A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Exploiting temporal information in parcel data to refine small area population estimates
https://orcid.org/0000-0002-1375-2792
https://orcid.org/0000-0001-5961-5809
Abstract Temporal analysis of small-area demographic data commonly relies on areal interpolation methods to create temporally consistent and compatible areal units. In this study, cadastral (parcel) data are used to identify residential land and to dasymetrically refine census tracts, with the goal of achieving more accurate small-area estimates. The built date recorded for residential parcel units is used to create residential land layers for two different time points used in the areal interpolation. Three different areal interpolation methods are employed with and without dasymetric refinement, including areal weighting (AW), target density weighting (TDW) and pycnophylactic modeling (PM). The methods interpolate tract-level population counts in Hennepin County, Minnesota, in 2000 into census tract boundaries from the year 2010. The mean absolute error, median absolute error, root mean square error and the 90th percentile of absolute error are calculated for each of the methods, and spatial variation in the interpolations are displayed in maps. Parcel-based refinements are also compared with refinements using the National Land Cover Dataset (NLCD). Results show that spatial refinement using residential parcels has the potential to improve the accuracy of areal interpolation for temporal analysis. Parcel-refined TDW out-performs the other tested methods, as well as the NLCD-refined TDW in this example. Parcel data identify residential land more reliably in rural areas. However, parcel units can have very large extents potentially biasing residential area delineation and population counts. Parcel-based refinement has the potential to further advance demographic change analysis over long time periods and large areas where the built date attribute is included in the dataset.
Highlights Three areal interpolation methods are tested with\without dasymetric refinement. The parcels built date attribute is used to determine residential land in different years. Parcel-refined target density weighting outperforms all other methods. In the study, parcel-based refinement is compared with land-cover-based refinement.
Exploiting temporal information in parcel data to refine small area population estimates
https://orcid.org/0000-0002-1375-2792
https://orcid.org/0000-0001-5961-5809
Abstract Temporal analysis of small-area demographic data commonly relies on areal interpolation methods to create temporally consistent and compatible areal units. In this study, cadastral (parcel) data are used to identify residential land and to dasymetrically refine census tracts, with the goal of achieving more accurate small-area estimates. The built date recorded for residential parcel units is used to create residential land layers for two different time points used in the areal interpolation. Three different areal interpolation methods are employed with and without dasymetric refinement, including areal weighting (AW), target density weighting (TDW) and pycnophylactic modeling (PM). The methods interpolate tract-level population counts in Hennepin County, Minnesota, in 2000 into census tract boundaries from the year 2010. The mean absolute error, median absolute error, root mean square error and the 90th percentile of absolute error are calculated for each of the methods, and spatial variation in the interpolations are displayed in maps. Parcel-based refinements are also compared with refinements using the National Land Cover Dataset (NLCD). Results show that spatial refinement using residential parcels has the potential to improve the accuracy of areal interpolation for temporal analysis. Parcel-refined TDW out-performs the other tested methods, as well as the NLCD-refined TDW in this example. Parcel data identify residential land more reliably in rural areas. However, parcel units can have very large extents potentially biasing residential area delineation and population counts. Parcel-based refinement has the potential to further advance demographic change analysis over long time periods and large areas where the built date attribute is included in the dataset.
Highlights Three areal interpolation methods are tested with\without dasymetric refinement. The parcels built date attribute is used to determine residential land in different years. Parcel-refined target density weighting outperforms all other methods. In the study, parcel-based refinement is compared with land-cover-based refinement.
Exploiting temporal information in parcel data to refine small area population estimates
https://orcid.org/0000-0002-1375-2792
https://orcid.org/0000-0001-5961-5809
Abstract Temporal analysis of small-area demographic data commonly relies on areal interpolation methods to create temporally consistent and compatible areal units. In this study, cadastral (parcel) data are used to identify residential land and to dasymetrically refine census tracts, with the goal of achieving more accurate small-area estimates. The built date recorded for residential parcel units is used to create residential land layers for two different time points used in the areal interpolation. Three different areal interpolation methods are employed with and without dasymetric refinement, including areal weighting (AW), target density weighting (TDW) and pycnophylactic modeling (PM). The methods interpolate tract-level population counts in Hennepin County, Minnesota, in 2000 into census tract boundaries from the year 2010. The mean absolute error, median absolute error, root mean square error and the 90th percentile of absolute error are calculated for each of the methods, and spatial variation in the interpolations are displayed in maps. Parcel-based refinements are also compared with refinements using the National Land Cover Dataset (NLCD). Results show that spatial refinement using residential parcels has the potential to improve the accuracy of areal interpolation for temporal analysis. Parcel-refined TDW out-performs the other tested methods, as well as the NLCD-refined TDW in this example. Parcel data identify residential land more reliably in rural areas. However, parcel units can have very large extents potentially biasing residential area delineation and population counts. Parcel-based refinement has the potential to further advance demographic change analysis over long time periods and large areas where the built date attribute is included in the dataset.
Highlights Three areal interpolation methods are tested with\without dasymetric refinement. The parcels built date attribute is used to determine residential land in different years. Parcel-refined target density weighting outperforms all other methods. In the study, parcel-based refinement is compared with land-cover-based refinement.
Exploiting temporal information in parcel data to refine small area population estimates
Zoraghein, Hamidreza (author) / Leyk, Stefan (author) / Ruther, Matthew (author) / Buttenfield, Barbara P. (author)
Computers, Environments and Urban Systems ; 58 ; 19-28
2016-03-22
10 pages
Article (Journal)
Electronic Resource
English
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