Advanced Spatial Statistical Techniques (Practical)

Paper Code: 
24DGEO614 (A)
Credits: 
02
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

Course Objectives:

  1. Understanding the application of statistical data in the spatial analysis. 
  2. Detailed analysis of statistical techniques in geographical study.
  3. Understanding of statistical applications to analyze both spatial and non-spatial data

 

Course Outcomes: 

 

Course

Learning Outcome

(at   course level)

Learning & Teaching Strategies

Assessment Strategies

Course Code

Course Title

 

 

 

 

 

 

 

24DGEO

614(A)

 

 

 

 

 

 

 

Practical:

Advanced Spatial

Statistical

Techniques

(Practical)

CO135: Interpret the basics of statistical data

CO136: Enhance knowledge

on the Probability theory 

CO137:  Explanations   on sampling plans for spatial-and non-spatial data analysis              

CO138:         Identify         the

Correlation     and     regression

analysis 

CO139: Explanation of time series analysis and its significance in geographical studies

CO140: Contribute effectively in course-specific interaction.

 

 Approach       in

Teaching:

Observation, Conduction  and Compiling data

 

 Learning activities for  the students:

Self-learning File  work/Report writing,  learning by doing.

 

 

 

Viva, Continuous assessment, Semester  end examinations, Individual and

Group team work

 

12.00

Statistics and Statistical Data:  Spatial and non-spatial;  indices of inequality and disparity. 

 

 

12.00

Probability theory,  probability density functions with respect to Normal, Binomial and Poisson distributions and their geographical applications.

12.00

Sampling: Sampling plans for spatial and non-spatial data, 

Sampling distributions; 

Sampling estimates for large and small samples tests involving means and proportions. 

 

 

12.00

Correlation and Regression Analysis: Rank order correlation and product moment correlation; 

Linear regression, residuals from regression, and simple curvilinear regression;  Introduction to multi-variate analysis. 

12.00

Time Series Analysis: Time Series processes; 

Smoothing time series; 

Time series components. 

 

 

Essential Readings: 

Essential Readings: 

 

  1. Bart, James, E, and Gerald, M. Barber. (1996). Elementary Statistics for Geographers. London, UK:

The Guieford Press. 

  1. Cressie, N.A.C. (1991). Statistics for Spatial Analysis. New York, USA: Wiley 
  2. Eldon, D. (1983). Statistics in Geography: A Practical Approach. London, UK: 

Blackwell. 

  1. Gregory, S., (1978). Statistical Methods and the Geographer (4th Edition). London, UK: Longman.Haining, R.P. (1990). Spatial Data Analysis in the Social  and Environmental Science. Cambridge, UK: Cambridge University Press. 
  2. Hammond, R. and McCullagh, P.S. (1974). Quantitative Techniques in Geography: An Introduction.

Oxford, UK: Clarendan Press. 

 

 

 

 

 

 

References: 

Suggested Readings: 

 

  1. Mathews, J.A. (1987). Quantitative and Statistical Approaches to Geography: A Practical Manual.

Oxford, UK: Pergamon. 

  1. Mc Grew, Jr. and Cahrles, B. M. (1993). An Introduction to Statistical Problem Solving in Geography.

New Jersey, USA: W.C. Brocan Publishers. 

  1. Rogerson, P. A. (2001). Statistical Methods for Geography. New Delhi, India: Sage Publications. 
  2. Yeates, M. (1974).  An Introduction to Quantitative Analysis in Human Geography. New York, USA:

McGrawhill. 

Academic Year: 
2024-2025