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