Computers Environment and Urban Systems最新文献

In recent years, social media has become an influential tool for engaging various participants and facilitating inclusivity in digital planning. While many studies highlight local governments' use of social media for formal participation, limited research assesses its impact on power dynamics in informal participation. This study aims to fill the gap by identifying key features of social media that facilitate informal participation and applying Castells' four forms of network power to understand power dynamics among civil society, journalism, citizens, and governments in planning processes. It also develops a novel mixed-methods approach that combines social media scraping, social network analysis (SNA), semi-structured interviews, and field observation. This approach is applied to investigate the Enning Road regeneration project in Guangzhou as a case study. Analyzing data from China's Weibo, the study reveals network disputes across three dimensions: graph, community, and network statistics. Hyperlink-Induced Topic Search (HITS) and community detection results suggest that civil society and journalism have substantial networked power as they strategically utilize social media to promote collaboration, mobilize citizens, and foster communities. They also excise network-making power by switching online and offline networks, thereby transmitting online debate to a wide range of audiences and compelling local governments to shift planning priorities from demolitions to preservation.

The COVID-19 pandemic, unprecedented in scale and impact, has significantly influenced consumer spending. This study leverages a longitudinal transaction dataset from South Korea to analyze how the pandemic, social distancing policies, and pandemic-related search interest have shaped spending within and across cities. We examine transaction volume and expenditure amount as city-level indicators of activity intensity and consumption demand across four stages of the early pandemic. The study finds that: (1) Social distancing caused reductions in both residents' and travelers' spending. The increase in search interest coincided with a rise in residents' spending but a decline in travelers' spending; (2) Resident transactions experienced a moderate and persistent decline across all stages, while expenditure rebounded after the 1st national outbreak. Traveler transactions and expenditure showed similar trends, with declines during outbreaks and recoveries during stable periods; (3) Disparities across cities were associated with proximity to outbreak centers and socioeconomic attributes. Cities with larger populations or those closer to epicenters experienced greater reductions in spending, while less densely populated cities saw increased traveler spending during the 2nd stable period; (4) Travelers' spending from distant cities significantly decreased during the 1st outbreak but gradually recovered as the pandemic continued, indicating evolving behavior and adaptation; (5) Changes across spending categories exhibited significant heterogeneity. Residents showed increased demand for essential goods and online shopping, while recreation-related industries struggled throughout. These findings highlight the characteristics and disparities among consumers, cities, and industries, providing information for policymakers to formulate tailored support programs for industries experiencing increased demand or significant impacts. This study emphasizes the need to develop robust strategies for crisis management and economic resilience to mitigate the impacts of future health crises.

Vehicle telemetry data is becoming more ubiquitous with increasingly sensorised vehicles, but making sense of the vehicles' purpose remains challenging without additional context. Clustering the vehicle activity data and identifying the underlying facilities where the activities occur reveals much insight, particularly for logistics planning. Unfortunately, current research typically only looks at a single point in time. This paper contributes by matching geospatial patterns, each representing a facility where trucks perform activities over multiple periods. The contribution is a necessary first step in studying how urban freight movement and its underlying inter-firm networks of connectivity change over time. We demonstrate how to overcome three challenges. Firstly, the complexity of identifying facilities from non-regular geometric polygons. Secondly, the challenge associated with the scale of comparing more than 200,000 facilities on a month-to-month basis over a multi-year period. Finally, overcoming the computational challenge of the workflow and getting the required performance on a consumer-grade laptop. The paper evaluates various machine learning algorithms, highlighting a SVM that outperforms more popular deep learning and neural network alternatives, with a mean average accuracy of 96.9 %.

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.