Geospatial Reasoning with Shapefiles for Supporting Policy Decisions

TL;DR

This paper presents a method to transform geospatial datasets into linked data formats to enable automated, ontology-based policy decision support, demonstrated through location-sensitive radio spectrum policies.

Contribution

It introduces a novel approach combining OWL, PROV-O, and GeoSPARQL standards to support geospatial policy reasoning and decision-making.

Findings

Successfully identified relationships between radio transmitters and policy regions

Enabled automated reasoning over geospatial policy data

Applied to real-world radio spectrum policies with domain expert requirements

Abstract

Policies are authoritative assets that are present in multiple domains to support decision-making. They describe what actions are allowed or recommended when domain entities and their attributes satisfy certain criteria. It is common to find policies that contain geographical rules, including distance and containment relationships among named locations. These locations' polygons can often be found encoded in geospatial datasets. We present an approach to transform data from geospatial datasets into Linked Data using the OWL, PROV-O, and GeoSPARQL standards, and to leverage this representation to support automated ontology-based policy decisions. We applied our approach to location-sensitive radio spectrum policies to identify relationships between radio transmitters coordinates and policy-regulated regions in Census.gov datasets. Using a policy evaluation pipeline that mixes OWL reasoning and GeoSPARQL, our approach implements the relevant geospatial relationships, according to a set of requirements elicited by radio spectrum domain experts.