Spatial Statistics and Analysis

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

Geographic Information Systems

Course Contents

From spatial data to information, Spatial analysis concepts, spatial dependency, Geographic data models and structures, fundamental spatial statistical measures describing the characteristics of spatial data, spatial patterns and distributions, spatial statistics and GIS, random and regular patterns, mapping of clusters, point and area pattern analysis in GIS, global and local measures of autocorrelation, geostatistical data analysis, modelling of spatial statistical relationships, spatio-temporal data analysis, geographic regression modeling and autoregressive functions.

Weekly Schedule

1) Spatial data- information-analysis concept, spatial data models and structures, spatial relations and dependency, spatial patterns and distributions,
2) Random and regular patterns, mapping of clusters. Point pattern analysis, quadrat analysis, nearest neighbor analysis, K- function, spatial statistics, density analysis (kernel etc.).
3) Area pattern analysis, spatial neighborhood concept, spatial modeling tools (regression etc.), neighborhood types and statistical analysis, overlap and neighborhood functions.
4) Spatial dependency and scale concept, spatial samping, modifiable areal unit problem (MAUP)
5) Spatial order and patterns, spatial autocorrelation concept, pattern analysis and statistics, global and local statistical measures, descriptive statistics, hypothesis tests and confidence intervals.
6) Average center, standard distance, weighted center and distance, analysis and statistics.
7) Midterm
8) Statistical interpretations of spatial pattern analysis, mean nearest neighbors, hot / cold spot clusters, multi-distance pattern analysis
9) Global and local measures of autocorrelation, Moran I statistics, local and global statistics (Getis-Ord, etc.)
10) Geostatistics data analysis, spatial structure functions of regional variables, covariance function and semivariograms, kriging functions and analysis
11) Spatial modeling, ordinary least squares and geographic regression modeling and spatial autoregressive functions
12) Introduction to network analysis, network topological structure, GIS based network analysis and spatial distribution and pattern definitions.
13) GIS based sample project applications, spatial autocorrelation and pattern definition and cluster analysis
14) GIS-based sample project applications, geographic modeling and visualisation
15) GIS-based sample spatial analysis project applications
16) Final exam

Recommended or Required Reading

1- Statistical Methods for Spatial Data Analysis (Texts in Statistical Science Series), Oliver Schabenberger and Carol A. Gotway (2004)
2- Statistics for Spatial Data (Wiley Series in Probability and Statistics), Noel A. C. Cressie (1993)
3- An Introduction to Applied Geostatistics, Edward H. Isaaks and R. Mohan Srivastava (1990)
4- Geographic Information Sytems, Oxford University Pres, New York
5- MAGUIRE, D. J., Geographical Information Systems: Principles and Applications, Longman, England
6- - Mitchell, Andy. The ESRI Guide to GIS Analysis: Volume 1: Geographic Patterns and Relationships, Redlands, CA: ESRI Press, 1999

Planned Learning Activities and Teaching Methods

1) Lecture
2) Question-Answer
3) Discussion
4) Drill and Practice
5) Modelling
6) Self Study
7) Problem Solving

Assessment Methods and Criteria

Contribution of Semester Studies to Course Grade	40%
Number Percentage Semester Studies Midterm Examination 1 60% Quiz 1 40%
Contribution of Final Examination to Course Grade	60%
Total	100%

Language of Instruction

Turkish

Work Placement(s)

Not Required