Urban Characteristics Research Articles

What Is My Plaza for? Implementing a Machine Learning Strategy for Public Events Prediction in the Urban Square

Plazas are an essential pillar of public life in our cities. Historically, they have been seen as public fora, hosting public events that fostered trade, interaction, and debate. However, with the rise of modern urbanism, city planners considered them as part of a larger strategic development scheme overlooking their social importance. As a result, plazas have lost their function and value. In recent years, awareness has risen of the need to re-activate these public spaces to strive for social inclusion and urban resilience. Geometric and urban features of plazas and their surroundings often suggest what kinds of usage the public can make of them. In this project, we explore the application of machine learning to predict the suitability of events in public spaces, aiming to enhance urban plaza design. Learning from traditional urbanism indicators, we consider factors associated with the features of the public space, such as the number of people and the high degree of comfort, which are evolved from three subcategories: external factors, geometric shape, and design factors. We acknowledge that the predictive capability of our model is constrained by a relatively small dataset, comprising 15 real plazas in Madrid augmented digitally to 2025 fictional scenarios through self-organising maps. The article details the methods to quantify and enumerate quantitative urban features. With a categorical target variable, a classification model is trained to predict the type of event in the urban space. The model is then evaluated locally in Grasshopper by visualising a parametric verified geometry and deploying the model on other existing plazas worldwide regarding geographical proximity to Madrid, where to share or not the same cultural and environmental conditions. Despite these limitations, our findings offer valuable insights into the potential of machine learning in urban planning, suggesting pathways for future research to expand upon this foundational study.

Evolution of the Planning Paradigm and Urban Design of Housing Complexes in the Context of Changing Challenges in Ukraine: Case of Lviv

The study provides a retrospective of the housing development in Lviv and the factors that affected it. Using the general process of housing evolution, the study focuses on the impact of the war as a catalyst that influenced the demand and volume of housing construction over the last decade. Moreover, it reflects the unprecedented demographic shift driven by the number of IDPs in Ukraine since the beginning of 2022. The study demonstrates that, due to the increased demand for housing, the approaches to the design of housing have changed in recent years, defining a new type of urban unit called a housing complex. The study aims to identify and examine the genesis and characteristics of the modern planning paradigm and urban design of residential architecture in Lviv in the context of shifting conditions. Spatial and urban characteristics, social indicators, resident focus, and urban influence evaluate the studied objects. The study results demonstrate key differences in planning philosophies and approaches to designing and constructing new housing, identifying qualitative differences between housing of the previous and current paradigms. A detailed examination and comparison of the studied complexes in Lviv reveals changes in the approach to their planning, reflecting the evolution from the inherited modernist approach to housing to the creation of sustainable planning. It also aims to determine the quality of Lviv's modern housing environment and the vector of its development.

A Multicriteria Decision-Making Approach for Urban Water Features: Ecological Landscape Architecture Evaluation

In recent decades, the issues of ecology and environmental sustainability have become a global concern in contemporary urban design. Among various urban elements, water features play a significant role in improving the ecological characteristics of their surrounding environment, especially in hot and arid areas. The aim of this study is to evaluate the ecological characteristics of urban water features comprehensively and quantitatively, which has been overlooked in previous studies, taking their physical characteristics into account. To this end, a multicriteria decision-making method, an analytic network process, was proposed to quantitatively evaluate the ecological characteristics of water features. In this approach, four ecological criteria—microclimate, biodiversity, greenery, and human wellbeing—and ten physical characteristics of water features were considered. Twenty-one experts were asked to complete a questionnaire for pairwise comparisons of all ecological criteria and the influence of physical characteristics. The results showed that vegetation and scale, with the relative influencing values of 0.255 and 0.188, respectively, were identified as the most decisive features influencing ecological criteria. Conversely, texture, with a value 0.023, had the least impact. Moreover, it was shown that water features have the greatest impact on the microclimate compared with other ecological criteria. The results were used to compare water features at the Koohsangi Transregional Park, located in a hot and arid city of Iran. The results of this study lead to a framework that can help urban designers integrate ecological criteria into water feature planning to enhance urban ecology and sustainability.

Correlation analysis between urban environment features and crime occurrence based on explainable artificial intelligence techniques

ABSTRACT Crime and urban environments are considered to be closely related. However, there exists no clear understanding of this phenomenon. Therefore, this study analyzes the relationship between various urban environmental variables and crime occurrences and provides insights into the optimal placement of crime prevention facilities by developing a crime prediction map based on a paradigm of the Daegu city. To achieve this, we used 373,387 crime reports from Daegu as dependent variables and 370,000 random points. Independent variables included information such as the point of interest, land use, land cover, floating population, and card sales. The developed crime prediction map created using the model was used to evaluate the adequacy of CCTV installation locations and identify areas requiring new CCTV installations. The performances of various machine-learning models were compared and the XGBoost model (accuracy of 89.7 % and precision of 89.8 %) was selected. Key variables influencing crime report data were identified using the SHAP(SHapley Additive explanation) method. To analyze the spatial explanatory power of the relationship between crime and urban environmental variables, various buffer distances were tested, and a 20 m buffer distance was derived. The results of this study are expected to provide valuable data for crime prevention policies.

Evolution of the Planning Paradigm and Urban Design of Housing Complexes in the Context of Changing Challenges in Ukraine: Case of Lviv

The study provides a retrospective of the housing development in Lviv and the factors that affected it. Using the general process of housing evolution, the study focuses on the impact of the war as a catalyst that influenced the demand and volume of housing construction over the last decade. Moreover, it reflects the unprecedented demographic shift driven by the number of IDPs in Ukraine since the beginning of 2022. The study demonstrates that, due to the increased demand for housing, the approaches to the design of housing have changed in recent years, defining a new type of urban unit called a housing complex. The study aims to identify and examine the genesis and characteristics of the modern planning paradigm and urban design of residential architecture in Lviv in the context of shifting conditions. Spatial and urban characteristics, social indicators, resident focus, and urban influence evaluate the studied objects. The study results demonstrate key differences in planning philosophies and approaches to designing and constructing new housing, identifying qualitative differences between housing of the previous and current paradigms. A detailed examination and comparison of the studied complexes in Lviv reveals changes in the approach to their planning, reflecting the evolution from the inherited modernist approach to housing to the creation of sustainable planning. It also aims to determine the quality of Lviv's modern housing environment and the vector of its development.

Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine Learning

Population mobility between cities significantly affects traffic congestion, disease spread, and societal well-being. As globalization and urbanization accelerate, understanding the dynamics of population mobility becomes increasingly important. Traditional population migration models reveal the factors influencing migration, while machine learning methods provide effective tools for creating data-driven models to handle the nonlinear relationships between origin and destination characteristics and migration. To deepen the understanding of population mobility issues, this study presents GraviGBM, an expandable population mobility simulation model that combines the gravity model with machine learning, significantly enhancing simulation accuracy. By employing SHAPs (SHapley Additive exPlanations), we interpret the modeling results and explore the relationship between urban characteristics and population migration. Additionally, this study includes a case analysis of COVID-19, extending the model’s application during public health emergencies and evaluating the contribution of model variables in this context. The results show that GraviGBM performs exceptionally well in simulating inter-city population migration, with an RMSE of 4.28, far lower than the RMSE of the gravity model (45.32). This research indicates that distance emerged as the primary factor affecting mobility before the pandemic, with economic factors and population also playing significant roles. During the pandemic, distance remained dominant, but the significance of short distances gained importance. Pandemic-related indicators became prominent, while economics, population density, and transportation substantially lost their influence. A city-to-city flow analysis shows that when population sizes are comparable, economic factors prevail, but when economic profiles match, living conditions dictate migration. During the pandemic, residents from hard-hit areas moved to more distant cities, seeking normalcy. This research offers a comprehensive perspective on population mobility, yielding valuable insights for future urban planning, pandemic response, and decision-making processes.

Remote Sensing - Based Analysis of LST and NDVI Correlation for the Coolest and Hottest Month of 2023 in Khanaqen City

Urban land cover characteristics often lead to variations in surface temperature. Remote Sensing (RS) and Geographic Information Systems (GIS) are valuable tools for obtaining detailed information about surface indices. This study evaluates the temporal variation of Land Surface Temperature (LST) in Khanaqen city, located in South eastern Iraqi Kurdistan and Eastern Iraq. The research investigates the relationship between LST and the Normalized Difference Vegetation Index (NDVI) for the coolest and hottest month of 2023 using Landsat images from the USGS Earth Explorer. The objective is to explore how green spaces influence LST during the cool, humid winter and hot, dry summer. Two satellite images from Landsat 9 were used to retrieve LST and NDVI for the study periods. The results indicate that green spaces significantly reduce LST in July, so that the result of the correlation analysis of variables, significant inverse correlation between LST and NDVI (R = -0.53, R2 = 0.28 and P value = 0.00) highlights the effect of vegetation cooling. Conversely, January shows a modest correlation (R = -0.01, R2 = 0.00, and P value = 0.90), indicating a minimal effect of vegetation cover on LST.

Cross-sectoral interventions to promote health-supportive urban environments

Abstract Background As dealing with the complexity of urban living conditions becomes more and more evident and urgent, it is fundamental to promote cross-sectoral interventions that allow cities to become more inclusive and supportive of health. The Emilia-Romagna region, Italy, has promoted a participatory initiative to structurally integrate health into urban planning tools, identifying a framework of tailored urban indicators linked to locally perceived population health issues. Methods Through internal discussion and systematic literature research, a set of urban planners, epidemiologists, and public health experts from regional offices, local health services, universities, and environmental protection agencies identified the urban and health dimensions most suitable for embedding population health into city planning. The result was summarised in a preliminary working paper, which included a matrix of associations between urban features and health dimensions framed according to the DPSEEA framework. Its applicability was then tested in a participatory process (March-May 2024) involving urban planners and public health departments of 7 different sized municipalities. Results A group of epidemiologists identified local-level computable indicators for the health dimensions already included in the matrix or added at request. Each municipality focused on specific topics based on local priorities: 3 groups assessing their already-approved urban plans and looking for computable urban indicators; the others integrating their under-construction plans with local health indicators. Conclusions The need for professional contamination and cross-sectoral interventions is significant. Epidemiological support is pivotal in integrating different professional languages and reading health data. The outputs of this experience will contribute to a technical document outlining how to incorporate health promotion into urban planning tools and practice while actively considering the local context. Key messages • Although the development of a common vocabulary can be difficult, multidisciplinary collaboration is essential in promoting a model for integrating health promotion into urban planning. • Local municipalities’ involvement in the experimentation can improve the model’s applicability by taking into account territorial specificities.

Research Progress in the Design Concept of Green Commuting Cities

Abstract: The connection between green commuting and urban design was intimately intertwined in this study. As urbanization progressed, issues such as population growth, housing, transportation, and the environment had become increasingly salient. Green commuting had emerged as a vital strategy to mitigate these challenges and enhance the quality of life for urban residents. This paper elucidated the principles underlying green commuting, highlighting a human-centric approach, the interconnections between public transit, walking, and cycling as modes of travel, and the significance of the digital city paradigm. By examining the transportation systems and urban design methodologies, techniques, and characteristics of Vienna and Copenhagen, the paper delineated the strengths and weaknesses in the development of these cities. It delved into the design approaches and philosophies in green commuting from these two cities that were exemplary and worthy of emulation. The paper proposed potential strategies for green and low-carbon design in future urban transportation, aiming to construct a more ecologically sustainable urban transportation system and infuse new dynamism into urban development.

Policy interventions and urban characteristics in modeling electric vehicle charging infrastructure utilization

The surge in electric vehicles adoption necessitates understanding the impact of policy interventions on public electric vehicle charging infrastructure in urban areas. This research investigates the influence of pricing frameworks on the usage of public charging facilities by analyzing both behavioral and spatial attributes of these infrastructures. Utilizing open data from Palo Alto, United States, this study employs descriptive statistical methods and interpretable machine learning approaches to scrutinize the relationship between policy initiatives and charging behaviors. The analysis underscores the significance of spatial attributes on charging behaviors. Policy interventions yield noticeable alterations in charging metrics, with locations near commercial hubs showing higher utilization, while local and frequent users resist fee adjustments. The research emphasizes the necessity for customized strategies to optimize infrastructure development and management, offering a framework for policymakers and stakeholders in sustainable urban transportation. Future research should explore similar interventions in diverse urban settings using real-time data and advanced optimization techniques to better tailor policies to the unique characteristics of specific facilities.

The impact of urban form on the energy efficiency of residential buildings in a hot climate. Case study: Ouargla city

Urban development is intensifying significantly in cities, mainly due to rapid population growth. However, this development is accompanied by increased vulnerability to climate change, leading to higher energy consumption. This energy demand is even more pronounced in hot climates, where temperatures are very high during the summer season. The Algerian Sahara is no exception, and is experiencing very accelerated development, which has given rise to the appearance of new urban districts, and different urban characteristics compared to the original core, which constitutes the urban form most adapted to the desert climate and in perfect harmony with the immediate environment. This study attempts to assess the effect of the different urban forms that make up the fabric of the city of Ouargla in south-east Algeria, taken as a case study, on the energy consumption of buildings in a hot climate. This research adopts a mixed approach to evaluate the energy consumption of three buildings located in three neighbourhoods with different urban characteristics. A quantitative estimate of the energy consumed for cooling during the summer season for the three typical buildings selected was simulated using DesignBuilder software, taking into account the city’s specific climatic data, as well as the urban characteristics of the three neighbourhoods selected for the study. As a result, this evaluation covers both the architectural and urban scales. For the qualitative assessment, a field survey was carried out using questionnaires to assess the quality of thermal comfort outside neighbourhoods and its impact on building interiors, and consequently on energy consumption. The results indicate the significant effect of urban form on energy consumption, confirming that the traditional form is the most appropriate to achieve better energy efficiency in a hot climate. Therefore, the research recommends taking into account various influential factors and applying design strategies, regarding urban form, from the first stage of the design process.

Urban Color Perception and Sentiment Analysis Based on Deep Learning and Street View Big Data

The acceleration of urbanization has resulted in a heightened awareness of the impacts of urban environments on residents’ emotional states. This present study focuses on the Lixia District of Jinan City. By using urban street view big data and deep learning methods, we undertook a detailed analysis of the impacts of urban color features on residents’ emotional perceptions. In particular, a substantial corpus of street scene image data was extracted and processed. This was performed using a deep convolutional neural network (DCNN) and semantic segmentation technology (PSPNet), which enabled the simulation and prediction of the subjective perception of the urban environment by humans. Furthermore, the color complexity and coordination in the street scene were quantified and combined with residents’ emotional feedback to carry out a multi-dimensional analysis. The findings revealed that color complexity and coordination were significant elements influencing residents’ emotional perceptions. A high color complexity is visually appealing, but can lead to fatigue, discomfort, and boredom; a moderate complexity stimulates vitality and pleasure; high levels of regional harmony and aesthetics can increase perceptions of beauty and security; and low levels of coordination can increase feelings of depression. The environmental characteristics of different areas and differences in the daily activities of residents resulted in regional differences regarding the impacts of color features on emotional perception. This study corroborates the assertion that environmental color coordination has the capacity to enhance residents’ emotions, thereby providing an important reference point for urban planning. Planning should be based on the functional characteristics of the region, and color complexity and coordination should be reasonably regulated to optimize the emotional experiences of residents. Differentiated color management enhances urban aesthetics, livability, and residents’ happiness and promotes sustainable development. In the future, the influences of color and environmental factors on emotions can be explored in depth, with a view to assist in the formulation of fine urban design.

Exploring the Impact of Mountain Urban Characteristics on Housing Prices: A Study Utilizing Street View Visual Features

Exploring the Impact of Mountain Urban Characteristics on Housing Prices: A Study Utilizing Street View Visual Features

Reimagining the urban stroller in Mumbai: Suketu Mehta as a flâneur in Maximum City

ABSTRACT Suketu Mehta’s reportage cum travelogue cum memoir, Maximum City: Bombay Lost and Found (2004), is an insightful account of the complex and multifaceted yet intriguing city of Mumbai. This article aims to position Suketu Mehta as a modern-day flâneur of Mumbai, emerging during a period of significant urban transformation, much like the classic flâneur of nineteenth-century Paris who navigated a changing cityscape. Mehta embodies a modernised urban wanderer whose flânerie incorporates elements of ethnography and journalism, investigating contemporary urban reality. Besides, Mehta’s flânerie exemplifies convergences between transnational travel and flânerie in a global city like Mumbai. The article also demonstrates that through the exploration of both diverse and archetypal urban characters and the analysis of the city's urban myths, the text encapsulates Mumbai's distinctive spirit, or “urban charisma”. Finally, it illustrates that Suketu Mehta's postcolonial diasporic identity introduces innovative perspectives for comprehending the ambivalence that inherently characterises the flâneur figure.

Soil animal communities demonstrate simplification without homogenization along an urban gradient.

Urbanization profoundly impacts biodiversity and ecosystem function, exerting an immense ecological filter on the flora and fauna that inhabit it, oftentimes leading to simplistic and homogenous ecological communities. However, the response of soil animal communities to urbanization remains underexplored, and it is unknown whether their response to urbanization is like that of aboveground organisms. This study investigated the influence of urbanization on soil animal communities in 40 public parks along an urbanization gradient. We evaluated soil animal abundance, diversity, and community composition and related these measures to urban and soil characteristics at each park. The most urbanized parks exhibited reduced animal abundance, richness, and Shannon diversity. These changes were influenced by many variables underscoring the multifaceted influence of urbanization on ecological communities. Notably, contrary to our expectation, urbanization did not lead to community homogenization; instead, it acted stochastically, creating unique soil animal assemblages. This suggests that urban soil animal communities are concomitantly shaped by deterministic and stochastic ecological processes in urban areas. Our study highlights the intricate interplay between urbanization and soil animal ecology, challenging the notion of urban homogenization in belowground ecosystems and providing insight for managing and preserving belowground communities in urban areas.

Neighborhood as Theater: Building a Goffmanian Framework for Comparative Urban Ethnography

AbstractWhile the history of urban sociology features myriad detailed descriptions of neighborhood life reflecting various theoretical, political, and practical purposes, the field lacks general templates for conducting in-depth neighborhood research and comparison. In this paper, as a remedy, we propose a Goffmanian framework for qualitative neighborhood description and analysis which we call "neighborhood as theater." While revisiting previous ethnographic research of two neighborhoods in Hollywood, Los Angeles, conducted in the late 1990s, we argue that local social life operates along three main dramaturgical axes—sets, characters, and performances—each divided into several subcategories. These three focal points reveal observable, yet easily overlooked, key elements of neighborhood life besides illuminating broader, structural aspects and connections. We argue that conceptualizing neighborhoods “as theater" offers a general template for ethnographic urban research that can advance comparative and critical spatial analysis while remaining grounded in meaning-centered, embodied experiences of mundane local life.

Spatial and Temporal Change Analysis of Urban Built-Up Area via Nighttime Lighting Data—A Case Study with Yunnan and Guizhou Provinces

As urbanization accelerates, characteristics of urban spatial expansion play a significant role in the future utilization of land resources, the protection of the ecological environment, and the coordinated development of population and land. In this study, Yunnan and Guizhou provinces were selected as the study area, and the 2013–2021 National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light (NTL) data were utilized for spatial and temporal change analysis of urban built-up areas. Firstly, the built-up areas in Yunnan and Guizhou provinces were extracted through ENUI (Enhanced Nighttime Lighting Urban Index) indices, and then the urban expansion speed and urban center of gravity migration were constructed and used to explore and analyze the spatial and temporal change and expansion characteristics of built-up areas in Yunnan and Guizhou provinces. The results showed the following. (1) Due to the complementarity between data types, such as NTL, EVI, NDBI, and NDWI, ENUI has better performance in expressing urban characteristics. (2) Influenced by national and local policies, such as “One Belt, One Road”, transportation infrastructure construction, geographic location, the historical background, and other factors, the urban expansion rate of Yunnan and Guizhou provinces in general showed a continuous advancement from 2013 to 2021, and there were three years in which the expansion rate was positive. (3) The center of gravity migration distance of most cities in Guizhou Province shows a trend of increasing and then decreasing, while the center of gravity migration distance in Yunnan Province shows a trend of continuous decrease in general. From the perspective of migration direction, Guizhou Province has the largest number of migrations to the northeast, while Yunnan Province has the largest number of migrations to the southeast. (4) Influenced by policy, economy, population, geography, and other factors, urban compactness in Yunnan and Guizhou provinces continued to grow from 2013 to 2021. The results of this study can help us better understand urbanization in western China, reveal the urban expansion patterns and spatial characteristics of Yunnan and Guizhou provinces, and provide valuable references for development planning and policymaking in Yunnan and Guizhou provinces.

Exploring the Adaptive Conservation and Development of Chongqing's Historic Buildings

Historical and cultural heritage is an essential part of human civilisation. Nowadays, in the progress of a society that emphasises sustainable development, the simultaneous modernisation and preservation of historical and cultural heritage has become a criterion for the degree of urban advancement. However, in China's large-scale urban renewal and renovation, many old urban structures and historical relics are being submerged by the pace of modernisation. Therefore, we need to strengthen the awareness of protection, while exploring deeply under the conditions of market economy to respond to the historical preservation issues in the development of ancient towns in a flexible and effective way. In tourism development, new ancient towns with traditional market style can be reconstructed to integrate them with modern life. This not only promotes the development of urban characteristics, but also injects new vitality into traditional culture and enriches the historical connotation of the city. However, some worrying situations of historical and cultural preservation have also emerged. For example, some people regard historical culture as a cash cow and blindly carry out temporary imitation of antique commodity blocks, leading to the chaos of cultural landscape; some people carry out one-sided and isolated protection, which makes the historical buildings incompatible with the modern life and so on. Therefore, there is an urgent need for domestic academics to explore effective methods for the conservation and utilisation of historic buildings, so as to ensure that the conservation and development of historic buildings can embark on a healthy path.

Comparing Urban Pattern and Characteristics of Different Towns in Tamil Nadu

Comparing Urban Pattern and Characteristics of Different Towns in Tamil Nadu

The challenges of mapping quiet urban areas from mobile sound surveys

Detailed spatial data on noise levels are needed to identify quiet areas. Yet noise maps derived from sound propagation models based on traffic flows or sensor networks are often incomplete. Surveys using mobile sound recording coupled with machine learning modelling have offered a possible solution, but so far have focused on averaged noise levels. We argue that quiet areas should ideally be consistently quiet, rather than simply quiet on average, yet mobile survey data usually lack the full temporal profile needed to quantify consistency in noise levels. Using data from our Southampton study area, we show that modification of the loss function used in machine learning modelling of mobile survey data can yield information on the likely range of noise levels at any location across an entire city. We use this approach not only to identify locations that are consistently quiet, but also to help understand which urban features may be associated with quiet, average and noisy events at any given location.