[Course information is subject to change]
GEOG 497 / 597: Spatial Quantitative Analysis (4 credits)
Contact Information: Heejun Chang (email@example.com)
Four credits with lectures and lab components.
This course is an application of statistical methods for geographers and other social, earth and
environmental scientists that use geospatial data (e.g., climate, hydrology, ecology, soils, geology,
population, crime, health, etc). It is intended to teach how to use statistics in understanding and
solving various geospatial problems. Topics include inferential statistics for spatial data (hypothesis
testing), spatial regression, point pattern analysis, spatial autocorrelation, and spatial interpolation.
The emphasis will be on developing analytical skills with practical applications. Topics covered in
class will be supplemented with computer exercises, which will use several graphical and statistical
software packages (Excel, SPSS, Surfer, ArcGIS, GeoDa) to perform graphical and numerical
analysis of geospatial data.
Textbooks (* required)
1 Rogerson, P. (2006) Statistical Methods for Geography (2
ed), Sage: London*
2 O’Sullivan, D. and Unwin, D. (2003) Geographic Information Analysis, Wiley: Hoboken
3 Wong, D. and Lee, J. (2005) Statistical Analysis of Geographic Information with ArcView GIS
and ArcGIS , Wiley: Hoboken (optional).
4 Fotheringham, A.S., Brunsdon, C., and Charlton, M. (2002) Quantitative Geography:
Perspectives on Spatial Data Analysis, Sage: London (optional).
5 McGrew, J. C. and C. B. Monroe (2000) An Introduction to Statistical Problem Solving in
edition, McGraw Hill: Boston (optional)
Undergraduate: Assignments (50%), Exams (50%)
Graduate: Assignments (35%), Exams (35%), Term project (30%)
Assignments: The assignments are designed to learn the various techniques of
statistics in solving geospatial problems. You are welcome to discuss the assignments
with other students or me, but the final product you hand in must be your own work.
Please be sure to submit assignments on due date. Late assignments will be penalized
10% of the credit per day —so if you are 3 days late you'll be marked out of 7 instead of 10.
Assignments over 3 days late will not be accepted.
Exams: There are two scheduled exams. The tests will not be cumulative, although you will find it
hard to do the second, if you have forgotten basic material in the first. Exams will consist of fill-in,
problem solving questions, and short essays. Material will be from lectures, readings, and
assignments. There will be no make-up exams except for documented medical or family
emergencies. University policies on academic honesty apply.
Term project (grad students only): The term project asks you to collect your own data based on a
survey, field work, or experiment and to critically analyze the data to support or reject a theory in
your area of interest. It will involve in the use of some inferential spatial statistics. Details of the
term paper requirements are described in a separate page (Due date 3/10 for oral presentation and
3/17 for written paper).
Tentative lecture schedule
Week Topic Reading (chapter #)
1 Introduction: statistics in geography Nature of spatial
R1 O&U1, 2
2 Hypothesis testing for spatial data R5
3 Point pattern analysis R10 O&U4,5
4 Spatial autocorrelation R10 O&U7
5 Correlation Mid-term exam (2/8) R7 supplementary
6 Regression R8,9
7 Spatial regression R11 O&U9
8 Spatial interpolation (Kriging) O&U8,9
9 Multivariate spatial data analysis O&U11 supplementary
10 Term project presentations
11 Final exam (12:30 – 14:20)