GGR538: SPATIAL DATA ANALYSIS

Fall 2005,  4 credit hours

prerequisites:   GGR 435/535 or GGR 436/536.

 

Instructor:           Ruihong Huang, Ph.D

Class meets:     MW 4:10-6:00pm in Lab034

Office:                Room 210, SWFSC (bldg 82)

 

Course Description

Spatial data analysis is a range of quantitative methods, usually implemented by GIS as spatial analysis tools, that are used for exploring and visualizing characteristics of spatial data , identifying spatial patterns and associations, and making prediction for unmeasured locations or future status.  Spatial analysis provides quantitative support for spatial decision making such as identifying the best location for new business establishments, modeling environment processes, maximizing the benefits of urban land use, as well as enhancing efficiency of transportation facilities.  This course focuses on vector-based spatial data analysis principles and techniques.  Contents include exploratory spatial data analysis, spatial modeling, and spatial statistics.

 

Student Learning Expectations and outcomes of the course

Students participating in this course are expected to have knowledge of basic statistics and have taken introductory GIS courses.  Upon completing the course students should have gained knowledge of spatial data analysis principles, enhanced comprehension of Geographic Information Science, and be able to perform analyses in GIS

• Spatial distribution analysis

• Spatial data visualization

• Spatial association / autocorrelation analysis

• Location analysis

• Network analysis

• Spatial interpolation

• Multivariate data analysis

Course structure/approach

The course will consist of lectures (including discussions) and labs each accounting for about 50% of the total time.  Principles will be illustrated by practical applications in lectures and enhanced by assigned literature reading and classroom discussions.  Spatial data analysis techniques will be trained in labs with the ArcGIS spatial analyst and GS+.

 

Graduate students are expected to do more and better in the course than undergraduate students.  More specifically, graduate students will

• Conduct course research and write a research paper each
• Be assigned additional journal articles in reading assignments
• Lead classroom discussions.  Each graduate student will organize and lead least two discussion sessions.
• Write an extended 1-2 page literature review for each reading assignment, due before discussion sessions.

 

For the course research, each graduate student needs to discuss with the instructor at least once in choosing a research topic and submits a research proposal before mid-term exam.  Thereafter, each graduate student needs to make at least two appointments with the instructor to report research progress and discuss problems.

 

Textbook and Required Materials

O'Sullivan, D. and D.J. Unwin, 2003. Geographic Information Analysis, John Wiley and Sons, New Jersey.  ISBN 0471211761.

ESRI, 2003.  Using ArcGIS Geostatistical Analyst.  ESRI press.

 

 

Recommended optional materials/references

Paul Longley and Michael Batty (ed.), 1996, Spatial Analysis: Modelling in a GIS Environment, Pearson Professional Ltd., New York.

Goodchild, M.F., et al, 1996, GIS and environmental modeling: progress and research issues, GIS World Books, Fort Collins, Colorado.

Morton E. O’Kelly, 1994, Spatial Analysis and GIS, Spatial Analysis and GIS, S. Fotheringham and P. Rogerson (ed.), Taylor & Francis. pp.65-79.

Trevor C. Bailey, 1994, A Review of Statistical Spatial Analysis in Geographical Information Systems, Spatial Analysis and GIS, S. Fotheringham and P. Rogerson (ed.), Taylor & Francis. pp.13-44.

Phillips, Jonathan D. Spatial, Structures and Scale in Categorical Maps. Geographical & Environmental Modelling, May2002, Vol. 6 Issue 1, p41-57.

Mark W Horner, Alan T Murray, Spatial representation and scale impacts in transit service assessment, Environment and Planning B: Planning and Design 2004, volume 31(5) September, pages 785 – 797

Precht, Francis L.; Evans, Daniel R.; Gates, J. Edward. Simulating Spatial Patterns of Brown-headed Cowbird Brood Parasitism in the Central Appalachians. Geographical & Environmental Modelling, Nov99, Vol. 3 Issue 2, p179-202.

Barry Boots and etc. Investigating Recursive Point Voronoi Diagrams, Proceedings Geographic Information Science, 2^nd International Conference, 2002, Max J. Egenhofer and David M. Mark (eds.). pp.1-21.

Elizabeth Burton, Measuring urban compactness in UK towns and cities, Environment and Planning B: Planning and Design 2002, volume 29(2) March, pages 219 – 250

Robin Flowerdew and Mick Green, 1994, Areal Interpolation and Type of Data, Spatial Analysis and GIS, S. Fotheringham and P. Rogerson (ed.), Taylor & Francis. pp.121-146.

Huang, Ruihong and Yehua D. Wei, 2002, Analyzing Neighborhood Accessibility via Transit in a GIS Environment, Geographic Information Science, Vol.8 No.1, pp.39-47.

Bill Macmillan and T. Pierce, 1994, Optimization Modeling in a GIS framework: the Problem of Political Redistricting. Spatial Analysis and GIS, S. Fotheringham and P. Rogerson (ed.), Taylor & Francis. pp.221-246.

Yongmei Lu, Junmei Tang, Fractal dimension of a transportation network and its relationship with urban growth: a study of the Dallas - Fort Worth area, Environment and Planning B: Planning and Design 2004, volume 31(6) November, pages 895 – 911

Anna Nagurney, June Dong, Urban location and transportation in the Information Age: a multiclass, multicriteria network equilibrium perspective, Environment and Planning B: Planning and Design 2002, volume 29(1) January, pages 53 – 74

Michael J de Smith, 2002, Distance transforms as a new tool in spatial analysis, urban planning, and GIS, Environment and Planning B, volume 31(1) January, pages 85 - 104

Mark Birkin, 1996, Retail Location Modeling in GIS, Spatial Analysis: Modleling in a GIS Environment, Paul Longley and Michael Batty (ed.), GeoInformation International. pp.207-225.

Lixin Li and Peter Revesz, A comparison of Spatio-temporal Interpolation Methods, Proceedings Geographic Information Science, 2^nd International Conference, 2002, Max J. Egenhofer and David M. Mark (eds.). pp.145-160.

Andrew D. Cliff and Peter Haggett, 1996, The Impact of GIS on Epidemiological Mapping and Modelling, Spatial Analysis: Modleling in a GIS Environment, Paul Longley and Michael Batty (ed.), GeoInformation International. pp.321-343.

Peter J. Diggle, Paulo J. Ribeiro Jr. and Ole F. Christensen, 2003, An Introduction to Model-based Geostatistics, Spatial Statistics and Computational Methods, Jesper Møller (ed.), Springer. pp.1-41

Jesper Møller and Rasmus P. Waagepetersen, 2003, An Introduction to Simulation-Based Inference for Spatial Point Process, Spatial Statistics and Computational Methods, Jesper Møller (ed.), Springer. pp.143-198.

Páez, Antonio. Anisotropic Variance Functions in Geographically Weighted Regression Models. Geographical Analysis, Oct.2004, Vol. 36 Issue 4, p299-314.

Peter H Verburg, Jan R Ritsema van Eck, Ton C M de Nijs, Martin J Dijst, Paul Schot, Determinants of land-use change patterns in the Netherlands,

Amnon Frenkel, Land-use patterns in the classification of cities: the Israeli case, Environment and Planning B: Planning and Design 2004, volume 31(5) September, pages 711 – 730.

Todd A Randall, Cameron J Churchill, Brian W Baetz, A GIS-based decision support system for neighbourhood greening, Environment and Planning B: Planning and Design 2003, volume 30(4) July, pages 541 - 563

 

 

Course Outline

Week 1:     Spatial data and geospatial data analysis

Week 2:     Vector-based GIS modeling and visualization

Week 3:     Descriptive spatial data statistics

Week 4:     Point pattern analysis 1:

                    Density-based, distance-based pattern measures, point-pattern statistics

Week 5:     Point pattern analysis 2:

                   Cluster detection, tesselations, thiessen (Voronio) polygons

Week 6:     Linear data analysis: networks, graphs and trees, shortest path

Week 7:     Polygon data analysis: spatial autocorrelation

Week 8:     Midterm review and exam

Week 9:     Deterministic spatial interpolations

Week 10:   Trend surface analysis

Week 11:   Semivariogram

Week 12:   Kriging 1: principles

Week 13:   Kriging 2: methods

Week 14:   Spatial regression (introduction)

Week 15:   Multivariate data analysis:

                    Distance, difference, similarity, cluster analysis

Week 16:   Final review and exam

 

Assessment of Student Learning Outcomes

Graduate student performance will be evaluated based on lab assignments, exams, literature review notes, discussions, final projects.

            Lab assignments:        300 points

            Midterm                        100 points

            Final exam                   100 points

            Literature study            100 points

            Discussions                 100 points

            Research paper           200 points

            Attendance                   -10 points per absence

            Total:                            900 points

 

Grading System

A                     > 90%

B                      80-90%

C                     70-80%

D                     60-70%

F                      < 60%

 

Course Policies

 

Attendance is required for the course and will be monitored in lectures and labs.  10 points will be deducted from the total points a student earned for each absence.

 

INCOMPLETES: will not be given without written recommendation by the Dean of Students

 

PLAGIARISM:  I encourage a certain amount of collaboration among students. However, each student is required to complete individual assignments. Plagiarism of another student’s work or of material from other uncited sources will cause the student to fail the class.

 

Northern Arizona University Policy Statements

Safe environment policy, Students with disabilities, Institutional review board, and Academic integrity: http://jan.ucc.nau.edu/academicadmin/plcystmt.html