Spatial Statistics: Applications          

Course Description

Nowadays spatial statistics become an important branch of statistics that used to explore and analysis observed data. Techniques of spatial analysis are involved and applied in various fields such as geography, spatial epidemiology and etc.

This course aims to consider topics in spatial statistics and providing necessary background to investigate geographical data. The course will focus on geostatistical computing methods that highly related to spatial data analysis. As software application, R programming language will be involved to analysis spatial data. Hence part of the covered topics in the course will contain an introduction to various R packages for the analysis of spatial data. This will include data import/export, data management and visualization, and other necessary R tutorial that serve the course. Below the main subjects that will be covered by the course. Moreover, ArcGIS is also going to be used and focusing on the spatial analyst ToolBox. It worth to mentioned that each theoretical lesson will have two hours of practical lesson, and will involve 3 – 4 hours as a workload.

Lectures and Materials

 01Introduction to Spatial Statistics Course
 02Introduction & Overview: What is Spatial Statistics;Spatial Statistical Computing
 03Exploring and visualizing spatial dataSupplement Lab: ggplot2 graphics
 04Modelling spatial structure from point samples -I
Visualizing spatial structure
 05Modelling spatial structure from point samples -IIModelling spatial structure from point samples
 06Introduction to GIS
07GIS Databases
 08GIS Databases: Projections and Geo-Positioning

 09GIS DatabasesMap Symbology and Attribute Table
 10 Satellite-based Positioning Field Work Session: Example Application
 11  Build a GIS Learning how to build a GIS
 12 Raster Operations, Spatial Analysis Interpolation techniques within GIS
 13Construct & Manipulate DEMs using ArcGIS Construct & Manipulate DEMs using ArcGIS
 14 Exercise: Exe01_Instructions
 Exe01: Creating a Map
 15 Exercise: Exe02_Instructions,
                  Idw_cadmium.pdf example
 Exe02: Spatial Interpolation

References & Textbooks

The most useful textbooks on spatial data analysis with R and ArcGIS are:

  • Bivand, R., Pebesma, E. J., & Gómez-Rubio, V. (2008). Applied spatial data analysis with R. New York: Springer.
  • O. Schabenberger and C. A Gotway (2005). Statistical Methods for Spatial Data Analysis, Chapman & Hall.
  • Rossiter, D. G. (2012). Introduction to the R Project for Statistical Computing for use at ITC. International Institute for Geo-information Science & Earth Observation (ITC), Enschede (NL), 3, 3-6.
  • Venables, W. N., Smith, D. M., & R Development Core Team. (2004). An introduction to R.
  • Development Core Team at ITC. (2013). GI science and earth observation : a process - based approach : also as e-book. (2010). Enschede: University of Twente Faculty of Geo-Information and Earth Observation ITC.
  • de By, R. A., Knippers, R. A., Weir, M. J. C., Georgiadou, Y., Kraak, M. J., Westen, C. J. V., et al. (2004). Principles of Geographical Information Systems: An introductory textbook (3 rd ed. Vol. 1). Enschede: ITC.