1. Generating Globe

2. Tissot’s Indicatrix Semi major axis -a Semi minor axis - b
Tissot’s ellipse is called an indicatrix
Assume a circle with radius 1
Any departure from circularity of the original circle indicates a distortion in angles;
Maps in which a=b are called conformal maps and indicates no distortion in angles
The product of a and b is related to the area of the ellipse;
If S=1 the area of the ellipse and the area of the circle are the same – an equal area projection
The two properties of equal area and equal angles are mutually exclusive.

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6. Lecture 6 Maps & Data Entry
Chapter 4 – pp
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7. http://what-is-questions. blogspot
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8. Lecture 6

9. Graticule Grid Lecture 6
Provide a reference so that locations can be easily estimated
Graticule –lines of lat/long
Grid – lines of constant x/y coordinates
Graticule
Grid
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10. Types of Maps There are many different types of maps.
Feature maps are the simplest as they map points lines or areas.
Choropleth maps depict quantitative information for areas.
Dot-density maps also depict quantitative information.
Isopleth maps/contour maps display lines of equal value.
These are the most common map types
There are several different types of choropleth maps
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11. Feature Map
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12. Choropleth Unique Value
Most choropleth maps display data by some administrative unit, state, city, census block
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13. Graduated Color Maps
The most important assumption in choropleth mapping is that the value in the enumeration unit is spread uniformly throughout the unit.
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14. Graduated Color Maps
It is traditional to use ratios instead of total values when creating graduated color maps.
Most mapping areas are unequal. The varying sizes and their values will alter the impression of the distribution.
Normalization of the data
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15. Proportional and Graduated Symbol Maps
Guidelines
Circles are the most common symbol used due to the ease with which they are interpreted.
All symbols should generally be the same color.
The difference between the largest and smallest symbols should be great enough to show differences in data values.
Largest symbols should not overlap so much that they obscure patterns on the map.
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16. Proportional and Graduated Symbol Maps
What are they?
Proportional Symbol
The size of a point symbol varies from place to place in proportion to the quantity that it represents.
Graduated Symbol
Size of a point symbol is based on which class the features value falls within.
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17. Chart
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18. Dot Density Maps What are they?
Dot density maps use a dot to indicate one or more occurrences of a phenomena.
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19. Dot Density Maps Guidelines
Choose a dot value that results in two or three dots being placed in the area with the least mapped quantity.
Select a dot value that is easily understood such as 5, 100, 1000, etc.
The dots should coalesce in the statistical area that has the highest density of the mapped value.
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20. Dot Density Maps Advantages Easily understood by the reader
Illustrates spatial density
Original data can be recovered from the map if the dots represent the actual locations of the phenomena
1 dot = 5 births
Therefore 6 dots = 30 births
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21. Dot Density Maps Disadvantages
Population
Disadvantages
A dot map that is computer generated typically involves a random distribution of dots within an enumeration area.
Solution - Use census blocks over tracts, counties over states, etc.
1 dot = 5000 persons
Population
1 dot = 5000 persons
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22. Isopleth Maps
Isopleth maps are used to visualize phenomena that are conceptualized as fields, and measured on an interval or ratio scale.
We can, however, also color them in such a way as to represent ordinal and nominal data as well.
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23. http://enb110-ert-2012. blogspot
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24. Statistical Analysis
Result of a T-test performed to identify areas of significant change in deer harvest.
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25. Images
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26. 3-D
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27. Map SCALE
The amount of reduction that takes place in going from real-world dimensions to the new mapped area on the map plane.
Defined as the ratio of map distance to earth distance, with each distance expressed in the same units of measurement.
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28. Concepts of Scale
Representative fraction – when the scale value is given as a fraction with a numerator as 1.
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29. Large Scale vs. Small Scale1
Large Scale e.g. 1:1000
1000 1
Small Scale e.g. 1:250000
250000
The terms “large scale” and “small scale” refer to scale shown as a fraction.
1:1000 is a relatively small denominator, yet it is a much bigger fraction (and thus a larger scale) than 1:250000
Large scale map features are relatively large. Small scale map features are relatively small.
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30. With a large scale map, the denominator is small, therefore the value of the fraction is large.
It covers a smaller area with greater detail
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31. Large Scale Shows a relatively small portion of the earth’s surface
e.g. 1: quad scale
e.g. 1: quarter quad scale
e.g. 1: tidelands maps
Shows a relatively small portion of the earth’s surface
Provides detailed information
Usually maps that are 1:24000 or larger are considered large scale
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32. Small Scale Shows relatively large areas of the earth
e.g. 1:250,000 - Hudson county
e.g. 1:3,300,000 - State of New Jersey
Shows relatively large areas of the earth
Provides limited detail
Generally maps smaller than 1:24000 are considered small scale.
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33. Verbal Scales One foot equals 24000 feet One inch equals one mile
Useful for a quick sense of ground units in familiar units.
Unreliable, subject to misinterpretation, invalidated by reduction and enlargement.
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34. Bar Scales Most effective
Map user can better measure and interpret distances within the map area.
Expands or shrinks along with other map distances, so it remains valid over all reductions and enlargements.
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35. Map Generalization Maps are abstractions of reality.
This abstraction introduces map generalization, the approximation of features.
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36. Penobscot Bay at Different Scales and Different Generalizations
Large Scale Map
Small Scale Map
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37. Types of Map Generalization
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38. Map Boundaries
Hard copy maps have edges, and discontinuities often occur at edges.
Most digital maps have been digitized from hardcopy maps so edge discontinuities have been carried into the present.
These errors are being corrected as newer data are being collected by digital means.
Differences in time of data collection for different map sheets can also cause errors at the edges.
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39. Analogue vs. Digital Data
Analogue (hardcopy)
Paper maps
Tables of statistics
Hard copy aerial photographs
Digital data is already in computer readable format and can come from a variety of sources:
The internet
Digital imagery
Data collection devices
If data were all in the same format, type, scale and resolution, encoding would be simple.
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40. Spatial Data Input from Hardcopy Sources
Common Input Methods:
keyboard entry
manual digitizing
automatic digitizing
scanning
format conversion
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41. Data Encoding The process of getting data into the computer.
Spatial data
Different sources
Different formats
Input via different methods
As a result, GIS data must be corrected or manipulated to be sure they can be structured according to the desired data model.
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42. Problems to Be Addressed
Reformatting
Reprojection
Generalization of complex data
Edge matching of adjacent map sheets
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43. Figure 5.1 The process of data encoding may be referred to as the data stream
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Heywood, Cornelius & Carver – Geographical Information Systems (4th Ed.)

44. Tabular Data Attribute data Spatial data Coordinate data
Add x,y data – it comes in as an event theme
Export to shapefile or feature class
Address data (requires a road file)
Geocoding converts addresses to x,y data
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45. Geocoding
Geocoding is the process of finding associated geographic coordinates (often expressed as lat/long) from other geographic data.
Address matching is the most common form of geocoding.
Plots street addresses as points.
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46. Applications of Geocoding
Internet Services: Google, Yahoo, Mapquest
Business: market/area analysis, real estate
Emergency Services
Crime Analysis
Public Health Services
Geocoding has become a part of life for most internet users, and has provided businesses, governmental agencies with powerful analytic tools.
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47. Manual Digitizing Tracing the location of “important” coordinates
Done from an image or map source
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48. Manual Digitization – Map Digitization
On-screen Digitizing/
Heads-up Digitizing
Digitizing Tablet
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49. Manual Digitizing Process from hardcopy map:
Fix map to digitizer table
Digitize control points (tics, reference points, etc.) of known location
Digitize feature boundaries in stream or point mode.
Proof, edit linework
Transform or register to known system (may also be done at start)
Re-edit, as necessary
Accuracies of between 0.01 and inches
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50. A well-distributed, precisely identifiable set of control points
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52. Figure 5.4 Point and stream mode digitizing (Heywood, Cornelius & Carver)
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53. Digitize Primarily from Cartometric Maps
Based on
coordinate
surveys
Plotted and
printed
carefully
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54. Manual Map Digitization, Pros and Cons
Advantages
low cost
poor quality maps (much editing, interpretation)
short training intervals
ease in frequent quality testing
device ubiquity
Disdvantages
upper limit on precision
poor quality maps (much editing, interpretation)
short training intervals
ease in frequent quality testing
device ubiquity
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55. DATA SOURCES, INPUT, AND OUTPUT Problems with source maps:
Dimensional stability (shrink, swell, folds)
Boundary or tiling problems
Maps are abstractions of Reality
Features are generalized:
classified (e.g., not all wetlands are alike)
simplified (lakes, streams, and towns in a scale example)
moved (offsets in plotting)
exaggerated (buildings, line roadwidths, etc).
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56. common errors that require editing
Manual Digitizing
common errors that require editing
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Figure Examples of spatial error in vector data (Heywood, Cornelius and Carver)

57. Digitizing Accuracy
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58. Interactive rubbersheeting:
Editing
Manual editing:
Line and point locations are adjusted on a graphic display, pointing and clicking with a mouse or keyboard. Most controlled, most time-consuming .
Interactive rubbersheeting:
Anchor points are selected, again on the graphics screen, and other points selected and dragged around the screen. All lines and points except the anchor points are interactively adjusted.
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59. Figure 5.20 Rubber sheeting (Heywood, Cornelius & Carver)
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60. Figure 5.15 Radius topology feature snapping (Heywood, Cornelius and Carver)
Source: 1Spatial. Copyright © Spatial Group Limited. All rights reserved
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61. Snapping Errors
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62. Manual Digitizing – Vertex Density
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63. To Few Vertices – Spline Interpolation
Create smooth, curving lines by fitting piecewise polynomial functions
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64. Too Many Vertices - Line Thinning
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65. Figure 5.18 The results of repeated line thinning (Heywood, Cornelius and Carver)
Sources: (a–f): From The Digital Chart of the World. Courtesy of Esri. Copyright © Esri. All rights reserved; (inset): From Esri, ArcGIS online help system, courtesty of Esri. Copyright © 2011 Esri. All rights reserved
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66. Common problem:
Features which occur on several different maps rarely have the same position on each map
What to do?
Re-drafting the data from conflicting sources onto the same base map, or
Establish a "master" boundary, and redraft map or copy after digitizing
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67. Digitizing Maps - Automated Scanners
Main alternative to manual digitizing for hardcopy maps
Range of scanner qualities, geometric fidelity should be verified
Most maps are now available digitally – however many began life as paper maps
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68. (Heywood, Cornelius and Carver)
Figure 5.7 Types of scanner
Sources: (a) Epson (UK) Ltd used by permission; (b) Stefan Kuhn, (c) Colortrac,
(Heywood, Cornelius and Carver)
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69. Practical Problems of Scanning
Optical distortion from flatbed scanners.
Unwanted scanning of handwritten information.
The selection of appropriate scanning tolerances.
The format of files produced for GIS input
The amount of editing required to produce data suitable for analysis.
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71. Digitizing Maps - Automated Scanners
Suitable threshholding allows determination of line or point features from the hardcopy map.
Scanners work best when very clean map materials are available.
Significant editing still required (thinning, removing unwanted features)
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72. Cell Thinning and Vectorizing– After Scan-Digitizing
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73. Georeferencing
In order to display images with shapefiles or features, it is necessary to establish an image-to-world transformation that converts the image coordinates to real-world coordinates. This transformation information is typically stored with the image.
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78. Rectified Image
GeoTiff - store the georeferencing information in the header of the image file. ArcView uses this information if it is present.
World Files - However, other image formats store this information in a separate ASCII file. This file is generally referred to as the world file, since it contains the real-world transformation information used by the image. World files can be created with any editor.
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79. World Files
It’s easy to identify the world file which should accompany an image file: world files use the same name as the image, with a "w" appended. For example, the world file for the image file mytown.tiff would be called mytown.tiffw or mytown.wtf
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80. The Contents of the World File
(x-scale factor)
(rotation)
(y-scale factor)
(x-translation)
(y-translation)
When this file is present, ArcView performs the image-to-world transformation.
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