Lecture 1 Course Organization General Introduction

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Lecture 1Course OrganizationGeneral Introduction

• Crop and Soil Science 620
• Spatial Modeling and Analysis

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Introduction to Course

• Review Syllabus
• Review Laboratory Objectives
• Review next four weeks
• Review rest of semester
• Discuss Objectives for Taking Course
• Discuss Off-site Collaboration

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Introduction to Course

• Expectations
• Introduction to GIS
• Understanding of Vector and Raster Models
• Understanding of Data Quality
• Hands on use of GIS
• Some, basic statistics and general math
• The ability to speak in class

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CSS 620 Goals (linked with NTRES 670)

• Laboratory Objectives
• You are on your own work as a researcher
• A.K.A. Thinking out of the Box
• Goal for publication
• Here are some examples of the students work.

• Lecture Objectives
• Review spatial model types (empirical,
  stochastic, physical, etc..)
• Programming techniques
• Point Pattern Analysis (nearest neighbor,
  quadrat, etc.)
• Linear Analysis (shortest path, dynamic
  segmentation, etc.)
• Areal Analysis (join count analysis,
  correspondence analysis, etc.)
• Student presentations

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Publications from CSS 620
Students from different backgrounds have a shared
experience working together on their research
projects

• Spatial Simulation of the Dynamics of
  Establishment of Secondary Forest in Abandoned
  Pastures in the Central Amazon. Karin T. Rebel,
  Susan J. Riha, Marco A. Rondon, Ted R.
  Feldpausch, and Erick C.M. Fernandes, Cornell
  University
• Sea Surface Temperature from AVHRR as a Predictor
  of Crustacean Zooplankton Density. David Warner,
  Cornell University and Art Lembo, Department of
  Soil and Crop Sciences, Cornell University, Rice
  Hall, Cornell University, Ithaca, NY 14853
• Armchair Flow Estimation in the Black River
  Watershed Zev Ross, Cornell University
• Modeling In-Stream Temperature of the Beaverkill
  Watershed. Beth Gardner, Cornell University
• Application of GIS in Studying the Energy,
  Economic, and Environmental Benefits of Using
  Dairy Manure as Renewable Energy Source and
  Designing Distributed Energy System in New York
  State. Jianguo Ma, Cornell University
• Assessing Our Cultural Divide An Analysis of
  Election 2000. Arthur J. Lembo, Jr., Cornell
  University, Paul Overberg, USA Today.
• Using GIS to Assess Shape and Area Correspondence
  Between Community-Based and Topographic Maps.
  Bjorn Sletto, Cornell University
• Using Ripleys K to Determine Clustering and
  Co-Dependence Applications in Kenya. Ingrid
  Rhinehart, Cornell University
• Combining USLE and GIS/ArcView for Soil Erosion
  Estimation in the Fall Creek Watershed in Ithaca,
  NY. Jianguo Ma, Cornell University

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Advanced Analysis with ArcGIS
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N6 1994
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N41 1995 pre-drop
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N77 1996
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N102 1997
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N132
1998
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N27 1999
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N4 2000
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N1 2001
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Procedures and Results
Land Cover Type
Wind Speed
1km1km Grid cells
SAS GENMOD procedure
Transformers and outages
Rainfall
Model validation, Simulation and application
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PROBLEM 1 Comparison of True HRU Slopes and
Average Subbasin Slopes
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Flow diagram
OPTIMIZING THE RIPARIAN BUFFER IN THE SKANEATELES
LAKE WATERSHED, NEW YORK
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Main Conclusion

• Approaches that incorporate both spatial and
  non-spatial data are likely to generate much
  greater environmental benefits

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Definitions of Course Title

• Spatial Modeling concerned with modeling a
  process, function, or phenomenon
• Spatial Analysis - concerned with the
  relationship among the GIS data
• That is, once we have data, what can we say about
  them

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Spatial Modeling Definitions

• Spatial modeling involves the construction of
  explanatory and predictive models for statistical
  testing (Chou, p. 24)
• Representation of a process (Fowler, 1997)
• Formal expression of the essential elements of
  some problem in either physical or mathematical
  terms (Jeffers, 1988)
• Simplified picture of reality..as a tool to solve
  problems (Jorgensen, 1994)

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Spatial Modeling Definitions

• Formal expression of the essential elements of
  some problem in either physical or mathematical
  terms (Jeffers, 1988)
• The studying of landscape processes using
  mathematical algorithms written in computer code
  (Burroughs, 1986)
• The processes of model development, formulation
  and application to simulate the system behavior
  of the real world.

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Components of Modeling and Analysis

• Point pattern analysis examination and
  evaluation of spatial patterns and the processes
  of point features

Biological survey where each point denotes the
observation of an endangered species. If a
pattern exists, like this diagram, we may be
able to analyze behavior in terms of
environmental characteristics

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Network Analysis

• Designed specifically for line features organized
  in connected networks, typically applies to
  transportation problems and location analysis

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Surface Analysis

• Spatial distribution of surface information in
  terms of a three-dimensional structure
• Surfaces do not have to be elevation, but could
  include population, crime, occurrence of disease,
  as well as topography

Cornell University
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Grid Analysis

• Processing of spatial data in a special,
  regularly spaced form

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Measurement Levels

• Nominal Data Simply a label or name and no
  assumption of ordering or distances The data is
  qualitative and categorical, such as origin, hair
  color, birthplace, school district.
• Zone 1 is not necessarily less than Zone 2
• Combining Zone 1 and Zone 2 does not equal
  Zone 3
• students in class x - blue eyes, y - brown eyes

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Measurement Levels

• Ordinal Data Meaningful in terms of rank order
  in each category relative to other items in the
  category. Examples include Social Class
  working, middle, and upper. Upper class is
  assumed to to rank higher than middle or working
• The degree of difference is not known. For
  instance, upper class is not two better than
  working class
• line up students in height order

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Measurement Levels

• Interval Useful for ordering and distance
  between categories. For example the difference
  between 50 and 49 degrees Celsius is the same as
  49 and 48 degrees. However, 50 is not twice as
  warm as 25.
• Examines differences between phenomena, but not
  their magnitude

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Measurement Levels

• Ratio similar properties to interval, except the
  zero point is defined by the measurement scheme.
  Therefore, you can say that 10 km. is twice as
  far as 5 km.
• Other ratio data would include height, weight,
  income

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Goals in Modeling

• Environmental Modeling has 2 aims
• Assist in understanding physical world
• Provide predictive tool for management
• Displayed as easy-to-read graphs, maps
  multi-media demonstrations

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Components of Modeling(Corwin, p. 20)

• Modeling
• Data Collection
• GIS

Data Collectors
Modelers, data collectors, and GIS designers have
different training, jargon, and approaches
GIS Designers
Model Builders