Syn-SC: Generating High-Volume Synthetic Point Data with Target Continuity and Smoothness

Resumo

When point data is aggregated and depicted on maps, the choice of thematic mapping technique depends on the cartographer's skill and the spatial structure of the data. MacEachren and DiBiase (1991) argued that two structural variables deserve explicit attention: continuity (the proportion of space occupied by events) and smoothness (the degree of variation between neighbouring locations). However, empirical studies seldom explicitly isolate these variables because real-world datasets rarely span a convenient range of values. Syn-SC closes this gap; it is a self-contained QGIS 3 Processing plug-in which synthesizes high-volume point datasets, and the continuity and smoothness of these can be defined independently. The Scale Assistant tool tessellates any area of interest with size-adaptive hexagons and reports a two-value smoothness window — floor and ceiling — defined by the binary extreme levels 1 and 100. A rule-based dispatcher then selects one of three generative solvers: an exhaustive brute-force solver for small grids of up to 16 cells, a checkerboard heuristic for large grids, and an iterative optimizer that rapidly converges whenever the requested smoothness lies within the reported window or below. Benchmarks demonstrate that Syn-SC matches the requested continuity precisely, achieves smoothness targets within ±1 percentage point and generates sets approaching one million points in seconds. Syn-SC, therefore, provides cartographers, usability researchers, and AI developers with shareable, perceptually parameterised point datasets that were previously unavailable.