
FREC 682: Spatial Analysis
Course Syllabus--Spring Semester, 2002
| Instructor: John
Mackenzie |
office: Townsend 215 |
e-mail: johnmack@udel.edu |
| phone: 302-831-1312 |
fax: 302-831-6243 |
cell: 302-373-3723 |
dept.: 302-831-2511 |
| office hours: TBA |
Class Meetings: Thursday evenings, 7-10 PM
Townsend 006 (GIS/CAD lab).
Grading: any 5 lab assignments (16% each):
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Mining Analysis
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Habitat Analysis
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Creating and
Analyzing DEM's
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TIGER Data
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Management
of Non-Point Pollution
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Intro to Image
Processing
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Spatial Regression
UNDER REVISION
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Geostatistics (TBA)
Final
Project (20%; your own topic; obtain instructor approval)
All assignments must be submitted as web pages, including maps saved
as GIF or JPEG format images.
For a quick introduction to HTML, check out Mad
Dog's HTML Reference.
Objectives: Develop advanced GIS and spatial analysis skills. The
principal GIS used in this course is GRASS 4.3 (definitive integer raster
version), a share-ware GIS with open data structures, extensive raster
functions and image processing capabilities running on Linux. GRASS source
code, compiled binaries for various UNIX platforms (including Solaris and
Linux), documentation and tutorials are all available by anonymous ftp
from www.baylor.edu/~grass/
Prerequisites: Undergraduates must have completed a prior GIS
course. Prior familiarity with UNIX is essential.
Texts, Etc.: There is no required textbook to buy. Students without
strong UNIX backgrounds should buy the CNS handbook Introduction to
UNIX at the University Bookstore. All students are encouraged to buy
Linux
in a Nutshell (O'Reilly, 1999), available at Borders or through Amazon.com.
Class notes and extensive GRASS documentation are available on-line.
UNIX, shell programming books and other references will be available in
the Townsend GIS/CAD lab.
CLASS SCHEDULE
1: February 7: introduction
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intro: course
organization, objectives, grading; basic GIS concepts: what a GIS does;
geographic features, raster vs. vector data
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SPATLAB cluster account set-up; logins; intro to X
2: February 14: UNIX basics and GRASS overview
3: February 21: raster analytics
4: February 28: shell programming, graphics and hardcopy generation
5: March 7: more raster tools
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profiles, volumes, etc.: r.mapcalc, d.profile, r.profile, r.transect,
r.cross, r.random, r.volume
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report generation: r.stats, r.average, r.median, r.mode, r.covar, r.coin
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basic AWK filters
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GRASS hardcopy
utilities: the CELL monitor and ps.map
6: March 14: 3D modelling, etc.
7: March 21: digitizing, etc.
8: March 28: digital elevation models
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Boolean vs. fuzzy logic in the context of GIS
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digital elevation models: uses; data sources; DLG formats
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DMA 1-degree DEM's: ftp data sources; m.dmaUSGS.read; m.rot90; r.in.ll;
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creating a
DEM from hypsography data: v.import; v.to.rast; r.reclass; r.surf.contour
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terrain analyses: r.slope.aspect, r.watershed, r.mapcalc, r.drain, r.los
( Spring Break: no class April 4 )
9: April 11: Census data and choropleth mapping
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overview of TIGER and 2000 Census data, USGS resources
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DLG hydrography, transportation, etc.: v.import, v.to.rast, r.reclass
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TIGER import and thematic mapping of Census data: AWK filters; v.in.tig.basic,
v.apply.census
10: April 18: fundamentals of remote sensing, image processing and image
interpretation
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data sources: SPOT, LANDSAT, aerial photos
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data import from digital media or scanner
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band-ratios; NDVI and other vegetation indices
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band stretches, histogram equalization, saturation, de-striping, etc.:
d.histogram;
r.colors; r.mapcalc
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color composites, filters: d.rgb; i.colors; i.composite, i.fft; r.mfilter
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satellite imagery import/rectification: i.tape tools, i.group, i.target,
i.points, i.vpoints, i.rectify
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colorspace transformations: i.rgb.his / i.his.rgb
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Anderson LU/LC categories
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unsupervised classification: i.cluster, i.maxlik
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supervised classification: i.class, i.gensig, i.maxlik
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alternative classification procedures: i.smap, neural networks,
etc.
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other tools: i.cca, i.pca
11: April 25: project administration and animation
12: May 2: statistical surfaces
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interpolation tools: r.surf.contour, r.surf.idw, s.surf.tps
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model validation and error surfaces
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surface sampling: r.what, r.random
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intro to spatial statistics
13: May 9: spatial econometrics and other issues
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taxonomy and consequences of spatial autocorrelation pathologies
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diagnostics for regression models
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EGLS, maximum likelihood and bootstrap regression methods
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other GIS's
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other Web resources
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compilation of user-contributed GRASS code
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course wrap-up and evaluations
SpatLab