RS4014 Spatial Data Modelling Syllabus:
RS4014 Spatial Data Modelling Syllabus – Anna University PG Syllabus Regulation 2021
OBJECTIVES :
To provide complete understanding of the concepts of Spatial Data Modelling
UNIT I MODELLING SPATIAL PROBLEMS
Introduction – Need for Spatial models- Conceptual model for solving spatial problems- steps involved , Types of Spatial Models- Descriptive and Process models- Types of Spatial Models Descriptive and Process models – Types of Process models – Creating Conceptual models – Site Suitability model – Case Study.
UNIT II MODEL BUILDER IN GIS ENVIRONMENT
Graphical Modeller of QGIS – Development of Models using Graphical Model Builder: Input to model Algorithm input – Running a Model – Nesting a Model- Arc GIS Model Builder: Building a Model, Input: Variables, Arrays – Iterative Models – Building and Running a Model – Converting a Model to Python Script
UNIT III GEOSTATISTICAL ANALYSIS AND MODELING–MAPPING
Stepwise Regression -Ordinary Least Squares (OLS)-Variogram and Kriging: Ordinary Kriging, Simple Kriging,Universal Kriging-Developing Variogram Model and Kriging -Spatial Autoregressive (SAR)-Binary Classification Tree (BCTs)-Cokriging-Geospatial Models for Presence and Absence Data-GARP Model-Maxent Model-Logistic Regression-Classification and Regression Tree (CART)- Envelope Model
UNIT IV GEOSPATIALMODELING
Concept – Cellular Automata Model : definition, type, application – integration with Fuzzy, ANN – Agent based modeling : concept, Agent, analysis, application- Big Data: definition, tools, Analysis and application, NetLogo Models integrated GIS : 2D, 3D visualization – VR- AR concepts -Case studies
UNIT V MACHINE LEARNING TOOLS
Artificial Intelligence: definition,types – Expert system – sources of Knowledge-Knowledge Acquisition Methods – Representation schemes -types of inference: forward and backward chaining Artificial Neural network-BPN-Fuzzy Logic- Integration with GIS- Case studies
OUTCOMES:
On completion of the course, the student is expected to be able to
CO1 Understand the descriptive and process spatial models
CO2 Understand model builder in GIS environment
CO3 Apply geostatistical analysis and modeling
CO4 Study various Spatio-Temporal model
CO5 Understand the machine learning tools
REFERENCES:
1. Manfred M. Fischer, Jinfeng Wang, Spatial Data Analysis, Springer-Verlag Berlin Heidelberg,2011,ISBN 978-3-642-21719-7
2. Christopher K. Wikle, Andrew Zammit-Mangion, Noel Cressie, Spatio-Temporal Statistics with R, 1st Edition, CRC Press, 2019.
3. Andrew Crooks, Nick Malleson, Ed Manley, Alison Heppenstall, Agent-Based Modelling and Geographical Information Systems: A Practical Primer (Spatial Analytics and GIS), 2018, 1st Edition, SAGE Publications Ltd
4. Noel Cressie, Christopher K. Wikle,2011,Wiley Publishers, 1 edition, Statistics for SpatioTemporal Data 1st Edition
5. Maguire, D., M. Batty, and M. Goodchild. 2005. GIS, spatial analysis, and modeling. ESRI Press, 2005
6. Andrew Crooks, Nick Malleson, Ed Manley, Alison Heppenstall, “Agent-Based Modelling and Geographical Information Systems”: A Practical Primer (Spatial Analytics and GIS) 1st Edition,2019,
7. Mastering Geospatial Development with QGIS 3.x: An in-depth guide to becoming proficient in spatial data analysis using QGIS 3.4 and 3.6 with Python,Packt Publishing; 3 edition (28 March 2019)
8. TsungChang-Kang, Introduction to Geographic Information Systems, Tata McGraw Hill Publishing Company and Limited NewDelhi, 4th Edition, 2017.