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Reliability Analysis of LandScan Gridded Population Data. The Case Study of Poland

Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland
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ISPRS Int. J. Geo-Inf. 2019, 8(5), 222; https://doi.org/10.3390/ijgi8050222
Received: 22 March 2019 / Revised: 23 April 2019 / Accepted: 4 May 2019 / Published: 8 May 2019
The issue of population dataset reliability is of particular importance when it comes to broadening the understanding of spatial structure, pattern and configuration of humans’ geographical location. The aim of the paper was to estimate the reliability of LandScan based on the official Polish Population Grid. The adopted methodology was based on the change detection approach, spatial pattern and continuity analysis, as well as statistical analysis at the grid-cell level. Our results show that the LandScan data can estimate the Polish population very well. The number of grid cells with equal people counts in both datasets amounts to 10.5%. The most and highly reliable data cover 72% of the country territory, while less reliable ones cover only 4.3%. The LandScan algorithm tends to underestimate people counts, with a total value of 79,735 people (0.21%). The highest underestimation was noticed in densely populated areas as well as in the transition areas between urban and rural, while overestimation was observed in moderately populated regions, along main roads and in city centres. The underestimation results mainly from the spatial pattern and size of Polish rural settlements, namely a big number of shadowed single households dispersed over agricultural areas and in the vicinity of forests. An excessive assessment of the number of people may be a consequence of the well-known blooming effect. View Full-Text
Keywords: global population data; uncertainty; change detection; disparity indices; spatial pattern global population data; uncertainty; change detection; disparity indices; spatial pattern
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Calka, B.; Bielecka, E. Reliability Analysis of LandScan Gridded Population Data. The Case Study of Poland. ISPRS Int. J. Geo-Inf. 2019, 8, 222.

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