R programming language in spatial analysis
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R programming language in spatial analysis






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R programming language in spatial analysis R programming language in spatial analysis Presentation Transcript

  • CONTENTS 2 1. Introduction to R programming language. 2. Spatial analysis in ‘R’ 3. R and GIS 4. Literature review 5. Case studies 6. Summary 7. References.
  • Introduction to R language 3  Environment for statistical computing and graphics - Free software  Associated with simple ,interpreted programming language  Versions of R exist of Windows, MacOS, Linux other Unix flavours  Easy to create your own functions in R  Simple GIS tasks like topological overlay, raster algebra etc., can be carried
  • R language includes 4  an effective data handling and storage facility,  a suite of operators for calculations on arrays, in particular matrices,  a large, coherent, integrated collection of intermediate tools for data analysis,  graphical facilities for data analysis and display either on- screen or on hardcopy, and  a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
  • R Function Libraries 5  Implement many common statistical procedures.  Provide excellent graphics functionality.  A convenient starting point for many data analysis projects.  Examples : maps: allows you to make maps of the world, the US, and smaller areas mapproj: allows you to do cartographic projections
  • 6 Fig 1 R project for statistical computing  www. r-project.org
  • Overview of packages used in R 7 Table 1 packages for spatial analysis in R
  • 8  R developers have written the R package ‘sp’ to extend R with classes and methods for spatial data . - Classes specify a structure and define how spatial data are organised and stored. - - - Methods are instances of functions specialised for a particular data class.
  • Analysis of spatial data in R using points: 9  Points are pairs of coordinates (x; y), representing events, observation posts, individuals, cities or any other discrete object denned in space.  Let's take a look at the dataset crime, which is just a table of geographic coordinates (decimal degrees) for crime locations in Baltimore, MD.  head(crime) ID LONG LAT  1 -76.65159 39.23941  2 -76.47434 39.35274  3 -76.51726 39.25874  4 -76.52607 39.40707  5 -76.51001 39.33571  6 -76.70375 39.26605
  • Points 10 Fig 1 Baltimore crime locations
  • Polygons and lines: 11  Polygons can be thought of as sequences of connected points, where the first point is the same as the last. - An open polygon, where the sequence of points does not result in a closed shape with a denned area, is called a line.  In the R environment, line and polygon data are stored in objects of classes SpatialPolygons and Spatial Lines")  Class Polygon [package "sp"] Name: labpt area hole ringDir coords Class: numeric numeric logical integer matrix  The data are stored as a SpatialPolygons dataframe, which is a subclass of SpatialPolygons containing a dataframe of attributes.
  • Preparation of a simple map in R 12 Fig 4- showing a simple map library(maps) library(mapdata) map("worldhires","canada", xlim=c(-141,-53), ylim=c(40,85), col="gray90", fill=TRUE) http://www.r-bloggers.com/maps-with-r-and-polygon- boundaries/
  • R and GIS 13 The aim of integrating R and ArcGIS is to provide an automated way of offering R scripts as ArcGIS Geoprocessing Tools. In some cases the analysis is composed by several steps, demanding different capabilities in such cases this kind of interface is most suitable.
  • 14 Examples of R packages providing an interface to GIS:  GRASS GIS can be connected through R package spgrass6.  R can access SAGAGIS modules through the R package RSAGA (currently Windows, Linux, FreeBSD and probably others); SAGA GIS is an open-source GIS with mainly raster processing capabilities such as terrain analysis.  R can also run ArcGIS geoprocessing tools through the R package RPyGeo (Windows only). -RPyGeo uses Python scripts to communicate with ArcGIS. RPyGeo/ArcGIS operates on files (raster and shapefiles).
  • 15 Figure 5- shows the workflow required to expose an R script as a Python toolbox and how the toolbox communicate with the original R script in order to run the algorithm.
  • Applications: 16  Geosciences  Water resources  Environmental science  Agriculture and soil science  Mathematics and statistics  Ecology  Geodesy  The exploitation of fossil fuels, and  Meteorology
  • LITERATURE REVIEW 17  Bivand(2001) gives the sketching of key modes of spatial data analysis (point pattern, continuous surface, areal/lattice), and how they integrate into legacy GIS data models.  Roger(2007) gave a brief description of how to access data and also covered how coordinate reference systems are handled, because they are the foundation for spatial data integration  Bajat(2012)presents possibilities of applying the geographically weighted regression, method in mapping population change index
  • 18 Bajat(2012) presents possibilities of applying the geographically weighted regression, method in mapping population change index in the spatial modelling of population concentration Shane(2013) described some statistical and mapping techniques developed for handling and interrogating large- scale multi-media geochemical datasets using the R with Python scripting languages along with GIS
  • CASE STUDY 1 19 Kate(2013) Utilized open-source programming languages to statistically and spatially analyse regional-scale geoenvironmental datasets. Objective  Making best use of open-source programming languages such as R in analysing regional-scale geoenvironmental datasets and developing a web mapping service and online viewer for the datasets. Study area The border region of Northern Ireland and interior of Northern Ireland.
  • 20 Fig 5: graphical plots produced in R after quality assurance and quality control assessment of analytical data Methodology  R–Statistical analyses: -R is employed initially to output a range of graphical plots for quality assurance and quality control assessment of analytical data with respect to laboratory reference materials (as shown in fig5).
  • 21  Exploratory data analyses are carried out to assess the data distribution. Multivariate analytical techniques such as robust factor analyses and hierarchical cluster analyses are used to investigate statistical and spatial correlations between elements. Mapping  R and Python code have been developed to automate the process of exploratory data analysis, spatial data analysis, data interpolation.  Map production using the arcPy mapping module is done. Online viewer  Finally , a web mapping service and online viewer for the mapped datasets, with live links to a managed database is developed.
  • 22 Source: http://spatial.dcenr.gov.ie/GeologicalSurvey/TellusBorder/index.html Fig 6 Tellus border online viewer
  • CASE STUDY 2 23 Acta Silvae (2013) illustrated the use of R programming language in the analyses of spatial data. Objective The aim of this article is to demonstrate the R’s potential for the spatial data processing and presentation. Study area  Snežnik (south Slovenia) forest measuring 20 ha.  Increases in altitude from 820 m to 880 m.  Silver fir and European beech are the dominant tree species. The terrain is characterized by abundant sinkholes.
  • 24 Methodology Manipulation of vector data : Coordinates were recorded using GPS devices and exported to a text file.  This text file was imported into the R environment using the library ‘map tools’ . Geospatial spatial interpolation: A spatial interpolation (kriging) of the temperature throughout the research area using the library gstat . A variogram model which is a function of the spatial dependence of random variables is to be selected. The point measurements were used to create a continuous temperature field in raster format .
  • 25 Fig 7 continuous surface of point measurements
  • 26 LiDAR data processing: R is used as a tool for large amounts of data processing, programming of the raw LiDAR data for 1 km2. Has a size of 539,468 KB (539 MB) and contains 20,736,221 rows and 62,208,663 data points. In R, an algorithm is written to eliminate points that represent forest trees in the whole cloud of points, yielding a point of the terrain.
  • 27 Fig. 8: The 3D point cloud (gray) of longitudinal profile in the research area. The red points are marked on the floor, which were determined based on the algorithm written in R.
  • 28 -A digital elevation model (DEM) was produced based on these classified points. Fig. 9: 3D elevation model based on LiDAR data. The surface coloured with a colour range of the altitude value
  • SUMMARY 29  R has become a high quality open-source software environment for statistical computing and graphics  It has a high performance GIS tool that can be used for geospatial data production, analysis, and mapping.  R allows the usage of many control flows, loops and user- defined functions, multiple input and output data formats.  It gives the opportunity to codify the existing data and functions.  The entire process of analyzing data within R is run through a written script and syntax, which means that it is simple to rerun these analyses if needed.
  • References 30  Bivand et Albrecht implementing functions for spatial statistical analysis using the r language journal of geographical systems, 2:307-317, 2000.  B.bajat (2012) spatial modelling of population concentration using geographically weighted regression method, journal of the geographical institute sass 01/2011; 61:151-167.  Howarth (1983) vol.2: ‘statistics and data analysis in geochemical prospection’, in handbook of exploration geochemistry, pages 69-73, elsevier, Amsterdam,
  • 31 M. Mcclelland et. wang(2010) ‘a python package for using r in python’, journal of statistical software, code snippets. Thibaul et al. Using an R package for exploratory spatial data analysis april 2012, volume 47, issue 2. (2012) ‘Statda: Statistical Analysis for Environmental Data.’ URL: http://CRAN.R-project.org/package=statda. R package version 1.6.2.
  • Any queries ? 32
  • 33 THANK YOU