Urban attractors: Discovering patterns in regions of attraction in cities.
Urban attractors: Discovering patterns in regions of attraction in cities.
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DOI：
10.1371/journal.pone.0250204
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
González MC
中科院分区：
文献类型：
--
作者：
Alhazzani M;Alhasoun F;Alawwad Z;González MC
了解城市地区吸引游客的动态在当今人口不断增加的城市中非常重要。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，在城市中的地区，他们的吸引力日常通勤和相关的兴趣点（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两个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，以及旅行开始的位置的空间分布。我们使用了层次聚类算法来分类城市中的所有地方的吸引力的特点。我们在上午发现了利雅得的三种主要类型的城市吸引力：全球，这是城市的重要场所，市中心，其中包含中央商务区，以及住宅吸引力。此外，我们发现了是什么使地区拥有一定的吸引力模式。我们使用统计显著性检验方法来量化兴趣点（POI）类型（服务）和检测到的城市吸引力模式之间的关系。
Understanding the dynamics by which urban areas attract visitors is important in today’s cities that are continuously increasing in population towards higher densities. 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 daily commuters 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 number of visitors a place received, the distribution of distance traveled by visitors on the road network, and the spatial spread of locations from where trips started. We used a hierarchical clustering algorithm to classify all places in the city by their features of attraction. We discovered three main types of Urban Attractors in Riyadh during the morning period: Global, which are significant places in the city, Downtown, which contains the central business district, and Residential attractors. In addition, we uncovered what makes districts possess certain attraction patterns. We used a statistical significance testing approach to quantify the relationship between Points of Interests (POIs) types (services) and the patterns of Urban Attractors detected.
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影响因子：
3.6
作者：
Bassolas, Aleix;Lenormand, Maxime;Ramasco, Jose J.
通讯作者：
Ramasco, Jose J.
DOI：
10.1016/j.trc.2015.04.022
发表时间：
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影响因子：
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