Instructor: Tony
E. Smith
274 Towne (898-9647)
tesmith@seas.upenn.edu
Office Hours (Spring, 2008):
M,W 3:00-4:30
Instructor: Tony
E. Smith
|
|
| Homework | 70% |
| Project | 30% |
| Lectures | Day/Date | Topic | Homework |
| INTRO | Th/Jan.17 | Introduction | |
| 1 | Tu/Jan.22 | Point Pattern Data | |
| 2 | Th/Jan.24 | CSR Hypothesis | |
| 3 | Tu/Jan.29 | Nearest-Neighbor Methods | |
| 4 | Th/Jan.31 | Data Applications | PS1 due |
| 5 | Tu/Feb.5 | K-Function Analysis | |
| 6 | Th/Feb.7 | Simulation Testing Methods | |
| 7 | Tu/Feb.12 | Bivariate K-Functions | |
| 8 | Th/Feb.14 | Tests of Pattern Similarity | |
| 9 | Tu/Feb.19 | Local K-Functions | PS2 due |
| 10 | Th/Feb.21 | Continuous Spatial Data | |
| 11 | Tu/Feb.26 | Spatial Variograms | |
| 12 | Th/Feb.28 | Variogram Estimation | |
| 13 | Tu./Mar.4 | Simple Kriging Model | |
| 14 | Th/Mar.6 | Kriging Predictions | PS3 due |
| 15 | Tu/Mar.11 | SPRING RECESS | |
| Th/Mar.13 | SPRING RECESS |
||
| Tu/Mar.18 | Simple Regression Model | Project Proposal due | |
| 16 |
Th/Mar.20 | Generalized Least Squares | |
| 17 |
Tu/Mar.25 | Universal Kriging Model | |
| 18 | Th/Mar.27 | Universal Kriging Estimation | PS4 due |
| 19 | Tu/Apr.1 | Data Applications | |
| 20 | Th/Apr.3 | Data Applications | |
| 21 | Tu/APR.8 | Regional Spatial Data | |
| 22 | Th/Apr.10 | Spatial Autocorrelation | |
| 23 | Tu/Apr.15 | Spatial Concentration | PS5 due |
| 24 | Th/Apr.17 | Spatial Autoregression | |
| 25 | Tu/Apr.22 | Spatial Lag Model | |
| 26 | Th/Apr.24 | Spatial Diagnostics | |
| 27 | Tu/Apr.29 | Additional Regression Topics | PS6 due |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| Assignment 7 |
1. PROJECT DESCRIPTION
In this class projects are typically done individually. For example, if you are a Graduate student working on (or thinking about) either a Dissertation topic or perhaps a Capstone Project for your Masters in Urban Spatial Analytics, I encourage you to choose a project that will contribute to this work. For everyone else (including Undergraduates) you may want to team up with someone else to work on a project. Teams of two individuals are encouraged. Teams of three are also permitted, but not encouraged -- and are expected to do more work.
Each team is expected to undertake a case study involving a
statistical
analysis of some spatial data
set. The only substantive requirement is
that
your analysis should focus on methods of spatial statistical analysis
presented in class. Your report should develop these methods in
sufficient detail to allow readers to understand the method and how it
is being applied. Use maps, graphs, and tables wherever appropriate in
presenting your results. (A picture is often "worth a thousand words").
But be sure to back these up with appropriate discussion. I do
not want to see graphics that are not even mentioned in the text. All
source material
(including software packages used) should be cited explicitly.
Finally, be
sure
to include page numbers in your report. (I write comments on every project,
and
am very unhappy when I have no page numbers to refer to!).
Example reports from past years are displayed in the section
below.
2. PROJECT SUBMISSION
First it is important to emphasize that for those of you who are
graduating at the end of the semester, you must submit your project by
the end of
Finals Week, so that I can give you a grade for the course. For
those who are not graduating (or do not otherwise need a grade
immediately), I encourge you to work on your project during the summer.
(That is one reason why this class is given in the Spring semester.) I am perfectly
willing to give "Incomplete" grades for this course, and change them
when your project is submitted.
As for the final subission of your projects, I need a hard copy of your report delivered to my office. In addition, you must send me an email attachment (preferably on the same day you turn in your project) including the following items:
There are no constraints on the subject of your case study. You might start by looking through the set of projects that are included below. (These projects are presented in their original form -- including possible errors. So don't assume that everything in them is correct. They are intended mainly to suggest possible topic areas and methode of analysis. ) A variety of interesting spatial data sources are also included below.
Students often find it difficult to obtain the data sets they want to study. So it is advisable to start looking as soon as possible. There is a list of web sites given below where you can start to search for existing data.
The final grade will be based on several factors: the
appropriateness
and sophistication of the analytical methods employed, the correctness
of
the analysis carried out, the logic and perceptiveness of the
conclusions
drawn, and the overall clarity of the presentation.
(return
to contents)
PENN CAMPUS RESOURCES
http://data.library.upenn.edu/index.html
http://www.library.upenn.edu/vanpelt/infofile/cdfram2.html
http://www.library.upenn.edu/lippincott/cdroms.html
http://www.cml.upenn.edu/
GENERAL SPATIAL DATA RESOURCES
http://www.geographynetwork.com/data/index.html
http://www.census.gov/geo/www/cob/index.html
http://fisher.lib.virginia.edu/index.html
http://www.gisdatadepot.com/
http://gis.about.com/
http://www.esri.com/data/download/index.html
http://www.maproom.psu.edu/dcw/
http://www.lib.ncsu.edu/stacks/gis/dcw.html
http://www.fedstats.gov/mapstats/
http://factfinder.census.gov/servlet/BasicFactsServlet
http://homer.ssd.census.gov/cdrom/lookup
http://www.nationalgeographic.com/maps/
http://nationalatlas.gov/natlas/natlasstart.asp
http://www3.cancer.gov/atlasplus/
http://www.csiss.org/clearinghouse/select-tools.php3
http://hope.hss.cmu.edu/(TrafficSTATS)
I. SPATIAL POINT PATTERN ANALYSIS
1.
Examples of Point Patterns
1.1
Clustering
versus Uniformity
1.2 Comparisons
between Point
Patterns
2.
Complete Spatial Randomness
2.1 Spatial
2.2 Complete Spatial Randomness
2.3 Poisson Approximation
2.4 Generalized Spatial Randomness
2.5 Spatial Stationarity
3.
Testing Spatial Randomness
3.1 Quadrat Method
3.2 Nearest-Neighbor
Methods
3.2.1 Nearest-Neighbor Distribution under CSR
3.2.2 Clark-Evens Test
3.3 Redwood Seedling
Example
3.3.1 Analysis of Redwood Seedlings using JMPIN
3.3.2 Analysis of Redwood
Seedlings using MATLAB
3.4 Bodmin Tors Example
3.5 A Direct Monte
Carlo Test of CSR
4.
K-Function Analysis of Point
Patterns
4.1 Wolf-Pack
Example
4.2 K-Function
Representations
4.3 Estimation of K-Functions
4.4 Testing the CSR Hypothesis
4.5 Bodmin Tors Example
4.6 Monte Carlo Testing Procedures
4.6.1 Simulation Envelopes
4.6.2 Full P-Value Approach
4.7 Nonhomogeneous CSR Hypotheses
4.7.1 Housing Abandonment Example
4.7.2 Monte Carlo Tests of Hypotheses
4.7.3 Lung Cancer Example
4.8 Nonhomogeneous CSR Hypotheses
4.8.1 Construction of Local K-Functions
4.8.2 Local Tests of Homogeneous CSR Hypotheses
4.8.3 Local Tests of
Nonhomogeneous CSR Hypotheses
5.
Comparative Analyses of Point Patterns
5.1 Forest
Example
5.2 Cross K-Functions
5.3 Estimation
of Cross K-Functions
5.4 Spatial Independence Hypothesis
5.5 Random-Shift
Approach to Spatial Independence
5.5.1 Spatial Independence Hypothesis for Random Shifts
5.5.2 Problem of Edge Effects
5.5.3 Random Shift Test
5.5.4 Application to the Forest Example
5.6 Random-Labeling Approach to Spatial Independence
5.6.1 Spatial Indistinguishability Hypothesis
5.6.2
Random Labeling Test
5.6 3 Application to the
Forest Example
5.7 Analysis of
Spatial Similarity
5.7.1 Spatial Similarity Test
5.7.2 Application to the
Forest Example
5.8 Larynx and
Lung Cancer Example
5.8.1 Overall Comparison of the Larynx and Lung Cancer Populations
5.8.2 Local Comparison in the Vacinity of the Incinerator
5.8.3 Local Cluster Analysis of Larynx Cases
II. CONTINUOUS SPATIAL DATA ANALYSIS
III. SPATIAL REGRESSION
ANALYSIS
IV. SOFTWARE
1. ARCMAP
1.2.1 Importing Text
Files to
ARCMAP
1.2.2 Changing Path
Directories
in Map Documents
1.2.3 Making a Column
of Row
Numbers in an Attribute Table
1.2.4
Masking in ARCMAP
1.2.5 Making Spline
Contours in Spatial Analyst
1.2.6 Excluding Values
from
Map
Displays
1.2.7 Importing ARCMAP
Images
to
the Web
1.2.8 Adding Areas to
Map
Polygons
1.2.9 Adding Centroids
to Map Polygons
1.2.11 Converting Strings to Numbers in ARCMAP
1.2.12 Displaying Proper Distance Units
1.2.14 Exporting Maps from ARCMAP to WORD
1.2.15 Making Legends for Exported Maps
1.2.16 Making Voronoi Tessellations in ARCMAP as Shapefiles
1.2.17 Running Local G* Tests of Concentration in ARCMAP
1.2.18 Joining Point Date to Polygon Shapefiles in ARCMAP
2. JMPIN
2.2.1 Printing Results from
JMPIN
2.2.2 Making a Random
Reordering
of Row Numbers
3. MATLAB
3.1
Opening MATLAB
3.2
Tips for using MATLAB
3.2.1 Exporting
Graphics from MATLAB to WORD
3.2.2 Making Boundary-Share
Weight Matrices in MATLAB
3.2.3