GEOGRAPHICAL SIMILARITY SEARCH IN OSAKA H3 HEXAGONS BASED ON VECTOR EMBEDDINGS OF HUMAN MOBILITY AND URBAN SPATIAL ATTRIBUTES

Abstract

In Japan, where the birthrate is declining and the population is ageing, it is necessary to promote urban planning that suits the times, such as compact city planning. We are promoting the digital twinning of cities, mainly based on the analysis of human flows in Osaka Prefecture, which can be linked to urban planning simulations. In this study, we attempted a geographical similarity serch for H3 hexagonal grid areas in Osaka Prefecture by integrating urban spatial attributes such as the number of stations and bus stops and arrival flow data. Embedded vectors created by distributed representation learning were used for similarity search. Experiments showed that the geographical areas with similar characteristics in terms of human flow + urban spatial attributes to the H3 area including JR Osaka Station were the neighbouring grid areas and the grid area of Kansai International Airport. The zones with intermediate characteristics were indentified from the human flow + urban spatial attributes data of the urban and town/village zones by the addition operation of the embedding vectors.