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With rapid development of socio-economics, the task of discovering functionalzones becomes critical to better understand the interactions between socialactivities and spatial locations. In this paper, we propose a framework todiscover the real functional zones from the biased and extremely sparse Pointof Interests (POIs). To cope with the bias and sparsity of POIs, the unbiasedinner influences between spatial locations and human activities are introducedto learn a balanced and dense latent region representation. In addition, aspatial location based clustering method is also included to enrich the spatialinformation for latent region representation and enhance the regionfunctionality consistency for the fine-grained region segmentation. Moreover,to properly annotate the various and fine-grained region functionalities, weestimate the functionality of the regions and rank them by the differencesbetween the normalized POI distributions to reduce the inconsistency caused bythe fine-grained segmentation. Thus, our whole framework is able to properlyaddress the biased categories in sparse POI data and explore the truefunctional 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 POIsdata from city of Raleigh. The results demonstrate that the proposed frameworkcan better identify functional zones than the benchmarks, and, therefore,enhance understanding of urban structures with a finer granularity underpractical conditions.

Discovering Urban Functional Zones from Biased and Sparse Points of Interests and Sparse Human Activities