Population estimation using remote sensing and GIS technologies

Wang, Le; Wu, Changshan

doi:10.1080/01431161.2010.496809

Cited by 24 publications

(15 citation statements)

References 12 publications

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“…Nonetheless, the knowledge of these aspects is essential for understanding and responding to problems in many fields (Wang & Wu, 2010). Obtaining proper cartographic representations of population distribution is an issue that dates back to the mid nineteenth century, when the first censuses and the earliest developments in thematic cartography emerged.…”

Section: Introduction: the Dasymetric Mapping Fundamentalsmentioning

confidence: 99%

A high-resolution population grid map for Europe

2013

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Population figures are usually collected by national statistical institutes at small enumeration units (e.g. census tracts or building units). However, still for many countries in Europe, data are distributed at coarser geographical units like municipalities. This level of resolution is insufficient for analysis in many fields. In addition, the heterogeneity of the size of the geographical units causes great distortions in analysis, i.e. the Modifiable Areal Unit Problem (MAUP). Dasymetric mapping techniques have long been applied worldwide to derive finer (and MAUP-free) depictions of the population distribution. These techniques disaggregate population figures reported at coarse source zones into a finer set of zones using ancillary geographical data. Previous attempts to map the European population at high resolution have used CORINE Land Cover (CLC) as the main source of ancillary data. In this article, we test new geographical datasets to produce an updated and improved European population grid map. It is tested whether using more detailed ancillary data in the dasymetric mapping significantly yields higher accuracies. As final outcome of this cartographic exercise, a European population grid map for the reference year of 2006, with a spatial resolution of 100 × 100 meters, is presented and validated against reference data. Resident population reported at commune level, a refined version of CLC and information on the soil sealing degree are used as the main inputs to produce the final map.

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Section: Introduction: the Dasymetric Mapping Fundamentalsmentioning

confidence: 99%

A high-resolution population grid map for Europe

2013

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“…In recent years, much research has been devoted to small area population estimation methods that utilize remote sensing and GIS technologies (Wu et al, 2005;Wang and Wu, 2010). Dasymetric mappingthe redistribution of areal enumeration data using ancillary information for the display of statistical surface dataonce confined to visualizing population distribution, is also increasingly being used for estimating the population of small areas (Mennis, 2009;Petrov, 2012).…”

Section: Methods For Small Area Population Estimatesmentioning

confidence: 99%

Demographic Techniques: Small-area Estimates and Projections

Rayer

2015

International Encyclopedia of the Social &Amp; Behavioral Sciences

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“…The first, basic problem is to find a correct way to convert the population from census units to units related to buildings (so-called limiting variables), including the diversity of buildings (so-called variable relationships) resulting from their nature (density, height, location, function, etc.). A more extensive overview of the conversion methods used can be found, for example, in works by Eicher and Brewer (2001), Wu et al (2005), Wang and Wu (2010), Całka et al (2016).…”

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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Population estimation using remote sensing and GIS technologies

Cited by 24 publications

References 12 publications

A high-resolution population grid map for Europe

A high-resolution population grid map for Europe

Demographic Techniques: Small-area Estimates and Projections

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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