Extracting urban functional regions from points of interest and human activities on location-based social networks

What is it about?

Data about points of interest (POI) have been widely used in studying urban land use types and for sensing human behaviors. However, it is difficult to quantify the right mix or the spatial relations among different POI types indicative of specific urban functions. In this research, we develop a statistical framework to help discover semantically meaningful topics and functional regions based on the co-occurrence patterns of POI types. The framework applies the latent Dirichlet allocation (LDA) topic modeling technique and incorporates user check-in activities on location-based social networks. Using a large corpus of about 100,000 Foursquare venues and user check-in behaviors in the ten most populated urban areas of the United States, we demonstrate the effectiveness of our proposed methodology by identifying distinctive types of latent topics and further, by extracting urban functional regions using K-means clustering and Delaunay triangulation spatial constraints clustering. We show that a region can support multiple functions but with different probabilities, while the same type of functional region can span multiple geographically non-adjacent locations. Since each region can be modeled as a vector consisting of multinomial topic distributions, similar regions with regard to their thematic topic signatures can be identified. Compared to remote sensing images which mainly uncover the physical landscape of urban environments, our popularity-based POI topic modeling approach can be seen as a complementary social sensing view on urban space based on human activities.

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Why is it important?

A region can have multiple functions but with different probabilities, while the same type of functional region can span multiple geographically non-adjacent locations. Compared with the remote sensing images that mainly uncover the physical landscape of urban environments, results derived from the popularity-based POI topic model can be seen as a complementary social sensing view of urban space based on human activities and the place settings of urban functions. However, there may exist gaps between the real-world business establishments and the online available POI information. Data-fusion and cross-validation relying on multiple sources may help reduce such gaps.