LISAM: an open source GIS-based model for liveability spatial assessment
- Published
- Accepted
- Subject Areas
- Databases, Spatial and Geographic Information Systems
- Keywords
- ecosystem services mapping, spatial multicriteria decision analysis, liveability mapping, urban services, landscape planning and management
- Copyright
- © 2016 Antognelli et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. LISAM: an open source GIS-based model for liveability spatial assessment. PeerJ Preprints 4:e2133v2 https://doi.org/10.7287/peerj.preprints.2133v2
Abstract
Ecosystem Services (ES) and Urban Services (US) influence place liveability in a comparable manner. Consequently, assessing landscape liveability considering both types of services can result effective for landscape planning and policy-making purposes. Since liveability depends also on local population preferences and perceptions, stakeholder involvement results essential for a more coherent liveability assessment. In this study a Spatial Multicriteria Decision Aiding (S-MCDA) approach guided the development of a LIveability Spatial Assessment Model (LISAM). Using a combination of GIS techniques (Euclidean distance, kernel density estimation, network analysis, viewshed analysis), consistent and comparable ES and US spatial indices were calculated in a study area located in central Italy. The indices were implemented in open-source geo-spatial software (QGIS, PostGIS and PostgreSQL). According to the Analytical Hierarchy Process (AHP), they were integrated with their percentage weights on liveability deriving from stakeholders interviews. Then, to investigate the liveability levels of local population, main statistics of liveability values were calculated per census section. Results include overall liveability indices at a local scale, and key statistics of liveability related to resident population. The work highlights the effectiveness of LISAM to assess local liveability and to deliver important information for policy-makers. LISAM approach opens the opportunity to integrate also ecosystem and urban disservices together with ES and US in liveability assessment to consider also the factors generated by landscape components that reduce the overall level of place liveability.
Author Comment
We improved the manuscript considering the comments sent by the reviewer.