clustered to identify spatially cohesive communities in urban areas. Our experiments across the U.S. demonstrate the effectiveness of network embeddings in capturing significant socioeconomic disparities between communities in various cities, particularly in factors such as median household income. The role of census mobility data in regional delineation is also noted, and we establish the utility of GNNs in urban community detection, as a powerful alternative to existing methods in this domain. The results offer insights into the wider effects of commute networks and their use in building meaningful representations of urban regions.
Lounis lbtissem, Leulmi Lamia, Gherzouli lazhar
et al.
This study analyzes urban sprawl in the Algerian cities of Skikda and Tébessa from 1985 to 2024, utilizing supervised classification of Landsat satellite imagery and GIS analysis. Skikda, a coastal city, experienced a 68% increase in built-up areas due to industrial growth and coastal geography, whereas Tébessa, an inland city, saw a 45% increase, with growth moderated by its topography and economic structure. The findings illustrate how socio-economic factors, land-use policies, and geographical characteristics influence urban expansion patterns. Skikda's rapid, scattered growth contrasts with Tébessa's controlled expansion. This study highlights the need for customized urban planning strategies that consider local contexts to manage urban sprawl effectively. By comparing the dynamics of coastal and inland cities, the research provides valuable insights for sustainable urban development in medium-sized Algerian cities, offering a framework for similar studies nationwide.
With a series of redevelopment activities, such as land consolidation and urban renewal, many cities in China have experienced land de-urbanization phenomena. These include the conversion of construction land into green spaces (such as parks, forests, and lawns), blue spaces (such as rivers, lakes, and wetlands), and farmland. However, there is currently limited research on diverse land de-urbanization types and pathways. This study focuses on investigating the land de-urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from 2014 to September 2023 using the Continuous Change Detection and Classification (CCDC) method. The results demonstrate that the GBA experienced 72.74 square kilometers of de-urbanization during the study period, primarily through the conversion of construction land to land with low plant coverage, including grassland and farmland. There were significant differences in the quantity and spatial agglomeration of de-urbanization between cities and within individual cities. Temporally, de-urbanization predominantly occurred in the period of 2016 to 2021, with a sharp decline in 2022. The temporal changes were significantly influenced by urban renewal policies and the impact of the COVID-19 pandemic. In terms of spatial clustering characteristics, the de-urbanization process in the GBA exhibited spatial agglomeration but was primarily characterized by low-level clustering. This study also examines the correlations between de-urbanization and factors including location and the stage of urbanization. The analysis showed that de-urbanization within cities tended to concentrate near the main urban roads within a range of 10–30 km from city centers. The trend of de-urbanization followed a pattern that is consistent with the Northam curve, where de-urbanization tends to increase during the rapid urbanization phase and decline as urbanization reaches a mature stage. Overall, this study provides valuable insights for the redevelopment of construction land within the context of ecological civilization construction. It also offers suggestions for urban land development and redevelopment in metropolitan areas.
In cities, a mosaic of different types of urban agriculture can be found. However, knowledge about advantages and disadvantages of the different types is still fragmented. This paper introduces an integrative evaluation framework for assessing the environmental, social, and economic sustainability of urban agriculture by applying a multi-criteria analysis based on an Analytic Hierarchy Process and a participatory approach. Based on a German case study and on the examples of vertical farming and community-supported agriculture, the results suggest that sustainable urban agriculture is a multi-dimensional approach informed by strong sustainability that places nature in the focus. Thus, the environmental dimension received the highest weight, followed by the social and, lastly, the economic dimension. Regarding the sub-criteria, species diversity achieved the highest total weight and food quality and safety the lowest. Conceptually, this paper provides scientific fundamentals for a systematic comparative evaluation of different types of agriculture for sustainable urban development.
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.
Ammar Sohail, Bojie Shen, Muhammad Aamir Cheema
et al.
As urban areas grapple with unprecedented challenges stemming from population growth and climate change, the emergence of urban digital twins offers a promising solution. This paper presents a case study focusing on Sydney's urban digital twin, a virtual replica integrating diverse real-time and historical data, including weather, crime, emissions, and traffic. Through advanced visualization and data analysis techniques, the study explores some applications of this digital twin in urban sustainability, such as spatial ranking of suburbs and automatic identification of correlations between variables. Additionally, the research delves into predictive modeling, employing machine learning to forecast traffic crash risks using environmental data, showcasing the potential for proactive interventions. The contributions of this work lie in the comprehensive exploration of a city-scale digital twin for sustainable urban planning, offering a multifaceted approach to data-driven decision-making.
This study addresses the challenge of urban safety in New York City by examining the relationship between the built environment and crime rates using machine learning and a comprehensive dataset of street view images. We aim to identify how urban landscapes correlate with crime statistics, focusing on the characteristics of street views and their association with crime rates. The findings offer insights for urban planning and crime prevention, highlighting the potential of environmental design in enhancing public safety.
Location-based services play an critical role in improving the quality of our daily lives. Despite the proliferation of numerous specialized AI models within spatio-temporal context of location-based services, these models struggle to autonomously tackle problems regarding complex urban planing and management. To bridge this gap, we introduce UrbanLLM, a fine-tuned large language model (LLM) designed to tackle diverse problems in urban scenarios. UrbanLLM functions as a problem-solver by decomposing urban-related queries into manageable sub-tasks, identifying suitable spatio-temporal AI models for each sub-task, and generating comprehensive responses to the given queries. Our experimental results indicate that UrbanLLM significantly outperforms other established LLMs, such as Llama and the GPT series, in handling problems concerning complex urban activity planning and management. UrbanLLM exhibits considerable potential in enhancing the effectiveness of solving problems in urban scenarios, reducing the workload and reliance for human experts.
Large language models(LLMs), with their powerful language generation and reasoning capabilities, have already achieved notable success in many domains, e.g., math and code generation. However, they often fall short when tackling real-life geospatial tasks within urban environments. This limitation stems from a lack of physical world knowledge and relevant data during training. To address this gap, we propose \textit{CityGPT}, a systematic framework designed to enhance LLMs' understanding of urban space and improve their ability to solve the related urban tasks by integrating a city-scale `world model' into the model. Firstly, we construct a diverse instruction tuning dataset, \textit{CityInstruction}, for injecting urban knowledge into LLMs and effectively boosting their spatial reasoning capabilities. Using a combination of \textit{CityInstruction} and open source general instruction data, we introduce a novel and easy-to-use self-weighted fine-tuning method (\textit{SWFT}) to train various LLMs (including ChatGLM3-6B, Llama3-8B, and Qwen2.5-7B) to enhance their urban spatial capabilities without compromising, or even improving, their general abilities. Finally, to validate the effectiveness of our proposed framework, we develop a comprehensive text-based spatial benchmark \textit{CityEval} for evaluating the performance of LLMs across a wide range of urban scenarios and geospatial tasks. Extensive evaluation results demonstrate that smaller LLMs trained with \textit{CityInstruction} by \textit{SWFT} method can achieve performance that is competitive with, and in some cases superior to, proprietary LLMs when assessed using \textit{CityEval}.
Mahla Ardebili Pour, Mohammad B. Ghiasi, Ali Karkehabadi
Floods are among the most prevalent and destructive natural disasters, often leading to severe social and economic impacts in urban areas due to the high concentration of assets and population density. In Iran, particularly in Tehran, recurring flood events underscore the urgent need for robust urban resilience strategies. This paper explores flood resilience models to identify the most effective approach for District 6 in Tehran. Through an extensive literature review, various resilience models were analyzed, with the Climate Disaster Resilience Index (CDRI) emerging as the most suitable model for this district due to its comprehensive resilience dimensions: Physical, Social, Economic, Organizational, and Natural Health resilience. Although the CDRI model provides a structured approach to resilience measurement, it remains a static model focused on spatial characteristics and lacks temporal adaptability. An extensive literature review enhances the CDRI model by integrating data from 2013 to 2022 in three-year intervals and applying machine learning techniques to predict resilience dimensions for 2025. This integration enables a dynamic resilience model that can accommodate temporal changes, providing a more adaptable and data driven foundation for urban flood resilience planning. By employing artificial intelligence to reflect evolving urban conditions, this model offers valuable insights for policymakers and urban planners to enhance flood resilience in Tehrans critical District 6.
آخرین یافتههای علمی موجود در حوزه پراکندهرویی شهری اغلب به گونهشناسی توجه کردهاند؛ ازاینرو در مقاله حاضر باهدف شناسایی انواع مختلفی از گونههای پراکندهرویی شهری در کلانشهر کرج سعی شده است دیدگاههای موجود در گونهشناسی، شاخصهای شناسایی هرگونه و چگونگی طبقهبندی گونههای مختلف در کلانشهر کرج مورد بررسی قرار گیرد. محدوده مکانی پژوهش، مناطق 10گانه کلانشهر کرج بوده است. دادهها با استفاده از روش کتابخانهای و میدانی جمعآوریشده است. نوع دادهها شامل دادههای جمعیتی مرکز آمار و دادههای مکان محور سازمان نقشهبرداری کشور و شهرداری کرج بوده است. روش تجزیهوتحلیل دادهها بهصورت کمی و با استفاده از تکنیک ضریب موران، تراکم کرنل، تحلیل چیدمان فضا، شاخص شکل و منطق بولین انجامشده است. نتایج حاصل حاکی از آن است که شهر کرج دچار رشد غیر سازمانیافته و بدقواره بوده و با گونههای مختلفی از پراکندهرویی شهری مواجه میباشد. گونهی تراکم پایین و پیوسته پراکندهرویی شهری بیشترین حجم از مساحت شهر را در برگرفته است و عمدتاً در لبههای شهری کرج قرار دارد. هرچند گونههای دیگری ازجمله پراکندهرویی خطی یا نواری، پراکندهرویی جهشی یا گرهگرهی نیز در این شهر وجود دارد؛ اما غلبه با گونهی پراکندهرویی پیوسته با تراکم کم یا پراکندهرویی لبهایی است؛ بنابراین میتوان گفت پراکندهرویی در شهر کرج با یک شکل و الگوی واحد رشد و توسعه پیدا نکرده است؛ بلکه با اشکال و گونههای مختلف همراه بوده است.