Search Results (54)

Urban vacant land can provide important habitat for birds, especially in cities with high concentrations of residential vacancy. Understanding which vegetation features best support urban biodiversity can inform greening strategies that benefit both wildlife and residents. This study addressed two questions: (1) How does bird species composition reflect the potential conservation value of these neighborhoods? (2) Which vegetation structures predict bird abundance across a fine-grained urban landscape? To answer these questions, we conducted avian and vegetation surveys across 100 one-hectare plots in St. Louis, Missouri, USA. These surveys showed that species richness was positively associated with canopy cover (β = 0.32, p = 0.003). Canopy cover was also the strongest predictor of American Robin (Turdus migratorius) and Northern Cardinal (Cardinalis cardinalis) abundance (β = 1.9 for both species). In contrast, impervious surfaces and abandoned buildings were associated with generalist species. European Starling (Sturnus vulgaris) abundance was strongly and positively correlated with NMS Axis 1 (r = 0.878), while Chimney Swift (Chaetura pelagica) abundance was negatively correlated (r = −0.728). These findings underscore the significance of strategic habitat management in promoting urban biodiversity and addressing ecological challenges within urban landscapes. They also emphasize the importance of integrating biodiversity goals into urban planning policies to ensure sustainable and equitable development. Full article

This study proposes replacing traditional single-value urban development intensity control with an elastic interval-based approach to address urban development challenges under population shrinkage. It constructs a Floor Area Ratio (FAR) assignment framework guided by “ideal value determination—interval value demarcation—specific value agreement”. The northern central urban area of Shantou City serves as an empirical case. The study, focusing on the conflict between inefficient expansion and population loss, delineates elastic development intensity intervals through multi-dimensional factor analysis: a baseline FAR is determined based on master plan objectives and resource carrying capacity; upper limits are calculated considering transportation and ecological constraints; and lower limits are set according to economic feasibility and social demands, forming a gradient-based control framework. Practically, the study area is divided into differentiated density units, with optimized pathways designed for newly developed, under-construction, and existing plots across multiple scenarios. A multi-stakeholder negotiation mechanism is established to dynamically adapt elastic intervals. Results demonstrate that this method maintains the regulatory authority of master plans while significantly enhancing the adaptability of spatial governance. It provides a theoretical and practical paradigm for balancing regulatory rigidity and flexibility in shrinking cities, offering actionable solutions for vacancy risk mitigation and land-use intensification. Full article

In view of the challenges brought by a complex environment, diverse data sources and urban development needs, our study comprehensively reviews the application of algorithms in urban residential vacancy rate observation. First, we explore the definition and measurement of urban residential vacancy rate, pointing out the difficulties in accurately defining vacant houses and obtaining reliable data. Then, we introduce various algorithms such as traditional statistical learning, machine learning, deep learning and ensemble learning, and analyze their applications in vacancy rate observation. The traditional statistical learning algorithm builds a prediction model based on historical data mining and analysis, which has certain advantages in dealing with linear problems and regular data. However, facing the high nonlinear relationships and complexity of the data in the urban residential vacancy rate observation, its prediction accuracy is difficult to meet the actual needs. With their powerful nonlinear modeling ability, machine learning algorithms have significant advantages in capturing the nonlinear relationships of data. However, they require high data quality and are prone to overfitting phenomenon. Deep learning algorithms can automatically learn feature representation, perform well in processing large amounts of high-dimensional and complex data, and can effectively deal with the challenges brought by various data sources, but the training process is complex and the computational cost is high. The ensemble learning algorithm combines multiple prediction models to improve the prediction accuracy and stability. By comparing these algorithms, we can clarify the advantages and adaptability of different algorithms in different scenarios. Facing the complex environment, the data in the observation of urban residential vacancy rate are affected by many factors. The unbalanced urban development leads to significant differences in residential vacancy rates in different areas. Spatiotemporal heterogeneity means that vacancy rates vary in different geographical locations and over time. The complexity of data affected by various factors means that the vacancy rate is jointly affected by macroeconomic factors, policy regulatory factors, market supply and demand factors and individual resident factors. These factors are intertwined, increasing the complexity of data and the difficulty of analysis. In view of the diversity of data sources, we discuss multi-source data fusion technology, which aims to integrate different data sources to improve the accuracy of vacancy rate observation. The diversity of data sources, including geographic information system (GIS) (Geographic Information System) data, remote sensing images, statistics data, social media data and urban grid management data, requires integration in format, scale, precision and spatiotemporal resolution through data preprocessing, standardization and normalization. The multi-source data fusion algorithm should not only have the ability of intelligent feature extraction and related analysis, but also deal with the uncertainty and redundancy of data to adapt to the dynamic needs of urban development. We also elaborate on the optimization methods of algorithms for different data sources. Through this study, we find that algorithms play a vital role in improving the accuracy of vacancy rate observation and enhancing the understanding of urban housing conditions. Algorithms can handle complex spatial data, integrate diverse data sources, and explore the social and economic factors behind vacancy rates. In the future, we will continue to deepen the application of algorithms in data processing, model building and decision support, and strive to provide smarter and more accurate solutions for urban housing management and sustainable development. Full article

As a pillar industry of China’s economy, the real estate sector has been challenged by the increasing prevalence of housing vacancies, which negatively impacts market stability. Traditional vacancy rate estimation methods, relying on labor-intensive surveys and lacking official statistical support, are limited in accuracy and scalability. To address these challenges, this study proposes a novel framework for assessing residential community-level housing vacancy rates through the integration of multi-source data. Its core is based on night-time lighting data, supplemented by other multi-source big data, for housing vacancy rate (HVR) estimation and practical validation. In the case study of Longquanyi District in Chengdu City, the main conclusions are as follows: (1) with low data resolution, the model estimates a root mean square error (RMSE) of 0.14, which is highly accurate; (2) the average housing vacancy rate (HVR) of houses in Longquanyi District’s residential community is 46%; (3) the HVR rises progressively with the increase in the distance from the city center; (4) the correlation between the HVR of Longquanyi District and the house prices of the area is not obvious; (5) the correlation between the HVR of Longquanyi District and the time of completion of the communities in the region is not obvious, but the newly built communities have extremely high HVR. Compared to the existing literature, this study innovatively leverages multi-source big data to provide a scalable and accurate solution for HVR estimation. The framework enhances understanding of urban real estate dynamics and supports sustainable city development. Full article

The expansion of urban underground spaces has broadened the range of urban activities by accommodating functions such as transportation, retail, and entertainment. Underground shopping malls (USMs) have been widely developed as a sustainable strategy to expand urban space capacity, alleviate surface congestion, and optimize land-use efficiency. However, the development and utilization of USMs often neglect user-centered evaluations, risking mismatches between design outcomes and long-term sustainability goals such as energy efficiency, user retention, and spatial adaptability. Therefore, this study analyzes 12 typical USMs in Nanjing, China, based on environmental psychology principles, employing mixed-methods research that combines objective measurements of spatial elements with subjective user perception surveys to establish a regression model investigating correlations between USM spatial–physical environments and user comfort perception. The results show that users generally have a positive impression of the current underground environment, but there are significant differences in their subjective perceptions of the different attributes of the USMs. The USMs present a trend of humanization, human culture, and landscape in terms of spatial characteristics. These improvements are critical for fostering long-term sustainable use by minimizing vacancy rates and retrofitting needs. The findings reveal that the human-centric comfort level of the USMs is largely determined by multi-dimensional architecture-space features, as well as personal and social activity level features. Building on these insights, we propose actionable strategies to advance sustainable USM design, prioritizing adaptive reuse, energy-efficient layouts, and culturally resonant esthetics. This work clarifies the direction of USM design optimization and improvement from the perspective of users’ subjective perception and provides a theoretical foundation for aligning underground development with global sustainability frameworks like the UN SDGs. Full article

As a crucial form of public transportation, subways are becoming essential infrastructure that cities in China increasingly prioritize for development. However, there is a lack of effective risk assessment methods for subway station and line siting. To address this gap, this paper uses the subway system in Kunming, China, as a case study, establishing a subway site risk evaluation framework (SIRE-BAF) that integrates three dimensions: balance (B), attractiveness (A), and financing mode (F). An extended NP-RV model is proposed to assess the balance (or imbalance) characteristics of subway stations based on sub-dimensions of traffic supply, land use, and urban vitality. Findings indicate that (1) the balance (or imbalance) of subway stations is distinctly distributed along the line and simultaneously exhibits a spatial pattern radiating from the urban core to the periphery. (2) Stations with high urban vitality and minimal imbalance are highly attractive and tend to face “undersupply” during operation, whereas stations with lower attractiveness are more prone to “oversupply”. A higher level of BAF coupling coordination suggests a more suitable subway site selection and lower investment risk, while lower coupling coordination indicates increased risk. (3) Excessive reliance on the “subway + real estate” model, without considering urban vitality, may lead to high vacancy rates and reduced efficiency in subway service. This paper further assesses the site selection risks for the proposed Kunming subway. This study contributes to risk assessments of existing subway operations and maintenance in Chinese cities, enhances planning rationality and site selection for proposed subways, and holds potential applicability for other cities. Full article

While many countries have witnessed the retreat of the state from social housing under neoliberalism, the Chinese government has taken the opposite trajectory, significantly expanding its involvement in public rental housing (PRH) over the past decade through substantial investments. However, the effectiveness of the PRH program has come under scrutiny due to its inability to meet the demand for housing units while grappling with a substantial vacancy rate. This study aims to unravel this paradox by utilizing a unique city-level database that encompasses information on public rental housing stock, land supply, waiting time, and allocation practices. The data suggest that there is a structural mismatch between supply and demand for PRH in China, with both high and low vacancy rates in different cities, and even high vacancy and high allocation rates co-existing in one city. The results of estimating the OLS regression model of PRH supply and demand indicate that the actual supply fails to align with the policy objectives and the actual housing demand. Rather, they are more a result of the power relationship between the central and local governments, and cities with high fiscal autonomy provide fewer PRH. Furthermore, local governments fail to set eligibility criteria in response to housing supply, demand, and allocation, further exacerbating the mismatch. This paper provides policy recommendations that aim to enhance the sustainability and effectiveness of the PRH program, contributing to more equitable urban development. Full article

Although urban greenspace enhances ecological functioning and human well-being through ecosystem services (ES), it is oftentimes inequitably distributed. Environmental justice (EJ) encompasses aspects of distributive, procedural, and interactive justice related to accessibility and allocation of environmental benefits. Vacant land in shrinking cities has the potential to address greenspace inequalities and provide ES. This study investigated the perceptions of residents regarding urban ES and EJ in their communities in St. Louis (MO, USA)—a shrinking city that was undergoing green development, through semi-structured interviews. Altogether, 27 residents were selected from socio-economically disadvantaged neighborhoods characterized by high levels of vacancy due to legacies of redlining and systemic racism. Interview analysis revealed four themes: green benefits (including recreation opportunities), green costs (e.g., concerns for increased crime and nuisance animals), injustice issues (e.g., access to community greenspaces), and changes in the community (e.g., higher property taxes). Results revealed that residents perceived ES as closely connected with EJ when it comes to urban greening projects in their city. This study helps inform the process of urban greening projects, particularly in shrinking cities at risk of inequities. Full article

The rapid urbanization process has brought about the shrinkage of rural space as a typical issue. Nevertheless, due to the dearth of effective assessment approaches, the patterns of rural spatial shrinkage remain poorly grasped. This study intends to establish a quantitative assessment model to scientifically disclose the spatiotemporal characteristics and mechanisms of rural spatial shrinkage. The “Population-Industry-Function-Land” (PIFL) assessment model has been rigorously constructed, encompassing eight assessment indices, such as the ratio of permanent residents, rural population density, and the rate of abandoned cultivated land. The model was adopted to conduct an analysis of the spatial shrinkage scenarios of the 18 administrative villages in Panxi Town spanning from 2011 to 2021. The results indicate that the temporal dimension of rural spatial shrinkage exhibits an accelerating trend, with discernible declines or increases in the ratio of permanent residents, rate of the elderly labor force, and housing vacancy rate. The shrinkage of rural spaces displays spatial heterogeneity, with more pronounced shrinkage characteristics observed in villages located further from the central town. According to the comprehensive shrinkage index, the villages are categorized into four types: relative shrinkage (0.2447 ≤ Z ≤ 0.2462), mild shrinkage (0.2463 ≤ Z ≤ 0.4423), moderate shrinkage (0.4424 ≤ Z ≤ 0.6125), and severe shrinkage (0.6126 ≤ Z ≤ 0.7988). The research findings possess significant reference value for the governance of rural spatial shrinkage. Full article

This study uses time series regression models and dynamic panel models of five-class housing to investigate the dynamics of the housing price-vacancy relationship in Hong Kong, offering insights distinct from previous cross-sectional analyses that take new housing completions as a supply proxy, without considering vacant homes as a source of housing supply. Two major contributions emerge: first, the results support the hypothesis that housing vacancies exert a negative impact on housing prices, holding other factors constant. Second, new builds supply is found to have a positive effect on housing prices, which is in line with many previous studies, but it contradicts the prediction. The results challenge the use of land supply or new housing completions as the proxy of housing supply and put forward a novel suggestion of including vacant homes in the housing price analysis. Advanced approaches to collecting housing vacancy data are also discussed. These findings have significant implications for policymakers, urban planners, and real estate investors, providing valuable insights for crafting targeted interventions and informing investment decisions. This is one of the first time series and dynamic panel analyses of housing vacancy’s effect on prices. Full article

Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model’s effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch. Full article

The green infrastructure of urban lots performs socio-ecological functions and provides several ecosystem services (ESs) in urban environments. By assessing the land cover patterns of such sites, one can deduce ecological functions and potential ESs. We represented the various land cover combinations of lots by mapping and classifying the vegetation quality of 2828 lots in the city of Belo Horizonte, Southeast Brazil. We performed cluster analysis of land cover with weighting according to ecological functions, potential for ES provision, and performance. Most lots (1024, 36.21%) were in the moderate vegetation quality class (trees/native vegetation between 25% and 50% or >50% herbaceous-shrubby vegetation), which included the largest plot of 383,300 m² and a median plot size of 403 m². A total of 244 (8.63%) lots were in the highest vegetation quality class (trees/native vegetation between >50% and 100%). The lots included diverse vegetation cover combinations of up to ten land cover types, with two dominant types: herbaceous-shrubby vegetation and tree clumps. Among the four land cover patterns obtained, those covered by tree clusters (1193 lots; 42.18%) had the highest ecological performance and the greatest potential for regulating and supporting ESs. This cluster had the highest average land cover of tree clumps (49%) and the highest averages for native vegetation formations (2–6%). Our study showed a variety of land cover patterns and an expressive percentage of lots with capabilities to provide ecological functions and ESs, which can support urban sustainability policies that have yet to be addressed. Full article

Urban vacancies have been a concern for neighborhood distress and economic decline and have gained more recent attention as potential green infrastructure is known to benefit communities in diverse ways. To investigate this, this study looked into the relationship between land cover, natural environment, and urban vacancies in Austin, Texas. Additionally, we investigated the spatial patterns of green infrastructure and urban vacancies by different income groups to see if low income communities would potentially lack the benefits of green infrastructure. To measure green infrastructure, we used different land covers such as forests and shrublands, as well as natural environments such as tree canopies and vegetation richness, using remote sensing data. Urban vacancy information was retrieved from the USPS vacant addresses and parcel land uses. Through a series of multivariate analyses examining green infrastructure variables one by one, the study results indicate that green infrastructure interacts with residential and business vacancies differently. Additionally, low-income communities lack green infrastructure compared with the rest of the city and are exposed to more urban vacancies in their neighborhoods. Further study is required to understand the dynamics of vacancies in underserved communities and examine how existing vacant land can benefit the communities as ecological resources. Full article

Vacant land in residual urban areas is a crucial resource to tackle the current climate and housing crises. In this study, we present the development of a geodatabase to determine the occurrence of vacant land in the urban core of Oklahoma City, USA (OKC), and assess its potential for infill housing. As a starting point, we define urban vacant land through a literature review. We present a description of the case study’s social and urbanistic context by highlighting its relevance to this study. We explain the methodology for the development of the geodatabase to quantify residual urban land in OKC’s urban core. We examine the spatial distribution and recurring characteristics of vacant parcels using QGIS, Python scripting for Rhinoceros 3D, and aerial imagery. We find that small parcels have higher vacancy rates than average-sized parcels and there is a correlation between higher vacancy rates and proximity to downtown and brownfields. Finally, we discuss the implications of the findings by assessing the urban vacant land potential for residential development and its contribution to OKC’s housing provision. Under all the proposed scenarios, the considered developable vacant land in the urban core could entirely fulfill the need for new housing units for the entire city. Full article

Vacant land in shrinking cities has long been associated with high crime rates and economic decline. While some efforts have been made to repurpose vacant land for tax revenue generation and temporary environmental restoration, a comprehensive framework for city-scale assessment and reprogramming remains lacking. To address this gap, our study introduced the Vacant Land Assessment System (VLAS), a multi-scale spatial analysis and planning tool that assesses the distribution and characteristics of vacant lots using publicly available spatial data. Taking Hartford, Connecticut as a case study, we assessed and categorized the characteristics of vacant lots into four typologies: Row House, Street Corner, Commercial/Industrial, and Main Street. Responding reuse programs for those typologies were generated and one design example of vacant lot greening was also provided based on identified sustainable goals and techniques. The VLAS serves as an effective planning support tool, enabling efficient assessment, classification, and planning for urban vacancy management across city, district, neighborhood, and site scales. This multi-scale planning and design approach to repurpose vacant lots with diverse physical characteristics offers valuable insights for transforming vacant land in other shrinking legacy cities for sustainability and neighborhood revitalization. Full article