From mobile phone data to the spatial structure of cities.
From mobile phone data to the spatial structure of cities.
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DOI：
10.1038/srep05276
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发表时间：
2014-06-13
影响因子：
4.6
通讯作者：
Barthelemy M
中科院分区：
文献类型：
--
作者：
Louail T;Lenormand M;Cantu Ros OG;Picornell M;Herranz R;Frias-Martinez E;Ramasco JJ;Barthelemy M
手机网络等无处不在的基础设施不仅可以捕获大量人类行为数据，还可以提供有关城市结构及其动态特性的信息。在本文中，我们通过研究 31 个西班牙城市 55 天内记录的电话数据来重点关注最后几个方面。我们首先定义一个城市扩张指数，该指数衡量白天人与人之间的平均距离如何变化，从而使我们能够突出不同类型的城市结构。然后我们关注热点，即城市中最拥挤的地方。我们提出了一种无参数方法来检测它们并测试我们结果的稳健性。这些热点的数量与人口规模呈次线性关系，这一结果与之前的理论论证和就业数据集的测量结果一致。我们研究了这些热点的生命周期，并特别表明，构成城市“心脏”的永久性热点的层次结构无论城市规模如何都非常稳定。这些热点的空间结构也很有趣，它使我们能够区分不同类别的城市，从空间分布非常依赖于土地利用的单中心和“隔离”城市，到土地利用之间的空间混合更为重要的多中心城市。这些结果表明有可能利用高分辨率时空数据对城市进行新的定量分类。
Pervasive infrastructures, such as cell phone networks, enable to capture large amounts of human behavioral data but also provide information about the structure of cities and their dynamical properties. In this article, we focus on these last aspects by studying phone data recorded during 55 days in 31 Spanish cities. We first define an urban dilatation index which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure. We then focus on hotspots, the most crowded places in the city. We propose a parameter free method to detect them and to test the robustness of our results. The number of these hotspots scales sublinearly with the population size, a result in agreement with previous theoretical arguments and measures on employment datasets. We study the lifetime of these hotspots and show in particular that the hierarchy of permanent ones, which constitute the ‘heart' of the city, is very stable whatever the size of the city. The spatial structure of these hotspots is also of interest and allows us to distinguish different categories of cities, from monocentric and “segregated” where the spatial distribution is very dependent on land use, to polycentric where the spatial mixing between land uses is much more important. These results point towards the possibility of a new, quantitative classification of cities using high resolution spatio-temporal data.
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DOI：
10.1073/pnas.0610245104
发表时间：
2007-05-01
影响因子：
11.1
作者：
Onnela, J.-P.;Saramaki, J.;Barabasi, A.-L.
通讯作者：
Barabasi, A.-L.
影响因子：
6.3
作者：
McMillen, DP;Smith, SC
通讯作者：
Smith, SC
DOI：
10.1068/a39382
发表时间：
2008-09-01
期刊：
ENVIRONMENT AND PLANNING A
影响因子：
--
作者：
Guerois, Marianne;Pumain, Denise
通讯作者：
Pumain, Denise
DOI：
10.1016/j.physa.2008.05.014
发表时间：
2008-09-01
影响因子：
3.3
作者：
Lambiotte, Renaud;Blondel, Vincent D.;Van Dooren, Paul
通讯作者：
Van Dooren, Paul
DOI：
10.1068/a160807
发表时间：
1984-01-01
期刊：
ENVIRONMENT AND PLANNING A
影响因子：
--
作者：
GOODCHILD, MF;JANELLE, DG
通讯作者：
JANELLE, DG