OpenStreetMap数据和社区的异质性特征
Characterizing the Heterogeneity of the OpenStreetMap Data and Community
摘要：
OpenStreetMap（OSM）是一个前所未有的、免费的地理信息源，由数百万人贡献，形成了一个海量和异构的数据库。在这项研究中，我们描述了在大数据的背景下，整个OSM数据库和历史档案的异质性。我们考虑了所有用户、地理元素和用户贡献，这些数据来自一个为期八年的数据归档，大小为692 GB。我们依赖于一些非线性的方法，如幂律统计和头/尾休息，揭示和说明潜在的标度特性。所有这三个方面（用户、元素和贡献）都表现出惊人的幂律或重尾分布。厚尾分布意味着小元素比大元素多得多，不活跃用户比活跃用户多得多，编辑较少的元素比编辑较多的元素多得多。此外，OSM核心组中约有500名用户在协作方面高度联网。关键词：OpenStreetMap，大数据，幂律，头/尾断裂，ht-index
英文摘要：
OpenStreetMap (OSM) constitutes an unprecedented, free, geographic information source contributed by millions of individuals, resulting in a database of great volume and heterogeneity. In this study, we characterize the heterogeneity of the entire OSM database and historical archive in the context of big data. We consider all users, geographic elements, and user contributions from an eight-year data archive, at a size of 692 GB. We rely on some nonlinear methods such as power-law statistics and head/tail breaks to uncover and illustrate the underlying scaling properties. All three aspects (users, elements, and contributions) demonstrate striking power laws or heavy-tailed distributions. The heavy-tailed distributions imply that there are far more small elements than large ones, far more inactive users than active ones, and far more lightly edited elements than heavily edited ones. Furthermore, about 500 users in the core group of the OSM are highly networked in terms of collaboration. Keywords: OpenStreetMap, big data, power laws, head/tail breaks, ht-index
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