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.

The objective of this paper is to manifest the intellectual and cognitive structure of CORINE Land Cover (CLC) research applications. Data from the Web of Science (WoS) was used to delimit publication on CLC during the period from 1985 until 2019 (29th April), retrieving a total of 873 documents. Through author citations, the origins and the most influential papers were identified. The main lines of research were identified from word co-occurrences extracted from the titles, keywords, and abstracts of the papers. In the view of both structures, it can be concluded that CORINE land cover constitutes a relatively young set of scientific data, with a constant expansion and a strongly interdisciplinary structure. The development of this application is dependent on the knowledge of such research areas as geography, remote sensing, ecology, forestry, agriculture, engineering, optics, and/or computer science. We believe that this information could be very useful for CLC users, as it reflects a large-scale analysis of the research lines of CLC and illuminates how research has changed over time in diverse areas of applications. Moreover, this study is intended to offer a useful tool for the CLC scientific community, showcasing the main research lines and the most noteworthy papers. Finally, the methodology used in this study can be replicated in many other fields of science to explore its intellectual and cognitive structure.

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 data – by unique building identifier – allows for regular, quick and census survey-independent 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 paper aimed to express the cognitive and intellectual structure of research executed in the field of GIS-based land use change modeling. An exploration of the Web of Science database showed that research in GIS spatial analysis modeling for land use change began in the early 1990s and has continued since then, with a substantial growth in the 21st century. By science mapping methods, particularly co-coupling, co-citation, and citation, as well as bibliometric measures, like impact indices, this study distinguishes the most eminent authors, institutions, countries, and journals in GIS-based land use change modeling. The results showed that GIS-based analysis of land use change modeling is a multi- and interdisciplinary research topic, as reflected in the diversity of WoS research categories, the most productive journals, and the topics analyzed. The highest impact on the world sciences in the field have can be attributed to European Universities, particularly from The Netherlands, Belgium, Switzerland, and Great Britain. However, China and the United States published the highest number of research papers.

The Global Human Settlement Population Grid (GHS-POP) the latest released global gridded population dataset based on remotely sensed data and developed by the EU Joint Research Centre, depicts the distribution and density of the total population as the number of people per grid cell. This study aims to assess the GHS-POP data accuracy based on root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) and the correlation coefficient. The study was conducted for Poland and Portugal, countries characterized by different population distribution as well as two spatial resolutions of 250 m and 1 km on the GHS-POP. The main findings show that as the size of administrative zones decreases (from NUTS (Nomenclature of Territorial Units for Statistics) to LAU (local administrative unit)) and the size of the GHS-POP increases, the difference between the population counts reported by the European Statistical Office and estimated by the GHS-POP algorithm becomes larger. At the national level, MAPE ranges from 1.8% to 4.5% for the 250 m and 1 km resolutions of GHS-POP data in Portugal and 1.5% to 1.6%, respectively in Poland. At the local level, however, the error rates range from 4.5% to 5.8% in Poland, for 250 m and 1 km, and 5.7% to 11.6% in Portugal, respectively. Moreover, the results show that for densely populated regions the GHS-POP underestimates the population number, while for thinly populated regions it overestimates. The conclusions of this study are expected to serve as a quality reference for potential users and producers of population density datasets.