Introduction to Geographic Information Systems

Miles Logsdonmlog@u.washington.edu

http://sal.ocean.washington.edu/

Spatial Information Technologies Geographic Information Systems – GIS Global Positioning System – GPS Remote Sensing and Image Processing - RS

Technologies to help answer: What is “here”? … give a position What is “next” to “this”? … given some description Where are all of the “???” … detecting or finding What is the spatial pattern of “???” When “X” occurs here, does “Y” also occur?

GIS Geographic Information System

GIS - A system of hardware, software, data, people, organizations and institutional arrangements for collecting, storing, analyzing, and disseminating information about areas of the earth. (Dueker and Kjerne 1989, pp. 7-8)

GIS - The organized activity by which people •Measure aspects of geographic phenomena and processes; •Represent these measurements, usually in a computer database;•Operate upon these representations; and •Transform these representations. (Adapted from Chrisman, 1997)

A KEY POINT: Geo-referenced Data

GIS - consists of:

Components People, organizational setting Procedures, rules, quality control Tools, hardware & software Data, information

Functions Data gathering Data distribution

Common “short hands”

CAM- Computer Aided Mapping

AM - Automated mapping CAD - Computer-Aided

Design LIS - Land Information

Systems AM/FM - Automated

Mapping/Facilities Management Systems

RS - Remote Sensing aerial Photography Photogrammetry Photo interpretation Thermal sensing Radar imaging Satellite Remote

SensingMeteorological Terrestrial

Image Processing

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 rememberRole of these Dimensions: One must be fixed, one controlled, one measured.

Components of Spatial Data

Temporal examples: Control: Measure:Time (hour) Attribute (water level) = strip chart (stream

guage)

The Basic Spatial Data Structures Control: Measure:Location Attribute => Raster (Location controlled by grid)Attribute Location => Categorical coverage (Vector)

Indirect measurement Control: Measure:

First: Attribute Location => Categorical Coverage (eg. land use category)Second: Category Attribute => Estimate for category (eg. % Corn yeild)

Composite Measurement Control: Measure:

First: Attribute Location => Collection Zones (eg. counties) Second: Location Attribute => Choropleth (eg. % vote for Initiative 187)

DATA - “more than one” DATUM - “only one item, or record”

Three Attributes of Data Thematic (Value Variable)

Nominal, … name, labelOrdinal, … rank orderedInterval / 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.

Spatial - thematic value types

Sta. 94, DOC 4.9

WELL200’

100’

100’

200’

Former Land Fill

URBANDuvall, pop 1170

FOREST

FOREST

AGRICULTURE

Snoqualmie River, 1

BrushCreek, 2

Stream,3

GeographiesLayers, Coverages, Themes

Land useSoils

Streets

Hydrology

Parcels

Concept of Spatial Objects

POINTS

LINES

AREA

Spatial Encoding - RASTER

0 00

00 0 0

01

POINT

1

0

1

11

0 0

00

0

5 5 3

3311 2

LINE

AREA

Spatial Encoding - VECTOR

POINT - x, y

LINE - x1, y1- x2, y2..- xN, yN

Area (Polygons)

- x1, y1- x2, y2..- xN, yN (closure Point)

* a single node with NO area

* a connection of nodes (vertices) beginning with a “to” and ending with a “from”

(Arcs)

* a series of arc(s) that close around a “label” point

Vector - Topology

Object Spatial Descriptive

1

2 3

45

15

1211

10

123

x1,y1x2,y2x3,y3

123

12

12

12

12

VAR1 VAR2

VAR1 VAR2

VAR1 VAR2

Fnode Tnode x1y1, x2y2

1 2 xxyy, xxyy2 3 xxyy,xxyy

10, 11, 12, 1510, …….

1

2 3

1

2

Raster Data Model

Set Selections

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 ]

[ 1 2 3 4 5 6 7 8 9 10 ]

AND, OR, XOR

1 2 32

AND = 2

OR

XOR

= 1,2,3

= 1

Spatial Overlay - UNION

1

2 3

4 5

1

2

3

1 23

4 5

6

78

9 10

1112

13 14

1516 17

12345

# attribute

123

# attribute

12345

# IN attribut OUT attribute

ABCD

102103

102 A A 102 B 102

Spatial Overlay - INTERSECT

1

2 3

4 5

1

2

3

1

12345

# attribute

123

# attribute

12345

# IN attribut OUT attribute

ABCD

102103

A 102 B 102 A 103 B 103

23

4 5

6 7

8 9

Spatial Overlay - IDENTITY

1

2 3

4 5

1

2

3

1

12345

# attribute

123

# attribute

12345

# IN attribut OUT attribute

ABCD

102103

A A 102 B 103 B

2

3 4

5

6 7

8 9

1011

12 13

Spatial Poximity - BUFFER

Constant Width

Variable Width

Spatial Poximity - NEAR

Assign a point to the nearest arc

Spatial Proximity - Pointdistance

123

123

2,0451,8991,743

DISTANCE

Spatial Proximity - Thiessen Polygons

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

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

Local Functions: Example•Multiply by constant value

X 3 =

•Multiply by a grid

X =

2 0 1 1

2 3 0 4

1 1 2

3 2

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

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

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

Focal Function: Examples

2 0 1 1

2 3 0 4

2 1 1 2

2 3 3 2

2 0 1 1

2 3 0 4

4 2 2 3

1 1 3 2

•Focal Sum (sum all values in a neighborhood)

=

=

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

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)

(3x3)16 13

17 19

Zonal FunctionProcess and analyze cells on the basis of ‘zones’

Zones define cells that share a common characteristic Cells in the same zone don’t 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

Zonal FunctionAn Example

•Zonal maximum – Identify the maximum in each zone

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.)

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

=

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

Golbal FunctionAn Example

•Distance Measures – Euclidean distance based upon cell size

Or – some function which must consider all cells before determining the value of any cell – (“cost” associated with a path across the surface)

1 1

1

2

2 1 0 0

1.4 1 1 0

1 0 1 1

1.4 1 1.4 2

=

Examples

outgrid = zonalsum(zonegrid, valuegrid)

outgrid = focalsum(ingrid1, rectangle, 3, 3)

outgrid = (ingrid1 div ingrid2) * ingrid3

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.