Course

Unit I

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. 

Unit II 

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. Google Earth Engine (GEE): Introduction to online GIS, Data repository of GEE, basic data extraction and analysis, automated workflows using remote sensing. 

Unit III

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.

Unit IV

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. 

Unit V

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: Practical knowledge of GIS software, statistical and time series analysis of geospatial data using python 

Course Outcomes 

CO1: Understanding of spatial data, its types and how to handle it. 

CO2:  Map generation and its understanding in a GIS software (including open-source software) 

CO3: Fundamentals of spatial statistics and introduction to R software 

CO4: Time series analysis in geospatial datasets 

TEXTBOOKS/REFERENCES: 

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.