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Applied Cartography and Introduction to GIS GEOG 2017 EL

Applied Cartography and Introduction to GIS GEOG 2017 EL. Lecture-2 Chapters 3 and 4. Vector Data Modeling. To prepare spatial data for computer processing: Use x , y coordinates to represent spatial features as points, lines, and areas.

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Applied Cartography and Introduction to GIS GEOG 2017 EL

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  1. Applied Cartography and Introduction to GISGEOG 2017 EL Lecture-2 Chapters 3 and 4

  2. Vector Data Modeling • To prepare spatial data for computer processing: • Use x,ycoordinates to represent spatial features as points, lines, and areas. • Organize geometric objects and their spatial relationships into digital data files that the computer can access, interpret, and process.

  3. A subway map of Taipei, Taiwan.

  4. Topology • We use topology to study arrangement of geometric objects and relationship between them. • Topology can therefore be used for geocoding and referencing. • TIGER: Topologically Integrated Geographic Encoding and Referencing. • An early application of Topology

  5. Digraph

  6. Georelational Data Model • Topology can be used to develop georelational data models. • In such a model: • geometries are stored in graphic files and • Attributes are stored in a relational database

  7. Georelational Data in ArcInfo An ArcInfo coverage has two components: graphic files for spatial data and INFO files for attribute data. The label connects the two components.

  8. The Coverage The coverage supports three basic topological relationships. • Connectivity:Arcs connect to each other at nodes. • Area definition:An area is defined by a series of connected arcs. • Contiguity:Arcs have directions and left and right polygons.

  9. Point Coverage

  10. Line Coverage

  11. Polygon Coverage

  12. Shapefile • The shapefileis a standard, non-topological data format used in ESRI products. • Although the shapefile treats a point as a pair of x-, y-coordinates, a line as a series of points, and a polygon as a series of line segments, no files describe the spatial relationships between these geometric objects. • Shapefiles can be read by other GIS software as well, such as MapInfo. • In general, a non-topological format is faster for the computer to process and display.

  13. Object-Based Data Model • The object-based data modeltreats spatial data as objects. It differs from the georelational data model in two important aspects. • The object-based data model stores both the spatial and attribute data of spatial features in a single system. • The object-based data model allows a spatial feature (object) to be associated with a set of properties and methods.

  14. Object-Based Model

  15. Geodatabase • The geodatabase is part of ArcObjects, a collection of thousands of objects, properties, and methods that provides the foundation for ArcGIS Desktop. • The geodatabase organizes vector data sets into feature classes and feature datasets • A feature class stores spatial data of the same geometry type. • A feature dataset stores feature classes that share the same coordinate system and area extent.

  16. Geodatabase Structure

  17. Advantages of Geodatabase • The hierarchical structure of a geodatabase is useful for data organization and management. • The geodatabase, which is part of ArcObjects, can take advantage of object-oriented technology. • The geodatabase offers on-the-fly topology, applicable to features within a feature class or between two or more participating feature classes. • Thousands of objects, properties, and methods in ArcObjects are available for GIS users to develop customized applications. • ArcObjects provides a template for custom objects to be developed for different industries and applications.

  18. Topology Rules • The geodatabase defines topology as relationship rules and lets the user choose the rules, if any, to be implemented in a feature dataset. • The geodatabase offers 25 topology rules by feature type.

  19. Topology Rules

  20. Composite Features • Composite features refer to those spatial features that are better represented as composites of points, lines, and polygons. • Composite features include TINs (triangulated irregular networks), regions, and routes.

  21. TIN A TIN approximates the terrain with a set of non-overlapping triangles.

  22. TIN Data Structure

  23. Regions • A region is a geographic area with similar characteristics. • A data model for regions must be able to handle two spatial characteristics: A region may have spatially joint or disjoint areas, and regions can overlap or cover the same area.

  24. Regions - Overlapping

  25. Region – Data Structure

  26. Routes A routeis a linear feature such as a highway, a bike path, or a stream but, unlike other linear features, a route has a measurement system that allows linear measures to be used on a projected coordinate system.

  27. Route – Data Structure

  28. Raster Data Model • A raster represents a continuous surface, but for data storage and analysis, a raster is divided into rows, columns, and cells. • Raster data represent points by single cells, lines by sequences of neighboring cells, and areas by collections of contiguous cells.

  29. Raster and Vector

  30. Elements of Raster Data Model • Cell value. Each cell in a raster carries a value, which represents the characteristic of a spatial phenomenon at the location denoted by its row and column. The cell value can be integeror floating-point. • Cell size. The cell size determines the resolution of the raster data model. • Raster bands. A raster may have a single band or multiple bands. • Spatial reference. Raster data must have the spatial reference information so that they can align spatially with other data sets in a GIS.

  31. Digital Elevation Model

  32. Types of Raster Data • Satellite Imagery • Digital Elevation Models (DEMs) • Digital Orthophotos (DOQ) • Land Cover Data • Bi-Level Scanned Files • Digital Raster Graphics (DRGs) • Graphic Files • GIS Software-Specific Raster Data

  33. Satellite Imagery

  34. Scanned Image

  35. Digital Raster Graphics

  36. Raster Data Encoding • Cell-by-Cell Encoding • Run Length Encoding • Quad Tree

  37. Cell-by-Cell Data Structure

  38. Run-Length Encoding Row 1: 5,6 Row 2: 4,6 Row 3: 3,7 Row 4: 3,7 Row 5: 3,7 Row 6: 2,7 Row 7: 2,7

  39. Quad-Tree Method

  40. Data Compression • Data compressionrefers to the reduction of data volume. • A variety of techniques are available for image compression. Compression techniques can be lossless or lossy. • The wavelet transform, the latest technology for image compression, treats an image as a wave and progressively decomposes the wave into simpler wavelets.

  41. Data Conversion The conversion of vector data to raster data is called rasterization, and the conversion of raster data to vector data is called vectorization.

  42. Rasterization and Vectorization

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