Download Week 15

Geog 370 – review
Your project
•
•
•
•
Introduction
Materials
Methods
Results
Course review
•
•
•
•
•
GIS
GPS
Remote sensing
Spatial analysis and simple statistics
Thematic mapping
What


is a GIS?
Components of a GIS
Basic characteristics of geographic data
- Location
- Dimensionality
- Continuity (continuous/discrete)
- Attributes (level of measurement)
Data models
1. Vector data model
- Spaghetti model
- Topological vector model (Arc-Node Data Structure)
2. Raster data model
- Cell size, spatial extent, cell position, and attributes
- Data compression
3. Vector model vs. raster model
Characteristic
Raster
Vector
Easy for programming
Often complex
Data structure
Storage
requirement
Positional
precision
Accessibility
Continues data
Topology
Triangulated Irregular Networks
• Advantage of TIN (vs. Raster model)
Sample points can be adapted to the terrain:
more points in areas of rough terrain and fewer in smooth
terrain
Size and shape of the Earth
- Flattening factor, great circle
Map Projections
- Standard point/lines
- Types of projections (based on distortion)
Common Map Projections in GIS
- Lambert conformal conic projection
- Transverse Mercator projection
The Universal Transverse Mercator Coordinate System
Starting at longitude 180 degrees West
60 zones, each 6° longitude wide, easterly direction
1. Distance between point A and point B
Point A: UTM Eastings = 450,000m; Northings = 4,500,000m
Point B: UTM Eastings = 550,000m; Northings = 4,500,000m
2. Longitude, latitude
State Plane Coordinate System vs. UTM
GPS
1.
System consists of three segments
2.
How is works (Triangulating, distance measure,
timing, satellite position)
3.
Error Sources (Satellite errors, atmospheric, Multipath distortion, receivers error)
4.
Error Correction (point averaging, Differential
Correction
Remote sensing
1. What is remote sensing? Why remote sensing?
2. Active system and passive system
3. Electromagnetic radiation energy
4. Characteristics of remote sensing data
5. Two types of sensors (across-track scanning
and along-track scanning)
6. NDVI
7. Image classification
8. Remote sensing applications
Spatial analysis
• Distance measure
Euclidean distance, Manhattan Distance
•
•
•
•
•
•
Buffering
Point in polygon analysis
DEM derivatives: slope and aspect
Boolean Operations with Raster Layers
Algebraic Operations w/ Raster Layers
Neighborhood Operations
The Revised Universal Soil Loss Equation
(RUSLE) (Wischemeier and Smith 1978)
A=RKLSCP
A: the average soil loss
R: the rainfall-runoff factor
K: soil erodibility factor
S: slope steepness
L: the slope length factor
C: crop management factor
P: the support practice factor
• Neighborhood Operations
- Mean filter
- Majority filter
- Variance
Measurements of point, line and polygons
- Points: centroid, clustering, density
– Lines: Length, sinuosity
– Polygons: Length, perimeter, area, shape
Measuring polygon: Shape
Perimeter = 7 miles
Area = 25 sqr miles
• Shape
• Perimeter to Area Ratio
CI = 7 / 25 = 0.28
– perimeter/area
– Expression of the
geographical complexity
of a polygon
CI = 15 / 25 = 0.60
• High ratio  complex
• Low ratio  simple
Area = 25 sqr miles
Perimeter = 15 miles
Graphic: Dr. Jean-Paul Rodrigue, Dept. of Economics & Geography, Hofstra University
Simple statistical measures
• Estimating Mean and Variance
• Histogram
• Simple Linear Regression
Spatial cross-correlation
Spatial autocorrelation
Spatial autocorrelation
• Positive spatial autocorrelation:
Features that are similar in location are also similar in
attributes
• Negative spatial autocorrelation:
Features that are similar in location are dissimilar in
attributes
• Zero autocorrelation
Features are independent of location
Thematic Mapping
• three very common thematic map types
• choropleth
• proportional symbol
• dot density
– understand decisions involved in classifying
quantitative data in thematic maps
Thematic mapping
• How many classes?
– too few - obscures patterns
– too many - confuses map reader
• difficult to recognize more than seven classes
• How do we create the classes?
–
–
–
–
–
–
assign classes manually: create meaningful classes, such as
population above/below poverty level
equal intervals: ignoring data distribution
“natural” breaks based on data distribution: minimize
within-class variation and maximize between-class variation
Quantiles: top 25%, 25% above middle, 25% below middle,
bottom 25%
– standard deviation: mean+1s, mean-1s, mean+2s, mean-2s, …
Additional mapping techniques
• Cartogram
• 3-D phenomena
• Internet Map/GIS Applications