Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437) Dr. David Arctur Lecturer, Research Fellow University of Texas at Austin Lecture 2 Jan 24, 2013 Map Design

Outline  Choropleth maps  Colors  Vector GIS display  GIS queries  Map layers and scale thresholds  Hyperlinks and map tips INF385T(28437) – Spring 2013 – Lecture 2 2

CHOROPLETH MAPS Lecture 2

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(28437) – Spring 2013 – Lecture 2 4

Choropleth map example  Percentage of vacant housing units by county 5 INF385T(28437) – Spring 2013 – Lecture 2

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(28437) – Spring 2013 – Lecture 2 6 Break points

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(28437) – Spring 2013 – Lecture 2 7

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(28437) – Spring 2013 – Lecture 2 8

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(28437) – Spring 2013 – Lecture 2 9

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(28437) – Spring 2013 – Lecture 2

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 INF385T(28437) – Spring 2013 – Lecture 2

U.S. population by state, INF385T(28437) – Spring 2013 – Lecture 2 Original map (natural breaks)

Not good because too many values fall into low classes 13 INF385T(28437) – Spring 2013 – Lecture 2 Equal interval scale

Shows that an increasing width (geometric) scale is needed 14 INF385T(28437) – Spring 2013 – Lecture 2 Quantile scale

Custom geometric scale  Experiment with exponential scales with powers of 2 or INF385T(28437) – Spring 2013 – Lecture 2

Beware empty statistics 16 INF385T(28437) – Spring 2013 – Lecture 2

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(28437) – Spring 2013 – Lecture 2 Normalizing data

Nonnormalized data Number of vacant housing units by state, INF385T(28437) – Spring 2013 – Lecture 2

Normalized data Percentage vacant housing units by state, INF385T(28437) – Spring 2013 – Lecture 2

California population by county, INF385T(28437) – Spring 2013 – Lecture 2 Nonnormalized data

21 INF385T(28437) – Spring 2013 – Lecture 2 California population density, 2007 Normalized data

COLORS Lecture 2 22 INF385T(28437) – Spring 2013 – Lecture 2

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(28437) – Spring 2013 – Lecture 2 Color overview

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(28437) – Spring 2013 – Lecture 2 Color wheel

 Light colors associated with low values  Dark colors associated with high values  Human eye is drawn to dark colors INF385T(28437) – Spring 2013 – Lecture 2 25 Light vs. dark colors

Contrast The greater the difference in value between an object and its background, the greater the contrast INF385T(28437) – Spring 2013 – Lecture 2 26

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(28437) – Spring 2013 – Lecture 2

Monochromatic map Values too similar 28 INF385T(28437) – Spring 2013 – Lecture 2

Monochromatic map A better map, more contrast 29 INF385T(28437) – Spring 2013 – Lecture 2

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(28437) – Spring 2013 – Lecture 2 Dichromatic color scale

31 INF385T(28437) – Spring 2013 – Lecture 2 Symmetric break points centered on 0 make it easy to interpret the map Dichromatic map

Color tips  Colors have meaning  Political and cultural  Cool colors  Calming  Appear smaller  Recede  Warm colors  Exciting  Overpower cool colors 32 INF385T(28437) – Spring 2013 – Lecture 2

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(28437) – Spring 2013 – Lecture 2 Color tips

34 If you have many polygons to symbolize, it may be better to use polygon centroid points with color rather than polygon choropleth maps. INF385T(28437) – Spring 2013 – Lecture 2 Color tips

Changing colors in ArcMap  Choose color, more colors… 35 INF385T(28437) – Spring 2013 – Lecture 2

 Website   Books  Brewer, Cynthia A Designed Maps: A Sourcebook for GIS Users. Redlands: ESRI Press  Brewer, Cynthia A Designing Better Maps: A Guide for GIS Users. Redlands: ESRI Press INF385T(28437) – Spring 2013 – Lecture 2 36 Learn more about GIS colors

VECTOR & RASTER DATA Lecture 2 37 INF385T(28437) – Spring 2013 – Lecture 2

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 38 INF385T(28437) – Spring 2013 – Lecture 2

Feature attribute tables  Store characteristics for vector features  Layers can be displayed using attributes 39 INF385T(28437) – Spring 2013 – Lecture 2

Displaying points  Single symbols  All CAD calls 40 INF385T(28437) – Spring 2013 – Lecture 2

Displaying points  Same features, different points  Based on attributes INF385T(28437) – Spring 2013 – Lecture 2 41

Displaying points  Industry specific (e.g. crime analysis)  Good for large scale (zoomed in) maps INF385T(28437) – Spring 2013 – Lecture 2 42

Displaying points  Industry specific (e.g. schools)  Not good for multiple features at smaller scales  Simple points better for analysis INF385T(28437) – Spring 2013 – Lecture 2 43

Displaying points  Quantities  Use exaggerated sizes INF385T(28437) – Spring 2013 – Lecture 2 44

Displaying lines For analytical maps, most lines are ground features and should be light shades (e.g. gray or light brown) INF385T(28437) – Spring 2013 – Lecture 2 45

Displaying lines Consider using dashed lines to signify less important line features and solid lines for the important ones INF385T(28437) – Spring 2013 – Lecture 2 46

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(28437) – Spring 2013 – Lecture 2 47

Displaying polygons Consider using texture for black and white copies INF385T(28437) – Spring 2013 – Lecture 2 48

 Assign bright colors (red, orange, yellow, green, blue) to important graphic elements  Features are known as figure All features in figure INF385T(28437) – Spring 2013 – Lecture 2 49 Graphic hierarchy

 Assign drab colors to the graphic elements that provide orientation or context, especially shades of gray  Features known as ground 50 Circles in figure, squares and lines in ground INF385T(28437) – Spring 2013 – Lecture 2 50 Graphic hierarchy

 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(28437) – Spring 2013 – Lecture 2 Graphic hierarchy 51

INF385T(28437) – Spring 2013 – Lecture 2 52 Graphic hierarchy example

Vector – Raster Comparison 53 INF385T(28437) – Spring 2013 – Lecture 2

Vector data example 54 INF385T(28437) – Spring 2013 – Lecture 2 Bolstad, Fig 2-26a

Raster data example 1 55 INF385T(28437) – Spring 2013 – Lecture 2

Raster data example 2 56 INF385T(28437) – Spring 2013 – Lecture 2

Converting between vector & raster 57 INF385T(28437) – Spring 2013 – Lecture 2

GIS QUERIES Lecture 2 58 INF385T(28437) – Spring 2013 – Lecture 2

59  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(28437) – Spring 2013 – Lecture 2 GIS queries

60  Simple query criterion   NatureCode = ' DRUGS '  DATE >= ' '  % 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(28437) – Spring 2013 – Lecture 2 Simple attribute queries

INF385T(28437) – Spring 2013 – Lecture 2 61 Simple attribute queries

62  Compound query criteria  Combine two or more simple queries with the logical connectives AND or OR  "NATURE_COD" = 'DRUGS' AND "DATE" >  Selects records that satisfy both criteria simultaneously  Result are drug crimes that were committed after August 1, 2004 INF385T(28437) – Spring 2013 – Lecture 2 Compound attribute queries

INF385T(28437) – Spring 2013 – Lecture 2 63 Compound attribute queries

LAYER GROUPS, SCALE THRESHOLDS Lecture 2

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(28437) – Spring 2013 – Lecture 2 65

Large Scale vs. Small Scale Which is larger scale: zoomed in (see small area) or zoomed out (see large area)? 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(28437) – Spring 2013 – Lecture 2 66

Map scales 67 INF385T(28437) – Spring 2013 – Lecture 2 1:5,000 is large scale 1:50,000,000 is small scale

Layer groups  Organizes layers  Groups and names logically INF385T(28437) – Spring 2013 – Lecture 2 68

Minimum scale threshold  When zoomed out beyond this scale, features will not be visible  Tracts not visible when zoomed to the USA 69 INF385T(28437) – Spring 2013 – Lecture 2

Minimum scale threshold  Tracts displayed when zoomed in 70 INF385T(28437) – Spring 2013 – Lecture 2

Maximum scale threshold  When zoomed in, features will not be visible  State population will disappear when zoomed in to a state 71 INF385T(28437) – Spring 2013 – Lecture 2

HYPERLINKS AND MAP TIPS Lecture 2

 Links images, documents, Web pages, etc. to features on a map INF385T(28437) – Spring 2013 – Lecture 2 73 Hyperlinks

Map tips  Provide an additional way to find information about map features  Pop up as you hover the mouse pointer over a feature 74 INF385T(28437) – Spring 2013 – Lecture 2

Summary  Choropleth maps  Colors  Vector GIS display  GIS queries  Map layers and scale thresholds  Hyperlinks and Map tips INF385T(28437) – Spring 2013 – Lecture 2 75