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

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

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

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: 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.

DATA 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

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

DATA 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 CHARACTERICTIS POINT X LINE POLYGON

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

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

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

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 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

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 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