Spatiality preservable factored Poisson regression for large-scale fine-grained GPS-based population analysis
Masamichi Shimosaka(Tokyo Tech), Yuta Hayakawa(Tokyo Tech), and Kota Tsubouch
The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 2019/1
機械学習 (Machine Learning) データサイエンス (Data Science) その他の取り組み (Misc.)
- With the wide use of smartphones with Global Positioning System (GPS) sensors, the analysis of the population from GPS traces has been actively explored in the last decade. We propose herein a brand new population prediction model to capture the population trends in a fine-grained point of interest (POI) densely distributed over large areas and understand the relationship of each POI in terms of spatiality preservation. We propose a new framework, called Spatiality Preservable Factorized Regression (SPFR), to realize this model. The SPFR is inspired by the success of the recently proposed bilinear Poisson regression and the concept of multi-task learning with factorization approach and the graph proximity regularization. Given that the proposed model is written simply in terms of optimization, we achieve scalability using our model. The results of our empirical evaluation, which used a massive dataset of GPS logs in the Tokyo region over 32 M count logs, show that our model is comparable to the stateof- the-art methods in terms of capturing the population trend across meshes while retaining spatial preservation in finer mesh areas.