Computers Environment and Urban Systems最新文献

The post-disaster recovery system is composed of the complex interplay between physical and social infrastructures. Despite the rise of coupled physical and social post-disaster recovery systems, less attention has been paid to the interdependent role of social support ties and physical infrastructure. This paper analyzes the data-driven models of post-disaster recovery system dynamics with the interdependence between the social and physical coupling to assess the post-disaster recovery policies. This paper utilizes the large-scale mobile phone location data, power outages, and socio-economic attributes for modeling the recovery dynamics during Hurricane Harvey in 2017. Parameter estimation results show that the model has regional heterogeneity and disparate impacts on socio-economic attributes to the model. The model's budget allocation scenarios also demonstrate that different budget allocation strategies affect the recovery period. The proposed model emphasizes the complex properties of the post-disaster recovery system and the importance of heterogeneous recovery policies across regions.

Up till now, a widely accepted definition of Digital Planning is missing. Following the Editorial, digital planning is defined as the application of digital technologies and data-driven approaches to enhance efficiency, effectiveness, and inclusivity in planning processes to improve social, economic, and environmental outcomes for a sustainable urban future. It is necessary to clarify the distinction between Digital Planning and two associated terminologies: Planning Support Systems (PSS) and Planning Support Science (PSScience). PSScience and Digital Planning (DP) are envisioned as distinctive but closely interconnected. PSScience acts as the scientific base of the foremost planning practice-oriented Digital Planning. Based on this double-sided distinction and interconnection with PSScience, the relatively new concept of Digital Planning is further elaborated upon, resulting in an integrated research and practice agenda. For both approaches, a quadruple collaboration will be needed between governmental organizations, market parties, societal organizations/individuals, and educational/research institutes.

The urgency to decarbonize the transportation sector has accelerated the adoption of micro-mobility solutions, with cycling network development witnessing remarkable growth. Robust and quantitative evaluation frameworks are needed to evaluate the quality of such developments. While a plethora of bike network evaluation approaches exist, their diversity creates issues of interpretability and comparability due to varying metrics and domain-specific terms. We present three contributions to address these challenges. First, we construct a formal ontology, VeloNEMO, that captures key attributes of evaluation metrics for harmonizing bike network evaluation metrics. Second, we generate a machine-readable knowledge base containing these metrics, enabling meta-analyses and resolving some of the existing terminological discrepancies. Third, we propose recommendations for transparent and comparable metric descriptions across various evaluation approaches, illustrated by exploratory metric selection scenarios for a forthcoming bike network evaluation tool. In summary, our research addresses the need for a structured and shared vocabulary for bike network evaluations. This ontology-based approach aims to improve the coherence of evaluation methods as the field of bike network planning continues to evolve, ultimately supporting decision-making for sustainable transportation planning.

Urban street profiling is the spatio-temporal pattern discovery of street-level urban areas, which plays a vital role in understanding urban structures and dynamics. Due to the natural topology and various geographic characteristics on the streets, it is necessary to combine multi-dimensional spatio-temporal information to understand different profiles of streets. This research aims to develop a street profiling framework according to the coupled characteristics of streets. At the start, a bidirected dual graph and a spatial weighted graph embedding method were used to solve the street representation. Then, the street profiles can be extracted by clustering embedding vectors of streets and feature importance analysis. As the case study, we employed the bike trajectories and street view images in Xiamen, China to depict the geographic attributes of streets. The results can reveal nine spatio-temporal street profiles from the biking perspective, including three spatial distribution patterns and two spatial semantic patterns. Urban streets in the study area show a significant hierarchical pattern because of locations and the spatial lags of the biking behaviors. Meanwhile, the spatio-temporal characteristics of biking behaviors are the main factors of street profiles, though the street environment attributes participate in over half the number of profile types. We further evaluated the profiling ability of the proposed framework and the importance of urban street profiles using coupled characteristics. Overall, this study explored the profiling method for coupling static and dynamic characteristics of urban streets. The profiling results also help understand street usage and experiences by bikers, which have a practical value on the human-oriented classification of streets and further urban development from a geographic view.

Inadequate supply of transport infrastructure is often seen as a barrier to a sustainable future for cities globally. Such barriers often perpetuate significant inequalities in who can and who cannot benefit from sustainable transport opportunities, and as a result there is momentum for transformative urban planning to promote sustainable transportation equity. This study introduces a new set of two-dimensional indicators, merging elements of supply and demand, to identify barriers and imbalances in sustainable transport equity. The accessibility indicators, which are generated for bus, rail, and cycle infrastructure, consider the proximity of administrative areas to good quality transport infrastructure, as well as mode-specific demand, to clearly identify areas where the supply of infrastructure is inadequate to support local populations. We present a policy case study for Liverpool City Region, which demonstrates how these indicators can be used in an analytical framework to support transformative urban planning in long-term. In particular, the indicators reveal policy priority areas where demand for sustainable transport is greater than supply, as well as neighbourhoods where multiple transport inequalities are intersecting spatially, highlighting the need for specific types of infrastructure investment to promote sustainable transport equity (e.g. more frequent services, additional cycle paths). Our framework lays the foundations for improved decision-making in urban systems, through development of mode-specific sustainable transport indicators at small area levels, which harmonise elements of supply and demand for the first time.

Urban ecological corridors are essential for sustainable urban development, but determining their width remains challenging. This paper addresses this issue by focusing on the unique habitat requirements of urban undercanopy bird species. We employ Species Distribution Model to simulate their potential living spaces in Shanghai and quantify their functional connectivity in urban mobility. We then use segmented linear regression models to identify turning points in functional connectivity within different buffer zones, which represent the physical width of the corridor. Our findings show that urban undercanopy birds are less sensitive to human activity and building distribution compared to surface temperature, land cover types, and vegetation canopy height. We also find that conventional linear weighting methods tend to overestimate the impact of environmental factors on undercanopy birds, leading to subtle deviations in corridor path recognition. Finally, we demonstrate that employing segmented linear regression helps to quantify the turning points of functional connectivity for each urban ecological corridor, allowing us to determine their physical width range. This study is the first attempt to quantitatively assess the functional connectivity of urban ecological corridors from the perspective of undercanopy birds and demarcate their extent.

Casual encounters with diverse groups of people in urban spaces have been shown to foster social capital and trust, leading to higher quality of life, civic participation, and community resilience to hazards. To promote such diverse encounters and cultivate social ties, policymakers develop social infrastructure sites, such as community centers, parks, and plazas. However, their effects on the diversity of encounters, compared to baseline sites (e.g., grocery stores), have not been fully understood. In this study, we use a large-scale, privacy-enhanced mobility dataset of >120 K anonymized mobile phone users in the Boston area to evaluate the effects of social infrastructure sites on the observed frequencies of inter-income and inter-race encounters. Contrary to our intuition that all social infrastructure sites promote diverse encounters, we find the effects to be mixed and more nuanced. Overall, parks and social businesses promote more inter-income encounters, while community spaces promote more same-income encounters, but each produces opposite effects for inter-race encounters. Parks and community spaces located in low-income neighborhoods were shown to result in higher inter-income and inter-race encounters compared to ordinary sites, respectively, however, their associations were insignificant in high-income areas. These empirical results suggest that the type of social infrastructure and neighborhood traits may alter levels of diverse encounters.

Road networks play an important role in the sustainable development of human society. Conventionally, there are two sources of road data acquisition: road extraction from Remote Sensing (RS) imagery and GIS based map production. Each method has its limitations. The RS road extraction methods are primarily raster-based and the extracted roads are not directly usable in GIS due to their fragmented and noisy nature, while vector-based methods cannot utilize rich raster information. Further more, the vector and raster data can have discrepancies for various reasons. Efficient road data production requires an image-vector conflation process that can match and combine raster and vector-based road data automatically.

In this study, we propose a full image-vector conflation framework that directly integrates image and vector road data by appropriately transforming extracted roads from imagery and establishing a match relation between these roads and a credible target GIS road dataset. Based on analyzing these match relations, we propose new metrics for measuring the degree of agreement between the raster and vector road data. The proposed framework combines state-of-the-art deep learning methods for image segmentation and optimization-based models for object matching. We prepared a large-scale high-resolution road dataset covering two counties in Kansas, US. Using trained models from one of the two counties, we were able to extract road segments in the other county and match them to the TIGER/Line roads.

Our experiments show that conventional performance metrics for road extraction (e.g. IoU) are insufficient for measuring the degree of agreement between image and vector roads as they are pixel-based and are too sensitive to spatial displacement. Instead, the newly defined vector-based agreement metrics are needed for image-vector conflation purposes. Experiments show that, by the vector-based metrics, nearly 90% of GIS road lengths in the study area were extracted and over 90% of extracted roads matched the target GIS roads. The new framework streamlines raster-vector conflation of roads and can potentially expedite relevant geospatial analyses regarding change detection, disaster monitoring and GIS data production, among others.

Addressing climate change and urban energy problems is a great challenge. Building Integrated Photovoltaics (BIPV) plays a pivotal role in energy conservation and carbon emission reduction. However, traditional approaches to assessing solar radiation on buildings with physical models are computing-intensive and time-consuming. This study presents a hybrid approach by integrating physical model-based solar radiation calculation and machine learning (ML) for city-wide building solar radiation potential (SRP) analysis. By considering urban morphology, land cover, and meteorological characteristics, local climate zones (LCZs) are classified. The SRP of representative LCZs is precisely evaluated using computing-intensive physical models integrated with 3D building models. A ML model is then developed to effectively predict the SRP of building roofs and facades throughout the city. An experiment was conducted in Shenzhen, China to validate the presented approach. The results demonstrate that Shenzhen has a total annual building solar radiation of $3.28\ast {10}^{11}\mathrm{kwh}$. Luohu District exhibits the highest SRP density. The LCZ-based analysis highlights that compact low-rise LCZs offer greater SRP for roofs, while compact high-rise LCZs do so for facades. Moreover, BIPV could cut CO₂ emission by up to 41.85 million tons annually. Notably, solar PV installation only on rooftops in Shenzhen could meet 87.81% of the city's electricity department's carbon reduction goal. This study provides an alternative for city-wide SRP estimation by combining physical modeling and ML and offers valuable insights for data-driven and model-driven urban planning and management in low-carbon cities.