Abstract. With new forms of digital spatial data driving new
applications for monitoring and understanding environmental change,
there are growing demands on traditional GIS tools for spatial data
storage, management and processing. Discrete Global Grid System (DGGS)
are methods to tessellate globe into multiresolution grids, which
represent a global spatial fabric capable of storing heterogeneous
spatial data, and improved performance in data access, retrieval, and
analysis. While DGGS-based GIS may hold potential for next-generation
big data GIS platforms, few of studies have tried to implement them as a
framework for operational spatial analysis. Cellular Automata (CA) is a
classic dynamic modeling framework which has been used with traditional
raster data model for various environmental modeling such as wildfire
modeling, urban expansion modeling and so on. The main objectives of
this paper are to (i) investigate the possibility of using DGGS for
running dynamic spatial analysis, (ii) evaluate CA as a generic data
model for dynamic phenomena modeling within a DGGS data model and (iii)
evaluate an in-database approach for CA modelling. To do so, a case
study into wildfire spread modelling is developed. Results demonstrate
that using a DGGS data model not only provides the ability to integrate
different data sources, but also provides a framework to do spatial
analysis without using geometry-based analysis. This results in a
simplified architecture and common spatial fabric to support development
of a wide array of spatial algorithms. While considerable work remains
to be done, CA modelling within a DGGS-based GIS is a robust and
flexible modelling framework for big-data GIS analysis in an
environmental monitoring context.
