  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 real functional zones from the biased and extremely sparse Point
of Interests (POIs). To cope with the bias and sparsity of POIs, the unbiased
inner influences between spatial locations and human activities are introduced
to learn a balanced and dense latent region representation. In addition, a
spatial location based clustering method is also included to enrich the spatial
information for latent region representation and enhance the region
functionality consistency for the fine-grained region segmentation. Moreover,
to properly annotate the various and fine-grained region functionalities, we
estimate the functionality of the regions and rank them by the differences
between the normalized POI distributions to reduce the inconsistency caused by
the fine-grained segmentation. Thus, our whole framework is able to properly
address the biased categories in sparse POI data and explore the true
functional zones with a fine-grained level. To validate the proposed 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.
