Travel surveys serve as vital resources for gaining insight into daily mobility patterns. Trip chains are one of the key features in such data sets, as they capture integrated travel patterns, including details of activity participation. Mapping the spatial distribution of intermediate activities, such as stops made during a standard commute, enhances understanding of travel behavior, particularly mode choice dynamics. However, such data are often incomplete or missing, which reduces the depth of the analysis. Addressing this concern, this study presents a Point of Interest (POI)-based time-geography methodology to approximate intermediate stop locations for activities in trip chains in travel surveys, with the potential to considerably improve computational efficiency compared to the traditional time-geographic density estimation (TGDE) technique. We apply the method to travel survey data from Eskilstuna, Sweden, combined with OpenStreetMap network data, achieving promising trip chain reconstruction results. However, although the proposed approach showed great potential, limitations in data availability prevented rigorous validation. Future studies should therefore test it using data with complete trip chains. Nevertheless, this study contributes to addressing the problem of missing data in travel surveys, which is relevant to researchers and policymakers in urban transport.