Module 4

1. Module 4

2. Visual Hierarchy Keiron BaileyGEOG416A Department of Geography and Regional Development University of Arizona Tier 1 Title/View LegendScale BarNorth Arrow Tier 2 Tier 3 AuthorSourceDate

3. Necessary Layout Elements Title - a title allows you to state what the primary message of the map is.View – this means the spatial data that you want to display Legend - a legend tells the map-reader what the symbols mean.Scale - The scale tells you how much paper space represents the ground, for example a 1:24000 scale map means that for every inch on the map, there are 24,000 inches in reality at that same place!North arrow - This tells you which direction north is. A north arrow is unnecessary on a small-scale map that shows entire continents. You should include when an unusual orientation used (i.e. west is up) or when direction is not obvious.

4. Necessary/Optional Layout Elements Source - this tells the map-reader where you got your information. This is very important to verify your data and results with others.Map Author and Date - This informs your readers whom to get a hold of to ask questions about the map. The date is important because it tells your readers when the map was created, and how old the data might be. Graticule - a graticule is only necessary on small-scale maps. This helps to form a reference grid for where features are located.Frame - a frame, or neatline outlines the map to help it stand out against the rest of the paper. Base map - a base map helps to frame where the spatial phenomenon is occurring in relation to other features.

5. The Nature of Spatial Data Point (point) Line (polyline) Area (polygon) Volumetric (3D) Discrete data Continuous data Keiron BaileyGEOG416A Department of Geography and Regional Development University of Arizona

6. Volumetric Data Keiron BaileyGEOG416A Department of Geography and Regional Development University of Arizona

7. Measurement Scales Nominal Ordinal Interval Ratio Keiron BaileyGEOG416A Department of Geography and Regional Development University of Arizona

8. Measurement Scales Keiron BaileyGEOG416A Department of Geography and Regional Development University of Arizona

9. Measurement Scales Keiron BaileyGEOG416A Department of Geography and Regional Development University of Arizona

10. Measurement Scales Keiron BaileyGEOG416A Department of Geography and Regional Development University of Arizona

11. Sources of Error Keiron BaileyGEOG416A Department of Geography and Regional Development University of Arizona Measurement ErrorError exists in all data. In it inescapable for a number of reasons, such as observer, instrument, environment, or observed material errors. All of these potential sources of error work together and are cumulative in nature. Observer errorObserver error is the poor performance of researcher, which results in biased measurements. Many times this error is unintentional, and probably can be corrected, if discovered. Some examples of observer error are mis-types, poorly worded survey questions, and poor eyesight/color blindness.Instrument errorInstrument error refers to malfunctioning instruments. The malfunction may be wide-ranging in nature from an incorrectly calibrated instrument (reads consistently high/low), a poor reading due to low battery, etc. Some of these errors may be corrected for, such as a consistent low reading might have a standard number added to all values to correct for the inaccuracy. Environmental errorAnother part of instrumental error refers to environment error. Instruments and people will only operate accurately within a certain temperature range. Occasionally the environment may cause errors in reading, such as when cloud cover obscures part of the ground for an aerial photo. These errors may be hard to account for.Observed material errorObserved material error is the change in data due to presence of the observer. These errors may be impossible to detect and correct.

12. Symbology Keiron BaileyGEOG416A Department of Geography and Regional Development University of Arizona SymbologySymbol types can be easily manipulated to fit whatever mapping situation you have. At the most basic categorization scheme, there are 5 visual variables that we can manipulate for each symbol type:1. Shape - different shapes mean different things. By changing the shape of a symbol, you can change the connotation of that symbol. 2. Size - smaller size means less of something, a larger size means more of something. 3. Hue - changing the color of a symbol can show varying levels of intensity or different classifications of the symbol (i.e. a blue line may show a two-lane road, while a red line may show a four-lane road.4. Orientation - by changing the orientation you can show differences in similar data categories. For example, a golf map might show how the putting green lies with a series of arrows indicating which way the golf ball would roll there. 5. Pattern - by changing the pattern of an area, you can show differences in categories or data types.