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104 Cards in this Set
- Front
- Back
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Geospatial data |
Data describing both the locations and characteristics of spatial features such as roads etc. on Earth's surface |
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EComponents of GIS |
Hardware Software Methods/procedures Geographic data People |
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Desirable characteristics of GIS |
1.) Quick and easy access to large volumes of data 2.) The ability to select detail by area or theme 3.) Link or merge one data set with another 4.) Search for particular characteristics or features in an area 5.) Update data quickly and cheaply 6.) Model data and assess alternatives 7.) Output results such as maps, graphs, address lists, summary statistics etc. tailored to particular needs |
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Layers |
Collection of geographic objects that make up GIS maps. May contain features or surfaces |
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Features |
Contained in layers. They have shape and size and can be represented as points, lines or polygons (vector data) Can be displayed at different sizes |
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Surfaces |
Contained in layers. Have number values rather than shapes- elevation, temperature etc. (Raster data) |
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Attributes |
The information that features are linked to (a countries table of attributes that shows population etc.) |
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Spatial relationships |
Relationships present in features. Who are it's neighbours? Does it cross any other features? I'd it contained within another feature? |
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Cartography |
The art, science, and technology of map making |
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What is a map? |
A diagramatic representation of Earth on a two-dimensional surface |
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Why are maps abstractions |
Because they are two-dimensional and present the real world in a simplified fashion at a reduced scale |
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What are maps used for? |
1.) The display of spatial data 2.) The analysis of spatial data |
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Relevance of maps to GIS |
1.) Maps are a common source of input data for a GIS 2.) Often GIS are used for projects of global or regional scales so consideration of the effect of the Earth's curvature is necessary 3.) Monitor screens are analogous to a flat sheet of paper |
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Cartographic process |
1.) Purpose 2.) Define the scale 3.) Select features 4.) Choose a method of representation of the features (points, lines, areas) 5.) Generalize these features for representation in two dimensions 6.) Adopt a map projection for placing features onto a flat plane 7.) Apply a spatial referencing system (UTM, lat, long etc.) to locate features 8.) Annotate map with keys, legends and text |
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How to express map scale |
1.) Statement 2.) Linear or line 3.) Representative fraction or ratio |
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Map Scale |
Tells us how distances on the map relate to real world distances |
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Large-scale map |
Displays a small area in greater detail (1:25,000) |
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Small-scale map |
Displays a larger area in lesser detail (1:100,000) |
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Projections |
How we represent the 3 dimensional Earth on a 2 dimensional surface (a systematic transformation) |
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Developable Surface |
If you shine a light through the globe, the image is projected onto a surface |
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Fundamental concepts of projections |
1.) Earth is almost round, but always changing shape 2.) All projections make compromises (preserve say sailors directions, or an element that is important) 3.) Distortions occur in properties of angles, areas, distances and directions 4.) Data from different projections cannot be combined |
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3 models to represent Earth when making projections |
1.) Sphere 2.) Ellipsoid 3.) Geoid |
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Sphere |
-Simplest of the models to represent Earth when making projections -Sufficient for geographic information and maps of very large areas -Requires only one parameter (radius) |
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Ellipsoid |
-Spinning of the Earth creates a centrifugal force that causes the Earth to bulge at the equator and flatten at the poles -Accurate enough for most geographic information and maps of smaller areas |
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Geoid |
-Takes into account differences in the Earth's gravitational field -Different weights of material in the Earth's core, differences in magnetic fields and movements of Earth's tectonic plates create irregularities in shape (i.e. it is not a perfect ellipsoid) -A geoid is the most accurate representation of Earth's surface using a common reference point -A reference ellipsoid is defined, then differences between are represented as geoid heights (+ or -) -Used for very detailed and accurate measurements of location -Geoid is constantly changing |
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Datum |
-A fixed, known reference point for calculating the geographic coordinates of a location -Both vertical (elevations, tide levels) and horizontal (location) |
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Compromise Projection |
Sometimes neither area or distance are preserved in order to make a more visually pleasing projection |
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The Developable Surface |
Three possible: 1.) Cone 2.) Cylinder 3.) Plane |
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Aspect |
Orientation of the developable surface relative to the globe 1.) Equatorial (shine of the equator, poles distorted) 2.) Transverse (cylindrical-horizontal) 3.) Oblique (not equator/poles, but any line of longitude) 4.) Polar (shine onto poles, equator distorted) |
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Tangent |
One point of contact between the developable surface and the spheroid, ellipsoid or geoid is the most accurate part of the projection |
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Secant |
2 points of contact between the developable surface and the spheroid, ellipsoid or geoid. The most accurate parts of the projection |
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Lambert: conformal conic |
-Looking onto the North Pole -Good for areas with east-west orientation |
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Mercator: conformal |
-Flat map -Flat, equal lines of constant bearing, good for navigation, popular with early sailors |
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Transverse Mercator |
-Flat map -Lines curve up to the poles -Used for topographical maps, since local shapes are preserved |
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Sinusoidal: equal area |
-3 eye-shaped maps -Lines curve up to the poles -Good for showing distribution patterns |
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Stereographic: Azimuthal |
-Rounded map -Lines curve -Direction is preserved -Great Circle routes shown as straight lines on flat surfaces but curved in real life -Good for airplane navigation |
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Robinson: Compromise |
-Squished ellipse shape with flat top and bottom and rounded edges -Lines curve -Adopted by National Geographic because it was 'visually pleasing' |
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Determining Distortion |
1.) Scale factor 2.) Local scale (at a particular place) divided by principle scale (at standard line) 3.) Tissot Indicatrix |
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Scale Factor |
-A method of determining distortion -No distortion = a scale factor of 1 (tangent or secant) |
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Tissot Indicatrix |
-A method of determining distortion -Project small circles from ellipsoid onto the map and inspect visually, or measure dimensions of projected circle -Example: an equatorial projection, the circles at the equator will be smaller and more round whereas the circles at the poles will be bigger and elongated due to distortion |
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What property does a conformal projection preserve? |
Angles |
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What property does a equal area/equivalent projection preserve? |
Areas |
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What property does a equidistant projection preserve? |
Distant |
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What property does a azimuthal projection preserve? |
Direction |
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2 Types of Coordinate Systems |
1.) Global or spherical coordinate systems (based on latitude and longitude) 2.) Projected coordinate systems (based on a map projection) |
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Graticule |
Lines of latitude and longitude superimposed on a globe |
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Latitude |
-Angular distance North or South of the equator -Values from 0 degrees at the equator and 90 degrees at the poles -Direction North or South must be given -Degrees in minutes and seconds -Equally spaces lines so called "parallels" |
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Longitude |
-Angular distance East and West of the Prime Meridian -Values range from 0 degrees at the Prime Meridian to 180 degrees East or West at the International Date Line -Lines are NOT parallel to each other |
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What does a geographic coordinate system contain? |
1.) A datum 2.) A prime meridian 3.) An angular unit of measurement (reference point) |
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Cartesian coordinate system |
-Linear coordinate system -Assigns x and y coordinates to every point on a flat surface -X and y values represent distances from the origin -Negative values are possible, so false eastings and false northings may be used to force positive values (changing the origin) |
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Polar coordinate systems |
-Used mainly in polar regions -Origin is at the pole and 2D locations are given as angles (azimuth) and distances from the origin -3D locations include a second angle to represent elevation |
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Spherical coordinate systems |
Similar to 3D polar (x,y,z) but the origin is the centre of the sphere, not the pole |
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UTM coordinates |
-A projected coordinate system used on topographic maps -Shape is preserved -Rectangular grid system (zones) superimposed on the Earth's surface -Cylindrical secant transverse Mercator projection is applied 60 different zones to minimize distortion |
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Locational Systems |
-Locally defined, and different between areas -Different reference points meant that boundaries overlapped |
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Survey Monuments |
Permanent markers with known UTM coordinates allowing the transformation of locally referenced data to a common coordinate system |
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CSRS |
Canadian Spatial Referencing System- network of monuments that can be measured with great precision by satellites |
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Data Models |
How real world spatial features are represented in a GIS (either raster or vector) |
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Types of Spatial Entities |
1.) Points 2.) Lines 3.) Areas/polygons (park boundaries) 4.) Networks (interconnected) 5.) Surfaces (value in every location) |
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Problems with the 5 spatial entities |
1.) The world is too complex to fit everything into 5 simple categories 2.) These 5 entities are 2 dimensional 3.) Do not account for changes over time 4.) Real features can be discrete (whole number) or continuous (can take on any value in a given range) 5.) Scale of analysis varies (use of the data, need a smaller scale etc.) |
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Tessellation |
'Tiling' of a plane with geometric shapes so there are no gaps and no overlaps. Raster format is a type of tessellation |
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Nominal cell value |
Just a label, has no mathematical meaning |
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Ordinal cell value |
Ranked data, but little mathematical information |
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Interval cell value |
Values can be added and subtracted but not divided since there is no meaningful zero (e.g. temperature) |
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Ratio cell value |
Has true zero, can perform all mathematical operations |
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Minimum Mapping Unit |
The size of a pixel/cell should be less than 1/2 the size of the smallest object to be mapped |
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Raster Data Structure |
Method or format for storing raster data in the computer |
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Types of Raster Data Structure |
1.) Cell by cell 2.) Run length encoding (RLE) 3.) Block coding 4.) Chain coding 5.) Quad trees |
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Cell by Cell |
-Simplest -Each cell value written into a file by row and column -Ideal for continuously changing cell values |
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Run Length Encoding (RLE) |
-Good for data with many repetitive cell values -More efficient -Cell values recorded by row and group |
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Block Coding |
-Like 2D RLE -Series of square blocks of same values |
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Chain Coding |
-Defines boundary of the entity -Sequence of unit cells starting from and returning to a given origin -Direction of travel around boundary given by numbers |
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Quad Trees |
-Divides and subdivides raster into hierarchy of quadrants until every quadrant contains only one cell value -Contains nodes (quadrants) and branches -Efficient for storing area data -Efficient data processing |
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Leaf Node |
The end point of a quadrant when it cannot be subdivided (used in quad trees) |
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Raster Data Compression |
-Needs considerable memory space -Data compression reduces data volume -Useful for background images, not analysis |
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Lossless Compression |
-Raster data -Preserved pixel values and allows original raster or image to be precisely reconstructed (RLE is an example) |
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Lossy Compression |
-Raster data -Cannot reconstruct original exactly, gives a 'blocky' appearance |
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Advantages of Raster Data |
1.) Geographic location is inherent in cell position (only need coordinates of origin, bottom left corner) 2.) Easy creation from image data 4.) Easy to overlay, analyze 3.) Ideal for representing surfaces4.) Easy to overlay, analyze5.) Efficient storage for dense, heterogeneous data (lots of points rather than just one point) surfaces4.) Easy to overlay, analyze5.) Efficient storage for dense, heterogeneous data (lots of points rather than just one point) 5.) Efficient storage for dense, heterogeneous data (lots of points rather than just one point) |
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Disadvantages of Raster Data |
1.) Must pre-define spatial resolution 2.) Requires large amounts of storage space 3.) Inefficient when data is sparse or homogeneous (single points) 4.) Deals poorly with linear features (rivers etc.) 5.) Harder to represent topology 6.) Output maps are not the best quality (not clear lines etc.) |
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Feature Class |
ArcGIS, each feature class is a separate layer. Points, lines and polygons are feature classes |
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FID & OID |
Feature ID. And table ID. Every feature has a unique internal ID to link spatial data with attribute data, set by software |
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Points |
-Basic vector feature -x,y coordinates -e.g. fire hydrants etc. |
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Lines |
-1 dimensional features -Location and length properties -Minimum 2 points: beginning and end nodes -Additional points between give shape to the line, called vertices -Shape could also be stored as a mathematical equation -e.g. roads, rivers, property boundaries etc. |
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Vertices |
Additional points between lines that give shape to the line |
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Polygons (areas) |
-2 dimensional objects bound by a continuous line -Single node may define beginning and ending point -Polygons can share common boundaries and also common nodes -Size and perimeter properties -e.g. provinces, soil types etc. |
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Topology |
-The mathematical study of the properties of objects that are not distorted under continuous deformations (deformations could be projections etc.) -Describes relationships between features, or how spatial data share geometry -Allows layers to be combined and analyzed -Also used to detect errors such as polygons that aren't closed -Requires additional data files to be stored |
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3 Essential Elements of Vector Data Structure |
1.) Adjacency 2.) Enclosure 3.) Connectivity |
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Adjacency |
-An essential element of vector data -Information about neighbours of different objects |
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Enclosure |
-An essential element of vector data structure -Information about spatial features that enclose other spatial features (e.g. a lake encloses an island) |
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Connectivity |
-An essential element of vector data structure -Information about links between spatial objects (e.g. which side of the street a specific address is on) |
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2 Types of Vector Models |
1.) Georelational 2.) Object-oriented |
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Georelational Vector Model |
-Oldest version -Includes coverages and shapefiles -Stores spatial and attribute data in separate files -Attribute data stored in a relational database -Object ID (OID or label) link spatial and attribute data |
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Coverage |
-A digital vector storage framework -Not a single file -Can be copied and pasted using file management tools (ArcCatalogue) -Collection of files and directories linked to get digital vector storage framework with built-in topology |
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Point Coverage |
Relates feature ID's and pairs of x,y coordinates with a point feature attribute table |
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Arc (line) Coverage |
-Information about arcs summarized in an arc-node list -Specifies the 'from node' and 'to node' and all of the vertices that define the line |
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Polygon Coverage |
-Also contains polygon left-right list (defines left and right polygons of every arc in the coverage according to the arc's directions) -Polygon-Arc list defines arcs that make up each polygon |
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Shapefiles |
-Contain non-topological vector data and attribute information in a dataset -No topology, so easier to process, store, analyze etc. -Consist of *.shp files (geometric shapes) |
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Object- Based Vector Model |
-Spatial data treated as objects -Both spatial and attribute data stores in a single system -More efficient but georelational data model is still widely used |
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Classes |
-A class is a collection of objects with the same attributes (States) -Grouped into superclass (U.S.) or divided into subclasses (counties within the state, with multiple attributes associated with each county) |
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Efficient Storage Ensures... |
-No node or line segment is duplicated -Line segments and nodes can be referenced to more than one polygon -All polygons have unique identifiers -Island and hole polygons can be adequately represented |
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Required Steps in Vectorization |
1.) Line thinning (lines in raster may be more than 1 cell wide) 2.) Line extraction (determine where individual lines begin and end) 3.) Topological reconstruction (connects extracted lines and shows where digitizing errors exist) 4.) Line smoothing (to get rid of step like features) |
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Voxels |
True representation of 3D data (3D raster) |
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Digital Terrain Models (DTM) |
-A surface model -Approximate a continuous surface using a finite number of observations |
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Raster approach to DTM's |
-Uses a grid of height values g. mountainous terrain adjacent to flat plain -Different resolutions required -Complex terrain requires finer grid-Difficulty handling e.g. mountainous terrain adjacent to flat plain-Different resolutions required-Compromise either stores redundant data in the flat area, or misses variability in the complex area-Data compression techniques (RLE, quad trees etc.) can help grid-Difficulty handling e.g. mountainous terrain adjacent to flat plain-Different resolutions required-Compromise either stores redundant data in the flat area, or misses variability in the complex area-Data compression techniques (RLE, quad trees etc.) can help -Difficulty handling e.g. mountainous terrain adjacent to flat plain-Different resolutions required-Compromise either stores redundant data in the flat area, or misses variability in the complex area-Data compression techniques (RLE, quad trees etc.) can help -Compromise either stores redundant data in the flat area, or misses variability in the complex area -Data compression techniques (RLE, quad trees etc.) can help |
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Vector Approach to DTM's |
-TIN's (Triangulated Irregular Network) -Looks like crumpled paper -Joins heights with straight lines to create a mosaic of irregular triangles -Vertices of triangles represent peaks, depressions etc. Edges represent ridges and valleys |
