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Introduction to Geographic Information Systems Fall 2013 (INF 385T-28620) Dr. David Arctur Research Fellow, Adjunct Faculty University of Texas at Austin Lecture 2 Sept 5, 2013 Map Design
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Outline Choropleth maps Colors Vector GIS display GIS queries Map layers and scale thresholds Hyperlinks and map tips INF385T(28620) – Fall 2013 – Lecture 2 2
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CHOROPLETH MAPS Lecture 2
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Choropleth maps Color-coded polygon maps Use monochromatic scales or saturated colors Represent numeric values (e.g. population, number of housing units, percentage of vacancies) INF385T(28620) – Fall 2013 – Lecture 2 4
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Choropleth map example Percentage of vacant housing units by county 5 INF385T(28620) – Fall 2013 – Lecture 2
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Classifying data Process of placing data into groups (classes or bins) that have a similar characteristic or value Break points Breaks the total attribute range up into these intervals Keep the number of intervals as small as possible (5-7) Use a mathematical progression or formula instead of picking arbitrary values INF385T(28620) – Fall 2013 – Lecture 2 6 Break points
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Classifications Natural breaks (Jenks) Picks breaks that best group similar values together naturally and maximizes the differences between classes Generally, there are relatively large jumps in value between classes and classes are uneven Based on a subjective decision and is the best choice for combining similar values Class ranges specific to the individual dataset, thus it is difficult to compare a map with another map INF385T(28620) – Fall 2013 – Lecture 2 7
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Classifications Quantiles Places the same number of data values in each class Will never have empty classes or classes with too few or too many values Attractive in that this method produces distinct map patterns Analysts use because they provide information about the shape of the distribution. Example: 0 – 25%, 25% – 50%, 50% – 75%,75% – 100% INF385T(28620) – Fall 2013 – Lecture 2 8
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Classifications Equal intervals Divides a set of attribute values into groups that contain an equal range of values Best communicates with continuous set of data Easy to accomplish and read Not good for clustered data Produces map with many features in one or two classes and some classes with no features INF385T(28620) – Fall 2013 – Lecture 2 9
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Classifications Use mathematical formulas when possible. Exponential scales Popular method of increasing intervals Use break values that are powers such as 2 n or 3 n Generally start out with zero as an additional class if that value appears in your data Example: 0, 1–2, 3–4, 5–8, 9–16, and so forth 10 INF385T(28620) – Fall 2013 – Lecture 2
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Classifications Use mathematical formulas when possible Increasing interval widths Long-tailed distributions Data distributions deviate from a bell-shaped curve and most often are skewed to the right with the right tail elongated Example: Keep doubling the interval of each category, 0 – 5, 5 – 15, 15 – 35, 35 – 75 have interval widths of 5, 10, 20, and 40. 11 INF385T(28620) – Fall 2013 – Lecture 2
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U.S. population by state, 2000 12 INF385T(28620) – Fall 2013 – Lecture 2 Original map (natural breaks)
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Not good because too many values fall into low classes 13 INF385T(28620) – Fall 2013 – Lecture 2 Equal interval scale
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Shows that an increasing width (geometric) scale is needed 14 INF385T(28620) – Fall 2013 – Lecture 2 Quantile scale
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Custom geometric scale Experiment with exponential scales with powers of 2 or 3. 15 INF385T(28620) – Fall 2013 – Lecture 2
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Beware empty statistics http://xkcd.com/1138 16 INF385T(28620) – Fall 2013 – Lecture 2
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Divides one numeric attribute by another in order to minimize differences in values based on the size of areas or number of features in each area Examples: Dividing the number of vacant housing units by the total number of housing units yields the percentage of vacant units Dividing the population by area of the feature yields a population density 17 INF385T(28620) – Fall 2013 – Lecture 2 Normalizing data
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Nonnormalized data Number of vacant housing units by state, 2000 18 INF385T(28620) – Fall 2013 – Lecture 2
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Normalized data Percentage vacant housing units by state, 2000 19 INF385T(28620) – Fall 2013 – Lecture 2
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California population by county, 2007 20 INF385T(28620) – Fall 2013 – Lecture 2 Nonnormalized data
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21 INF385T(28620) – Fall 2013 – Lecture 2 California population density, 2007 Normalized data
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COLORS Lecture 2 22 INF385T(28620) – Fall 2013 – Lecture 2
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23 Hue is the basic color Value is the amount of white or black in the color Saturation refers to a color scale that ranges from a pure hue to gray or black INF385T(28620) – Fall 2013 – Lecture 2 Color overview
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24 Device that provides guidance in choosing colors Use opposite colors to differentiate graphic features Three or four colors equally spaced around the wheel are good choices for differentiating graphic features Use adjacent colors for harmony, such as blue, blue green, and green or red, red orange, and orange INF385T(28620) – Fall 2013 – Lecture 2 Color wheel
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Light colors associated with low values Dark colors associated with high values Human eye is drawn to dark colors INF385T(28620) – Fall 2013 – Lecture 2 25 Light vs. dark colors
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Contrast The greater the difference in value between an object and its background, the greater the contrast INF385T(28620) – Fall 2013 – Lecture 2 26
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Monochromatic color scale Series of colors of the same hue with color value varied from low to high Common for choropleth maps The darker the color in a monochromatic scale, the more important the graphic feature Use more light shades of a hue than dark shades in monochromatic scales The human eye can better differentiate among light shades than dark shades 27 INF385T(28620) – Fall 2013 – Lecture 2
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Monochromatic map Values too similar 28 INF385T(28620) – Fall 2013 – Lecture 2
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Monochromatic map A better map, more contrast 29 INF385T(28620) – Fall 2013 – Lecture 2
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30 An exception to the typical monochromatic scale used in most choropleth maps Two monochromatic scales joined together with a low color value in the center, with color value increasing toward both ends Uses a natural middle point of a scale, such as 0 for some quantities (profits and losses, increases and decreases) INF385T(28620) – Fall 2013 – Lecture 2 Dichromatic color scale
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31 INF385T(28620) – Fall 2013 – Lecture 2 Symmetric break points centered on 0 make it easy to interpret the map Dichromatic map
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Color tips Colors have meaning Political and cultural Cool colors Calming Appear smaller Recede Warm colors Exciting Overpower cool colors 32 INF385T(28620) – Fall 2013 – Lecture 2
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33 Do not use all of the colors of the color spectrum, as seen from a prism or in a rainbow, for color coding If you have relatively few points in a point layer, or if a user will normally be zoomed in to view parts of your map, use size instead of color value to symbolize a numeric attribute INF385T(28620) – Fall 2013 – Lecture 2 Color tips
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Graphics for colorblind users 34 INF385T(28620) – Fall 2013 – Lecture 2 Two-meter air temperature anomalies (i.e., differences from the 1971–2000 mean) for January 1998 (during a recent El Niño):
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Graphics for colorblind users 35 INF385T(28620) – Fall 2013 – Lecture 2 Two-meter air temperature anomalies (i.e., differences from the 1971–2000 mean) for January 1998 (during a recent El Niño):
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36 If you have many polygons to symbolize, it may be better to use polygon centroid points with color rather than polygon choropleth maps. INF385T(28620) – Fall 2013 – Lecture 2 Color tips
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Changing colors in ArcMap Choose color, more colors… 37 INF385T(28620) – Fall 2013 – Lecture 2
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Website http://colorbrewer2.org/ http://colorbrewer2.org/ Books Brewer, Cynthia A. 2008. Designed Maps: A Sourcebook for GIS Users. Redlands: ESRI Press Brewer, Cynthia A. 2005. Designing Better Maps: A Guide for GIS Users. Redlands: ESRI Press INF385T(28620) – Fall 2013 – Lecture 2 38 Learn more about GIS colors
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VECTOR & RASTER DATA Lecture 2 39 INF385T(28620) – Fall 2013 – Lecture 2
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Points, lines, polygons Point x,y coordinates Line starting and ending point and may have additional shape vertices (points) Polygon three or more lines joined to form a closed area 40 INF385T(28620) – Fall 2013 – Lecture 2
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Feature attribute tables Store characteristics for vector features Layers can be displayed using attributes 41 INF385T(28620) – Fall 2013 – Lecture 2
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Displaying points Single symbols All CAD calls 42 INF385T(28620) – Fall 2013 – Lecture 2
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Displaying points Same features, different points Based on attributes INF385T(28620) – Fall 2013 – Lecture 2 43
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Displaying points Industry specific (e.g. crime analysis) Good for large scale (zoomed in) maps INF385T(28620) – Fall 2013 – Lecture 2 44
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Displaying points Industry specific (e.g. schools) Not good for multiple features at smaller scales Simple points better for analysis INF385T(28620) – Fall 2013 – Lecture 2 45
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Displaying points Quantities Use exaggerated sizes INF385T(28620) – Fall 2013 – Lecture 2 46
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Displaying lines For analytical maps, most lines are ground features and should be light shades (e.g. gray or light brown) INF385T(28620) – Fall 2013 – Lecture 2 47
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Displaying lines Consider using dashed lines to signify less important line features and solid lines for the important ones INF385T(28620) – Fall 2013 – Lecture 2 48
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Displaying polygons Consider using no outline or dark gray for boundaries of most polygons Dark gray makes the polygons prominent enough, but not so much that they compete for attention with more important graphic features INF385T(28620) – Fall 2013 – Lecture 2 49
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Displaying polygons Consider using texture for black and white copies INF385T(28620) – Fall 2013 – Lecture 2 50
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Assign bright colors (red, orange, yellow, green, blue) to important graphic elements Features are known as figure All features in figure INF385T(28620) – Fall 2013 – Lecture 2 51 Graphic hierarchy
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Assign drab colors to the graphic elements that provide orientation or context, especially shades of gray Features known as ground Circles in figure, squares and lines in ground INF385T(28620) – Fall 2013 – Lecture 2 52 Graphic hierarchy
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Place a strong boundary, such as a heavy black line, around polygons that are important to increase figure Use a coarse, heavy cross-hatch or pattern to make some polygons important, placing them in figure INF385T(28620) – Fall 2013 – Lecture 2 Graphic hierarchy 53
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INF385T(28620) – Fall 2013 – Lecture 2 54 Graphic hierarchy example
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Vector – Raster Comparison 55 INF385T(28620) – Fall 2013 – Lecture 2
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Vector data example 56 INF385T(28620) – Fall 2013 – Lecture 2 Bolstad, Fig 2-26a
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Raster data example 1 57 INF385T(28620) – Fall 2013 – Lecture 2
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Raster data example 2 58 INF385T(28620) – Fall 2013 – Lecture 2
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Converting between vector & raster 59 INF385T(28620) – Fall 2013 – Lecture 2
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GIS QUERIES Lecture 2 60 INF385T(28620) – Fall 2013 – Lecture 2
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61 Powerful relationship between data table and vector-based graphics—unique to GIS Records from a feature attribute table are selected by using query criteria Query will automatically highlight the corresponding graphic features INF385T(28620) – Fall 2013 – Lecture 2 GIS queries
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62 Simple query criterion NatureCode = ' DRUGS ' DATE >= ' 20040701 ' % wild card % symbol stands for zero, one, or more characters of any kind NAME like ' BUR%' Selects any crime with names starting with the letters BUR, including burglaries (BUR), business burglaries(BURBUS), and residential burglaries (BURRES) INF385T(28620) – Fall 2013 – Lecture 2 Simple attribute queries
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INF385T(28620) – Fall 2013 – Lecture 2 63 Simple attribute queries
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64 Compound query criteria Combine two or more simple queries with the logical connectives AND or OR "NATURE_COD" = 'DRUGS' AND "DATE" > 20040801 Selects records that satisfy both criteria simultaneously Result are drug crimes that were committed after August 1, 2004 INF385T(28620) – Fall 2013 – Lecture 2 Compound attribute queries
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INF385T(28620) – Fall 2013 – Lecture 2 65 Compound attribute queries
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LAYER GROUPS, SCALE THRESHOLDS Lecture 2
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First: What is Scale? Q. What does it mean when a map says Scale 1:2 million (1 inch on map = 2 million inches on land) Q. How about Scale 1:63,360 (1 inch = 1 mile) (5280 ft x 12 in/ft = 63,360 in) Q. How about Scale 1:1 (actual size) INF385T(28620) – Fall 2013 – Lecture 2 67
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Large Scale vs. Small Scale Which is larger scale: zoomed in (small areas appear large) or zoomed out (large areas appear small)? Which is larger: 1/400 or 1/20,000? Which is larger scale? 1:400 or 1:20,000? When we say Scale 1:n, what we’re saying is that each feature on the map is 1/n of its real size. So small denominator = LARGE scale (zoomed in) and large denominator = SMALL scale (zoomed out) INF385T(28620) – Fall 2013 – Lecture 2 68
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Map scales 69 INF385T(28620) – Fall 2013 – Lecture 2 1:5,000 is large scale 1:50,000,000 is small scale
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Layer groups Organizes layers Groups and names logically INF385T(28620) – Fall 2013 – Lecture 2 70
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Minimum scale threshold When zoomed out beyond this scale, features will not be visible Tracts not visible when zoomed to the USA 71 INF385T(28620) – Fall 2013 – Lecture 2
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Minimum scale threshold Tracts displayed when zoomed in 72 INF385T(28620) – Fall 2013 – Lecture 2
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Maximum scale threshold When zoomed in, features will not be visible State population will disappear when zoomed in to a state 73 INF385T(28620) – Fall 2013 – Lecture 2
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HYPERLINKS AND MAP TIPS Lecture 2
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Links images, documents, Web pages, etc. to features on a map INF385T(28620) – Fall 2013 – Lecture 2 75 Hyperlinks
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Map tips Provide an additional way to find information about map features Pop up as you hover the mouse pointer over a feature 76 INF385T(28620) – Fall 2013 – Lecture 2
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Summary Choropleth maps Colors Vector GIS display GIS queries Map layers and scale thresholds Hyperlinks and Map tips INF385T(28620) – Fall 2013 – Lecture 2 77
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