Abstract
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.
Highlights
With increasing sources of geographically referenced data such as growing satellitebased imaging archives, GPS tracking data, and transactional data stores; new challenges are emerging that relate to storing, accessing, analyzing, visualizing, and sharing big data as components of Geographical Information Systems (GIS) [1, 2].One of the key challenges is the development of new data models capable of integrating data from different sources with varying levels of accuracy and uncertainty [3]
We propose here that combining a Cellular automate (CA) model and discrete global grid systems (DGGS) data model may enable the use of machine learning and other AI algorithms for rule set definition
This paper aims to couple a CA model with a DGGS data model in a distributed database system, to realize an in-database approach with associated computational advantages
Summary
With increasing sources of geographically referenced data such as growing satellitebased imaging archives, GPS tracking data, and transactional data stores; new challenges are emerging that relate to storing, accessing, analyzing, visualizing, and sharing big data as components of Geographical Information Systems (GIS) [1, 2].One of the key challenges is the development of new data models capable of integrating data from different sources with varying levels of accuracy and uncertainty [3]. Different architectures and data models have been proposed to realize a digital earth vision, including data cubes [6] and discrete global grid systems (DGGS) [7,8,9]. DGGS as a data model for big geo-data is able to store, manage and manipulate the large volume of heterogeneous data, including combining both vector and raster data into one spatial representation [8, 17]. An in-database DGGS data structure provides potential advantages over vector and raster approaches in that single data model can support a wide array of analytic algorithms with simple datatypes. Classic examples of CA applications in GIS environments include wildfire modeling [28], land use change [29], and invasive species modelling [30] Despite their wide use, conventional CA models have problems in defining simulation parameter values, transition rules and model structures [31]. This paper aims to couple a CA model with a DGGS data model in a distributed database system, to realize an in-database approach with associated computational advantages
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