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DATA VISUALIZATION Introduction to GIS| Winter 2015 Understanding your map data Qualitative vs. Quantitative Data Qualitative Quantitative Data classified by category e.g. Soil types, Animal Species Data grouped by numerical value e.g. Population (# of people), Forest cover (%) Why is understanding this so important? Data type will influence your choice of map symbolization and visualization Showing qualitative data on maps Features Features (Showing Location) Each geographic feature is represented by a single color Categories—Unique Values Categories (Unique Values) Each geographic feature is represented by a different color Categories—Grouped Values Categories (Unique Values – Grouped) Geographic features are grouped and each group is represented by a color Showing quantitative data on maps Quantitative Map Types Choropleth (Classification) Isoline Cartogram Density 3D Visualization Choropleth Maps Widely-used method for showing quantitative data Based on numeric attributes of non-overlapping areas Areas are shaded based on the value of the attribute “Spatially-sensitive” values should be normalized Convert “raw” count data to ratio (density); this is done by dividing raw data by another attribute (e.g. area) e.g. Raw count data: Total Population per County e.g. Density data: Population/mi2 per County Choropleth Maps |Graduated Colors Graduated Colors in Arc is used for creating a Choropleth Map How do these % values get grouped into classes? Choropleth maps | Classification Different ways to show how data is grouped into classes Classification goals: Make the map easy to read and understand Communicate info that is not self-evident about an area Cartographer must decide which method to use… Natural Breaks (Jenks) Default in ArcGIS and appropriate for most data Classes are based on natural groupings of data Statistical method that minimizes the total variance within each group (i.e. similar things are put into the same group) Cartographer sets number of classes; ArcMap finds natural groupings Natural Breaks Cartographer chooses classes Quantile method Each class contains (approx) the same # of features Best suited for data that is uniformly distributed; data that does not have a disproportionate number of features with similar values Cartographer sets number of classes; ArcMap assigns data to each class Quantile method Cartographer sets classes, Arc assigns values Each class has 50 values, regardless of the range of variance Interval nethods Equal Intervals: Features are divided equally into a set number of intervals. Cartographer sets number of intervals (e.g. 3); ArcMap calculates interval range (e.g. 0-5, 6-10, 11-15 etc.) and assigns data to each interval. Defined Intervals: Features are divided based on a set interval range. Cartographer sets intervals (e.g. 0-7, 7-9, 9-15); ArcMap assigns data to each interval. Equal Interval method Cartographer sets the number of intervals Arc calculates interval range, in this case intervals of 23 Defined Interval method Arc assigns classes needed to accommodate chosen interval Cartographer sets interval size Standard Deviation method Shows distance from the data mean (average) Places class breaks at intervals (1/4, 1/5, or 1) away from mean, based on the type of standard deviation Cartographer sets type; ArcMap assigns data to intervals Standard Deviation method Arc assigns classes needed to accommodate standard deviation chosen Cartographer sets standard deviation desired Normal Distribution Curve Which method(s) are appropriate for data values that naturally “clump” into different groups? Which method(s) are appropriate for data that has uniformly distributed values? Which method(s) are appropriate for data values that follows a normal distribution? Classification Methods Natural groupings of features into classes Class values are equally spaced Features classified by distance from mean Same # of features in each class Normalizing Data When geographic features vary in size, then data should be displayed as densities; i.e. “Spatiallysensitive” values should be normalized “Raw count” data converted to a ratio (density) Divide raw data by another attribute (e.g. area) Let’s look at a visual example… If Raw count data is Total Population per State, then… The corresponding density is Population per mi2 (by State) Isoline Maps Lines joining points of equal value (usually generalized) Used for continuous surfaces; phenomena must vary smoothly across the map Common example is contour lines… but isolines can show other data besides elevation! Two types: Isometric (measured values) Isopleth (areal averages) Example - Isometric map Example - Isopleth map Cartograms Distort area, shape or distance of map symbols for a specific purpose Reveal or enhance patterns that might not be visually apparent on a “normal” map Sometimes used to promote legibility National Debt compared to GDP, by nation Source: Jonathan Crowe, http://www.mcwetboy.net/ Density Maps Uniform symbols repeated across map Exact quantities not shown Instead, goal is to give an overall impression of data distribution across an area (i.e. density) Distribution of symbols represents distribution of data 3D Visualization Can be created with a wide variety of tools Google Earth ArcScene Sketch up Data needs to have a Z-factor (x,y,z) 3D Visualization 3D Visualization Temporal Visualization GAPMINDER WORLD Hans Rosling – Statistics shown using Graphs & Maps http://www.gapminder.org/world Interactive/Crowd-Sourced Visualizations TED Talk: Aaron Koblin “Artfully visualizing our humanity” www.ted.com/talks/aaron_koblin.html www.aaronkoblin.com/work/flightpatterns