Urban Planning Strategies Research Articles

An assessment of ground subsidence from rock salt mining in Maceió (Northeast Brazil) from 2019 to 2023 using remotely sensed data

This study investigates ground subsidence in Maceió, the capital of Alagoas, Brazil, utilizing Sentinel-1 Synthetic Aperture Radar (SAR) data from November 2019 to December 2023. Ground subsidence poses significant risks to urban infrastructure and requires comprehensive monitoring and mitigation strategies. We employed the Interferometric Wide Swath (IW) mode of Sentinel-1A, acquiring high-resolution data in descending orbit with VV polarization. Through interferometric processing, including co-registration, phase unwrapping, and coherence estimation, we produced detailed subsidence maps for key neighborhoods such as Bebedouro, Farol, Mutange, Pinheiro, and Bom Parto. Our results reveal that subsidence in Maceió is highly variable across different neighborhoods, with cumulative subsidence reaching up to 3.83 meters in the most affected areas. The Bebedouro neighborhood, for instance, experienced subsidence up to 0.33 meters over an area of 41.85 hectares, while Farol saw significant ground movement impacting 59.49 hectares. The analysis indicates that high-density urban areas are particularly susceptible to subsidence, correlating with increased structural damage and flood vulnerability. The study underscores the importance of integrating geotechnical, urban planning, and environmental management strategies to address subsidence. We recommend the implementation of adaptive urban planning measures and enhanced monitoring techniques using satellite data to mitigate the impact of subsidence on infrastructure and communities. These findings provide a critical foundation for developing resilient urban environments in coastal cities prone to geological and anthropogenic subsidence.

Calculation of the Optimal Scale of Urban Green Space for Alleviating Surface Urban Heat Islands: A Case Study of Xi’an, China

Research has demonstrated that urban green spaces play a crucial role in mitigating the severe urban heat island (UHI) effect. However, existing studies often suffer from limitations such as the neglect of the cooling effect of water bodies within the green spaces and incomplete considerations of the overall cooling effect. These limitations may lead to inaccuracies in the research findings. Therefore, the present study takes the city of Xi’an as a case study to explore the optimal green space size for achieving efficient cooling. The results indicate that (i) urban green spaces exhibit robust cooling effects, with variations observed among the various types; (ii) for community parks without water, and for street gardens, the optimal areas of these green spaces are 3.44 and 0.83 hectares, respectively; (iii) for community parks with water, the area of internal water bodies should ideally be maintained at around 29.43% of the total green space area in order to achieve an optimal cooling efficiency. In conclusion, this study introduces a new perspective and new optimization methods for urban green space planning, thereby offering scientific guidance to urban planners in formulating effective development and management policies and urban planning schemes.

Climate change and urban sprawl: Unveiling the escalating flood risks in river deltas with a deep dive into the GBM river delta

River deltas, such as the Ganges-Brahmaputra-Meghna (GBM) delta, are highly vulnerable to flooding, exacerbated by intense human activities and rapid urban growth. This study explores the evolution of urban flood risks in the GBM delta under the combined impacts of climate change and urban expansion. Unlike traditional assessments that focus on a single flood source, we consider multiple sources—coastal, fluvial, and pluvial. Our findings indicate that future urban expansion will significantly increase flood exposure, with a substantial rise in flood risk from all sources by the end of this century. Climate change is the main driver of increased coastal flood risks, while urban growth primarily amplifies fluvial, and pluvial flood risks. This highlights the urgent need for adaptive urban planning strategies to mitigate future flooding and support sustainable urban development. The extreme high emissions future scenario (SSP5–8.5) shows the largest urban growth and consequent flood risk, emphasizing the necessity for preemptive measures to mitigate future urban flooding. Our study provides crucial insights into flood risk dynamics in delta environments, aiding policymakers and planners in developing resilience strategies against escalating flood threats.

Exploring the Complexities of Urban Forms and Urban Heat Islands: Insights from the Literature, Methodologies, and Current Status in Morocco

The Urban Heat Island phenomenon (UHI), characterized by elevated temperatures in urban areas compared to their rural surroundings, is highly exacerbated by urbanization and climate change. Understanding the complex relationship between UHI effect and urban form is crucial for effective urban planning and climate mitigation strategies. This paper examines the multifaceted connection between UHIs and urban forms, exploring various methods used to study this relationship. Through a review of the existing literature, we analyze the influence of various urban characteristics on the intensity and spatial distribution of UHIs. Furthermore, we discuss the key methods and technologies, such as remote sensing and modeling, used in advancing our understanding of UHI–urban form interactions. This study then delves into the literature on UHIs within the specific context of Morocco, identifying research gaps and emphasizing the need for more comprehensive research to address them. By translating study findings into actionable urban solutions, this paper suggests contextual mitigation strategies based on the research outcomes. Finally, by synthesizing current research findings and methodologies, this paper seeks to provide insights into the complexities of UHI dynamics and their implications for climate resilience, highlighting the potential of research for forging sustainable and climate-conscious planning strategies in Morocco and across the broader MENA region.

Evaluation of Green Infrastructure Benefits in Urban Areas in Paris

Purpose: The aim of the study was to investigate the evaluation of green infrastructure benefits in urban areas in Paris. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: An evaluation of green infrastructure benefits in urban Paris found that parks and street trees effectively mitigate urban heat island effects and improve air quality by reducing pollutants such as nitrogen dioxide and particulate matter. Recommendations emphasize integrating green infrastructure into urban planning to sustain these benefits and promote environmental resilience, highlighting its critical role in enhancing urban livability and sustainability in Paris. Unique Contribution to Theory, Practice and Policy: Ecosystem services framework, social-ecological Systems (SES) theory & urban political ecology (UPE may be used to anchor future studies on the evaluation of green infrastructure benefits in urban areas in Paris. Foster interdisciplinary collaboration between urban planners, landscape architects, environmental scientists, and social researchers to enhance the design, implementation, and management of green infrastructure projects. Advocate for policy frameworks that integrate green infrastructure into urban planning and development strategies at local, regional, and national levels. This includes incentivizing green infrastructure adoption through financial incentives, tax breaks, and regulatory support.

Analysis of the Spatiotemporal Evolution and Driving Mechanisms of Impervious Surfaces along the Jiaozhou Bay (China) Coast over the Past Four Decades

Impervious surfaces serve as critical indicators for monitoring urbanization processes and assessing urban ecological conditions. The precise extraction and analysis of the spatiotemporal variations in impervious surfaces are essential for informing urban planning strategies. The unique location advantage of Jiaozhou Bay makes it an important urban gathering area. Based on Landsat remote sensing image data, the extraction effect and accuracy of urban built-up area index, biophysical index, and random forest classification were compared and analyzed. Then, the optimal random forest method was used to extract impervious water information from 8 Landsat satellite images of the coastal area of Jiaozhou Bay from 1986 to 2022. Over the past four decades, the impervious surface area in the Jiaozhou Bay coastal region has expanded dramatically from 71.53 km2 in 1986 to 1049.16 km2 in 2022, with the most significant increase, nearly doubling, occurring between 2011 and 2017. Spatially, the distribution of impervious surfaces has expanded progressively from coastal to inland areas and from central to peripheral zones, particularly toward the southwest in Huangdao District and Jiaozhou City. The distribution of impervious surfaces in the Jiaozhou Bay coastal area is primarily confined to flat and gently sloping nearshore regions due to natural constraints like terrain slope. Concurrently, policy initiatives, along with population and economic growth, have catalyzed the rapid expansion of these surfaces. These insights are invaluable for comprehending the urban spatiotemporal dynamics and patterns along the Jiaozhou Bay coast and offer fresh perspectives for research into urban transformations and the sustainable development of ecological environments in other coastal regions.

Predictive analytics for traffic flow optimization in urban logistics: A transformer-based time series approach.

In this study, we focus on the analysis and prediction of urban logistics traffic flow, a field that is gaining increasing attention due to the acceleration of global urbanization and heightened environmental awareness. Existing forecasting methods face challenges in processing large and complex datasets, particularly when extracting and analyzing valid information from these data, often hindered by noise and outliers. In this context, time series analysis, as a key technique for predicting future trends, becomes crucial for supporting real-time traffic management and long-term traffic planning. To this end, we propose a composite network model that integrates gated recurrent unit (GRU), autoregressive integrated moving average (ARIMA), and temporal fusion transformer (TFT), namely the GRU-ARIMA-TFT network model, to enhance prediction accuracy and efficiency. Through the analysis of experimental results on different datasets, we demonstrate the significant advantages of this model in improving prediction accuracy and understanding complex traffic patterns. This research not only theoretically expands the boundaries of urban logistics traffic flow prediction but also holds substantial practical significance in real-world applications, especially in optimizing urban traffic planning and logistics distribution strategies during peak periods and under complex traffic conditions. Our study provides a robust tool for addressing real-world issues in the urban logistics domain and offers new perspectives and methodologies for future urban traffic management and logistics system planning.

Long-term temporal and spatial divergence patterns of urban heat risk in the Beijing–Tianjin–Hebei urban agglomeration

As urbanization accelerates and global warming intensifies, urban heat risks are increasingly becoming a critical issue and addressing this challenge to foster more harmonious and livable urban environments is essential. In our study, multisource remote sensing and point-of-interest (POI) data were applied to assess the urban heat risks in the Beijing-Tianjin-Hebei (BTH) urban agglomeration from 2001 to 2020. The results indicated that the heat hazard, exposure, and sensitivity were notably greater in the southeastern of the BTH. Conversely, the heat adaptability was lower in the southeast, leading to a severe high heat risk in this region. From 2001 to 2020, the heat risk index (HRI) values for these cities generally displayed an upward trend, peaking in 2015. Among the cities, Handan had the highest heat risk, followed by Baoding and Beijing, with HRIs of 0.32, 0.32, and 0.31, respectively. In urban areas, commercial zones had the highest average HRI at 0.3, while public management zones had the lowest at 0.28. These findings highlight significant variations in heat risk, indicating the need for targeted urban planning and heat mitigation strategies in the most affected areas of the BTH.

Air quality impact of natural protected areas: A case study of Sarayiçi Tavuk Forest, Edirne, Türkiye

This study investigated the regulatory ecosystem services of Sarayiçi Tavuk Forest, a natural protected urban forest in Edirne, Turkey, with a focus on its ability to improve air quality. The i-Tree Canopy Tool was used to categorise land-cover classes within the forest and assess air quality impacts using pollutant removal and carbon sequestration estimates. The results show that the Sarayiçi Tavuk Forest eliminates 5,014.68 kg/yr of pollutants, sequesters 183,000 kg/yr of carbon, and stores a total of 4,596,680 kg of carbon throughout the lifespan of its trees. The economic value of these regulatory ecosystem services is estimated at 864,177 USD annually and substantially improves air quality. The results of this study provide valuable insights for researchers, landscape managers, and policymakers involved in regional planning, decision-making, and green space improvement in cities. This study highlights the vital role of natural protected urban forests in improving air quality and underscores the need for their preservation and integration into landscape and urban planning strategies for sustainable development in response to 21st-century challenges.

Mapping Tomorrow’s Cities: GeoAI Strategies for Sustainable Urban Planning and Land Use Optimization

Mapping Tomorrow’s Cities: GeoAI Strategies for Sustainable Urban Planning and Land Use Optimization

Factor Analysis of Housing Choice in Flood Prone Areas: Focusing on The Effect of Place Attachment on Risk Perception

Coastal regions are particularly susceptible to the effects of climate change because of their geographical proximity to the sea. Despite this vulnerability, these areas continue to be perceived and utilized primarily as recreational spaces where individuals can own and enjoy scenic landscapes rather than as zones at risk from climatic threats. The paradoxical demand for coastal amenities in spite of the risk of flooding is investigated in this study by applying place attachment factors from a sociopsychological angle. We employed the theories of vulnerability, risk perception, and place attachment to build a conceptual framework and formulate hypotheses regarding their relationships. Our analysis, based on a structural equation model, showed that people accept these risks for the benefits of living near the coast because their perception of risk is increased by disaster exposure but lessened by place attachment. These findings highlight the urgent need for urban planning strategies that mitigate the risks associated with climate change.

Evaluating the Spatial-Temporal Dynamics of Urbanization in Prefecture Cities of China Using SNPP-VIIRS Nighttime Light Remote Sensing Data

Ensuring the well-being of urban communities hinges on sustainable urban planning strategies informed by current data, particularly in China since urbanization has been one of the most significant demographic shifts in recent decades. Therefore, our research aimed to evaluate the spatio-temporal dynamics of urbanization and sub urbanization across prefecture and provincial levels in China by utilizing consistent SNPP-VIIRS-like and NPP-VIIRS nighttime data spanning the years 2000 to 2020. The k-means method was applied to derive urban and sub urban features from above datasets. The findings uncovered a significant expansion of urban entities at the prefecture level, escalating from 16,209 km2 to 89,631 km2 over the specified period showing a 5% growth. Among five main urban agglomerations, the Yangtze River Delta stands out with the highest urbanization rate, witnessing a remarkable expansion of urban entities from 2,684 km2 to 41,465 km2. This growth reflects an average growth rate of 72.2% per annum. The analysis revealed that the overall area of suburbs expanded from 59,151 km2 to 120,339 km2 between 2012 and 2020 indicating a proportional growth rate ranging from 0.4% to 1.9%. The peak growth rate of suburbs was recorded between 2012 and 2014, reaching 18%. Guizhou, Hunan, and Hubei provinces have exhibited growth rates of 334%, 258%, and 246% respectively while Beijing, Guangdong, Tianjin, and Shanghai have experienced relatively low growth rates of 50%, 56%, 46%, and 17%. The analysis of urban growth with GDP, population, and electricity consumption revealed an inverse relationship during the specified period. Therefore, the findings of this research can provide immense support to sustainable urban planning initiatives at both the provincial and prefecture-level cities in China. The findings can assist city planning authorities in making informed decisions regarding optimizing resource distribution, all while prioritizing the preservation of ecological footprint within urban environments. Also, the limitations addressed in our study must be taken into account in future research works aimed at deriving reliable urban extraction results using nighttime light remote sensing data.

Exploring the non-linear impacts of urban features on land surface temperature using explainable artificial intelligence

High land surface temperatures (LST) have emerged as crucial threats to urban ecosystems and sustainable urban development. To better understand and mitigate their impacts, it is essential to analyze the contributing urban features. Against this background, we developed a random forest model enhanced by Explainable Artificial Intelligence (XAI) to analyze the impact features of LST in Beijing, China. By applying the XAI method, our results suggest that the major impact features of LST in Beijing are elevation (44.19%), compactness of impervious surface (17.27%), Normalized Difference Vegetation Index (11.12%), proportion of impervious surface area (8.04%), and tree height (3.83%). Compactness of impervious surface exhibited an overall cooling effect, which became weaker at high values. LST increased with building height, and the trend became weaker as building height reached 5 m. The most important features impacting LST in the inner city are the proportion and height of buildings, whereas in the outer city these features are tree height and the compactness of impervious surfaces. The study applies XAI to explain the non-linear interactions between LST and urban features, offering innovative insights to policy-makers to develop sustainable urban planning strategies. Our findings suggest that increasing green spaces and water bodies as well as controlling building density and height can effectively mitigate heat in dense urban areas and enhance cooling effects.

Community Responsiveness on Climate Change and Natural Hazards Towards Sustainable Cities: An Empirical Study of Da Nang, Vietnam

Understanding of community responsiveness on climate change and the natural hazards is an essential component of urban planning and adaptation strategies. The coastal cities are seriously affected by the impact of rising sea levels and climate change-induced hazards. In this paper, we investigate the urbanization level on climate change and natural hazards awareness of local communities at the households’ level in Da Nang city, the largest municipality in Central Vietnam using 1903 surveyed households. The results showed that the urbanization level significantly influenced the local communities' responsiveness to climate change. Communities with robust infrastructure and strong economic status demonstrate greater resilience and awareness of climate change and natural hazards compared to others. Therefore, it is recommended to prioritize enhancing local people’s responsiveness to develop bottom-up climate change response strategies and policies, instead of enforcing top-down decisions.

Determinants of housing prices: Serbian Cities’ perspective

This study investigates the spatial and temporal dynamics of housing prices in Serbia, addressing the critical need to understand the drivers of real estate prices and their implications for economic and social welfare. Employing a panel data analysis approach on a unique dataset covering 24 distinct urban areas in Serbia from 2011 to 2021, we examine the relevance of diverse economic, demographic, and infrastructural indicators, providing novel insights within a developing country context. Our findings reveal that the housing market stock-flow model effectively predicts housing price appreciation trends, explaining over 60 percent of variation in property prices. Notably, disparities in labour income, captured by average wages and registered employment rates, emerge as significant determinants of real estate prices, underlining socio-economic disparities within Serbian cities. Housing prices exhibit a positive response to the population/housing stock ratio, suggesting higher prices in cities experiencing faster population growth relative to housing supply. Intensified construction is associated with elevated housing prices. Additionally, we find positive association between the inflation variable and housing prices, underlining real estate’s potential as an inflation hedge. Public service provision and infrastructural amenities also emerge as contributors to higher housing prices in urban areas, emphasizing the importance of comprehensive urban planning strategies. Our study contributes to the literature by providing specific quantitative evidence, advancing the understanding of urban housing market dynamics in developing countries. By offering nuanced insights into determinants of housing prices, our research informs policymakers and urban planners seeking to foster equitable and sustainable urban development strategies.

A cost-and-effect simulation model for compact city approaches: A case study in Japan

Japan's compact-city policy addresses rapid population decline and aging to ensure sustainable development. However, its implementation requires significant time, financial resources, and complex stakeholder collaborations. Faced with these challenges, we asked: How can the cost-and-effect of planning be simply and automatically recognized by stakeholders? To address this, a simulation model was developed for evaluating urban planning strategies within the compact-city framework. Our model integrates pseudo-people-flow data and includes three fundamental modules and five sub-models to simulate and predict urban metrics, including population, administrative costs, visitors, and traffic volume from 2015 to 2040. Four scenarios-baseline, location optimization, new road and new station installation-were analyzed. Findings suggest that 1) new station project could mitigate population decline, 2) both location optimization and new station projects improve public services efficiency and sustainability, 3) urban vitality could be enhanced by new station construction, generating a new urban core, and 4) compact city policy may also reduce and concentrate traffic volume, shortening car trips. The model's efficiency and uncertainty were examined, and user experiments highlighted the potential of digital tools to democratize urban planning, making it accessible for non-expert stakeholders and fostering informed participation. This research advances the use of technology in participatory urban planning.

The 15-minute city concept: The case study within a neighbourhood of Thessaloniki.

Cities, crucial cultural hubs, mould individual and group identities. The global urban expansion, with over half the population in urban areas, presents interconnected challenges such as pollution, poverty, inequality, ageing infrastructure, resource overconsumption, land use changes, biodiversity impact and climate change. Addressing these demands ambitious actions targeting political, social and economic systems for transformative change. The theoretical framework guiding city transformation centres on an interdisciplinary approach influenced by the Smart and Green Transition. The '15-minute city' concept, emphasizing human scale and urban experience, proposes that cities enable residents to meet daily needs within a short walk or bike ride. The aim of this study was the exploration of its implementation in Greek cities, particularly Thessaloniki, which reveals inherent characteristics supporting the 15-minute concept. Through an interdisciplinary approach rooted in the Smart and Green Transition framework, the research provides concrete guidance for policymakers in tailoring urban planning strategies, allocating resources effectively and crafting policies conducive to successful and sustainable urban transformations. Moreover, prioritizing public engagement highlights the significance of community involvement in shaping urban development plans, ensuring that proposed initiatives align with residents' needs and desires. In essence, this research contributes tangible insights and actionable recommendations for Greek cities, paving the way for more liveable, resilient and sustainable urban environments.

Enriching Real-Estate Through Sustainable Public Spaces: The case of Dubai- UAE

In the process of the ongoing urban planning strategies for developing smart cities, the concept of urban public square has not yet been given the attention of urbanists and city planners in the Middle East and North Africa (MENA) region. More focus was given by architects and developers on saturating the market with iconic smart buildings rather than developing more livable communities with public spaces and plazas for people to connect. The overall aim of this paper is to highlight the key role that the real state sector can play to foster developing public spaces and squares as an integral part of smart urbanism. The issue of developing urban public squares in Dubai new communities and formulating policies for including such spaces in new districts and cities should be closely examined. The focus should be on enriching the real estate sector by regulating a concept of injecting urban public squares in the new smart and sustainable urbanism in Dubai to perform as a sociocultural entity within the fabric of new communities for the sake of improving social life and developing vibrant environment. With keywords such as smart city, public squares, social, urban design, livability; and sustainability, this write up makes the case of sustainable urbanization.

Achieving urban vibrancy through effective city planning: A spatial and temporal perspective

Achieving urban vibrancy is a critical goal for urban planning and policy-making, and the city's physical plan plays an important role in shaping and supporting urban vibrancy. However, the effectiveness of urban planning strategies in optimizing resource allocation and fostering a vibrant city remains unclear. This study aims to contribute to this understanding by exploring the spatial and temporal effects underlying the association between urban vibrancy and urban forms. Using mobile-phone location-request data along with urban nighttime light data as a proxy for urban vibrancy in Shenzhen, China, we conducted a geographically and temporally weighted regression (GTWR) analysis to examine the relationship between land use, street configuration, and urban vibrancy. The results showed that a diverse land use mix and the proportions of land allotted for sports and cultural usage, parks, and greenspace generally have a positive impact on urban vibrancy. We noted the facilitating role of degree centrality and closeness centrality on urban vibrancy, compared to the negative impact of betweenness centrality. Our analysis of local spatial variations in the influence of road networks suggests a potential connectivity threshold for attracting people to specific areas. Finally, we identified a divergence between weekdays and weekends in terms of temporal variations.

Forecasting urban temperatures through crowdsourced data from Citizen Weather Stations

This work explores the potential of using crowdsourced data from Citizen Weather Stations (CWS) to forecast urban temperatures. Five case studies were selected (Madrid, London, Rome, Paris and Berlin), using data from the manufacturer Netatmo to gather the hourly temperatures of the years 2021 and 2022 for 776 CWS. A quality-control process was implemented to enhance data accuracy, and heat maps were generated. Multiple linear regression models were created for each CWS and month using only reference weather data as input, resulting in the development of accurate models (RMSE lower than 1.5 °C) for an average of 374 CWS. Then, reference weather predictions were used on the 2nd of April 2023 to forecast the hourly temperatures of those CWS for the period from the 3rd to the 9th of April (168 h). The outcomes showed that accurate CWS data and precise prediction models for the reference weather are crucial to improve the accuracy of the forecasts using crowdsourced data. The study demonstrates the potential benefits of using CWS data and simple models to forecast urban temperatures in a cost-efficient way even 168 h ahead, with implications for various sectors such as urban planning, energy consumptions, health impacts, or climate change adaptation strategies.