ABSTRACT

This paper proposes a new method for urban population density prediction at 30 m resolution. Using data for Bangalore, the paper demonstrates that population within each 30 m residential built-up cell can be modeled as a function of cell-level data on street density and building heights and ward-level data on car ownership. Building-height data were generated from Cartosat-1 stereo imagery using an open-source satellite stereo image processing software. Using this building-height data in conjunction with the other datasets, the paper demonstrates that a 30 m resolution population density surface can be generated such that, when summed to the ward level, the median absolute percentage error between predicted population and known census population at the ward level is 8.29%. The paper also shows that the relationship between population density, street density, building height, and ward level car ownership is spatially non-stationary. A fine-grained understanding of urban population densities, as enabled by the proposed method, can be beneficial to research, policy, and practice related to cities.

摘要

本文提出了一种30 m分辨率的城市人口密度预测新方法。利用班加罗尔的数据，该论文表明，可以将每个30 m居民楼内的人口建模为街道密度和建筑物高度的小区级数据以及汽车拥有量的病房级数据的函数。建筑高度数据是使用开源卫星立体图像处理软件从Cartosat-1立体图像生成的。结合使用该建筑高度数据和其他数据集，该论文表明可以生成30 m分辨率的人口密度表面，这样，当加总到病房水平时，预测人口与已知普查人口之间的中值绝对百分比误差为病房率为8.29％。该论文还表明人口密度之间的关系，街道密度，建筑物高度和病房级别的汽车在空间上是不稳定的。通过所提出的方法，对城市人口密度的细粒度了解可以有益于与城市有关的研究，政策和实践。