Community Dynamics in Smart City Digital Twins: A Computer Vision-based Approach for Monitoring and Forecasting Collective Urban Hazard Exposure

Abstract

Urbanization and the growth of human population are leading to increased complexities in the interactions of citizens with public spaces, creating cities that must be more responsive to community dynamics. Despite the critical need for community-based city management, current decision-making approaches are often uninformed by the collective behavior of communities across time and in different locations. Here, we introduce a computer vision-based monitoring and forecasting approach for Smart City Digital Twins (SCDT) that enables integrating collective behavior into the spatiotemporal assessments of exposure to urban heat stress. Our results from a pilot case study in the city of Columbus, GA, demonstrate the significance of integrating community dynamics into monitoring and forecasting urban hazard exposure. This ongoing study highlights how SCDT can make community dynamics more accessible to and responsive by city managers.

A multi-dimensional digital twin use cases classification framework

“…Likewise, Wang et al (2020) exploited the smart rail card ticket data to help protect the safety of urban public transportation. Another study used computer vision to anticipate the risk of heat stress on pedestrians (Mavrokapnidis et al, 2021).…”

“…A DT use case can basically target an individual infrastructure asset, a whole infrastructure system comprising multiple assets and networks; or ultimately an integrated system of systems [SoS] where interdependencies between these infrastructure systemswhich have been conventionally seen as independentbe them geospatial, cyber, physical and logical interdependencies (Whyte et al, 2019) are taken into consideration (ISO, 2018). In the first case, several studies captured infrastructure sub-systems, like basins as a part of the stormwater network (Bartos & Kerkez, 2020); pedestrian routes, road or rail networks as components of the transportation system (Mavrokapnidis et al, 2021;Honarvar & Sami, 2019;Wang et al, 2020;Barmpounakis & Geroliminis, 2020;Nallaperuma et al, 2019); or energy consumption of buildings as an element of those connected to the energy grid. In the second case involving full infrastructure systems, Kourtit and Nijkamp (2018) and Anejionu et al (2019) considered all means of transportation in developing their DTs.…”

“…Bartos and Kerkez (2020) included infrastructure along with natural environment, while others considered the interactions between infrastructure and socio-economic layers (Jadli & Hain, 2020;Wang et al, 2020;Barmpounakis & Geroliminis, 2020;Nallaperuma et al, 2019). Some authors alternatively involved varying combinations of three city layers while delivering DT purposes (Anejionu et al, 2019;Honarvar & Sami, 2019;Kourtit & Nijkamp, 2018;Francisco et al, 2020;Mavrokapnidis et al, 2021;Barkham et al, 2018;Mayaud et al, 2019), while others included all four as required within some other applications (Pettit et al, 2018;Yabe & Ukkusuri, 2019).…”

“…Few DTs focus only at a neighborhood scale with a neighborhood resolution as well (Panagoulia, 2017). For example, Mavrokapnidis et al (2021) developed a DT bounded to a specific district to predict the heat exposure on citizens within this district. Nonetheless, various DTs have spatially incorporated full cities, albeit with different levels of resolution.…”

“…For example, Kourtit and Nijkamp (2018) relied on DT city-scale urban indicators and KPIs to guide setting city redevelopment strategies. Moreover, the continuous monitoring of the heat stress that pedestrians' experiences informed decision makers of redevelopments, including building shades, among other facilities, to overcome this issue (Mavrokapnidis et al, 2021). Another DT approach can be used to direct future redevelopments of road network in such a manner that brings about less air pollution (Honarvar & Sami, 2019).…”