Lecture07

Lecture07 - What is a Map Data Structure? Analytical and...

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Analytical and Computer Cartography Lecture 7: Spatial Data Structures for Mapping What is a Map Data Structure? z Map data structures store the information about location, scale, dimension, and other geographic properties, using the primitive spatial data structures (zero-, one-, and two-dimensional objects), or more complex objects such as arrays z Minimum requirement for computer mapping systems z The purpose is to support computer cartography, and NOT necessarily analytical cartography. z A Map data structure plus an attribute data structure is the minimum requirement for the additional analytical functions in Analytical Cartography, and GISystems. Map Data Structures are largely Input- Determined Output constraints on Map Data Structures ?
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Vector or Raster? z Advantages and Disadvantages (Burrough, 1986) z Choices determined by Purposes z Peuquet (1979) showed that “ most algorithms using a vector data structure have an equivalent raster-based algorithm, in many cases more computationally efficient ” (Clarke, 1995) z Vector I/O devices are being increasingly replaced by raster I/O devices z Most GIS software packages support both vector and raster data structures OR Vectors just seemed more correcter z Can represent point, line, and area features very accurately. z Far more efficient than raster data in terms of storage. z Preferred when topology is concerned z Support interactive retrieval, which enables map generalization Vectors are more complex z Less intuitively understood z Overlay of multiple vector map is very computationally intensive z Display and plotting of vectors can be expensive, especially when filling areas Rasters are faster. .. z Easy to understand z Good to represent surfaces, i.e. continuous fields z Easy to read and write – A grid maps directly onto a programming computer memory structure called an array z Easy to input and output – A natural for scanned or remotely sensed data – Easy to draw on a screen or print as an image z Analytical operations are easier, e.g., autocorrelation statistics, interpolation, filtering
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Rasters are bigger z Inefficient for storage Raster compression techniques might not be efficient when dealing with extremely variable data Using large cells to reduce data volume causes information loss z Poor at representing points, lines and areas Points and lines in raster format have to move to a cell center. Lines can become fat z Areas may need separately coded edges z Each cell can be owned by only one feature z Good only at very localized topology, and weak otherwise. z Suffer from the mixed pixel problem. z Must often include redundant or missing data. Entity-by-Entity Data Structures z Cartographic entities are usually classified by dimension into point features, line features, and area features z The simplest means to digitally representing cartographic entities as objects is to use the feature itself as the lowest common denominator z Entity-by-Entity data structures are concerned
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This note was uploaded on 12/28/2011 for the course GEOG 128 taught by Professor Staff during the Fall '08 term at UCSB.

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Lecture07 - What is a Map Data Structure? Analytical and...

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