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Understanding Geospatial Data Analysis Using GIS Technology

Explore the fundamentals of geographical information technology through this introduction to GIS, including vector and raster data models, topological relationships, triangulated irregular networks, raster data structures, and data compression techniques. Discover the advantages and disadvantages of vector and raster data, as well as the essential knowledge required for using GIS software such as ArcView 3.X.

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Understanding Geospatial Data Analysis Using GIS Technology

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  1. GG3019/GG4027/GG5019 An Introduction to Geographical Information Technology and GIS Geographical Information Systems and Geospatial Data Analysis David R. Green G12 – 2324 d.r.green@abdn.ac.uk www.abdn.ac.uk/geospatial

  2. Website www.abdn.ac.uk/geospatial/GG3019

  3. Data Models Lecture 2 Objectives • Representing the ‘Real World’ • Examine Vector Data Model • Examine Raster Data Model

  4. Real World

  5. Real World

  6. Representation

  7. Vector Data Model • Points Lines and Areas (Polygons) • Represent ‘real world’ / ‘spatial’ features • X,Y co-ordinates • Point (zero dimension) • Line (1D) • Area (2D) • Nodes, vertex / line, link / area, polygon

  8. Vector Data Model Point: x,y Line: x1,y1: x2,y2 Area/Polygon: x1,y1:x2,y2:x3,y3:x4,y4:x1,y1 x,y……… northings and eastings

  9. Vector Data Model

  10. Vector Data Model • Line features can intersect (network) • Line features can join • Area features can be linked • Area features can be connected • Area features can have ‘holes’ • Representation depends on ‘scale’

  11. Topology • Non-Topological Data Structures • Do not always require topology • CAD - DXF • Shapefiles • No files to describe topology! • X,Y co-ordinates only • Topology can be built ‘on-the-fly’ • Needed for spatial analysis

  12. Topology • Topology • Relationships between objects • Based on Graph Theory (study of geometric objects and relationships) • 3 basic relationships • Connectivity (arcs connected at nodes) • Area Definition (arc connections define area) • Contiguity (left and right direction) • Data Structures - Topological

  13. Topology

  14. Topology Connectivity: Arcs are connected to others (at nodes). This identifies possible routes and networks, such as rivers and roads, via the lists of arcs and nodes in the database. Containment: An enclosed polygon has a measurable area; lists of arcs define boundaries and closed areas. Contiguity: The adjacency of polygons can be determined by shared arcs. These are fundamental to GIS analysis and queries, for example: a. From point A, how can I get to point B using the city road system? b. What is the area of the combined areas of all residential housing? c. Which residential areas are next to city parks?

  15. TIN • ALSO: Vector data structure for terrain mapping and analysis • Triangulated Irregular Network (TIN) • Approximate surface with a set of non-overlapping triangles • Constructed using Delaunay Triangulation • X,Y, Z values and edges

  16. TIN

  17. Raster Data Model • Grids / Rasters / Tessellations • Raster Map / Surface Cover • Square cells • Rows and Columns (address of each cell) • Cell contains a value / digital number (DN) • Point, Line, Area Features represented by ‘overlay’ of grid

  18. Raster Data Model Raster / Grid / Tessellation (regular) Image: number (0-255) or 8-bit GIS: number (code) Columns (j) 1,1 Addressing can be used to manipulate data store in each cell e.g. digital image processing 32 Row, column or x,y numbers provide an address for each cell e.g. 1,1; 3,3 etc… (similarity of spreadsheet) 3,3 Rows (i) e.g. mat (2,2) = 32 Multiple matrices e.g. bands of data/imagery; raster maps; digital images

  19. Raster Data Model • Airborne & Satellite Imagery • Scanned aerial photographs • Scanned images, maps, graphics • Often used as backdrops for digitising, context • May be geocorrected (image fitted or warped to a map (geometrically or planimetrically correct)

  20. Z Y X Raster Data Model • Digital Elevation Models (DEM) • Grid of cells comprising heights (z-value) • Uniformly spaced • Create ‘surfaces’

  21. Raster Data Model • Data Structures for Raster files • Cell values written - cell-by-cell encoding • Also: Run Length Encoding (homogenous areas) • Also: Chain Codes, Block Codes, Quadtrees (2D features)

  22. Raster Data Model • Data Compression Techniques • Standard file encoding for remotely sensed images not always efficient • Use more advanced techniques • Lossy (JPEG) and Lossless (TIF and GIF) compression • Mr.SID / ECW • Differential compression based on detail in image

  23. Vector and Raster • Conversion • Vector to Raster • Raster to Vector • Vectorisation • Rasterisation • Integration • ArcView / Idrisi

  24. Vector and Raster Advantages & Disadvantages • Vector: compact • Raster: bulky (large file sizes) • Vector: network analysis • Raster: cell-based modelling • etc…….. • Choice dependent on purpose!

  25. Using GIS – What you need to know! • Software and Hardware • Mathematics (arithmetic, geometry, co-ordinate systems) • Statistics (univariate and multivariate) • Data Formats • Map Projections • Datums

  26. Introducing ArcView 3.X • Views, Themes/Layers, Legends, Tables, Layouts and Project Files • Data Files (.shp, .dbf, .shx etc..) • ArcView Extensions and Scripts • Maps • Map Projections and Datums • Basic GIS Functionality

  27. Homework Topology: http://www.esri.com/news/arcuser/0401/topo.html Map Projections and Datums: http://www.colorado.edu/geography/gcraft/notes/mapproj/mapproj_f.html Crime Mapping Exercise: On GG3019 Website (instructions, three documents, website links, and map files for use in ArcView GIS) Summary of Introduction to ArcView

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