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Geographic Information Systems

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 NEED FOR GIS. the real world has a lot of spatial data

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Geographic Information Systems

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  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 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 312-314 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: • 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 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. Lake River Pond Reality - Hydrography Lake River Pond Reality overlaid with a grid 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 = No Water Feature 1 = Water Body 2 = River 0 0 0 0 0 0 0 2 1 1 1 0 0 1 1 2 0 0 2 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 Resulting raster Creating a Raster

  11. 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

  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 • 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

  15. 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

  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

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

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

  24. Projections

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

  26. An estimate of the ellipsoid is called a datum

  27. 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.

  28. Coordinate systems.. UTM

  29. State plane…

  30. Ok… let’s get GISy

  31. 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

  32. 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

  33. Digitizing….. • Nodes • Vertices • Et al

  34. Topology • P. 46 in my super secret book….

  35. 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…

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

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

  38. 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

  39. Let’s get ARCy….

  40. 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

  41. 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

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