WP1 has completed the empirical groundwork for our module by demonstrating how social spatial interaction is distributed in Singapore. Utilizing GPS-based mobile phone data acquired from CityData.ai, which covers 245,327 individuals, mobility data was transformed into a network of spatial interactions encompassing the entire city of Singapore (Fig. 1). A socio-spatial interaction (or co-location) of two individuals, and thus a link in the network, is defined as an observation of the individuals within the same grid cell within the same time interval.
This method also allows for the measurement of travel distances and paths for the interactions, reflecting transportation and energy consumption efficiency. The analysis of the clustering of urban functions —such as work, shopping, and leisure— in relation to social spatial interaction further gives insights into the properties of places where social interaction may occur more frequently. In addition, by analyzing travel distances, places of high and low resource efficiency for social interaction were identified. As depicted in Fig. 2 (media output), the analysis in Singapore revealed significant disparities in spatial interaction efficiencies, with residential zones showing higher efficiency than mixed use areas.
Moving forward, WP1 will delve into a detailed analysis of mixed land use areas, identifying effective intensification strategies for urban transportation and land use. This will involve a strategic bundling of similar trips—considering similarities in origins, destinations, or routes—to reveal resource-efficient transportation system planning. This approach aims to optimize urban mobility paths, potentially leading to significant improvements in overall urban efficiency and functionality. Moreover, the analysis will be extended to Zurich as a comparative case study.
Based on these findings, WP2 and WP3 will conduct deeper socio-economic investigations to explore the reasons behind the patterns found.