On the Use of MATLAB to Import and Manipulate Geographic Data: A Tool for Landslide Susceptibility Assessment

Michele Placido Antonio Gatto, Salvatore Misiano and Lorella Montrasio
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Michele Placido Antonio Gatto: Department of Engineering and Architecture, University of Parma, 43121 Parma, Italy
Salvatore Misiano: Department of Engineering and Architecture, University of Parma, 43121 Parma, Italy
Lorella Montrasio: Department of Civil, Environmental, Architectural Engineering and Mathematics, University of Brescia, 25121 Brescia, Italy

Geographies, 2022, vol. 2, issue 2, 1-13

Abstract: Most of the methods for landslide susceptibility assessment are based on mathematical relationships established between factors responsible for the triggering of the phenomenon, named the conditioning factors. These are usually derived from geographic data commonly handled through Geographical Information System (GIS) technology. According to the adopted methodology, after an initial phase conducted on the GIS platform, data need to be transferred to specific software, e.g., MATLAB, for analysis and elaboration. GIS-based risk management platforms are thus sometimes hybrid, requiring relatively complex adaptive procedures before exchanging data among different environments. This paper describes how MATLAB can be used to derive the most common landslide conditioning factors, by managing the geographic data in their typical formats: raster, vector or point data. Specifically, it is discussed how to build matrices of parameters, needed to assess susceptibility, by using grid cell mapping units, and mapping them bypassing GIS. An application of these preliminary operations to a study area affected by shallow landslides in the past is shown; results show how geodata can be managed as easily as in GIS, as well as being displayed in a fashionable way too. Moreover, it is discussed how raster resolution affects the processing time. The paper sets the future development of MATLAB as a fully implemented platform for landslide susceptibility, based on any available methods.

Keywords: landslide susceptibility assessment; MATLAB Mapping Toolbox; GIS; landslide conditioning factors (search for similar items in EconPapers)
JEL-codes: Q1 Q15 Q5 Q53 Q54 Q56 Q57 (search for similar items in EconPapers)
Date: 2022
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