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

机译：从基于位置的社交网络中的兴趣点和人类活动中提取城市功能区

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Data about points of interest (POI) have been widely used in studying urban land use types and for sensing human behavior. However, it is difficult to quantify the correct 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 behavior in the 10 most populated urban areas of the US, 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 with 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.
机译：关于兴趣点（POI）的数据已被广泛用于研究城市土地使用类型和传感人类行为。然而，难以量化指示特定城市功能的不同POI类型的正确组合或空间关系。在这项研究中，我们开发了一个统计框架，以帮助基于POI类型的共同发生模式发现语义有意义的主题和功能区域。该框架应用潜在Dirichlet分配（LDA）主题建模技术，并在基于位置的社交网络上结合了用户登记活动。在美国最庞大的城市地区，使用大约10万个四分之一场地和用户办理入住行为的大型语料库，我们通过确定独特类型的潜入题目和进一步提取城市功能区，展示了我们提出的方法的有效性使用k-means群集和delaunay三角测量空间约束群集。我们表明一个区域可以支持多个功能，但具有不同的概率，而相同类型的功能区域可以跨越多个地理上非相邻位置。由于每个区域可以被建模为由多项式主题分布组成的向量，因此可以识别出关于其主题主题签名的类似区域。与主要揭示城市环境物质景观的遥感图像相比，我们的受欢迎程度的POI主题建模方法可以被视为基于人类活动的城市空间的互补社会传感观点。

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