Estimation of Spatio-Temporal Missing Data for Expanding Urban LOD

The illegal parking of bicycles has been an urban problem in Tokyo and other urban areas. We have sustainably built a Linked Open Data (LOD) relating to the illegal parking of bicycles (IPBLOD) to support the problem solving by raising social awareness. Also, we have estimated and complemented the temporally missing data to enrich the IPBLOD, which consisted of intermittent social-sensor data. However, there are also spatial missing data where a bicycle might be illegally parked, and it is necessary to estimate those data in order to expand the areas. Thus, we propose and evaluate a method for estimating spatially missing data. Specifically, we find stagnation points using computational fluid dynamics (CFD), and we filter the stagnation points based on popularity stakes that are calculated using Linked Data. As a result, a significant difference in between the baseline and our approach was represented using the chi-square test.