The MerQur Map Tab: Spatial Density, Autocorrelation and Hot-Spot Analyses
DOI:
https://doi.org/10.53463/mjdsm.20260461Keywords:
spatial analysis, KDE, Hexbin, Moran's I, spatial autocorrelation, Getis Ord Gi*, DBSCANAbstract
Spatial data — where each observation is tied to a geographic location (latitude-longitude) — violates the independence assumption of ordinary statistics: “everything is related to everything else, but near things are more related than distant things” (Tobler’s first law of geography). This study introduces in detail the five spatial analyses offered in the Map tab of the MerQur desktop software: KDE (kernel density estimation), Hexbin (hexagonal density), Getis-Ord Gi* (local hot/cold spots), DBSCAN (density-based spatial clustering) and Moran’s I (global spatial autocorrelation). For each method, the following are presented: (i) the spatial question it answers and its application context, (ii) required inputs (coordinates, value field, neighborhood/bandwidth) and assumptions, (iii) form fields in the MerQur Map tab, (iv) reported statistics (Moran’s I/z, Gi* hot-/cold-spot counts, cluster count), and (v) an interpretation guide for a typical research question. All worked examples were produced with real MerQur output on the synthetic Landscape Architecture dataset distributed with MerQur. Overall, the MerQur Map tab presents a complete spatial-exploration workflow — from visualizing point data (KDE/Hexbin) to statistically diagnosing spatial clustering (Moran’s I), mapping the exact location of clusters (Getis-Ord Gi*) and density-based grouping (DBSCAN) — within a single interactive map interface.
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