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.

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.