ESE 502 Home Page




Instructor: Tony E. Smith


274 Towne (898-9647)
tesmith@seas.upenn.edu


Office Hours
(Spring, 2008):
M,W 3:00-4:30

     Phil_igc_image


 

TABLE OF CONTENTS



COURSE DESCRIPTION

The course is designed to introduce students to modern statistical methods for analyzing spatial data. These methods include nearest-neighbor analyses of spatial point patterns, variogram and kriging analyses of continuous spatial data, and autoregression analyses of areal data. The underlying statistical theory of each method is developed and illustrated in terms of selected GIS applications. Students are also given some experience with ARCMAP, JMPIN, and MATLAB software.
 

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PREREQUISITES

  ESE 302 (or comparable course in statistics and/or regression)

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REQUIRED MATERIALS

Required Text

Interactive Spatial Data Analysis, T.C. Bailey and A.C. Gatrell

Also Recommended:


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COURSE TOPICS

Spatial Point Pattern Analysis

Continuous Spatial Data Analysis

Regional Data Analysis


COURSE GRADING

Homework 70%
Project 30%

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COURSE TIME AND LOCATION 
 


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CETS Labs
 


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TENTATIVE SCHEDULE FOR SPRING 2008


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

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HOMEWORK ASSIGNMENTS

Example Assignment
Example Answer
Assignment 1
Assignment 2
 Assignment 3
 Assignment 4
 Assignment 5
 Assignment 6
Assignment 7

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EXTRA MATERIALS

Using LeSage Models.     This gives a brief introduction to the package of MATLAB
                                        programs available on Jim Lesage's web site.


Using ArcView 9.                 This manual, written by Amy Hillier, provides a brief
                                         introduction to some of the more useful procedures
                                         in ArcView 9. 

Using MATHTYPE.
           These notes show you how to access MATHTYPE in
                                         WORD, and use it to write both mathematical equations
                                         and in-line expresssions in your reports.

Matrix Regression.              These slides give an introduction to MATRIX ALGEBRA
                                         in the context of  MULTILE REGRESSION


CML Data and Maps.        This is a set of instructions on how to download data sets
                                         and map shapefiles from the Neighborhood Information
                                         System (NIS) in the Cartographic Modeling Lab (CML).

Reference Materials.           This is a secure site containing additional reference materials
                                         for the course that are copywrited.


Lab Data Access.                  These notes are intended for those students who do not have
                                          a class account, but would still to access the class data sets and
                                          use the software in the lab.

Remote Data Access.           These notes are intended for those students who have access to
                                          the ARCMAP, JMPIN, and MATLAB software elsewhere, and
                                          want to access the class data sets from this remote location.

 

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PROJECT MATERIAL


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:

It is strongly recommended that you include all files in a single ZIP file with one of your names (or initials) in the title. If you send separate files, be sure to put a name (or initials) in each of the file names. 

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.

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EXAMPLE STUDENT PROJECTS

Hillier Project

Holton Project

Paiva-Turra Project

Johnson Project

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SPATIAL DATA WEB SITES


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)

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NOTEBOOK ON SPATIAL DATA ANALYSIS

INTRODUCTION 

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
Laplace Principle
        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

    6.  Space-Time Point Processes

        
6.1  Space-Time Clustering
         6.2  Space-Time K-Functions
         6.3  Temporal Indistinguishability Hypothesis
         6.4  Random Labeling Test
         6.5  Application to the Lymphoma Example

      APPENDIX TO PART I
 

II. CONTINUOUS SPATIAL DATA ANALYSIS

  1. Overview of Spatial Stochastic Processes
        1.1  Standard Notation
        1.2  Basic Modeling Framework

  2. Examples of Continuous Spatial Data
        2.1  Rainfall in the Sudan
       

III. SPATIAL REGRESSION ANALYSIS
 

IV. SOFTWARE 

     1.  ARCMAP

       1.1  Opening ARCMAP

       1.2  Tips for Using 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
              1.2.10  Adding Coordinate Fields to Attributes of Point Shapefiles
              1.2.11  Converting Strings to Numbers in ARCMAP
              1.2.12  Displaying Proper Distance Units

              1.2.13  Editing Point Styles in ARCMAP
              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

       2.1  Opening JMPIN

       2.2  Tips for using 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  
Making Boundary-Share Weight Matrices using ARCMAP and MATLAB
              3.2.4  
Clipping Grids in ARCMAP for use in and MATLAB
                      

 REFERENCES

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Last modified: February 18, 2008