1. Spatial Data Analysis: Course Outline Ifgi, Muenster, Fall School 2005 Gilberto Câmara INPE, Brazil

2. INPE - brief description National Institute for Space Research  main civilian organization for space activities in Brazil  staff of 1,800 ( 800 Ms.C. and Ph.D.) Areas:  Space Science, Earth Observation, Meteorology and Space Engineering

3. CBERS-2 Launch (21 October 2003) CBERS-2

4. CBERS-2 CCD, Minas Gerais, Brazil

5. CBERS-2 image from Louisiana, EUA Obtained from on-board data recorder

6. Amazon Deforestation 2003 Fonte: INPE PRODES Digital, 2004. Deforestation 2002/2003 Deforestation until 2002

7. source: Greenpeace Amazônia in 2005

8. Amazônia in 2015? fonte: Aguiar et al., 2004

9. R&D in GIScience at INPE Graduate programs in Computer Science and Remote Sensing Research areas  Spatial statistics  Spatial dynamical modelling  Spatio-temporal databases  Image databases and image processing Technology  TerraLib – open source library for ST DBMS

10. Course outline Motivation: why do need spatial data analysis? Point pattern analysis Areal data analysis Surface data analysis (geostatistics) Trends in spatial data analysis

11. Course outline: 1 st week Monday – Introduction  10:30 – 12:00 (2) Tuesday – Basic concepts  10:30 – 12:00 (2) Wednesday – Areal analysis I (LAB work)  9:00 – 10:30 and 11:00 – 12:30 (4) Thursday – Areal analysis II  9:00 – 10:30 and 11:00 – 12:30 (4) Friday – Areal analysis III (LAB work)  9:00 – 10:30 and 11:00 – 12:30 (4) Saturday – QUIZ  14:00 – 17:00 (LAB)

12. Course outline: 2 nd week Monday – Introduction to R (LAB)  10:30 – 12:00 (2) Tuesday – Surface analysis (LAB)  9:00 – 10:30 and 11:00 – 12:30 (4) Wednesday – Surface analysis II (LAB)  9:00 – 10:30 and 11:00 – 12:30 (4) Thursday – Point pattern analysis (LAB)  9:00 – 10:30 and 11:00 – 12:30 (4) Friday – Trends in spatial data analysis  10:30 – 12:00 (2) Saturday – Quiz  14:00 – 17:00

13. Course material Course homepage  www.dpi.inpe.br/gilberto/tutorials.html www.dpi.inpe.br/gilberto/tutorials  Bailey and Gattrel, “Spatial Data Analysis by example” Software  R – statistical suite (open source) www.r-project.org  GeoDa – analysis of areal data (gratis)  TerraView – visualisation and analysis (open source) www.terralib.org  TerraLib – GIS library (open source)