This study proposes a method for estimating mobility demand using area mobility models derived from a relatively small dataset of mobility data and population distribution estimates. The approach assumes that individual mobility patterns are a function of residential location and approximates mobility behavior by resampling movement data from individuals in neighboring areas. By weighting and aggregating these mobility patterns based on population density, the method generates an area-wide mobility model. The proposed method is applied to estimate travel demand among elderly individuals in suburban areas with limited access to retail facilities. Three potential public transportation routes connecting residential settlements to a shopping district are analyzed, comparing a direct route with alternatives passing through the traditional central area of Hojo. The results reveal distinct demand patterns for each route, highlighting key differences in mobility behavior among residents. In particular, while demand from settlements along the shortest route to Oho is significant, Hojo residents tend to fulfill their shopping needs locally, leading to relatively low demand for travel to Oho.