1. Geographic Information Systems
Spatial and non-spatial data, getting spatial data into Arc, and databases

2. Geographic Information Systems
An information system that handles geographic data.
Duhhhhhh!!!

3. the real world has a lot of spatial data
THE NEED FOR GIS
the real world has a lot of spatial data
manipulation, analysis and modeling can be effective and efficiently carried out with a GIS
the neighborhood of the intended purchase of house
the route for fire-fighting vehicles to the fire area
location of historical sites to visit
Military purposes
Surveillance (pro and con)
the earth surface is a limited resource
rational decisions on space utilization
fast and quality information in decision making

4. What are GIS systems being used for..
City, county, state, tribal, etc planning.. Mentioned this last class
Wildlife biology, natural resources
Public health
Data visualization
Business planning
Agriculture
Others on page of book

5. Geographic Information Systems
Old School
Map-Overlay analysis
New School
Computer based

6. Geographical Information Science (GISc)
Deals with making appropriate or best use of geographical information
Closely related to GIS
Examples
Analysis techniques
Visualisation techniques
Algorithms for geographical data
A shout out to Ian Gregory U. of Portsmouth

7. Types of data 1. Spatial data: 2. Attribute data:
Says where the feature is
Co-ordinate based
Vector data – discrete features:
Points
Lines
Polygons (zones or areas)
Raster data:
A continuous surface
2. Attribute data:
Says what a feature is
Eg. statistics, text, images, sound, etc.

8. DATA MODEL OF RASTER AND VECTOR
REAL WORLD 1 2 3 4 5 6 7 8 9 10
GRID RASTER
VECTOR

9. RASTER DATA MODEL
derive from formulation that real world has spatial elements and objects fills those elements
real world is represented with uniform cells
list of cells is a rectangle
cell comprises of triangles, hexagon and higher complexities
a cell reports its own true characteristics
per units cell does not represent an object
an object is represented by a group of cells

10. Creating a Raster 0 = No Water Feature 1 = Water Body 2 = River Lake
Pond
Reality - Hydrography
Lake
River
Pond
Reality overlaid with a grid 1 1 1 1 1 1 1 2 1 1 1
0 = No Water Feature
1 = Water Body
2 = River 1 1 2 2 2 2 2
Resulting raster
Creating a Raster

11. DATA MODEL OF RASTER AND VECTOR
REAL WORLD 1 2 3 4 5 6 7 8 9 10
GRID RASTER
VECTOR

12. VECTOR CHARACTERICTIS
POINT X
LINE
POLYGON

13. RASTER TO VECTOR RIVER CHANGED FROM RASTER TO VECTOR FORMAT
RIVER THAT HAS BEEN VECTORISED
ORIGINAL RIVER

14. PRO AND CONS OF RASTER MODEL
raster data is more affordable
simple data structure
very efficient overlay operation
cons
topology relationship difficult to implement
raster data requires large storage
not all world phenomena related directly with raster representation
raster data mainly is obtained from satellite images and scanning

15. PRO AND CONS OF VECTOR MODEL
more efficient data storage
topological encoding
suitable for most usage and compatible with data
good graphic presentation
cons
overlay operation not efficient
complex data structure

16. Types of data
nominal, ordinal, ratio, (interval).
P. 163 in book

17. Allowed mathematical operations
Nominal; counting the number of occurrences in the measurement class
Ordinal; make judgments about greater than and less than
Interval-Ratio;allow a full range of mathematical operations

18. Spatial data….

19. point

20. line

21. Area / polygon

23. More stuff about data
Precision vs. Accuracy
Garbage in – garbage out

24. Stuff to know about your spatial data
Projection
Datum
Coordinate system
Lat and long
UTM
State plane
Why you need to know this stuff??

25. Projections

27. Stuff to know about your spatial data
Projection
Datum
Coordinate system
Lat and long
UTM
State plane
Why you need to know this stuff??

28. An estimate of the ellipsoid is called a datum

29. Datum
1) the North American Datum of 1927 (NAD 27) which is based on the Clarke 1866 ellipsoid; 2) the North American Datum of 1983 (NAD83);
2) the world geodetic system (WGS84) based on the GRS80 ellipsoid.

30. Coordinate systems.. UTM

32. State plane…

33. Ok… let’s get GISy

34. Layers
Data on different themes are stored in separate “layers”… book calls ‘em ‘data planes’
As each layer is geo-referenced layers from different sources can easily be integrated using location
This can be used to build up complex models of the real world from widely disparate sources

35. Geo-referencing data Capturing data Geo-referencing
Scanning: all of map converted into raster data
Digitising: individual features selected from map as points, lines or polygons
Geo-referencing
Initial scanning digitising gives co-ordinates in inches from bottom left corner of digitiser/scanner
Real-world co-ordinates are found for four registration points on the captured data
These are used to convert the entire map onto a real-world co-ordinate system
Danke to Ian Gregory

36. Digitizing…..
Nodes
Vertices
Et al

37. Topology
P. 46 in my super secret book….

38. Labeling Feature Attribute Tables
We are now in the world of “attribute data”
What the spatial stuff is
This also falls into categories of nominal, ordinal, ratio etc…

39. Example: Think back to last week’s lab

40. another type of spatial data to know about..
Digital Elevation Models (DEM’s)

41. 30 or 10 meter spacing
15 to 7 meter elevation accuracy
7.5 min
30 min (60 M)
1 degree
Can turn into raster, TINs

42. Let’s get ARCy….

45. Geographical Information Systems (2)
2. GIS: A tool-kit
Manipulate spatially:
Calculate distances and adjacencies
Change projections and scales
Integrate disparate sources
Analyse spatially:
Quantitative analysis
Exploratory spatial data analysis
Qualitative analysis
Visualise data:
Maps!
Tables, graphs, etc.
Animations
Virtual landscapes

46. Querying GIS data Attribute query Spatial query
Select features using attribute data (e.g. using SQL)
Results can be mapped or presented in conventional database form
Can be used to produce maps of subsets of the data or choropleth maps
Spatial query
Clicking on features on the map to find out their attribute values
Used in combination these are a powerful way of exploring spatial patterns in your data