Fine-resolution population mapping using OpenStreetMap points-of-interest
Fine-resolution population mapping using OpenStreetMap points-of-interest
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DOI：
10.1080/13658816.2014.909045
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发表时间：
2014-01-01
影响因子：
5.7
通讯作者：
Zipf, Alexander
中科院分区：
文献类型：
--
作者：
Bakillah, Mohamed;Liang, Steve;Zipf, Alexander
建筑物层面的人口数据是需要用于各种目的。然而，为了保护隐私，政府人口数据是汇总的。更精细尺度的人口估计数可以通过面积插值法获得，这是一个将数据从第一个空间单位系统转移到另一个系统的过程。可以利用辅助数据进行面积插值，以指导人口的重新分布。对于建筑物级别的人口估计，常见的辅助数据包括通过昂贵的过程（如LiDAR）获得的建筑物三维数据。与此同时，自愿提供的地理信息正在作为一种新的数据类别出现，并已用于与城市管理有关的目的。本文的目的是提出一种替代的方法，使用VGI作为辅助数据的建筑物水平面积插值。所提出的方法集成了现有的插值技术，即，多类dasymetric映射和插值表面体积集成;从OpenStreetMap（OSM）中提取的建筑物足迹和兴趣点（POI）数据用于改进建筑物级别的人口估计。以汉堡市为例进行了案例研究，并使用不同类型的POI对结果进行了比较。结果表明，VGI可以用来准确地估计人口分布，但需要进一步的研究来了解POI如何揭示人口分布模式。
Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.