A 100 m hexagonal statistical grid for Santa Catarina, Brazil: cell geometry, dasymetric population allocation, and functional establishment classification

A regular hexagonal statistical grid with a 100 m cell diameter covering the entire state of Santa Catarina, southern Brazil (11,024,064 cells), together with 2022 Demographic Census attributes transferred to cells through address-weighted dasymetric allocation, and a functional classification of establishment counts derived from the Brazilian National Address Registry for Statistical Purposes (CNEFE).The grid was built as a stable, high-resolution spatial support for territorial analysis that must integrate urban, rural and natural areas and remain comparable over time. Because the cells are geographically fixed — unlike census tracts and municipal boundaries, which are redrawn at every census — heterogeneous data sources can be co-registered on a single geometry and longitudinal comparison becomes meaningful. Because hexagonal tessellation has unambiguous uniform adjacency (six equidistant neighbours per cell), the grid can be treated directly as an undirected graph, supporting step- and ring-based neighbourhood metrics without the arbitrary contiguity choices that rectangular lattices impose.The grid is a planar projected tessellation in SIRGAS 2000 / UTM zone 22S (EPSG:31982), aligned to the reference frame of the Brazilian statistical grid rather than to an icosahedral discrete global grid system, a deliberate trade-off favouring interoperability and metric exactness within the UTM zone over global coverage. Census counts published by census tract were apportioned to cells using dwelling counts from CNEFE address points as the distribution ratio, in a pycnophylactic dasymetric estimator; establishment records were reclassified from eight into fourteen functional classes; and settlements were delineated empirically by Voronoi tessellation and classified into five hierarchical levels.This is a derived, modelled product, not microdata: cell values are dasymetric estimates apportioned from published aggregate totals, not observed counts. Full methodological detail, the attribute dictionary and known limitations are given in the README.Funded by the Government of the State of Santa Catarina through FAPESC.