Introduction to Geographic Information Systems Miles Logsdon mlogu

Introduction to Geographic Information Systems Miles Logsdon mlog@u. washington. edu http: //sal. ocean. washington. edu/

Spatial Information Technologies z Geographic Information Systems – GIS z Global Positioning System – GPS z Remote Sensing and Image Processing - RS Technologies to help answer: z What is “here”? … give a position z What is “next” to “this”? … given some description z Where all of the “? ? ? ” … detecting or finding z What is the spatial pattern of “? ? ? ” z 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: z. Components y. People, organizational setting y. Procedures, rules, quality control y. Tools, hardware & software y. Data, information z. Functions y. Data gathering y. Data distribution

Common “short hands” CAM- Computer Aided Mapping AM - Automated mapping CAD - Computer-Aided Design LIS - Land Information Systems z AM/FM - Automated Mapping/Facilities Management Systems z z z RS - Remote Sensing y aerial Photography y Photogrammetry y Photo interpretation y Thermal sensing y Radar imaging y Satellite Remote Sensing x. Meteorological x. Terrestrial y Image Processing

Geographic Data z Spatial Data ylocation yshape yrelationship among features z Descriptive Data yattributes, or ycharacteristics 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.

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” z. Three Attributes of Data y. Thematic (Value Variable) x. Nominal, … name, label x. Ordinal, … rank ordered x. Interval / Ratio, … measurement on a scale y. Spatial (location) y. Temporal Spatial Data: the spatial attribute is explicitly stated and linked to thematic attribute for each data item.

Spatial - thematic value types 200’ Sta. 94, DOC 4. 9 Stream, 3 Former Land Fill 100’ FOREST URBAN FOREST 200’ 100’ WELL Duvall, pop 1170 AGRICULTURE Snoqualmie River, 1 Brush Creek, 2

Geographies Layers, Coverages, Themes Land use Soils Streets Hydrology Parcels

Concept of Spatial Objects z POINTS z LINES z AREA

Spatial Encoding - RASTER POINT 0 0 1 0 0 AREA LINE 1 0 0 1 5 5 3 1 1 3 2

Spatial Encoding - VECTOR POINT - x, y * a single node with NO area LINE (Arcs) - x 1, y 1 - x 2, y 2. . - x. N, y. N * a connection of nodes (vertices) beginning with a “to” and ending with a “from” Area (Polygons) * a series of arc(s) - x 1, y 1 that close around - x 2, y 2 a “label” point. . - x. N, y. N (closure Point)

Vector - Topology Spatial Object 1 2 3 1 3 2 1 2 x 1, y 1 x 2, y 2 x 3, y 3 Fnode Tnode x 1 y 1, x 2 y 2 1 2 xxyy, xxyy 2 3 xxyy, xxyy Descriptive VAR 1 VAR 2 1 2 3 VAR 1 VAR 2 1 2 2 1 VAR 2 2 3 15 10 5 1 11 4 12 1 2 10, 11, 12, 15 10, ……. 1 2

Raster Data Model

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 ]

AND, OR, XOR 2 1 AND 3 = 2 OR = 1, 2, 3 XOR =1

Spatial Overlay - UNION 1 1 1 2 3 6 2 2 4 5 3 7 3 8 11 12 9 4 10 5 13 14 16 17 15 # 1 2 3 4 5 attribute A B C D # 1 2 3 attribute 102 103 # 1 2 3 4 5 IN attribut OUT attribute 102 A A B 102

Spatial Overlay INTERSECT 1 1 1 2 2 2 3 3 4 5 3 6 4 5 8 # 1 2 3 4 5 7 attribute A B C D # 1 2 3 attribute 102 103 # 1 2 3 4 5 9 IN attribut A B OUT attribute 102 103

Spatial Overlay IDENTITY 1 1 1 2 2 2 5 3 3 4 6 7 8 9 3 4 5 10 11 12 # 1 2 3 4 5 attribute A B C D # 1 2 3 attribute 102 103 # 1 2 3 4 5 13 IN attribut A A B B OUT attribute 102 103

Spatial Poximity - BUFFER ta s Con Va ria b idth W nt le W idt h

Spatial Poximity - NEAR Assign a point to the nearest arc

Spatial Proximity Pointdistance DISTANCE 1 2 3 2, 045 1, 899 1, 743

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 2 2 0 3 1 0 1 4 1 1 2 3 2 X 3 = 6 6 0 9 3 0 3 12 3 3 6 9 6 • Multiply by a grid 2 2 0 3 1 1 0 1 4 1 2 3 2 X 2 3 0 3 2 3 2 2 2 1 1 = 4 6 0 9 2 0 2 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, 2 x 2, 3 x 3, 4 x 4 cells Types of focal functions might be: • focal sum, • focal mean, • focal max, • focal min, • focal range

Focal Function: Examples • Focal Sum (sum all values in a neighborhood) 2 2 0 3 1 0 1 4 2 1 1 2 2 3 3 2 (3 x 3) = 16 13 17 19 • Focal Mean (moving average all values in a neighborhood) 2 2 0 3 1 0 1 4 4 2 2 3 1 1 3 2 (3 x 3) = 1. 8 1. 3 1. 5 2. 2 2. 0 1. 8 2. 2 2. 0 2. 2 2. 3 2. 5

Zonal Function Process 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 Function An Example • Zonal maximum – Identify the maximum in each zone 2 2 1 2 3 1 3 3 2 1 1 2 1 5 2 6 3 7 4 8 1 2 3 4 5 6 7 8 = 5 5 8 5 7 8 7 7 5 8 8 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. )

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 Function An Example • Distance Measures – Euclidean distance based upon cell size 1 2 1 1 = 2 1 1. 4 1 0 0 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)

Examples outgrid = zonalsum(zonegrid, valuegrid) outgrid = focalsum(ingrid 1, rectangle, 3, 3) outgrid = (ingrid 1 div ingrid 2) * ingrid 3

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.