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.