Novel gross and actual density insights for density pattern classification in spatial point datasets - Jalal, Shazad Jamal (Applied geomatics, 2025)
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.