ABSTRACT

High-resolution population density data are crucial for advanced geographical analysis but are difficult to obtain owing to personal data protection. This paper presents a method to obtain these data through spatial disaggregation of aggregate data using random forests. Ancillary topographic data are used from open data sources, namely OpenStreetMap, Urban Atlas, and the NASA Shuttle Radar Topography Mission (SRTM). An attempt to increase disaggregation accuracy is made through a systematic conceptualization of proximity, neighborhood features. The method is implemented as a toolbox for Python and PostGIS and is tested on three cities in Central and Eastern Europe: Prague, Maribor, and Tallinn. It is shown that this approach produces more accurate predictions than other comparable approaches.

摘要

高分辨率人口密度数据对于高级地理分析至关重要，但由于个人数据保护而难以获得。本文提出了一种通过使用随机森林对聚合数据进行空间分解来获取这些数据的方法。辅助地形数据来自开放数据源，即OpenStreetMap，Urban Atlas和NASA航天飞机雷达地形任务（SRTM）。试图通过对邻近性，邻域特征的系统化概念来提高分类精度。该方法被实现为Python和PostGIS的工具箱，并在中欧和东欧的三个城市进行了测试：布拉格，马里博尔和塔林。结果表明，与其他类似方法相比，该方法可产生更准确的预测。