空间点数据集中密度模式分类的新总体和实际密度见解

IF 2.3 Q2 REMOTE SENSING
Shazad Jamal Jalal
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引用次数: 0

摘要

空间科学的一个重要方面是确定和分类空间点数据的密度模式。这一过程包括确定整个地理区域的点覆盖率(PCR),这对于分析各种空间科学相关主题至关重要。然而,在点的公共密度值中,通常不考虑整个区域中的点和空区域位置。因此,本研究引入了新的概念和公式来计算总密度和实际密度(Dg和Da),使用点之间的最小距离来确定个体和多个整个区域的PCR。该方法是在一个假设数据集上实施的,该数据集包括10个场景和两个不同的数据集,包括尼日利亚第1区(东部和南部各州)和第2区(北部和西部各州,包括联邦首都地区(FCT),阿布贾)的45,443个农村聚落群。因此,10和5尺度的密度模式可以定量表征实际密度利用PCR。这有助于更好地定量分析单个或多个空间点数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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 (Dg and Da) 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.

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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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