Modeling Land Use Change With Logic Scoring Of Preference Method Gis And Cellular Automata
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Modeling Land-use Change with Logic Scoring of Preference Method, GIS and Cellular Automata
Multicriteria Evaluation (MCE) has been used as a land suitability method and is often coupled with Cellular Automata (CA) for land-use and urban growth modeling. MCE, however, exhibits limitations including data loss and insufficient logical requirements. The Logic Scoring of Preference (LSP) is an alternative approach that can overcome these issues. LSP is a soft computing system evaluation method efficient in nonlinear aggregation with flexible logic requirements. The main objective of this research study was to develop an integrated LSP and geographic information system (GIS) methodology for modeling land-use change. Moreover, the LSP method was further integrated into a CA to model urban growth. The LSP methodology was tested with geospatial datasets of coastal BC, Canada and several scenarios of future land-use change were generated. This research contributes to the field of spatio-temporal modeling with a novel integration of LSP, GIS, and CA for land-use suitability analysis and modeling.
Integrating Soft Computing, Complex Systems Methods, and GIS for Modeling Urban Land-use Change
The Logic Scoring of Preference (LSP) method is a part of general multicriteria decision making approach that has origins in the soft computing. The method can model simultaneity, replaceability, and a wide range of other aggregators to suit various evaluation objectives. As soft computing method, LSP is based on fuzzy reasoning and can aggregate an unlimited amount of inputs without loss of significance. The main objective of this research is to develop and test integrated methods that use LSP, complex systems theory and geographic information systems (GIS) to model urban land-use change. In this research study LSP is integrated into a GIS to determine land-use suitability and is integrated into both cellular automata (CA) and agent-based models (ABMs) to simulate urban growth at both regional and local spatial scales. LSP approaches were implemented with geospatial datasets for Metro Vancouver, Canada and several scenarios of land-use change have been created.
Dynamic land use/cover change modelling
Author: Jamal Jokar Arsanjani
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-10-01
The thesis is an original and novel contribution to land use/land cover change analysis using methods of geosimulation and agent-based modeling. The author implements several traditional methodologies of land use change by means of remote sensing and GIS techniques. An Agent-Based Model was developed in order to simulate land use change in the Tehran metropolitan area, comparing the outcomes of each particular methodology. All methods are compared, and advantages and disadvantages discussed.