An Ontology-Based Information Extraction System for Residential Land Use Suitability Analysis

TL;DR

This paper introduces an ontology-based information extraction system that automates land use suitability analysis from regulatory documents, producing maps to aid urban planning and growth prediction.

Contribution

The paper presents a novel OBIE system that extracts land use criteria from regulations and integrates it into a decision-making model for urban planning.

Findings

Effective extraction of land use criteria from documents

Successful generation of suitability maps for Regina

Integration with urban growth modeling demonstrated

Abstract

We propose an Ontology-Based Information Extraction (OBIE) system to automate the extraction of the criteria and values applied in Land Use Suitability Analysis (LUSA) from bylaw and regulation documents related to the geographic area of interest. The results obtained by our proposed LUSA OBIE system (land use suitability criteria and their values) are presented as an ontology populated with instances of the extracted criteria and property values. This latter output ontology is incorporated into a Multi-Criteria Decision Making (MCDM) model applied for constructing suitability maps for different kinds of land uses. The resulting maps may be the final desired product or can be incorporated into the cellular automata urban modeling and simulation for predicting future urban growth. A case study has been conducted where the output from LUSA OBIE is applied to help produce a suitability map for the City of Regina, Saskatchewan, to assist in the identification of suitable areas for residential development. A set of Saskatchewan bylaw and regulation documents were downloaded and input to the LUSA OBIE system. We accessed the extracted information using both the populated LUSA ontology and the set of annotated documents. In this regard, the LUSA OBIE system was effective in producing a final suitability map.