City Planning Research Articles

Evaluation of perceptual indicators of physical environment affecting the inducement of citizen’s sense of place in urban neighborhoods

Cities in the past made it simple for people and space to interact. However, in today’s world, city planning and foreseeing strategies and options to create a desirable place that fits human life have become more important due to the spread of urbanization and the emergence of new problems brought about by the frequent implementation of foreign and Western patterns in urban neighborhoods. Sense of place is a behavioral science concept that plays a crucial role in forming user communication bases and creating an environment that eventually fosters a sense of belonging by attracting users’ happiness. Because of this, architects and urban planners are now more cognizant of the ideas and signs of a feeling of place in the actual physical surroundings of metropolitan areas. However, the debate remains as to what these indicators actually are and how they might affect the sense of place that residents have. This study combines analytical and descriptive methods. This study employed a questionnaire and survey approach to determine the physical characteristics impacting the feeling of place. The statistical population consisted of 150 residents of Tehran Municipality District 1′s Zaferanieh area. Questions were posed using a basic random technique to the statistical population. For data analysis, SPSS was utilized. This form of external validity applies to the questionnaire instrument. To assess this questionnaire’s reliability, Cronbach’s alpha coefficient was employed. The reliability of the questionnaire is deemed adequate, as indicated by the coefficients that exceed 0.7. Certain factors significantly contribute to creating a sense of place in urban communities, according to the findings of the regression analysis and correlation coefficient. With regard to the physical elements affecting the Zaferanieh neighborhood’s feeling of place, factor analysis revealed that the use and activity index, at 11.252%, has the least influence and the spatial quality index, at 17.021%, has the most.

Percursos de pedestres: um índice a partir das inter-relações entre caminhabilidade e conforto ambiental

Abstract The study of environmental comfort in open urban spaces has undergone constant updates in the last decades. In the meantime, the specific analysis of these aspects within the context of walkability stands out as an area with considerable potential to be investigated. In this context, this article aims to explore walkability in pedestrian routes and its relationship with environmental comfort, proposing a more systematized approach for its application in the strategic planning of cities. Therefore, we proposed the construction of a Pedestrian Walkability and Comfort Index (ICCP), composed of three partial indices that consider thermal, acoustic, and ergonomic aspects of pedestrian paths. The methodology is based on street visualization tools and Geographic Information System (GIS) tools to spatialize and systematize the interactions between walkability and environmental comfort. The research concludes that ICCP is a valid tool for measuring walkability at meso and micro scales, and it can be replicated in other urban contexts. Furthermore, the development and application of ICCP also highlight that rethinking walkability with environmental comfort in urban areas requires an integrated approach to the entire mobility system and urban infrastructure of cities.

Semantic segmentation of optical satellite images for the illegal construction detection using transfer learning

As urban populations grow, the demand for new infrastructure intensifies. However, some builders often neglect to obtain necessary permits, resulting in unstable structures that endanger public safety and disrupt city planning. Illegal construction poses significant challenges to economic development and social harmony. To tackle this issue, there is an urgent need for an automated illegal building monitoring system that can accurately identify and notify authorities about unlawful constructions. Current methods are often inefficient due to limited data access, inadequate analysis, and lack of automation.Recent technological advancements suggest that using satellite imagery alongside semantic segmentation is a highly effective approach for detecting illegal buildings. This process not only ensures the safety and compliance of structures but also enhances overall regulatory enforcement. This research distinguishes itself from previous studies by primarily focusing on illegal construction. It employs a diverse range of machine learning models, including the U-Shaped Network and Visual Geometry Group, and incorporates customized evaluation metrics that are not typically found in earlier research. Additionally, it utilizes real-world data, enhancing its practical relevance, and features IoT integration for real-time monitoring capabilities.We chose U-Net as our architecture due to its symmetrical design, which facilitates effective feature extraction. Its skip connections play a crucial role in preserving important features during the segmentation process. U-Net is known for its high accuracy and adaptability across various domains, making it versatile for our needs. Additionally, it performs well even with limited data, demonstrating robustness in diverse conditions. Furthermore, its compatibility with transfer learning enhances its applicability. For all these reasons, U-Net was selected as our model.Our experiments revealed that the Unet_mini model outperformed all others, demonstrating its effectiveness in addressing illegal construction challenges in urban areas. Qualitative results further illustrate the model's robustness, showing a significant reduction in false positives and improved accuracy in identifying illegal structures. This comprehensive approach not only improves detection rates but also contributes to more efficient regulatory enforcement, thereby promoting safer urban environments.This research distinguishes itself from previous studies by primarily focusing on illegal construction detection rather than general segmentation tasks. It employs a diverse range of machine learning models, including the U-Shaped Network and Visual Geometry Group, and integrates customized evaluation metrics that are not typically found in earlier research. Furthermore, it utilizes real-world data, enhancing its practical relevance, and incorporates IoT integration for real-time monitoring capabilities, setting it apart from existing studies.

Can regional polycentric spatial structure narrow the urban–rural income gap? Evidence from China

PurposePolycentricity has become a crucial spatial development strategy. This paper aims to clarify the impact and mechanism of polycentric spatial structure on the urban–rural income gap, provide better countermeasures to narrow it and make development suggestions for cities of different scales.Design/methodology/approachBased on the panel data of Chinese cities from 2010 to 2019, this paper empirically examines the specific impact of polycentric spatial structure on the urban–rural income gap by utilizing a two-way fixed effects model.FindingsThe results show that the polycentric spatial structure is favorable for reducing the urban–rural income gap, and upgrading the industrial structure and improving transportation infrastructure are important mechanisms. Moreover, the role of polycentric spatial structure is more significant in large cities. However, in small and medium-sized cities, the role of polycentric spatial structure is not significant.Research limitations/implicationsThe limitations of this study include two main aspects. One is that this paper only analyzes the mediating effect of industrial structure and transport infrastructure, and we do not examine other mechanisms. The second is that due to data availability, this paper uses data from 2010 to 2019, and the research period can be further extended in future studies.Practical implicationsThis study has policy implications for building a polycentric city network and also provides insights into the planning of cities of different scales.Social implicationsThe polycentric spatial structure is important for narrowing the urban–rural income gap, especially in large cities. Improvement of transportation infrastructure and upgrading of industrial structure are important mechanisms. However, the polycentric spatial structure does not play a significant role in small and medium-sized cities. Thus, the polycentric spatial structure is not suitable for every kind of city. Relevant departments should carry out urban planning according to local conditions.Originality/valueAs a critical concern of urban planning, polycentricity significantly affects the urban–rural income gap. Relevant studies have analyzed the impact of polycentric spatial structure on regional income disparity, and few studies have focused on the relationship between polycentric spatial structure and the urban–rural income gap. Using city panel data in China, this paper explores the impact of polycentric spatial structure on the urban-rural income gap. Meanwhile, we test the mediating role of transportation infrastructure improvement and industrial structure upgrading. This study broadens the analysis of the urban–rural income gap’s influencing factors and provides a path reference for narrowing the urban–rural income gap.

Advancing Sustainability in Urban Planning by Measuring and Matching the Supply and Demand of Urban Green Space Ecosystem Services

As global cities rapidly develop, the realization of sustainable urban development has become a pressing global concern. As a critical component of the urban built environment, urban green spaces are essential for maintaining the sustainability of urban ecosystems by offering a variety of ecological benefits. However, there are some problems in the design and construction of urban green framework, which often disregard the real needs of residents and hinder the sustainable development of urban areas. Therefore, urban planning that takes into account the supply and demand relationship of urban green space is considered to be a key technology that should help to improve residents’ happiness and promote urban sustainable development. This paper takes Zunyi City as an example; selects six indicators; combines UAV images, population, economic, and environmental data to evaluate ecosystem services valuation existing green space in the city; and analyzes its social and ecological needs. Affected by land utilization and development density, the supply value showed low spatial density characteristics in the central urban area and high spatial distribution characteristics in the surrounding urban area. The correlation between demand value and population density is the largest, and the distribution pattern is similar to that of population density. The supply-demand ratio in the study area is −0.54, indicating a severe imbalance between supply and demand. The correlation analysis between the comprehensive supply and demand ratio and each influencing factor shows that the correlation with population density is the largest. The combination of supply and demand maps addresses a major problem in planning, namely, ensuring that communities have priority in providing urban green space based on society’s need for ecosystem services. In addition, these maps will help develop the best green space planting plans to meet the specific needs of different communities. Taken together, the findings can help city planners and decision makers improve the layout of urban green areas, increase ecosystem service potential, and improve urban environmental quality and sustainability.

Assessment and the Future Development of the Zagreb 3D City Model

The first 3D model of the city of Zagreb was developed in 2008. Since then, the model has been improved and updated. Today, it is used for various purposes in the city planning and management. It is also publicly available to citizens through the ZG3D Web application. In this paper, an assessment of the existing 3D model is provided by considering different factors (i.e. consistency between models, standardization, data quality, data interoperability, data maintenance/governance, and use cases) that need to be tackled in order for the 3D city model to be further improved and used towards the creation of the digital twin and ultimately to support the smart city concept. The assessment considered some general criteria and also the 3D City Index assessment tool developed by the Urban Analytics Lab, National University of Singapore (NUS). The assessment results show that the existing model is used for various purposes, but to evolve towards a true digital twin, it must be improved.

Balancing Economic Growth and Environmental Conservation: Assessing Supportive Policies in Resources-Based Cities in China

This study investigates the impact of comprehensive reforms on the economic development and environmental protection of resource-based cities in China within the context of the `National Sustainable Development Plan for Resource-based Cities (2013–2020)’. Employing a difference-in-differences methodology, we find that these reforms not only bolster economic stability but also significantly enhance urban ecological environment, resulting in a win–win outcome for economic prosperity and environmental preservation. Our analysis reveals that the reforms drive sustained economic growth and ecological enhancement by downsizing industries, adopting green technologies, and optimizing industrial composition. Moreover, we identify that these impacts are more pronounced in mature and declining cities and in regions with robust official promotion strategies and stringent environmental regulations. These findings provide valuable insights for addressing the “resource curse” within social systems and for formulating policies that balance stable economic growth and environmental protection in resource-based cities.

DEMOCRATIC PARTICIPATION IN THE DEFENSE OF SUSTAINABLE CITIES

This article reflects on the management of a neighborhood residents' association located in a large city in the state of São Paulo. This study makes an important contribution to the debate on popular participation in city planning as a management strategy to achieve the UN Sustainable Development Goal (SDG) number 11, which states: "Make cities and human settlements inclusive, safe, resilient and sustainable." The methodology developed was bibliographic and field research with a qualitative approach, using semi-structured interview techniques and observation. The data collected in the interviews were analyzed from a theoretical-critical perspective that ensures a reading of social reality and favors the understanding of social relations in their essence, that is, beyond what is presented in the appearance of facts. The study brought indicators that, although residents' associations are privileged spaces for popular participation, in the institution investigated, no practice of protagonism of residents in social mobilization and participation in collective affairs was observed. It is clear that the residents' association is unable to perform its political functions, much less facilitate the population's approach to municipal management, aiming at participation in the elaboration of public policies and sustainable local development.

CONSORTIUM URBAN OPERATIONS, A MUNICIPAL MANAGEMENT TOOL: AN ANALYSIS OF ITS IMPLEMENTATION ATTEMPT IN THE MUNICIPALITY OF MANAUS

This research aimed to evaluate the implications of the City Statute on the application of Joint Urban Operations? Was there any attempt to carry out Joint Urban Operations in Manaus? Joint Urban Operations (OUC) are an instrument for the implementation of large urban projects using public and private investments. It shows the importance of popular participation, the relationship with progressive IPTU and with the Master Plans of cities, especially Manaus. A methodology was used regarding technical procedures, the research is classified as bibliographic and documentary; regarding the objectives, it was an exploratory study, analyzing some cases to arrive at the one in Manaus. It was concluded that there is a low level of popular participation, a lack of transparency that makes society's participation unfeasible and the use of progressive taxes would have a certain effect, but without solving the problem. The introduction of Joint Urban Operations could be an option to reduce the housing deficit.

Outdoor Artificial Light at Night and Insomnia-Related Social Media Posts

Understanding the outcomes of artificial light at night (ALAN) on insomnia is crucial for public health, particularly in rapidly urbanizing regions. However, evidence of the association between ALAN exposure and insomnia is limited, despite the large number of people exposed to ALAN. To explore the association between outdoor ALAN exposure and insomnia among the Chinese population. This cross-sectional study used social media data from Weibo (Sina), a social media platform, and satellite-derived nighttime light images. The study period spans from May 2022 to April 2023. The study encompasses 336 cities across China's mainland, providing a comprehensive national perspective. Data include insomnia-related posts from the platform users, representing a large and diverse population sample exposed to varying levels of ALAN. Outdoor ALAN exposure (in nanowatts per centimeters squared per steradian [nW/cm2/sr]) was measured using satellite-derived nighttime light images at a spatial resolution of 500 m. The incidence of insomnia among residents at the city level was measured by the number of insomnia-related posts on social media. Multiple linear regression models were used to estimate the association between ALAN exposure and population insomnia, adjusting for population characteristics and meteorological factors at the city level. The study included data from 1 147 583 insomnia-related posts. Daily mean ALAN exposure across the 336 cities ranged from 3.1 to 221.0 nW/cm2/sr. For each 5 nW/cm2/sr increase in ALAN exposure, the incidence of insomnia increased by 0.377% (95% CI, 0.372%-0.382%). The association was greater in less populated cities and under extreme temperature and poor air quality conditions. The observed exposure-response functions between ALAN exposure and insomnia demonstrated an upward trend, with steeper slopes observed at low exposures and leveling off at higher exposures. This study provides evidence of the association between increased ALAN exposure and higher incidence of insomnia. These findings expand the current knowledge on adverse health outcomes of ALAN exposure and emphasize the potential health benefits of well-planned artificial nighttime lighting in China and other developing countries in the early stages of city planning.

Quantitative Identification of Mixed Urban Functions: A Probabilistic Approach Based on Physical and Social Sensing Data

ABSTRACTThis paper proposes a diversity identification method based on information fusion for quantitatively identifying mixed urban functional zones (UFZ), addressing the critical need for better city planning and management. This method integrates both social and physical sensing data, considering the frequency of urban functional occurrences and the intensity of human activity. Specifically, we extract “dynamic” human activity features from crowdsourced smart device data and “static” visual features from street view images. Based on the fused multi‐modal data, our method infers the large‐scale distribution of UFZs more accurately. We also create a standardized mixed UFZ dataset for model training and testing, which includes residential, commercial, public services, industrial, and ecological categories. In general, the method transforms the functional label recognition task into a probability distribution recognition task. It addresses complex land use distributions rather than simply assigning a single label to each zone. The result shows that our method could achieve a Cosine similarity of (0.542 ± 0.143), the lowest Chebyshev of (0.785 ± 0.043), and L1 distances of (0.264 ± 0.080), indicating more accurate and consistent predictions and closer match to true distributions.

WebGIS map for smart city development and decision support system: A case study of Dehradun Smart City, Uttarakhand, India

India is one of the countries in the world which has made continuous progress in the technological revolution. The development of Smart Cities has led to a revolution in e-governance and the citizen-centric approach. The Geographical Indicators and Location-based assets can provide quick action and decision-making approaches to City Administrators. All Smart City GIS Layers on a single platform are beneficial for Smart City and Urban Planners and Administrators to find common solutions for retrofitting the environment. The common GIS Layer helps in utility services planning and drainage mapping which allows for the underground laying of all utility pipes and cables. This research is about the mapping of the common GIS Layer of a Smart City of India on a single WebGIS map to its core. The common GIS Layer will help in decision support and quick action redressal in emergency scenarios as well.

Masked Graph Autoencoder for Self‐Supervised Transportation Mode Recognition

ABSTRACTTransportation mode recognition reveals the traveling patterns of urban residents and plays an important role in travel analysis and city planning. Research has been done to identify transportation modes using deep learning methods. However, large‐scale and far‐ranging labeled trajectory data are hard to acquire, which hinders the training and application of supervised models. In this study, we propose a self‐supervised method for trajectory transportation modes recognition. First, we construct sequence and dependency graph for trajectory points. Next, we apply mask and remask strategy to graph node features to enhance its learning ability in semantic information. Notably, in order to learn features without labeled data, we introduce graph autoencoder into the framework. This method is able to learn the sequence and dependency feature reconstruction of the travel paths. It can obtain underlying semantic information with the mask and remask strategy. We applied our model to GPS trajectory dataset from the Microsoft GeoLife Project, achieving an average accuracy of 68.26% in identifying the transportation modes among seven categories. The result indicates that the proposed method offers a solution to identifying transportation modes without abundant labeled data. This method can also help to extract space–time features at different scales from trajectory data, which could be applied to various downstream tasks.

Assessing Rainwater Risks and Rainwater Harvesting Opportunities for the New Capital City of Indonesia

In the context of planning and construction of the new capital city of Indonesia, referred to as Ibu Kota Negara (IKN), this article addresses the spatial risks and opportunities of rainwater resources in the area where IKN is planned. The article relies on an inventory of various physical data, which were used to derive a flood susceptibility map, as well as rainfall data derived from public and open sources. The geospatial study drew on geospatial software (ArcGIS Pro, 2.1.) and the Google Earth Engine platform (GEE). After this analysis, we followed a management design, which took IPCC climate change scenarios into account. The results demonstrated that the southern coast has higher precipitation than the northern coast in the IKN area. To enhance the efficacy of rainwater management planning, a grid is proposed to mitigate the flood risk and to harvest rainwater. Although rainwater varies throughout the IKN area, and may vary even more with different climate change predictions, it is possible to capture rainwater and create a system to reduce reliance on traditional water sources, alleviate stormwater runoff and mitigate the impact of urban flooding. While IKN will be developed by both regulated planning and other population-driven developments, monitoring and reflecting on existing plans will still be necessary to make IKN sufficiently resilient and sustainable.

Inventário e Análise da Arborização de praças no município de Campo Belo, MG

Due to the population growth in urban areas, it is necessary to rethink city planning. Parks, as accessible green spaces, play an important role in providing leisure, social interaction, education, and ecosystem services, contributing to improving quality of life.he objective was to carry out a qualitative and quantitative inventory of trees located in Cônego Ulisses and Menotti d’Áurea squares in Campo Belo, Minas Gerais. On-site data collection included identification, measurement (DBH, height, and cankers), and visual assessment (inadequate pruning, mechanical damage, apparent nutritional deficit, and pests). By cross-referencing the data, a classification of the trees' phytosanitary status was conducted. A total of 257 trees were found, with 70% belonging to native species and classified as large-sized. Only 3.5% of the trees showed apparent nutritional deficits. Regarding pests, 4.66% suffered attacks by termites, 5.44% by borers, and two individuals were attacked by caterpillars. It was observed that 10.89% had larger canker dimensions. It was noted that 43.97% of the trees were pruned irregularly, outside of the recommended technical standards, and three species suffered mechanical damage due to vandalism. It was identified that 52% of the trees classified under criterion III (precarious tree) were Pau Ferro. The inventory in the squares showed a predominance of native species, but planning for the planting of new specimens should respect specific diversity criteria. Monitoring needs to be carried out, and replanting planning should prioritize the replacement of precarious trees. Training should be provided for professionals performing pruning, and environmental education strategies should be developed for the population.

Inclusive engagement for equitable resilience: Community case study insights

Abstract Incorporating equity into climate resilience planning, especially through participatory processes, is important to adequately address social vulnerability and avoid reproducing inequities. Recent analyses of resilience and adaptation plans in the United States suggest that there is increasing attention on equity and justice, but a wide variation in how it is being incorporated and implemented. Available studies of resilience planning are limited by their focus on larger urban areas and on plan contents. This research contributes a qualitative analysis of engagement in resilience planning in smaller cities and rural areas. It presents findings from community case studies used as part of human-centered design research to develop an equitable resilience planning tool. Materials from the tool were used to conduct participatory engagement activities including storytelling, mapping, and brainstorming actions that elicited community members’ experiences with hazards and disasters and ideas for equitable resilience actions. Themes that emerged from the qualitative analysis of the workshop discussions were: community members’ include both environmental and social concerns in addressing resilience, challenges associated with social vulnerability framing, the underlying social systems that perpetuate inequities, recognizing different types of trauma, the power of convening, and challenges with sustaining engagement without dedicated resources. This article provides insights that inform efforts to better incorporate equity into resilience planning and advance the study of equitable resilience.

Co-designing transport models as a heuristic planning tool.

Recently the transportation sector has witnessed several new technologically driven disruptions that have amplified the complexity of city planning and policymaking. Traditional well-established processes of decision-making in urban planning and transportation are proving insufficient to deal with this degree of complexity and uncertainty. This paper proposes an alternative approach, combining qualitative and normative urban design, with quantitative and predictive transport modelling. This requires urban designers and transport modellers to co-create goal-driven and agile transport models that act as a heuristic tool to guide planning decisions in early design stages. Heuristic modelling is informed by design optioning and vice versa in an iterative loop. A case study is presented to demonstrate how this approach is operationalized to study the impacts of automated vehicles on urban planning. Design workshops are used as a method to elicit responses from stakeholders, which are used to co-create the simulation models. This collaborative process grounds the research in real-world practice and enhances the communication of design proposals and research findings across disciplines. By integrating design thinking methods with agent-based transport simulations, this approach provides a better understanding of emergent effects in complex urban systems and improves stakeholder engagement in the planning process.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

High-resolution multi-source traffic data in New Zealand

Traffic information is crucial for managing transportation and city planning, but obtaining national-scale data is difficult due to privacy concerns. Consequently, most current traffic datasets have limitations in terms of time and location coverage, leading to a lack of comprehensive public access to national traffic data. To address this issue, a multi-source highway traffic dataset has been created, featuring 2042 sensors in New Zealand over a 9-year period with 15-minute intervals and accompanying metadata. The dataset includes data of both light-duty and heavy-duty vehicles, as well as weather information like temperature and precipitation. This dataset has diverse potential research applications such as traffic flow prediction and congestion management.

Automatic reconstruction of semantic façade model of architectural heritage

Façade elements, such as windows, doors, balconies, sculptures, and totems, in architectural heritage images with incomplete structures should be automatically reconstructed for applications in 3D analysis, 3D modeling, virtual tourism, city planning, and the protection and reconstruction of architectural heritage. This study segments façade elements of architectural heritage semantically using YOLOv9. A parameterized expression for the semantic façade model is designed. In addition, the façade layer graph (FLG) and element layer graph (ELG) algorithms are developed based on topological, geometric, and structural constraints to automatically reconstruct the semantic façade model for architectural heritages. The results showed that the average precision (AP) and mean intersection over union (MIoU) achieved using YOLOv9 + FLG-ELG are 86.91% and 85.63%, respectively, on the dataset concerning façade elements of architectural heritages. The AP values obtained from the proposed method are 98.5% on the ECP2011 dataset and 95.3% on the Graz2012 dataset. The YOLOv9 + FLG-ELG method automatically reconstructs regular, irregular, and complex façade layouts with high accuracy and robustness.

How 3D Printing Technology Makes Cities Smarter: A Review, Thematic Analysis, and Perspectives

This paper presents a comprehensive review of the transformative impacts of 3D printing technology on smart cities. As cities face rapid urbanization, resource shortages, and environmental degradation, innovative solutions such as additive manufacturing (AM) offer potential pathways for sustainable urban development. By synthesizing 66 publications from 2015 to 2024, the study examines how 3D printing improves urban infrastructure, enhances sustainability, and fosters community engagement in city planning. Key benefits of 3D printing include reducing construction time and material waste, lowering costs, and enabling the creation of scalable, affordable housing solutions. The paper also addresses emerging areas such as the integration of 3D printing with digital twins (DTs), machine learning (ML), and AI to optimize urban infrastructure and predictive maintenance. It highlights the use of smart materials and soft robotics for structural health monitoring (SHM) and repairs. Despite the promising advancements, challenges remain in terms of cost, scalability, and the need for interdisciplinary collaboration among engineers, designers, urban planners, and policymakers. The findings suggest a roadmap for future research and practical applications of 3D printing in smart cities, contributing to the ongoing discourse on sustainable and technologically advanced urban development.