Introduction to Geographic Information Systems

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

2. 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?

3. 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

4. GIS - consists of: • Components • People, organizational setting • Procedures, rules, quality control • Tools, hardware & software • Data, information • Functions • Data gathering • Data distribution

5. 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 Sensing Meteorological Terrestrial Image Processing Common “short hands”

6. 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.

7. 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)

8. 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.

9. 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

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

11. Concept of Spatial Objects • POINTS • LINES • AREA

12. 0 Spatial Encoding - RASTER POINT 0 0 0 0 1 0 0 0 0 5 5 3 AREA 1 3 3 1 1 2 LINE 1 0 0 0 1 0 0 1

13. 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)

14. Vector - Topology Descriptive Spatial Object VAR1 VAR2 1 2 3 x1,y1 x2,y2 x3,y3 1 2 3 Fnode Tnode x1y1, x2y2 VAR1 VAR2 1 1 2 1 2 xxyy, xxyy 2 3 xxyy,xxyy 1 2 3 2 2 1 VAR1 VAR2 2 3 1 2 10, 11, 12, 15 10, ……. 1 2 15 10 1 4 12 5 11

15. Raster Data Model

16. 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 ]

17. 2 2 1 3 AND = 2 = 1,2,3 OR XOR = 1 AND, OR, XOR

18. # attribute # attribute # IN attribut OUT attribute 1 2 3 4 5 1 2 3 1 2 3 4 5 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

19. # attribute # attribute # IN attribut OUT attribute 1 2 3 4 5 1 2 3 1 2 3 4 5 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

20. # attribute # attribute # IN attribut OUT attribute 1 2 3 4 5 1 2 3 1 2 3 4 5 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

21. Spatial Poximity - BUFFER Constant Width Variable Width

22. Spatial Poximity - NEAR Assign a point to the nearest arc

23. Spatial Proximity - Pointdistance DISTANCE 2,045 1,899 1,743 1 2 3 1 2 3

24. Spatial Proximity - Thiessen Polygons

25. 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

26. 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

27. 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

28. 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

29. 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) 16 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) =

30. 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

31. 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.)

32. 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

33. 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)

34. Examples outgrid = zonalsum(zonegrid, valuegrid) outgrid = focalsum(ingrid1, rectangle, 3, 3) outgrid = (ingrid1 div ingrid2) * ingrid3

35. 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.