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Spatial Factor—Using a Random Forest Classification Model to Measure an Internationally Comparable Urbanity Index

机译：空间因素 - 使用随机森林分类模型来衡量国际可比的城市指数

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Travel behavior can be determined by its spatial context. If there are many shops and restaurants in close proximity, various activities can be done by walking or cycling, and a car is not needed. It is also more difficult (e.g., parking space, traffic jams) to use a car in high-density areas. Overall, travel behavior and dependencies on travel behavior are influenced by urbanity. These relationships have so far only been examined very selectively (e.g., at city level) and not in international comparison. In this study we define an Urbanity Index (UI) at zip code level, which considers factors influencing mobility, international comparability, reproducibility as well as practical application and the development of a scalable methodology. In order to describe urbanity, data were collected regarding spatial structure, population, land use, and public transport. We developed the UI using a supervised machine learning technique which divides zip codes into four area types: (1) super-urban, (2) urban, (3) suburban/small town, (4) rural. To train the model, the perception from experts in known zip codes concerning urbanity and mobility was set as ground truth. With the UI, it is possible to compare countries (Germany and France) with a uniform definition and comparable datasets.
机译：旅行行为可以通过其空间上下文来确定。如果有许多靠近靠近的商店和餐馆，可以通过行走或骑自行车来完成各种活动，并且不需要汽车。它也更加困难（例如，停车位，交通拥堵），以在高密度区域使用汽车。总体而言，旅行行为和旅行行为的依赖性受城市的影响。到目前为止，这些关系仅被非常有选择地检查（例如，在城市等级）而不是国际比较中。在这项研究中，我们在邮政编码级别定义了城市指数（UI），其考虑了影响移动性，国际可比性，再现性以及实际应用以及可扩展方法的发展的因素。为了描述城市，有关空间结构，人口，土地利用和公共交通的数据。我们使用监督机器学习技术开发了UI，该技术将邮政编码划分为四种面积类型：（1）超城，（2）城市，（3）郊区/小镇，（4）农村。要培训模型，从有关城市和流动性的已知邮政编码的专家的看法被设定为基础事实。通过UI，可以将国家（德国和法国）与统一的定义和可比数据集进行比较。

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