Web-based geospatial multiple criteria decision analysis using open software and standards

ABSTRACT

The emerging ubiquity of geospatial information is providing an unprecedented opportunity to apply Geographical Information Systems (GIS)-based multiple criteria decision analysis (MCDA) to a broad spectrum of use cases. Volunteered geographic information, open GIS software, geoservice-based tools, cloud-based virtualized platforms, and worldwide collaboration of both domain experts and general users have greatly increased the quantity and accessibility of geospatially referenced data resources. Currently, there is a lack of GIS-based MCDA tools that integrate this decision-driven process within a widely accessible, robust geoframework environment, designed for user-friendly interaction. In this contribution, we present a conceptual workflow and proof-of-concept software application, Geocentric Environment for Analysis and Reasoning (GEAR), which provides a viable transition path to enhance geospatial MCDA in the age of open GIS. We propose a Web-based platform that leverages open-source geotechnologies to incorporate a wide variety of geospatial data formats in a common solution space to allow for spatially enhanced and time-relevant decision analysis. Through the proposed workflow, a user can ingest and modify heterogeneous data formats, exploit temporally tagged data sources, create multicriteria decision analysis models, and visualize the results in an iterative and collaborative workspace. A sample case study applied to disaster relief is used to demonstrate the prototype and workflow. This proof-of-concept Web-based application provides a notional pathway of how to connect open-source data to open-source analysis through a geospatially enabled MCDA workflow that could be virtually accessible to many levels of decision makers from individuals to entire organizations.

KEYWORDS:

• Multiple criteria decision analysis
• disaster relief
• open-source GIS
• participatory GIS
• Web-based GIS
• scenario analysis

Acknowledgements

This research was conducted at the US Army Corps of Engineers, Engineer Research, and Development Center, Geospatial Research Laboratory. The authors would like to thank Andrew Jenkins, John Eylander, Mike Ekegren, Sean Wallace, Micah Brachman, Luke Lambert, and Shane Brennan for their contribution to this research project.

Disclosure statement

No potential conflict of interest was reported by the authors.