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

Abstract

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.

GHS-POP Accuracy Assessment: Poland and Portugal Case Study

“…As a result of the difficulty of interpreting built-up areas, the GHS-POP underestimates the population number for densely populated regions, while for thinly populated ones it overestimates. A similar relation was noticed by Calka and Bielecka [33] analyzing LandScan data.…”

“…The absolute estimation error (AEE) was used for the analysis. It measures the total difference between number of people in administrative units and the population number from corresponding spatial grid cells from the GHS-POP; it is expressed as Equation (1) [33]:…”

Earth Observation for the Implementation of Sustainable Development Goal 11 Indicators at Local Scale: Monitoring of the Migrant Population Distribution

“…Uncertainty of the output maps is especially important for decision making and was discussed previously in many papers [10,32,34]. MAE and TAE values were used in our paper to verify that the aggregation of output cell data reproduced the totals for the original census areas [7].…”

“…Thus, the distribution map of regular migrant population obtained may have underestimated population counts, as explained in [7]. In fact, cells occupied by tall buildings may host more people than the ones occupied by lower buildings [7,34]. Information height could be obtained from available Laser Imaging Detection and Ranging (LIDAR) data, which are costly, or from the new Copernicus building-heights dataset.…”

“…In order to handle the dasymetric modeling uncertainty related to the output map, two indicators were used, i.e., the Mean Absolute Error (MAE) and the Total Absolute Error (TAE), as described elsewhere [10,34]. These indicators were computed by comparing the input population value provided for each census zone (P j,census ) with the sum of all pixels population output values included in each census zone (P j ).…”