Urban mobility data are important to areas ranging from traffic engineering to the analysis of outbreaks and disasters. In this paper, we study mobility data from a major Brazilian city from a geographical viewpoint using a Complex Network approach. The case study is based on intra-urban mobility data from the Metropolitan area of Rio de Janeiro (Brazil), presenting more than 480 spatial network nodes. While for the mobility flow data a log-normal distribution outperformed the power law, we also found moderate evidence for scale-free and small word effects in the flow network’s degree distribution. We employ a novel open-source GIS tool to display (geo)graph’s topological properties in maps and observe a strong traffic-topology association and also a fine adjustment for hubs location for different flow threshold networks. In the central commercial area for lower thresholds and in high population residential areas for higher thresholds. This set of results, including statistical, topological and geographical analysis may represent an important tool for policymakers and stakeholders in the urban planning area, especially by the identification of zones with few but strong links in a real data-driven mobility network.

中文翻译：

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城市流动性（地理）图的拓扑特性如何填充地图？

城市交通数据对于从交通工程到爆发和灾难分析的各个领域都很重要。在本文中，我们使用复杂网络方法从地理角度研究了巴西主要城市的出行数据。该案例研究基于来自里约热内卢（巴西）大都市地区的城市内部移动性数据，该数据提供了480多个空间网络节点。尽管对于流动性流量数据，对数正态分布优于幂律，但我们还发现了流量网络度分布中无标度和小词效应的适度证据。我们采用新颖的开源GIS工具在地图上显示（geo）graph的拓扑属性，并观察到强大的交通拓扑关联，并针对不同的流量阈值网络对集线器位置进行了精细调整。在较低阈值的中央商业区和在较高阈值的高人口住宅区。这组结果，包括统计，拓扑和地理分析，可能代表城市规划领域的政策制定者和利益相关者，尤其是通过在真正的数据驱动型移动网络中识别出很少但牢固联系的区域，这是一种重要的工具。