Cornell University CSS/NTRES 6200 Spatial Modelling and Analysis for agronomic, resources and environmental applications. Spring semester 2015

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1 Cornell University CSS/NTRES 6200 Spatial Modelling and Analysis for agronomic, resources and environmental applications Spring semester 2015 Course information D G Rossiter Department of Crop & Soil Sciences November 15, 2014

2 Introduction to course 1 Orientation to course 1. for whom? 2. objectives 3. instructor 4. method 5. assignments & grading 6. project 7. schedule 8. some key references

3 Introduction to course 2 For whom? Graduate and senior undergraduate students who want to advance their understanding of geographic information science and technology; * Including introduction to the R environment for (spatial) statistical computing and visualization, and the QGIS open-source GIS * Student should have followed an undergraduate-level course in GIS theory and practice * Helpful but not necessary: remote sensing, statistical modelling, computer programming Especially useful for students starting a graduate research project that uses spatial information in agronomy, soil science, natural resource management, hydrology, ecology and similar * spatial : information is from known locations in some coördinate reference system (global, national, state, local, arbitrary... )

4 Introduction to course 3 Objectives Core competency: student is able to analyze complex spatial problems with appropriate theory and tools * especially their own problems Enhance student skills in processing, analyzing, and visualizing spatial data; with emphasis on open-source computer programs and publically-available data Provide opportunities to analyze students own geospatial data under instructor supervision

5 Introduction to course 4 Instructor David G. Rossiter Adjunct Associate Professor, CSS * Guest Researcher (Gastmedewerker), ISRIC World Soil Information, Wageningen (NL) Visiting Professor ( ), Chinese Academy of Sciences, Soil Science Institute Nanjing ( ) Retired from University of Twente (NL), Faculty of Geoinformation Science & Earth Observation

6 Introduction to course 5 Instruction method Graduate-level course, emphasis is on guided self-instruction; * including critical reading of primary literature, lab. self-paced tutorials Lectures are overviews / orientations to introduce fundamental spatial analysis concepts and methods, widely applicable methods; * Emphasizes theory and practice of using geospatial data for resource inventory and analysis, biophysical process modeling, and land surveys Emphasise ability to read, understand and apply methods in journal papers, advanced textbooks and reference books; necessary in graduate career Student project is about half the course (and lab. periods); First six (of 14) labs. are tutorial exercises to process, visualize, and analyze geospatial data; other lab. periods for project work. * Emphasis on methods and computation; example applications to illustrate these

7 Introduction to course 6 Assignments Weeks 2 4, 6 8: six (6) set lab. exercises, small hand-in assignment; due before Tues. of following week Week 9: Prelim = literature review & project flow chart Weeks 10, five (5) short question sets based on reading; due before Thursday lecture (to be discussed in class) (Week 11: spring break) Week 16: Project presentations

8 Introduction to course 7 Grading Six lab. exercise assignments: graded 0 3; 20% of final Five question sets: graded 0 3; 20% of final Prelim: 10% of final Project: 50% of final (breakdown: 60% project, 30% reporting, 10% oral presentation/discussion) * So, project, including prelim, 60% of final Grading on absolute scale, not curve.

9 Introduction to course 8 Project An independent project in which the student applies spatial analysis methods to a problem of interest relevant to student s field of study; * May work in pairs provided that independent contributions of team members can be evaluated. Spatial analysis must be prominent; Prefer use of own data, similar obtained from colleague/advisor, or publically-available data; little credit for extensive data manipulation (not the main purpose of this course); Should be able to complete during allocated lab. time and related self-study time; graded as such; Milestones: (1) brief proposal: 10-March (week 8 Tuesday) ; (2) prelim (literature review / detailed project plan): 23-March (week 10 Monday, after hearing comments on proposal presentation week 9); (3) report: 14-May (on scheduled exam date, TBD)

10 Introduction to course 9 Project proposal structure Title Your Name; CSS/NTRES 6200 Background Area of interest Brief literature review Problem statement Objectives Approach General description of study area General description of spatial data needed Proposed analytical methods Spatial analysis flow diagram Anticipated Outcomes Graphics Maps References

11 Introduction to course 10 Project report structure Title Your Name; CSS/NTRES 6200 Lab Section Background Area of interest Problem statement Brief literature review Objectives Approach General description of study area General description of spatial data needed Proposed analytical methods Spatial analysis flow diagram Results and discussion Graphics Maps Summary and conclusions; recommendations for future work References

12 Introduction to course 11 Week schedule Lectures: Tuesday, Thursday Bradfield 110 Computer laboratory: Tuesday or Wednesday Bradfield 108 you can come to one or both labs brief orientation but most time is self-paced with instructor available Bradfield 108 always available (card access for enrolled students)

13 Introduction to course 12 Schedule lectures Week 1 Course Intro, expectations, naïve analysis (Th only) Week 2 Typology of spatial analysis Week 3 The R environment; non-spatial regression Week 4 Spatial correlation; spatial interpolation Week 5 Model-free interpolation (Th only) Week 6 Spatial regression Week 7 Area-Based spatial analysis Week 8 Point Pattern analysis...

14 Introduction to course Week 9 Review Week 10 Time series; Spatio-temporal analyis Week 11 (spring break) Week 12 Spatial sampling theory Week 13 Data sources Week 14 Tools: remote sensing Week 15 Tools: terrain analysis Week 16 Summary: the typology revisited

15 Introduction to course 14 Schedule labs Week 1 (no lab) Week 2 open-source GIS Week 3 The R environment; non-spatial regression Week 4 geostatistics with R/gstat Week 5 (no lab) Week 6 R: Generalized least squares, REML Week 7 R/spdep and GeoDa Week 8 R/spatstat...

16 Introduction to course Week 9 prelim presentations Weeks 10, individual project work (staff available for consultation) Week 16 student project presentations/discussion

17 Introduction to course 16 Key texts/references Spatial modelling in general O Sullivan, D., & Unwin, D. (2010). Geographic information analysis, 2nd ed. Wiley. Available on-line via CU Library. Ecological modelling Austin, M. (2007). Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling, 200(1-2), doi: /j.ecolmodel Borcard, D., & SpringerLink (Online service). (2011). Numerical ecology with R. New York: Springer. Fisher, W. L., & Rahel, F. J. (Eds.). (2004). Geographic information systems in fisheries. Bethesda, Md: American Fisheries Society. Legendre, P. (2012). Numerical Ecology (3rd ed.). San Diego: Elsevier Science & Technology Books.

18 Introduction to course 17 Spatial analysis in R Bivand, R. S., Pebesma, E. J., & Gómez-Rubio, V. (2008). Applied Spatial Data Analysis with R. Springer. [also e-book, full text on-line] Datasets etc. at abbreviated as ASDAR; full text downloadable from CU library via catalog and then Springer Link; &DB=local Hengl, T. (2009). A Practical Guide to Geostatistical Mapping. Amsterdam. Retrieved from

19 Introduction to course 18 General GIS Burrough, P. A., & McDonnell, R. A. (1998). Principles of geographical information systems. Oxford: Oxford University Press. Neteler, M. (2007). Open source GIS: a grass gis approach, third edition. New York: Springer. Geostatistics Webster, R., & Oliver, M. A. (2008). Geostatistics for environmental scientists. John Wiley & Sons Ltd. Goovaerts, P. (1997). Geostatistics for natural resources evaluation. New York; Oxford: Oxford University Press. Diggle, P. J., & Ribeiro Jr., P. J. (2007). Model-based geostatistics. Springer. Krivoruchko, K. (2011). Spatial statistical data analysis for GIS users: DVD containing book + data. Redlands: ESRI.

20 Introduction to course 19 Statistics review StatSoft Electronic Statistics Textbook: Statistical modelling Fox, J. (2008). Applied regression analysis and generalized linear models (2nd ed.). Los Angeles: Sage. Fox, J., & Weisberg, S. (2011). An R companion to applied regression (2nd ed.). Thousand Oaks, Calif.: SAGE Publications. Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). Wiley-Interscience Publication. Venables, W., & Ripley, B. (2002). Modern Applied Statistics with S. Fourth Edition. Springer. Land surface modelling Hengl, T., & Reuter, H. I. (Eds.). (2009). Geomorphometry: concept, software, applications. Developments in soil science.

21 Introduction to course 20 Spatial sampling de Gruijter, J., Brus, D. J., Bierkens, M. F. P., & Knotters, M. (2006). Sampling for Natural Resource Monitoring. Springer. full text downloadable from CU library via catalog and then Springer Link &DB=local

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