1. GIS Data Models Representing the Earth
Week 3 & 4
March 2 &
Institute of Space Technology, Karachi

2. Data Model: Chapter 2 of Text Book
Assignment 1: Read Chapter 2

3. Definition
A data model may be defined as the objects in a spatial database plus the relationships among them (Bolstad)

4. GIS Data Model An abstraction or a simplified view of the real world
Because every computer system has limits, only a subset of the essential characteristics are represented for each entity.
Only lake boundaries and essential lake characteristics have been saved in this example. All other information
for the area may be ignored, e.g., information on the roads, buildings, slope, or soil characteristics.

5. GIS Data GIS data includes information on Spatial location
Non-spatial properties

6. GIS Representation
Real world entities are approximated with spatial objects or features
Entities are “things” in real world
Rivers
Roads
Land use

7. Data Representation

8. Objects or features are the representation of the entities in a data model
Examples
Fire Hydrant: by location points
Roads: by series of straight lines connected at nodes
Lakes: can be represented by set of polygons

9. Exercise: How GIS features can be extracted from this Image?

10. GIS Representation

11. Representation of GIS Data Model
Legends
Buildings
Land
Road

12. What entities we may represent as Point, Line, Polygon or Surface?
Points
Buildings, fire hydrants, location of accidents, traffic signals, etc.
Lines
Roads, pipelines, rivers, water mains, traffic routes, etc.
Polygons
Land parcels, lake, countries, etc.
Surface
Elevation, temperature, spectral data
A point normally represents a geographic feature too small to be displayed as a line or area.
Line: shape of geographic features too narrow to be displayed as an area at the given scale (contours, street centerlines, or streams), or linear features with no area. More complex lines made up of many line segments.
Polygon: A feature used to represent areas.

13. Selection of data type also depends on Map Scale
Example
How to represent buildings, in a map points or polygons?
Map scale around 1:25,000 or 1:10,000
As points
In a more detailed map of scale of 1:1000
Buildings may better be represented as polygons, rather than point

14. Coordinates and attributes are used to represent entities
Coordinates: a pair of numbers that specify location in relation to an origin.
Single or groups of coordinates are organized to represents shapes and boundaries that define objects.

15. Attribute Data Type Qualitative Quantitative (Numerical)
Nominal Attribute (names or labels): provide descriptive information about an object (color, landuse type, city name, etc.).
Ordinal: values assigned to objects or events represent the rank order. May be either descriptive or numeric (small, medium, large; road class I, II, III)
Quantitative (Numerical)
often recorded as real numbers, most often on a linear scale
Intervals: number that are separated by the same interval. The "zero point" on an interval scale is arbitrary; and negative values can be used. (Temperature in oC and oF)
Ratios: have all the features of interval measurement and also have meaningful ratios between arbitrary pairs of numbers. Physical quantities like mass, length, or energy are measured on ratio scale
Nominal Attributes: also called ‘categorical attributes
Nominal: descriptive information associated with a spatial entity
Ordinal Attributes: The order reflects only rank, and does not specify the form of the scale. An object with an ordinal attribute that has a value of four has a higher rank for that attribute than an object with a value of two. But do not infer that the attribute value is twice as large, because we cannot assume the scale is linear.
Ordinal: rank order or scale by their values. Such as small, medium, large (soil erosion class, drainage class…). Does not infer a specific scale.
Temperature is a measurement of the average kinetic energy of the molecules in an object or system. At absolute zero, a hypothetical temperature, all molecular movement stops - all actual temperatures are above absolute zero

16. Representation of a feature ‘Fire Hydrant’
Attributes are presented in tables. Each row corresponds to an individual spatial object and column to an attributes.

17. Thematic Layers Each layer representing a theme
Thematic layers. Each layer organizes the spatial and attribute data for a given set of spatial objects based on a certain theme.
We may combine data to create a new data layer

18. Data Representation: Discrete Vs. Continuous

19. Spatial Data Types - Discrete

20. Spatial Data Types – Continuous
Data is organized into surfaces where one attribute value vary across the space, examples: Elevation, temperature, rainfall, ocean salinity, etc.
Continuous values across the space.

21. GIS Data Models Two Primary data models Vector Data Model
Edges and vertices are defined by series of coordinate pairs (x, y) and connected by arcs
Raster Data Model
Map area is divided into grid cells
Each cell contains a value (Categorical or Quantitative values)

22. Data Model vs. Data Structure
A data model is a conceptual model of the real world
The representation of this model in computer is the data structure
Data models do not necessarily imply any particular data structures
Data structure is the specific format with which the data are stored on computers
Data structures are simple encodings that work well in a computer setting. (Encoding is the process of transforming information from one format into another)
A data structure is a particular way of storing and organizing data in a computer so that it can be used efficiently.
Different kinds of data structures are suited to different kinds of applications.
Data structures can represent the same data model while still being very different from one another.
For example, you could represent a vector data model using coverages, shapefiles, or geodatabases. Although these all take the same basic approach in representing the model, there are still significant differences between them.

23. Data Model vs. Data Structure
Different types of data structures can be used to represent the same data model
Example: consider a feature represented as a line (vector data model)
To draw and analyze this feature as line, computer needs some information – location of nodes/vertex
This information can be provided in the form of a table listing the coordinates of nodes/vertices and also identifying which lines goes through which node/vertices
This table is the basic data structure (coverages and shapefile use this type of structure)

24. Representing the Earth
VECTOR
RASTER

25. Data Models
There are instances when working with vector tools and formats is the best practice, and instances when working with raster tools and formats is the best practice. There are times when both formats come together.
Which one is better? Nature of the data and the processing desired determines the appropriate data structure.
A point feature is represented as a value in a single cell, a linear feature as a series of connected cells that portray length, and an area feature as a group of connected cells portraying shape

26. Comparison between Vector and Raster Data Models
Point: Position, no area
Line: Length, no width
Polygon: Area and perimeter
Raster
Point: 1 cell
Line: Multiple cells joined at edges or corners, usually with only 1 or 2 neighbors
Polygon: Group of contiguous cells joined at edges or corners

27. Data Models

28. Vector Data Model

29. Vector Data Model
A vector based GIS is defined by the vectorial representation of its geographic data
In vector based model geospatial data is represented in the form of coordinates
In vector data, the basic units of spatial information are points, lines (arcs) and polygons
Composed of two components: the one that manages spatial data and the one that manages thematic data
A unique key element called ‘identifier’ for each object allows the system to connect both databases
Stores only those points which define features and all space outside these features is 'non-existent’

30. Some Common Vector Data Formats
Coverage
Shapefile
Geodatabase
TIN (Triangulated irregular networks)

31. Advantages of Vector Data Formats
Good representation of the landscape being mapped
Looks great
Generalization of the graphics is possible while still maintaining the great looks
Topology can be completely described including network linkages ….
A GIS Topology is a set of rules and behaviors that model how points, lines, and polygon share geometry. For example adjacent features such as two counties will share a boundary.

33. Raster Data Model

34. How to Represent Point Features in Raster Data Models

35. How to Represent Line Features in Raster Data Models

36. How to Represent Polygon Features in Raster Data Model

37. Raster Data Models For Raster Model there are
Array of pixels (each pixel representing a specific value)
A matrix consisting of rows and columns with each grid or pixel representing a specific value

38. Raster Data Model
Raster data is an abstraction of the real world where spatial data is expressed as a matrix of cells or pixels with spatial position implicit in the ordering of the pixels
They store each cell in the matrix regardless of whether it is a feature or simply 'empty' space
Typically these cells are square and evenly spaced in the x and y directions

39. Raster Data Model - Example

40. Cell Value
Cell values can be either positive or negative, integer, or floating point
Integer values are best used to represent categorical (discrete) data
Floating-point values to represent continuous surfaces
Cells can also have a ‘No Data’ value to represent the absence of data

41. Grid Size and Resolution
Pixel/cell refers to the smallest unit of information available in an image or raster map
Cell dimension specifies the length and width of the cell in surface units, e.g. the cell dimension may be specified as 30 meters on each side
volume of data increases as the cell dimension gets smaller
Reducing the cell dimension by four causes a sixteen fold increase in the number of cells
Smaller cell size provides greater spatial detail

42. Grid Size and Resolution
For a given area, a linear decrease in cell size cause an exponential increase in cell number,

43. http://ceng572. cankaya. edu

44. Generic Structure for a Grid
Source:

45. Raster Data Model Larger Cell Size Lower resolution
Lower feature spatial accuracy
Faster display
Faster processing
Smaller file size
Smaller Cell Size
Higher resolution
Higher feature spatial accuracy
Slower display
Slower processing
Larger file size
16X16 = 256

47. Data Accuracy with Cell
Mixed Pixel Problem
Winner takes all
Wat/Veg dominates
Edges separate??
Value is correct when variable value is uniform over the raster cell
In case of within cell variation then average, central or most common value prevails

48. http://ceng572. cankaya. edu

49. Types of Raster Data Thematic Raster
Like a map describes the features and characteristics of an area and their relative position in space
Cell values are measured quantity or classification of a particular phenomenon (either integers or real numbers
Stored in a single band
Image Raster
Cell values represent reflected or emitted light/energy
Usually in 3 bands
Satellite image or scanned photographs
Source: Smart land-use analysis: the LUCIS model land-use conflict identification ...
 By Margaret H. Carr, Paul Dean Zwick

50. Raster – As Thematic Map
Rasters representing thematic data can be derived from analyzing other data. A common analysis application is classifying a satellite image by land-cover categories .
Also through a geoprocessing model to create a raster dataset .
By grouping the values of multispectral data into classes (such as vegetation type) and assigns a categorical value

51. Source: Smart land-use analysis: the LUCIS model land-use conflict identification ...
 By Margaret H. Carr, Paul Dean Zwick

52. Rasters – As Base Map
Example orthophotographs: Background display for other feature layers (for other layers’ spatial alignment)
Three main sources of raster base maps are orthophotos from aerial photography, satellite imagery, and scanned maps

53. Raster – As Surface Map
Rasters provide an effective method of storing the continuity as a surface

54. Raster – As Attributes of a Feature
Above is a digital picture of a very large, old tree that could be used as an attribute to a landscape layer that a city may maintain.
Rasters used as attributes of a feature may be digital photographs, scanned documents, or scanned drawings related to a geographic object or location

55. Raster Attribute Table
Raster values and other attributes are stored in the Value Attribute Table (VAT)
A thematic raster contains at least two items in its VAT
Value:
Represents some characteristics being mapped
Count:
Number of cells that share the same value

56. Raster Attribute Table
When a raster attribute table is generated, there are three default fields created in the table: OID, VALUE, and COUNT. It is not possible to edit the content in these fields. The ObjectID (OID) is a unique, system-defined, object identifier number for each row in the table. VALUE is a list of each unique cell value in the raster datasets (in a grid, this is an integer). COUNT represents the number of cells in the raster dataset with the cell value in the VALUE column. Cell values represented by NoData are not calculated in the raster attribute table.

57. Source: www.utsa.edu/LRSG/Teaching/ES2113/L3_...

58. Zone and Region Cells with same value makeup ‘Zone’
The size of the zone is defined by the ‘count’ item
A set of contiguous cells with the same value is called a ‘Region’
Source: Smart land-use analysis: the LUCIS model land-use conflict identification ...
 By Margaret H. Carr, Paul Dean Zwick

59. Raster Overlay
Source:

60. Raster Overlay
Source:

61. Raster Overlay
Source:

62. ‘NoData’ Value Represents missing or unknown information
When a cell is vacant, it’s assigned ‘NoData’ value
‘NoData’ remain always ‘NoData’ for ESRI rasters unless specifically requested
Combining 2 or more ESRI rasters will retain ‘NoData’ values in the outer raster
Sometimes also referred to as null value.
NoData to identify areas where GIS analyst does not wish to compute real values. The assignment of NoData to areas the analyst wants to remove from considerations called ‘masking’ and the raster containing the masked areas is called a mask raster.

63. Acquiring Raster Data Satellite Remote Sensing Aerial Imaging
Other Raster sources
DEM: Digital elevation model (DEM) is a digital representation of ground surface topography …
Scanned map datasets don't normally contain spatial reference information
Image rectification converts images to a standard map coordinate system. This is done by matching ground control points (GCP) in the mapping system to points in the image. These GCPs calculate necessary image transforms.

64. Data Sources Digitizing existing maps Scanning existing maps
Digital photogrammetric map production
Entry of computed coordinates from field measurements
Editing for improvement of data quality is required sometimes

65. Advantages of Raster Data Formats
Can represents different types of continuous surfaces and ability to perform surface analysis
Computing/processing is fast
Surface data faster to display
Overlaying maps is easy
Integration of remotely sensed imagery is straightforward
Tiling facilitates make easy handling of large data
Good for accomplishing complex analysis operations through complex raster expressions (A huge variety of complex spatial and advanced statistical analyses are supported)
Only solution for some application which can not handled by vector
Hydrologic modeling, spread of wild fire, air pollution dispersion etc.
Overlay: no possibility of silver polygons developing since all raster cell borders are coincident
Remote sensed imagery: satellite images or scanned photos.
Fast overlays with complex datasets
Tiling an image segments it into a number of smaller rectangular areas called tiles

66. Disadvantages of Raster Data Formats
Spatial inaccuracies due to the limits imposed by the raster dataset cell dimensions.
Very large datasets needs more memory space and more processing time
Changing cells to one-half the current size requires as much as four times the storage space
There is also a loss of precision
Large datasets: Resolution increases as the size of the cell decreases. For a given area, changing cells to one-half the current size requires as much as four times the storage space, depending on the type of data and storage techniques used.
There is also a loss of precision that accompanies restructuring data to a regularly spaced raster-cell boundary.

67. Advantages/Disadvantages of Raster and Vector
The selection of data model has a major impact on how we capture, store, and use the
spatial data.
“Yes raster is faster, but raster is vaster, and vector just seems more corrector”
Source:

68. Conversion Between Raster and Vector Data Models
Raster and vector two basic data structures for storing and manipulating geospatial data.
Spatial resolution of a raster is determined by the resolution of the acquisition device and the quality of the original data source.

71. Spatial Data Conversion
Vector to Raster or Rasterization
Raster to Vector or Vectorization
Converted data is less accurate than original data
Spatial data may be converted between raster and vector data models
Normally some data/information are lost in the conversion process therefore converted data is less accurate than original data

72. Vector to Raster (V2R)
Assign a cell value for each position occupied by vector features
The cell in which the point resides is given a number or other code identifying the point feature occurring at the cell location.

73. Vector to Raster Encoding Methods
Center Cell Method
The center location of the cell determines the raster value encoded from the vector data
Majority of Cell Method
The value in the vector dataset that covers the majority of the cells determines the cell value
Weighted Cell Method
Analyst determines which vector value is most important by weighting the options
Percent of cell method
Encodes the cell by multiple values based on the percentage of the cell taken up by each feature

74. Vector to Raster Encoding Methods
Source: Smart land-use analysis: the LUCIS model land-use conflict identification ...
 By Margaret H. Carr, Paul Dean Zwick

75. Conversion of Vector Point Feature
Represented by a value in a raster cell
Assigned to the cell containing the point coordinate
Have at least the dimension of the raster cell after conversion
Problem:
If the cell size is too large, two or more vector points may fall in the same cell
To avoid this problem a cell size is chosen having the diagonal dimension smaller than the distance between the two closest point features

76. Conversion of Vector Line Feature
Output depends on the input algorithm used
Raster cells may be coded using different criteria/rules
Assign a value to a cell if a vector line intersects with any part of the cell
Line connections maintained
Wider lines
Vector line features in a data layer may also be converted to a raster data model
We may get a different output data layer when a different conversion algorithm is used, even though we use the same input.

77. Conversion of Vector Line Feature
Assign a cell as occupied by a line only when the cell center is “near” a vector line segment
May lead to discontinuity in lines
Thinner linear features
“Near” may be defined as some sub-cell distance, e.g., 1/3 the cell width

78. Conversion of Vector Area Feature
Boundaries among different polygons are identified as in vector to- raster conversion for lines
Assign the cell to the area if more than one half the cell is within the vector polygon OR
Assign a raster cell to an area feature if any part of the raster cell is within the area contained within the vector polygon
Interior regions are then identified
Each cell in the interior region is assigned a given value
Assignment results will vary with the method used

79. Raster to Vector (R2V)
Point, line, or area features represented by raster cells may be converted to corresponding vector data coordinates and structures
The quality and resolution of the raster image are key factors for the quality and accuracy of the vectorized data
Automatic digitizing traces lines automatically from the scanned raster image using image processing and pattern recognition techniques.
It is not easy to do an automatic conversion from raster to vector, or so called vectorization process, although the opposite direction (from vector to raster) is quite trivial.

80. R2V - Point Feature A single raster cell represents point feature
Each vector point feature is assigned the coordinate of the corresponding cell center

81. R2V - Linear Feature
Linear features represented in a raster environment may be converted to vector lines
Conversion to vector lines typically involves identifying the continuous connected set of grid cells that form the line.
Cell centers are typically taken as the locations of vertices along the line
Lines may then be “smoothed” using a mathematical algorithm to remove the “stair-step” effect.

82. R2V - Linear Feature

83. R2V - Area Feature Each raster cell is assigned an attribute value
Boundaries are set up between different attribute classes
A polygon is created by storing x and y coordinates for the points adjacent to the boundaries

84. R2V - Conversion Errors
Example:
The original river after R2V conversion appears to connect the loop back.
Deletion of small features e.g. small polygons may by lost?
Vector to Raster and Raster to Vector conversions generally involves a loss in precision

85. ArGIS Tools for Conversion
Spatial Analyst, ArcScan and ArcToolbox Conversion Tools
Raster to polygon conversion
Contour Generation
Surface Interpolation from point data
Etc.
R2V module is included in commercial RS software packages including ENVI

86. Raster Operation in ArcGIS
Simple Mathematical Operations
Smart land-use analysis: the LUCIS model land-use conflict identification ...
 By Margaret H. Carr, Paul Dean Zwick

87. Conditional Analysis
Conditional Tool: ArcToolbox> Spatial Analysis Tools > Conditional Toolset

88. Extraction
Extraction Tool: ArcToolbox> Spatial Analysis Tools > Extraction Toolset
Extract by Attribute

89. Extraction
Extract by Mask

90. Reclassify To reassign raster values in order to create new values
Spatial Analyst > Reclass Toolset

91. Single Output Map Algebra
Spatial Analyst > Map Algebra toolset
To write single line equations with map algebra expressions
Examples:

92. Cell Statistics Tools

93. References Chapter 2 of the text http://mason.gmu.edu/~mvenigal/
David P. Lusch, 1999
Ron Briggs UT Dallas
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