Geographic Information Systems Spatial and non-spatial data, getting spatial data into Arc, and...
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Geographic Geographic Information SystemsInformation Systems
Geographic Geographic Information SystemsInformation SystemsSpatial and non-spatial data, getting Spatial and non-spatial data, getting spatial data into Arc, and databasesspatial data into Arc, and databases
Geographic Information Systems
• An information system that handles geographic data.
• Duhhhhhh!!!
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
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 312-314 of book
Geographic Information Systems
• Old School– Map-Overlay analysis
• New School– Computer based
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
Types of data• 1. Spatial 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.
DATA MODEL OF RASTER AND VECTORDATA MODEL OF RASTER AND VECTOR
REAL WORLD 1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
6
7
8
9
10
GRID RASTER VECTOR
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
Pond
Lake
River
Pond
Lake
River
1 1 0
11
1 1 1
11 1
2
2
22
2
2
11
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0
Reality - Hydrography
Reality overlaid with a grid
Resulting raster
Creating a RasterCreating a Raster
0 = No Water Feature1 = Water Body2 = River
DATA MODEL OF RASTER AND VECTORDATA MODEL OF RASTER AND VECTOR
REAL WORLD 1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
6
7
8
9
10
GRID RASTER VECTOR
VECTOR CHARACTERICTISVECTOR CHARACTERICTIS
POINT X
LINE
POLYGON
RASTER TO VECTORRASTER TO VECTOR
RIVER CHANGED FROM RASTER TO VECTOR FORMAT
RIVER THAT HAS BEEN VECTORISED
ORIGINAL RIVER
PRO AND CONS OF RASTER MODEL
• pro– 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
PRO AND CONS OF VECTOR MODEL
• pro– 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
Types of data• nominal, ordinal, ratio, (interval). • P. 163 in book
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
Spatial data….
point
line
Area / polygon
More stuff about data
• Precision vs. Accuracy • Garbage in – garbage out
Stuff to know about your spatial data
• Projection• Datum• Coordinate system
– Lat and long– UTM– State plane– Why you need to know this stuff??
Projections
Stuff to know about your spatial data
• Projection• Datum• Coordinate system
– Lat and long– UTM– State plane– Why you need to know this stuff??
An estimate of the ellipsoid is called a
datum
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.
Coordinate systems.. UTM
State plane…
Ok… let’s get GISy
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
Geo-referencing data• Capturing data
– 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
Digitizing…..• Nodes• Vertices• Et al
Topology• P. 46 in my super secret book….
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…
Example:
Think back to
last week’s
lab
another type of spatial data to know about..
• Digital Elevation Models (DEM’s)
• 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
Let’s get ARCy….
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
Querying GIS data• Attribute 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