SPEMI

Abstract

Region embedding is a primary task for a wide variety of urban-related downstream applications. However, many existing embedding techniques neglected the fact that the regions in a city have been developed differently by many factors such as planning policies, economic, and population mitigation. Such a spatial imbalance problem may result in a quite different region embedding to distinguish differences between regions, even though the regions could be similar in terms of the certain application tasks. In this paper, we propose a SPatial EMInence (SPEMI) model that normalizes region embeddings to mitigate the effects from spatial imbalance. In particular, we present a context-aware spatial feature, called spatial eminence, that measures a region's importance to its spatial context. The experimental results of store placement recommendation using real-world urban data show that SPEMI improves the performance of citywide region embeddings by up to 27.92%.