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This 3 page Class Notes was uploaded by Cortney Leuschke on Saturday September 12, 2015. The Class Notes belongs to GEOG 494M at West Virginia University taught by Staff in Fall. Since its upload, it has received 13 views. For similar materials see /class/202691/geog-494m-west-virginia-university in Geography at West Virginia University.
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Date Created: 09/12/15
Geog 494m Geodemographics Spring 2009 Example 1 Choropleth Mapping and Display Obiectives 1 Understand the di rerence between classification methods in choropleth map display 2 Understand how the method ofclassification can impact the meaning or interpretation ofthe map display 3 Choose an appropriate classification method to display poverty datafor US counties Data 2004 Election Counties shapefile Step 1 Open and explore the data I Open ArcMap and add the 2004 Election data I Examine the attribute table to familiarize yourself with the type of information available in this dataset Step 2 Symbolize data I Go to the layer properties for the 2004 Election Choose quantities from the 39show39 menu and use pctpoor as the value Keep all of the other default settings I ArcMap by default uses Natural Breaks Ienks classification method with 5 classes 39 What do you think thlS type Of classification Figure 1 Choropleth map of poverty percentages means Do you think it accurately represents classified using natural breaks jenks the data Step 3 Experiment with different classi cation methods I Go back to the layer properties and change the classification method to equal interval but leave the number of classes at 5 I How does this change the appearance of the map If the data is the same why does the map convey a completely different message by just changing the method of classification I How do we know which classification method produces the most accurate representation of the data I Try other classification methods and describe how they change the Visual patterns on the map display Figure 2 Choropleth map of poverty percentages classified using equal interval Step 4 Examine the histogram I One easy way to examine the histogram distribution of the data is to go back to layer properties and go to 39classify39 Geog 494m Geodemographics Spring 2009 I The bars in the histogram describe the frequency and distribution ofyour data and the blue lines represent the breaks in your classification scheme whichever you may be using I The general shape of the histogram will provide some clues for which classification method is most appropriate for displaying your data I The following are the different methods of classification and some clues for when to use each one 1 EqualInterval method divides the entire range of attribute values into equally sized classes gives visual weight to the middle of the distribution When to use when the distribution is nearly equal 2 When not to use when the distribution is skewed or when large outliers exist N Quantiles method places an equal number of features within each class gives the same visual weight to the tails as to the middle of the distribution 3 When to use when data is linearly distributed with proportionate numbers of features with similar values or when outliers exist 2 When not to use if disproportionate numbers of features with similar values because then it may be possible for features with the same value to fall into different classes 3 Natural Breaks jenks method finds natural patterns and groupings in the data and creates classes around those natural groups 2 When to use for general purpose displays and as a quick and easy classification method gt When not to use when you want to show the maximum visual differentiation between classes n mwn nnnnn Figure 3 Poverty percentage histogram WiLll equalr interval classification method Figure 4 Poverty percentage histogram with quantile classification method i Figure 5 Poverty percentage histogram wi39h natural breaks jenks classification method Geog 494m Geodemographics Step 5 Step 6 Step 7 Spring 2009 4 Geometric interval method is a newly introduced classi cation method for ArcGIS but the ESRI mapping center does not recommend using this method for strictly Visualization purposes Standard deviation method creates classes as portions of standard deviations above and below the attribute39s mean 2 Men to use when you want the classi cation to re ect how far values are from the average 3 Men not to use when there are large outliers in the data in deviation classification method Choose the most appropriate classification method There is always the option of creating your own manual classification if none of the class choices seem appropriate for your data Manual classi cation is the most exible because you decide where each class begins and ends but may be more effort than necessary if a prede ned structure ts well with the data Based on the previous information which methods do you think most appropriately classi es the poverty percentage data What information do we risk losing or misrepresenting by using other classes Could more than one classi cation method be appropriate for displaying this data Why or why not Choose an appropriate number of classes for display In general four classes should be the minimum number used and ten or eleven classes should be the maximum number used Too few classes may not accurately display variation in the data while too many classes may appear too similar to be differentiated One exception to the rule of using too many classes is when you want your data to look Visually continuous for a global Visual impression rather than individual class distinctions Choose a different attribute and determine how your classification strategy might change
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