2005 , Vol. 60 >Issue 1: 158 - 164

• 1. 清华大学水沙科学教育部重点实验室,北京 100084;
2. 郑州市国土资源局,郑州 450006;
3. 中国测绘科学研究院,北京100039

修回日期: 2004-05-29

网络出版日期: 2005-01-25

Modeling Urban Population Density with Remote Sensing Imagery

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• 1. School of Civil Engineering, Tsinghua University, Beijing 100084, China;
2. Zhengzhou Land Resource Bureau, Zhengzhou 450006, China;
3. Chinese Academy of Surveying and Mapping, Beijing 100039, China

Received date: 2004-01-21

Revised date: 2004-05-29

Online published: 2005-01-25

Supported by

SDPC Hi-tech Application Project in 2000 (Technology Support System of China PGIS Construction)

### Abstract

The population in the study region is the sum of the area of each habitation type multiplying its respective sampled population density. The population density of each habitation type is estimated. In some experimental region, there is mathematical relation among the area, the population density and its total population of each habitation type. The population density of each habitation type can be estimated via the mathematical relation. A new land use density method is proposed based on least square principle. The main idea is: First, habitation type is defined in the study region; and the boundaries of all the habitation types are lined out based on remote sensing imagery and the area of each habitation type are calculated; then mathematical models according to the population data of every sub-region are established; the best population density estimation of each habitation type is calculated with the least square principle when the number of sub-regions is more than the number of habitation types. The population estimation of any region can be calculated since the population density of each habitation type is known as well. The method need not sample the population density of each habitation type. The estimation workload of population density of each habitation type is low. The mathematical models are not influenced by random error of samples.

### 参考文献

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