GI3601 Geospatial analysis with R Programming Syllabus:

GI3601 Geospatial analysis with R Programming Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

 To expose the variables, expressions, control stations of R
 To use R Programming for Analysis of data and visualize outcome inform of graphs, charts
 To analysis data using various statistical tools like correlation and regression

UNIT I INTRODUCTION TO R

Introduction – History and overview – elements and data structures – Sessions and Functions – Variables – DataTypes – Vectors – Scalars – Conclusion – DataFrames – Lists – Matrices – Arrays – Classes – Data input/output – Data storage formats – Sub setting objects – Vectorization

UNIT II PROGRAMMING IN R

R Programming – Arithmetic and Boolean Operators and values – Structures – Control Statements – Loops – Pointers – Recursion – Scoping Rules – Loop functions – Array and Matrices

UNIT III DATA MANIPULATION

Math and Simulation – Functions – Math Function – Probability Calculation – Cumulative Sums and Products-Minima and Maxima Data sorting – Linear Algebra Operation on Vectors and Matrices – Set Operation.

UNIT IV DATA VISUALISATION AND PROBABILITY DISTRIBUTION

Graphics – Creating Graphs – Customizing Graphs – lattice library- Visualization – Box plot – Histogram – Pareto charts – Pie graph – Line chart – Scatter plot – Developing graphs – Probability Distributions: Normal – Binomial – Poisson and Other Distributions.

UNIT V STATISTICAL DATA ANALYSIS

Basic Statistics – Outlier – regression Analysis: Linear – Multiple – Logistic – Poisson – Survival Analysis and Nonlinear Models: Splines – Decision Tree – Random Forests – Support Vector Machine – Clustering – Correlation – Covariance – Statistical simulation – T-Tests.

TOTAL:45 PERIODS
COURSE OUTCOMES:

On completion of the course, the student is expected to
CO1 State the capabilities of R and its data, variable types.
CO2 Describe the various operators, control statements and scoping rules in R.
CO3 Apply R programming for manipulation of datasets.
CO4 Produce various graphs and distribution plots using R.
CO5 Analyze dataset using Statistical Tools available in R.

REFERENCES:

1. Mark Gardener, Beginning R -The Statistical Programming Language, John Wiley and Sons, Inc., ISBN: 9781118164303, 2012.
2. Chris Brunsdon, Lex Comber, An Introduction to R for Spatial Analysis and Mapping, 2nd Revised Edition, Sage Publications Ltd (UK), ISBN: 9781446272954, 2019.
3. Jared P. Lander, R for Everyone Advanced Analytics and Graphics, 2nd Edition, AddisonWesley Professional PTG, ISBN: 9780134546926, 2017
4. Hamid Reza Pourghasemi, Spatial Modeling in GIS and R for Earth and Environmental Sciences, Elsevier (S&T), ISBN: 9780128152263, 2019
5. Michael J. Crawley, The R Book, 2nd Edition, Wiley-Blackwell, ISBN: 9780470973929, 2012.