According to the World Bank, more than half of the world's population now lives in cities, creating burdens on the degraded city infrastructures and driving up the demand for new ones. Construction sites are abundant in already dense cities and have unavoidable impacts on surrounding environments and residents. However, such impacts were rarely quantified and made available to construction teams and local agencies to inform their planning decisions. A challenge in achieving this was the lack of availability of data that can provide insights about how urban residents respond to changes in their environment due to construction projects. Wider availability of data from city agencies nowadays provides opportunities for having such analysis possible. This paper provides the details of a generic data-driven approach that enables the analysis of impact of construction projects on quality of life in urban settings through the quantification of change on widely accepted quality of life indicators in cities. This paper also evaluated the approach using data from publicly construction projects' information and open city data portals from New York City. Historical 311 Service Requests along with 27 road reconstruction projects were used as testbeds. The results showed that 61% of the projects analyzed in this testbed experienced higher 311 requests after the commencement of construction, with main complaints of 'noise', 'air quality', and 'sewer' at the beginning of construction, and 'sanitation' and 'waste' towards the end. Prediction models, built using regression machine learning algorithms, achieved an R-Squared value of 0.67. The approach is capable of providing insights for government agencies and construction companies to take proactive actions based on expected complaint types through different phases of construction.
Urban Metaverse: The Smart City in the Industrial Metaverse. Opportunities of the metaverse for real-time, interactive, and inclusive infrastructure applications in urban areas
Urban Comfort Assessment in the Era of Digital Planning: A Multidimensional, Data-driven, and AI-assisted Framework
Department of Architecture, National University of Singapore（新加坡国立大学建筑系）；School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）；Department of Real Estate, National University of Singapore（新加坡国立大学房地产系）
Scaling indicator and planning plane: an indicator and a visual tool for exploring the relationship between urban form, energy efficiency and carbon emissions
Monitoring Urban Forests from Auto-Generated Segmentation Maps
Remote Sensing Technology Institute, German Aerospace Center（德国航空航天中心遥感技术研究所）；Technical University of Munich（慕尼黑工业大学）；TJ Watson Research Center, IBM Research（IBM研究院托马斯·J·沃森研究中心）
AI Urban Scientist: Multi-Agent Collaborative Automation for Urban Research
Vanke School of Public Health, Tsinghua University, Beijing, China（范克学校公共卫生学院，清华大学，北京，中国）；Zhongguancun Academy, Beijing, China（中关村学院，北京，中国）；Department of Electronic Engineering, Tsinghua University, Beijing, China（电子工程系，清华大学，北京，中国）
The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective
《美国大城市的生与死》（The Death and Life of Great American Cities）写于1961年，是当今最具影响力的城市规划书籍之一。在这本书中，简·雅各布斯提出了促进城市生活的四个条件。然而，这些条件直到最近才得到实证检验。这主要是因为很难收集有关“城市生活”的数据。首尔市最近通过规模空前的调查收集了行人活动，这项工作持续了十多年，使研究人员能够进行第一项研究，成功测试雅各布斯的状况。在本文中，我们确定了一个有价值的替代活动调查数据的冗长和昂贵的收集：移动电话数据。我们从这些数据中提取人类活动，从意大利人口普查和开放街道地图中收集土地使用和社会人口信息，并在六个意大利城市中测试这四个条件。虽然这些城市与雅各布斯的条件所描述的地方非常不同（即，伟大的美国城市）和他们最近测试的地方（即，亚洲城市首尔），我们发现这些条件确实与意大利的城市生活有关。我们的方法有望对城市研究产生巨大影响，尤其是因为，如果复制，它将使大规模测试雅各布斯的理论成为可能。
Urban Rhapsody: Large-scale exploration of urban soundscapes
New York University（纽约大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Universidade Federal Fluminense（弗鲁米嫩塞联邦大学）；New Jersey Institute of Technology（新泽西理工学院）
