Introduction to Geographic Information Systems

1
Introduction to Geographic Information Systems

• Miles Logsdon
• mlog_at_u.washington.edu

http//sal.ocean.washington.edu/
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GIS - consists of

• Components
• People, organizational setting
• Procedures, rules, quality control
• Tools, hardware software
• Data, information
• Functions
• Data gathering
• Data distribution

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Geographic Data

• Spatial Data
• location
• shape
• relationship among features

• Descriptive Data
• attributes, or
• characteristics of the features

After Sinton, 1978 Components of spatial
information time, space, theme
(attribute) Sounds obvious. useful starting
point to remember Role of these Dimensions One
must be fixed, one controlled, one measured.
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DATA - more than one DATUM - only one item,
or record

• Three Attributes of Data
• Thematic (Value Variable)
• Nominal, name, label
• Ordinal, rank ordered
• Interval / Ratio, measurement on a scale
• Spatial (location)
• Temporal

Spatial Data the spatial attribute is
explicitly stated and linked to the thematic
attribute for each data item.
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Spatial - thematic value types
200
Sta. 94, DOC 4.9
Stream,3
Former Land Fill
100
FOREST
URBAN
Duvall, pop 1170
Brush Creek, 2
FOREST
AGRICULTURE
100
200
Snoqualmie River, 1
WELL
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Geographies
Layers, Coverages, Themes
Land use
Soils
Streets
Hydrology
Parcels
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Concept of Spatial Objects

• POINTS
• LINES
• AREA

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Spatial Encoding - RASTER
POINT
0
0
0
0
0
1
0
0
0
5
5
3
AREA
3
3
1
1
1
2
LINE
1
0
0
0
1
0
1
0
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Spatial Encoding - VECTOR
a single node with NO area
POINT
- x, y
- x1, y1 - x2, y2 . . - xN, yN
LINE
a connection of nodes (vertices)
beginning with a to and ending with a
from
(Arcs)
Area (Polygons)
a series of arc(s) that close around a
label point
- x1, y1 - x2, y2 . . - xN, yN (closure Point)
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Vector - Topology
Descriptive
Spatial
Object
VAR1 VAR2
1 2 3
x1,y1 x2,y2 x3,y3
1 2 3
VAR1 VAR2
Fnode Tnode x1y1, x2y2
1
1 2
1 2
1 2 xxyy, xxyy 2 3
xxyy,xxyy
3
2
2
1
VAR1 VAR2
2
3
1 2
1 2
10, 11, 12, 15 10, .
15
10
1
4
12
5
11
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Raster Data Model
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Set Selections
1 2 3 4 5 6 7 8 9 10
Reduce Select - RESEL GT 5 6 7 8 9 10 Add
Select - ASEL EQ 5 5 6 7 8 9
10 Unselect - UNSEL GE 9 5 6 7 8
Null Select - NSEL 1 2 3 4 9 10
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AND, OR, XOR
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Spatial Overlay - UNION
1
1
2
1
3
6
2
4
5
2
3
7
8
11
3
12
9
10
4
5
13
14
16
17
15
A B C D
102 103
102 A
A 102 B
102
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Spatial Overlay - INTERSECT
1
1
1
2
2
2
3
3
4
5
3
6
7
4
5
8
9
A B C D
102 103
A 102 B
102 A 103 B
103
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Spatial Overlay - IDENTITY
1
1
1
2
5
2
3
4
2
3
6
7
3
8
9
4
5
10
11
12
13
A B C D
102 103
A A
102 B 103 B

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Spatial Poximity - BUFFER
Constant Width
Variable Width
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Spatial Poximity - NEAR
Assign a point to the nearest arc
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Spatial Proximity - Pointdistance
DISTANCE
2,045 1,899 1,743
1 2 3
1 2 3
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Spatial Proximity - Thiessen Polygons
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Map Algebra

• In a raster GIS, cartographic modeling is also
  named Map Algebra.
• Mathematical combinations of raster layers
• several types of functions
• Local functions
• Focal functions
• Zonal functions
• Global functions
• Functions can be applied to one or multiple layers

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Local Function

• Sometimes called layer functions -
• Work on every single cell in a raster layer
• Cells are processed without reference to
  surrounding cells
• Operations can be arithmetic, trigonometric,
  exponential, logical or logarithmic functions

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Local Functions Example

• Multiply by constant value

2 0 1 1 2 3 0 4
1 1 2 3 2
6 0 3 3 6 9 0 12
3 3 6 9 6
X 3

• Multiply by a grid

2 0 2 2 3 3 3 3
2 2 2 1 1
4 0 2 2 6 9 0 12
2 2 4 3 2
2 0 1 1 2 3 0 4
1 1 2 3 2

X
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Focal Function

• Focal functions process cell data depending on
  the values of neighbouring cells
• We define a kernel to use as the neighbourhood
• for example, 2x2, 3x3, 4x4 cells
• Types of focal functions might be
• focal sum,
• focal mean,
• focal max,
• focal min,
• focal range

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Focal Function Examples

• Focal Sum (sum all values in a neighborhood)

2 0 1 1 2 3 0 4 2
1 1 2 2 3 3 2
(3x3)
12 13

17 19

• Focal Mean (moving average all values in a
  neighborhood)

2 0 1 1 2 3 0 4 4
2 2 3 1 1 3 2
1.8 1.3 1.5 1.5 2.2 2.0 1.8 1.8 2.2 2.0
2.2 2.3 2.0 2.2 2.3 2.5
(3x3)

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Zonal Function

• Process and analyze cells on the basis of zones
  
• Zones define cells that share a common
  characteristic
• Cells in the same zone dont have to be
  contiguous
• A typical zonal function requites two grids
• a zone grid which defines the size, shape and
  location of each zone
• a value grid which is processed
• Typical zonal functions
• zonal mean,
• zonal max,
• zonal sum,
• zonal variety

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Zonal Function An Example

• Zonal maximum Identify the maximum in each zone

2 2 1 1 2 3 3 1
3 2 1 1 2 2
1 2 3 4 5 6 7 8 1
2 3 4 5 6 7 8
5 5 8 8 5 7 7 8
7 5 8 8 5 5

Useful when we have different regions
classified and wish to treat all grid cells of
each type as a single zone (ie. Forests, urban,
water, etc.)
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Global function

• In global functions -
• The output value of each cell is a function of
  the entire grid
• Typical global functions are distance measures,
  flow directions, or weighting measures.
• Useful when we want to work out how cells
  relate to each other

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Golbal Function An Example

• Distance Measures Euclidean distance based upon
  cell size

1 1 1
2
2 1 0 0 1.4 1 1 0 1
0 1 1 1.4 1 1.4 2

Or some function which must consider all cells
before determining the value of any cell
(cost associated with a path across the surface)
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Examples
outgrid zonalsum(zonegrid, valuegrid) outgrid
focalsum(ingrid1, rectangle, 3, 3) outgrid
(ingrid1 div ingrid2) ingrid3
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Spatial Modeling

• Spatial modeling is analytical procedures applied
  with a GIS. Spatial modeling uses geographic data
  to attempt to describe, simulate or predict a
  real-world problem or system.
• There are three categories of spatial modeling
  functions that can be applied to geographic
  features within a GIS
• geometric models, such as calculating the
  Euclidean distance between features,
• coincidence models, such as topological overlay
• adjacency models (pathfinding, redistricting, and
  allocation)
• All three model categories support operations on
  spatial data such as points, lines, polygons,
  tins, and grids. Functions are organized in a
  sequence of steps to derive the desired
  information for analysis.
• The following references are excellent
  introductions to modeling in GIS
• Goodchild, Parks, and Stegaert. Environmental
  Modeling with GIS. Oxford University Press, 1993.
• Tomlin, Dana C. Geographic Information Systems
  and Catograhic Modeling. Prentice Hall, 1990.