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GG3019/GG4027/GG5019 An Introduction to

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Presentation on theme: "GG3019/GG4027/GG5019 An Introduction to"— Presentation transcript:

1 GG3019/GG4027/GG5019 An Introduction to
Geographical Information Technology and GIS Systems and Geospatial Data Analysis David R. Green G12 – 2324

2 Website

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) 1,1 32 3,3
Columns (j) Image: number (0-255) or 8-bit GIS: number (code) 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 Raster Data Model Digital Elevation Models (DEM)
Grid of cells comprising heights (z-value) Uniformly spaced Create ‘surfaces’ X Y Z

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: Map Projections and Datums: Crime Mapping Exercise:
Map Projections and Datums: 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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