OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks

OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks
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DOI:
10.1016/j.compenvurbsys.2017.05.004
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发表时间:
2017-09-01
影响因子:
6.8
通讯作者:
Boeing, Geoff
Boeing, Geoff
中科院分区:
地球科学1区
文献类型:
--
作者:
Boeing, Geoff

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城市学者以各种方式研究街道网络,但目前的城市规划/街道网络分析文献存在数据可用性和一致性限制。为了应对这些挑战,本文介绍了OSMnx,这是一种新的工具,可以从图论,交通和城市设计的角度简单,一致,自动化和合理地收集数据并创建和分析街道网络。OSMnx为研究人员和实践者提供了五个重要功能:第一,自动下载政治边界和建筑物足迹;第二,从OpenStreetMap定制和自动下载和构建街道网络数据;第三,网络拓扑的算法校正;第四,将街道网络保存为shapefile,GraphML或SVG文件的能力。第五,分析街道网络的能力,包括计算路线、投影和可视化网络以及计算度量和拓扑测量。这些措施包括城市设计和交通研究中常见的措施,以及网络结构和拓扑结构的高级措施。最后,本文给出了一个简单的案例研究,使用OSMnx构建和分析俄勒冈州波特兰的街道网络。(C)2017爱思唯尔有限公司版权所有
Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon. (C) 2017 Elsevier Ltd. All rights reserved.