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. The understanding of the flow of people in a city, which is known as the analysis of urban dynamics, is of great importance in urban planning, emergency management, and commercial activity. Questionnaire-based surveys, such as the person trip survey and the traffic flow survey, have been useful for understanding urban dynamics (Sekimoto et al. 2011) . With the spread of smart devices, a large amount of mobility log data, such as the global positioning system (GPS) and cell tower logs, has been accumulated. Therefore, studies have paid attention to the analysis of urban dynamics using mobility logs from smartphones without other additional survey costs. With the structured property of the population data across regions, time, and days obtained from GPS logs, the analytics based on spatio-temporal tensor factorization (Fan, Song, and Shibasaki 2014) and discriminative approach, such as neural network (Jiang et al. 2018) , and Poisson regression (Okawa, Kim, and Toda 2017) have been established as urban dynamics analysis in the last decade. In the former approach, the tensor data, where the population data with respect to locations, time-zones, and days are accumulated, are
Spatiality preservable factored Poisson regression REF incorporates point of interest to overcome data sparsity and degraded performance in even ner grains population prediction.
Spatiality Preservable Factored Poisson Regression for Large Scale Fine Grained GPS-­based Population Analysis
AAAI
Computer Science