Dasymetric Modeling and Uncertainty

Nagle, Nicholas N.; Buttenfield, Barbara P.; Leyk, Stefan; Spielman, Seth E.

doi:10.1080/00045608.2013.843439

Cited by 87 publications

(57 citation statements)

References 39 publications

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“…Liu, Kyriakidis, & Goodchild, 2008;Wu & Murray, 2005). Despite this methodological progress, some challenges remain, including the identification of residential areas in rural settings (Leyk, Ruther, Buttenfield, Nagle, & Stum, 2014;Zandbergen & Ignizio, 2010) and the accurate estimation and validation of populations for various demographic attributes (Nagle, Buttenfield, Leyk, & Spielman, 2014).…”

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

Self Cite

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“…In recent years, open source data is used for the dasymetric modelling, e.g., Langford (2013) added open access ancillary data into population interpolation tools (Langford, 2013). Meanwhile, flexible ancillary data in intelligent method is used for the population estimation, such as method of intelligent dasymetric mapping (Mennis and Hultgren, 2006), maximum entropy dasymetric model (Leyk et al, 2013b;Nagle et al, 2014) and random forest modelling method (Stevens et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Scale Population Spatialization Model Constrained by Multiple Factors: A Case Study of Russia

Liu

2016

The Cartographic Journal

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Population spatialization is the foundation for the visualization and analysis of population integrated with other information, such as environmental resources, economy, and public health. The existing population spatialization models have solved many problems for population distribution, but most of these studies have focused on a specific, single-scale approach and ignored the scale transformation for population spatialization. However, multi-scale visualization and the analysis of spatial information need multi-scale information. Meanwhile, the population distribution map as one kind of thematic map is always overlaid with the digital vector map or remote sensing map and visualized in the Web Geographic Information Systems (Web GIS), so it should adapt to the map scale showed in browser, when the user zoom in and zoom out. Hence adaptive multi-scale is necessary for population spatialization. Therefore, in this study, an adaptive multi-scale population spatialization model (APSM) is proposed with comprehensive factors constrained. These factors are residential area, land cover, public transport, hydrology, terrain, and climate. All of them are closely associated with population distribution. The overall methodology of APSM and the process for APSM are expounded in this paper. Meanwhile, with a case study of Russia, the processes of APSM for Russia are stated, and a population spatialization tool is implemented for expanding the application of APSM. The experimental analysis showed that APSM satisfied the requirement of geospatial analysis well and obtained a reliable accuracy.

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“…They consist in the adoption of other reference fields -in cartograms these are imposed areas, usually administrative, in a dasymetric approach -these are areas related to the spatial distribution of the phenomenon (Goleń and Ostrowski, 1994). As a result, the representation of the spatial distribution of the depicted phenomenon with the use of cheap dasymetric methods is more reliable than in the case of a cartogram (Longley et al, 2006). In the case of population maps in a simple cartogram, the reference units are census units, in dasymetric methods -the range of development.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the usefulness land use and land cover maps for estimating the population of urban areas – valorisation of multi-variant population maps based on the GUS kilometre network

Pirowski¹,

Timek²

2019

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The paper is a continuation and summary of a series of publications related to the dasymetric estimation of the distribution of the population of Krakow. The conversion of the population from the original census units is based on the development data from three sources, the Corine Land Cover project (CLC), the Urban Atlas project (UA) and the object classification (OBIA) of the RapidEye data. The experiment was conducted using archival statistical data from 2009 from 141 urban units (u.u.) of the city.In the first two parts of the cycle population conversion was presented on the basis of CLC, UA and OBIA maps, obtaining a total of 12 maps of Krakow's population. The obtained error distributions were presented and the calculated weights of population density for each category of residential buildings were discussed. In the third part of the cycle (Pirowski and Berka, 2019) the results were analyzed in detail by reference to the reference, high-resolution population map of the Bronowice district (north-western part of the city).In this publication, ending the cycle, population maps were verified on the basis of a kilometre grid of the Central Statistical Office (GUS), which is an aggregation of data from the National Census of Population and Housing 2011, made available by the Office in 2017. The results of high-resolution verification carried out in the Bronowice district were compared with the data of the CSO (GUS). In the GUS grid the best results were obtained for surface and weight UA methods (RMSE 908-917 people; MAPE 42-46%). The estimation of population distribution using OBIA data (RMSE 1115-2073 people; MAPE 121-184%) was found to be incorrect. After the correction of OBIA by UA data, a significant improvement in the results for surface-weighted methods was obtained (RMSE 930-1067 people; MAPE 53-68%), however, the error rate was still higher than for UA itself, which eliminates the OBIA method from practical applications in this area.A correlation was found between the RMSE and MAPE errors recorded in UC at the stage of weight selection and the RMSE and MAPE errors recorded in the GUS grid, respectively R2(RMSE)=91%, R2(MAPE)=65%. Therefore, the correlation detected indicates that the low errors obtained at the selection stage translate into reliable population estimates. The proposed weighting methodology limits the subjectivity of the method, based on the minimisation of RMSE and MAPE in the original census units. The disadvantage of the method is that it is necessary to define the boundary conditions for the selection of weights, in case of obtaining unreal weights and the possibility of occurrence of equifinality phenomenon, difficult to detect in the absence of additional reference data. Słowa kluczowe: dane spisowe GUS, modelowanie dazymetryczne, mapy pokrycia i użytkowania terenu, korelacja poziomu błędów Abstrakt Artykuł jest kontynuacją i podsumowaniem cyklu publikacji związanych z dazymetrycznym szacowaniem rozmieszczenia ludności Krakowa. Przeliczanie ludności z pierwotnych jednostek spisowyc...

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Dasymetric Modeling and Uncertainty

Cited by 87 publications

References 39 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

Adaptive Multi-Scale Population Spatialization Model Constrained by Multiple Factors: A Case Study of Russia

Analysis of the usefulness land use and land cover maps for estimating the population of urban areas – valorisation of multi-variant population maps based on the GUS kilometre network

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