Comparison of Dasymetric Mapping Techniques for Small-Area Population Estimates

Zandbergen, Paul A.; Ignizio, Drew A.

doi:10.1559/152304010792194985

Cited by 107 publications

(88 citation statements)

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“…The different types of ancillary data used in dasymetric modeling include road network density (Reibel & Bufalino, 2005), Landsat TM imagery (Harvey, 2002) and cadastral data (Maantay et al, 2007). In addition, research has been carried out using combinations of different types of data, such as land-cover, imperviousness, road networks, and nighttime lights (Zandbergen & Ignizio, 2010) or address points and parcels (Tapp, 2010).…”

Section: Dasymetric Modelingmentioning

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

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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.

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Section: Dasymetric Modelingmentioning

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

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“…The more often land cover/land use data derived from satellite imageries (Eicher and Brewer, 2001;Mennis, 2003;Ciołkosz and Bielecka, 2005;Gallego, 2010;Azar et al, 2013;Langford, 2013), topographic data (Su et. Al., 2010;Wu et al, 2008), street network (Zandbergen and Ignizio, 2010) or even cadastral data (Maantay et al, 2007) are used as ancillary information. However, recently buildings extracted from LIDAR data (Sridharan and Qiu, 2013) or imperviousness layer Azar et al, 2010;Wu and Murray, 2005) are the source of additional information for the purposes of dasymetric population modelling.…”

Section: Introductionmentioning

confidence: 99%

Redistribution population data across a regular spatial grid according to buildings characteristics

Całka

Bielecka

Zdunkiewicz

2016

Geodesy and Cartography

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Population data are generally provided by state census organisations at the predefi ned census enumeration units. However, these datasets very are often required at userdefi ned spatial units that differ from the census output levels. A number of population estimation techniques have been developed to address these problems. This article is one of those attempts aimed at improving county level population estimates by using spatial disaggregation models with support of buildings characteristic, derived from national topographic database, and average area of a fl at. The experimental gridded population surface was created for Opatów county, sparsely populated rural region located in Central Poland. The method relies on geolocation of population counts in buildings, taking into account the building volume and structural building type and then aggregation the people total in 1 km quadrilateral grid. The overall quality of population distribution surface expressed by the mean of RMSE equals 9 persons, and the MAE equals 0.01. We also discovered that nearly 20% of total county area is unpopulated and 80% of people lived on 33% of the county territory.

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“…Other types of lower-resolution ancillary data like spectral and/or textural metrics obtained from remotely sensed data (Harvey, 2002;Li & Weng, 2005;Liu, Clarke, & Herold, 2006) or demographic information and distance-to-services metrics (Deng, Wu, & Wang, 2010) have also been applied, but the results obtained in these studies suffered from low accuracies. Impervious surface fractions, on the other hand, have proven to perform equally well as or even better than land-use data as a source for disaggregating population data (Lu, Weng, & Li, 2006;Wu & Murray, 2007;Zandbergen & Ignizio, 2010). Recently, nonparametric modelling has been applied to disaggregate population based on a large number of remotely sensed and other geospatial variables (Patel et al, 2015;Stevens, Gaughan, Linard, & Tatem, 2015).…”

Section: Introductionmentioning

confidence: 99%

Incorporating spatial non-stationarity to improve dasymetric mapping of population

Cockx

Canters

2015

Applied Geography

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Comparison of Dasymetric Mapping Techniques for Small-Area Population Estimates

Cited by 107 publications

References 0 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

Redistribution population data across a regular spatial grid according to buildings characteristics

Incorporating spatial non-stationarity to improve dasymetric mapping of population

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