Multi-Class Multi-Tiers Dasymetric Demographic Model
Date Issued
2007
Date
2007
Author(s)
Hsieh, Hsin-I
DOI
zh-TW
Abstract
The population distribution plays a crucial role in many research fields. However, the map of the population distribution is usually based on administrative divisions, and only shows the population density or the number of population in the region. Traditional maps of population distribution have problems like inadequate area size that is too large, the inapplicability of the administrative division unit in many applications and the change of the administrative boundaries over time.
Many estimation methods of the population distribution have been proposed in literatures to improve the defects of the population distribution maps. However these proposed methods have its advantages and disadvantages. Most researches use a single estimate method. Some recent researches proposed the concept of the multi-layered estimation which integrates various estimation methods in the framework.
A multi-layer and multi-class framework is proposed in this study to improve the accuracy in estimating the population distribution. Grid data structure with 40m of resolution was used. Each layer (building, land-use, and traffic accessibility layers) uses individual estimate methods (binary, multi-class, and accessibility) to estimate populations in each cell for better capture of spatial distribution of regional populations.
The proposed framework can better capture the true population distribution in comparison with the traditional population density maps based on administrative divisions. The standard deviation of estimation errors decreases from 10.39 to 9.29 in the first layer, to 8.71 in the second layer, and 8.71 in the third layer. The results are also better than decomposing the total population based on administration units, the standard deviation of errors is 9.91(town) and 8.87(village) respectively.
The numbers of cell without errors also increases and average errors in the erroneous cells decrease with improvements through layers. The mean error is 10.42 in the first layer, 8.65 in the second layer and 8.57 in the third one. It is also found that the improvement is most significant in the first layer and diminished in the following ones.
Subjects
分區密度圖
人口分布
多層
多類別
可及性
二元
dasymetric map
population distribution
multi-layer
multi-class
accessibility
binary
Type
thesis
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