In this context, the assessment of social vulnerability has an important role for evaluating the capacity of a community to prepare for, respond to and recover from disasters. Italy is one of the European countries that are most heavily exposed to a wide range of natural hazards, which might cause large economic losses. This paper also introduces an example of classification of roads on the basis of slope via box plot representation by interfacing R in GRASS Environment. It is also capable to provide introductory knowledge of both open software’s packages with their flexibility, robustness capability. and proves very beneficial with the perception of research and educational purposes. Integration also enables all R plotting and analytical functions i.e., kriging prediction kernel density pattern estimation etc. In this paper, GRASS-GIS i.e., GIS subsystem act as a simple interface for R i.e., statistical computing subsystem for both raster and vector spatial data which provides commands to GRASS program via R system () function.
Monroe county grass gis software#
GRASS is open source software freely available, used for data management, analysis of geospatial data, spatial modelling with visualization whereas R (Open Source Package) enables all statistical environments with better quality plots providing linear or non-linear modelling, time series analysis with classification and clustering.
However, the integration of GRASS-GIS and R statistical package play a very important role to fulfil all needs related to computation, analyse, retrieve, image processing, graphics production and query spatial data. Many researchers always want to explore, analyse, complex analysis of spatial data with statistical problems and dealing with large areas in less amount of time and memory within individual software but this is not possible without integration. As lots of GIS (Geographic Information systems) applications for statistical purposes are available in the market but still there is lots of demand of integration of GRASS-GIS i.e., Geographic Resources Analysis support GIS with the R statistical package.
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