Comparing the effects of an NLCD-derived dasymetric refinement on estimation accuracies for multiple areal interpolation methods

Ruther, Matt; Leyk, Stefan; Buttenfield, Barbara P.

doi:10.1080/15481603.2015.1018856

Cited by 26 publications

(38 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Dasymetric techniques have recently been examined to refine areal interpolation over multiple time periods. Buttenfield, Ruther, and Leyk (2015) and Ruther, Leyk, and Buttenfield (2015) compared different areal interpolation techniques with dasymetrically refined source zones using developed land classes from the National Land Cover Database (NLCD). Their results demonstrated that dasymetrically refined and unrefined methods performed differently for shorter and longer time periods, with lower error rates in most cases when dasymetric refinement was used.…”

Section: Introductionmentioning

confidence: 99%

“…Buttenfield et al (2015) showed that improvements can manifest in some areas whether the dasymetric method refines source or target estimates, or both. Ruther et al (2015) included four different demographic settings in their study, and found distinct spatial and temporal variations in the error of estimates for different demographic conditions. High estimation errors were discovered in fast growing subregions, in areas that were more fully developed for both the source and target time periods, and in rural settings where NLCD is known to underestimate developed residential land.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting temporal information in parcel data to refine small area population estimates

Zoraghein

Leyk

Ruther

et al. 2016

Computers, Environment and Urban Systems

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploiting temporal information in parcel data to refine small area population estimates

Zoraghein

Leyk

Ruther

et al. 2016

Computers, Environment and Urban Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…It leverages additional complementary ancillary variables such as NLCD developed classes (21, 22, and 23) and road network buffer zones (100m buffer distance). The NLCD class selection follows Ruther et al (2015) for delineating refined areas.…”

Section: Third Spatial Refinementmentioning

confidence: 99%

“…Therefore, recent studies have developed spatially refined interpolation techniques with the objective of decreasing population estimation errors Ruther et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal enabled Content-based Image Retrieval

Belgiu¹,

Sudmanns²,

Tiede³

et al. 2016

GIScience

View full text Add to dashboard Cite

Wireless Sensor Networks (WSNs) are widely used for monitoring and observation of dynamic phenomena. A sensor in WSNs covers only a limited region, depending on its sensing and communicating ranges, as well as the environment configuration. For efficient deployment of sensors in a WSN the coverage estimation is a critical issue. Probabilistic methods are among the most accurate models proposed for sensor coverage estimation. However, most of these methods are based on raster representation of the environment for coverage estimation which limits their quality. In this paper, we propose a probabilistic method for estimation of the coverage of a sensor network based on 3D vector representation of the environment.

show abstract

“…Areal interpolation coupled with spatial refinement has been demonstrated as an effective approach to reduce estimation errors in temporally interpolating population enumerated in a set of source zones (source census year) to target zones defined by the boundaries of the target census year (e.g. Ruther et al 2015;Zoraghein et al 2016). In this study, this approach is tested for estimating urban population of census tracts in 1990 and 2000 (i.e., the source zones) within census tract boundaries in 2010 (i.e., the target zones) using different ancillary variables for spatial refinement to create a temporally consistent time series of urban population distributions at the tract level.…”

Section: Introductionmentioning

confidence: 99%

Estimating changes in urban land and urban population using refined areal interpolation techniques

Zoraghein

Leyk

2018

Proc. Int. Cartogr. Assoc.

Self Cite

View full text Add to dashboard Cite

Abstract:The analysis of changes in urban land and population is important because the majority of future population growth will take place in urban areas. U.S. Census historically classifies urban land using population density and various land-use criteria. This study analyzes the reliability of census-defined urban lands for delineating the spatial distribution of urban population and estimating its changes over time. To overcome the problem of incompatible enumeration units between censuses, regular areal interpolation methods including Areal Weighting (AW) and Tar-get Density Weighting (TDW), with and without spatial refinement, are implemented. The goal in this study is to estimate urban population in Massachusetts in 1990 and 2000 (source zones), within tract boundaries of the 2010 census (target zones), respectively, to create a consistent time series of comparable urban population estimates from 1990 to 2010. Spatial refinement is done using ancillary variables such as census-defined urban areas, the National Land Cover Database (NLCD) and the Global Human Settlement Layer (GHSL) as well as different combi-nations of them. The study results suggest that census-defined urban areas alone are not necessarily the most meaningful delineation of urban land. Instead, it appears that alternative combinations of the above-mentioned ancillary variables can better depict the spatial distribution of urban land, and thus make it possible to reduce the estimation error in transferring the urban population from source zones to target zones when running spatially-refined temporal areal interpolation.

show abstract

Comparing the effects of an NLCD-derived dasymetric refinement on estimation accuracies for multiple areal interpolation methods

Cited by 26 publications

References 36 publications

Exploiting temporal information in parcel data to refine small area population estimates

Exploiting temporal information in parcel data to refine small area population estimates

Spatiotemporal enabled Content-based Image Retrieval

Estimating changes in urban land and urban population using refined areal interpolation techniques

Contact Info

Product

Resources

About