Novel gross and actual density insights for density pattern classification in spatial point datasets

An essential aspect of spatial science is determining and classifying density patterns in spatial point data. This process includes determining the point coverage ratio (PCR) across the entire geographical area, which is crucial for analysing various spatial science-related topics. Nevertheless, the...

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Veröffentlicht in:Applied geomatics Jg. 17; H. 4; S. 791 - 802
1. Verfasser: Jalal, Shazad Jamal
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2025
Springer Nature B.V
Schlagworte:
Artificial intelligence
Bandwidths
Datasets
Density
Earth and Environmental Science
Geographical Information Systems/Cartography
Geography
Geophysics/Geodesy
Land area
Machine learning
Mapping
Measurement Science and Instrumentation
Nucleotide sequence
Pattern classification
Remote Sensing/Photogrammetry
Spatial analysis
Surveying
Spatial point dataset
Net density
Actual density
Spatial gridding
Gross density
Density pattern, rural settlement cluster, Nigeria
ISSN:1866-9298, 1866-928X
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Zusammenfassung:An essential aspect of spatial science is determining and classifying density patterns in spatial point data. This process includes determining the point coverage ratio (PCR) across the entire geographical area, which is crucial for analysing various spatial science-related topics. Nevertheless, the point and the empty area positions in a whole area are usually not considered in the common density value of points. Therefore, this study introduced novel concepts and formulas for calculating the gross and actual densities ( D g and D a ) using the minimum distance between points to determine the PCR for individuals and multiple entire areas. The methodology was implemented on a hypothetical dataset comprising 10 scenarios and two distinct datasets, including 45,443 rural settlement clusters across Region1 (the eastern and southern states) and Region 2 (northern and western states, which includes the Federal Capital Territory (FCT), Abuja) of Nigeria. Consequently, the 10 and five-scale density patterns could quantitatively characterise the actual density utilising the PCR. This contribution assists in better analysing single or multiple spatial point datasets quantitatively.
Bibliographie:ObjectType-Article-1
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ISSN:1866-9298
1866-928X
DOI:10.1007/s12518-025-00646-2