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