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Vector fields as a framework for modelling the mobility of commodities

Fig 6

Distribution and maps of trading distances and spatial lag values.

(A) Municipalities are grouped into five predefined distance-based clusters: short (50 km), short–medium (50–100 km), medium (100–150 km), medium–long (150–200 km), and long (>200 km). For each municipality, the trade vector magnitude was computed as the mean of all monthly vectors across four years. The x and y components of the vectors were computed as the Euclidean differences between the latitude and longitude coordinates of the points and then converted into distance values (km). The distributions of vector magnitudes and distances for each cluster are shown. (B) Map of municipalities coloured by distance-behaviour cluster, with trade vector magnitudes and distances represented as in (A). (C) Spatial lag values of the vector magnitudes in (B), calculated using Queen-based spatial weights. These highlight municipalities whose values differ from their neighbours, revealing patterns such as ‘doughnuts’ (low values encircled by high ones) and ‘diamonds’ (high values encircled by low ones). Spatial lag values are grouped into five ranges based on the boxplot distribution of all 853 municipalities. The base maps in this figure are freely available (not copyrighted) [34].

doi: https://doi.org/10.1371/journal.pone.0340109.g006