城市吸引力：城市吸引力区域的发现模式
Urban Attractors: Discovering Patterns in Regions of Attraction in Cities
摘要：
了解城市地区吸引游客的动态对于城市的城市发展具有重要意义。此外，确定与具有高度吸引力的地区有关的服务，有助于制定关于安置这些地方的政策。因此，我们提出了一个框架，在城市的吸引力，对游客的兴趣点（POI）类型的地区的吸引力模式进行分类。我们使用了从手机数据中挖掘出来的出发地-目的地矩阵（OD），这些数据捕捉了沙特阿拉伯利雅得每对地方之间的旅行流量。我们根据三个主要的统计特征来定义一个地方的吸引力概况：一个地方接待的游客数量，游客在道路网络上行驶的距离分布，以及游客来自哪里的空间分布。我们使用一个层次聚类算法来分类城市中的所有地方的吸引力的特点。我们在利雅得发现了三种类型的城市吸引力：全球，这是城市的重要场所，市中心，中央商务区和住宅吸引力。此外，我们揭示了是什么使这些地方在吸引力模式方面有所不同。我们使用统计显著性检验方法来严格量化兴趣点（POI）类型（服务）与我们检测到的3种城市吸引力模式之间的关系。所提出的框架可以用于检测的吸引力模式的类型相关的每个模式的服务。这是为旅行分布模型提供信息的关键信息。
英文摘要：
Understanding the dynamics by which urban areas attract visitors is significant for urban development in cities. In addition, identifying services that relate to highly attractive districts is useful to make policies regarding the placement of such places. Thus, we present a framework for classifying districts in cities by their attractiveness to visitors, and relating Points of Interests (POIs) types to districts' attraction patterns. We used Origin-Destination matrices (ODs) mined from cell phone data that capture the flow of trips between each pair of places in Riyadh, Saudi Arabia. We define the attraction profile for a place based on three main statistical features: The amount of visitors a place received, the distribution of distance traveled by visitors on the road network, and the spatial spread of where visitors come from. We use a hierarchical clustering algorithm to classify all places in the city by their features of attraction. We detect three types of Urban Attractors in Riyadh during the morning period: Global which are significant places in the city, Downtown which the central business district and Residential attractors. In addition, we uncover what makes these places different in terms of attraction patterns. We used a statistical significance testing approach to rigorously quantify the relationship between Points of Interests (POIs) types (services) and the 3 patterns of Urban Attractors we detected. The proposed framework can be used for detecting the attraction patterns given by type of services related to each pattern. This is a critical piece of information to inform trip distribution models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
