Population density is one of the key parameters for assessing the magnitude of population exposed to risk, and the better quality data we have, the better the assessment of risk. The aim of this study is to elaborate a high-resolution spatially distributed population density grid, which estimates population at the commune scale with a reliability of over 90%. The novelty of the approach is population density estimation in a regular European grid, based on buildings vector data collected in the national topographic database. Using abductive reasoning in combination with statistics and spatial analysis, the authors extract approximate information about a population from the large-scale topographic data. Moreover, linking the obtained population data with the cadastral databy unique building identifierallows for regular, quick and census surveyindependent updates of the population surface. A shortcoming of the approach is the issue of the possible existence of two houses per family, which leads to an overestimation of population. However, in the study area it affected only two of the total 14 communes by 7%-9%.
The aim of this study is to describe uncertainty of the Global Rural-Urban Mapping Project (GRUMP) data based
on Polish population reference grid created by the Central Statistical Office of Poland, using INSPIRE grid coding system. The
adopted population data uncertainty analysis methodology combined three different approaches, i.e. simple change detection
algorithm to obtain discrepancies at the grid cell level, statistical analytical approach to investigate these discrepancies’ frequency
distribution, and GIS approach to analyse spatial pattern of distinguished population difference classes. The results
showed significant differences in population count at the grid cell level. The maximum magnitude of GRUMP vs. Polish
Reference Grid overestimation equals 4087 people per 1 sq. km, while the underestimation equals 20,086 people per 1 sq. km.
Very few grid cell shows no difference in population count, i.e. 1.5% of total grid cell count. GRUMP data overestimates
Polish total population by 0.15%, while it underestimates the average population density by 50%. The highest population
underestimations were identified in the centers of the cities, while suburban areas were characterised by the large and regular
population overestimations within GRUMP dataset. These GRUMP dataset imperfections can be attributed to country-specific
administrative divisions and to the varying effectiveness of the urban centers delimitation mapping using the night sky light
intensity, including blooming effects as well as not frequently illuminated small settlements.
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