Discovering Urban Functional Zones By Latent Fusion of Users GPS Data and Points of Interests

Abstract

With rapid development of socio-economics, the task of discovering functional zones becomes critical to better understand the interactions between social activities and spatial locations. In this paper, we propose a framework to discover the functional zones by analyzing urban structures and social behaviors. The proposed approach models the inner influences between spatial locations and human activities by fusing the semantic meanings of both Point of Interests (POIs) and human activities to learn the latent representation of the regions. A spatial based unsupervised clustering method, Conditional Random Filed (CRF), is then applied to aggregate regions using both their spatial information and discriminative representations. Also, we estimate the functionality of the regions and annotate them by the differences between the normalized POI distributions which properly rank various functionalities. This framework is able to properly address the biased categories in sparse POI data, when exploring the unbiased and true functional zones. To validate our framework, a case study is evaluated by using very large real-world users GPS and POIs data from city of Raleigh. The results demonstrate that the proposed framework can better identify functional zones than the benchmarks, and, therefore, enhance understanding of urban structures with a finer granularity under practical conditions