Course

Syllabus

Cartography & GIS: Intro to Geographic Information Systems (GIS) and their applications; Vector and Raster data operations. Spatial phenomena and its distribution, diversity of representation forms, map types, scale, projections, coordinate system. Concepts of map making: Data Posting, symbolizations, typography; Contour Map; primary and derivative map, features and resolution. Map making ArcGIS, digitization. 

Google Earth: Exporting vector and raster maps to KML; Reading KML files through R, obtaining data via Google service, export of maps to Google Earth. 

Spatial statistics: Conventional Analysis (non-geostatistical), Why Geostatistics, Environmental variables, source of spatial variability, deterministic and scholastic processes. Analysis of discrete and continuous random variables. probability density function; Variances, joint variation, covariance, correlation, regression, different types of error like root mean square; Probability theory: Univariate, bivariate, multivariate statistics, Gaussian Distribution, Central Limit Theorem, Variogram Statistics, Nugget, Higher Dimensions & Statistical Anisotropy, Model-Fitting “Rules of Thumb”. Hands-on different R tools like gstat, geoR, etc 

Time series analysis: Examples of time series; Purposes of analysis; Components (trend, cycle, seasonal, irregular); Stationarity and autocorrelation; Approaches to time series analysis; Simple descriptive methods: smoothing, decomposition; Regression. 

Skills acquired : Practicalknowledge of GIS softwares, statistical and time series analysis of geospatial data using python 

Course Outcomes 

CO1 : Describe and categorize the different types of spatial data, and how to handle it. 

CO2: Generate and interpret maps in a GIS software (including open source software) 

CO3 : Explain the fundamentals of spatial statistics and demonstrate the use of R software 

CO4 : Apply time series analyses to geospatial datasets 

1. Islam, T., Srivastava, P. K., Gupta, M., Zhu, X., & Mukherjee, S. (Eds.). (2014). Computational intelligence techniques in earth and environmental sciences. Springer Netherlands. 
2. Wackernagel, H. (2013). Multivariate geostatistics: an introduction with applications. Springer Science & Business Media. 
3. Chun, Y., & Griffith, D. A. (2013). Spatial statistics and geostatistics: theory and applications for geographic information science and technology. Sage.