Abstract
This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize straightforward point-to-point matching. Using our approach, in each point data set, two geometric features (i.e., the distance and angle from the centroid) were calculated and represented as probability density functions (PDFs). The PDF similarity of each geometric feature was measured using nine metrics, with values ranging from zero (very contrasting) to one (exactly the same). The overall similarity was defined as the average of the distance and angle similarities. In terms of sensibility, the method was shown to be capable of measuring, at a human visual sensing level, two pairs of hypothetical patterns, presenting reasonable results. Meanwhile, in terms of the method′s sensitivity to both spatial and temporal displacements from the hypothetical origin, the method is also capable of consistently measuring the similarity of spatial and temporal patterns. The application of the method to assess both spatial and temporal pattern similarities between two deforestation data sets with different resolutions was also discussed.
Highlights
The similarity between multiple spatial patterns has often been approached by a ‘distance0 concept, often after normalization in which the range is used as a unitary metric
The overall similarity was calculated as the average probability density functions (PDFs) similarity for θ and δ
We developed a generic method for measuring the spatial and temporal pattern similarity between deforestation data sets with different spatial resolutions based on similarity-based pattern analysis
Summary
The similarity between multiple spatial patterns (or their representations) has often been approached by a ‘distance0 concept, often after normalization in which the range (maximum–minimum) is used as a unitary metric. Direct visualization is more challenging, but Euclidean distances can be readily calculated. Measurement of the spatial agreement between deforestation maps from various producers is usually carried out based on the point-to-point degree of matching (i.e., with regard to their omission and commission disagreements; see, e.g., [1,2,3,4,5]). Spatial discrepancies due to omission and commission disagreements between deforestation maps from various producers often trigger political debates, regardless of the overall number, the temporal trend, the spatial pattern, or the operational forest definition (threshold) used, which are mostly counterproductive to efforts for providing salient, legitimate, and credible data on deforestation for relevant policy formulation as guidance on the ground [6]
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