A Multi-Layer and Multi-Class Dasymetric Model for Reconstructing Spatial Population Distribution
Date Issued
2011
Date
2011
Author(s)
Lin, Mei-Chun
Abstract
Detailed and correct spatial distributions of population are the foundation for sound regional planning and management decisions. Population data are usually disseminated in aggregated form for confidentiality concerns. However, this approach of spatial aggregation may distort the original spatial pattern from the modified areal unit problem (MAUP). And the frequent changes of boundaries over time may make the across temporal analysis impossible. It is difficult to estimate population at risk in disaster because boundary of the disaster area may not coincide with the population aggregation units. Although population at risk may be estimated using areal interpolation method, errors may arise from unreasonable assumption of uniform distribution in the aggregation area. Effective algorithms to disaggregate the aggregated population into smaller spatial units get more and more important.
A Multi-Layer Multi-Class Daymetric (MLMCD) model was developed in this study to reconstruct spatial distributions from spatially aggregated population data. Ancillary data, such as remote sensing imageries, census, land use, traffic network and other infrastructure were used to disaggregate the aggregated population data into smaller grids. These disaggregated grid data were then summed up to different spatial levels for error comparisons. Mean Absolute Percentage Error (MAPE) was used to examine the effectiveness of the proposed MLMCD model in this population disaggregation process. Error matrix and Kappa Index as in remote sensing were used to compare the spatial distribution pattern using hotspot analysis.
From the case study in Taipei metropolitan area, the results show the error is decreased as the layer increased and more ancillary data were used. The MAPE are significantly improved from layer 0 to layer 3. MAPE decreased from 0.99 to 0.13 (compared at grid level), from 0.866 to 0.583 (compared at census tract level) and from 0.809 to 0.458 (compared at Li administration level). Besides, the increases of Kappa indices from 0.351 to 0.814 (at grid level) and from 0.669 to 0.888 (at census tract level) shows that the proposed MLMCD model effectively preserve the spatial distribution characteristics of population in the disaggregation process.
Subjects
population
disaggregate
multi-layer multi-class dasymetric model (MLMCD)
grid
error
Type
thesis
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