The final map will be made from two separate maps.\u00a0 The first will show the percentage of voters who voted remain, while the second will show the number houses purchased under the \u2018Right to Buy\u2019scheme.\u00a0 The example uses QGIS<\/a>, but the principles will be the same for ArcGIS and most other GIS.<\/p>\n To start, a Vector Layer<\/em> is added showing the Local Authority Districts:<\/p>\n The dataset can be added next.\u00a0 The step is the same as before only with a\u00a0Delimited Text Layer.\u00a0\u00a0<\/em>The layers can then be joined together.\u00a0 This is done using the Joins<\/em> option under the Layer Properties<\/em> menu.\u00a0 It is important the fields used to make the join contain identical information. In this example the titles of the matching data is different in each dataset \u2013 \u201cArea_Code\u201d and \u201clad17cd\u201d:<\/p>\n Under the Symbology<\/em> option in the Layer Properties<\/em> menu the type can now be set as Graduated<\/em>, and the data to be shown selected as \u201cEU-referendum-result-data_Pct_Remain.\u201d\u00a0 A bright and clear color should be selected \u2013 in this case red \u2013 and the number of classes can be set to three.1<\/a><\/sup> In this case the best algorithm to use for rendering is Quantile<\/em>.2<\/a><\/sup>\u00a0 This completes the first choropleth map.<\/p>\n The second map is completed by repeating the above steps but using the housing sales data instead of the voting data.\u00a0 When complete there will be four layers: Local Authority Districts (for voting data); Voting Data; Local Authority Districts (for housing data); and Housing data.\u00a0\u00a0Turning off the Visibility<\/em> of the layers from the first map (below left) leaves the second map (below right) showing house purchase data by Local Authority District:<\/p>\n All that is required at this stage is to overlay map two onto map one.\u00a0 All the layers must be turned on, and by clicking on the color bar in the Symbology<\/em> option under the Layer Properties<\/em> menu, the Opacity<\/em> of the layer can be changed.\u00a0 Some experimentation with the percentage value will be required depending on preferences, however 55% works for this map.<\/p>\n Zooming into the map more closely reveals the areas where the colors of the two variables are most clearly blended \u2013 the gray\/mauve districts are those with a high number of \u2018Right to Buy\u2019 purchases, and a high percentage of remain votes.<\/p>\n This is a quick and accessible method which shows that analytical mapping is not always a complicated operation.\u00a0 Nonetheless, this technique is not the most refined or flexible, and more advanced ways to render a map with improved accuracy, colors, and visual contrast should be used for larger projects, especially those purposely developed for display.3<\/a><\/sup>\u00a0Nonetheless, the convenience of such an approach is that reasonably advanced maps can be produced easily and can help identify key data trends quickly if needed.<\/p>\n 1.<\/a> As a general rule, maps should have as few data classes as possible as this makes them easier to read.<\/p>\n 2.<\/a> For more on how to properly select algorithms for rendering social data, see John Nelson \u201cTelling the Truth\u201d, Last Modified: October 12, 2011, Available: <http:\/\/uxblog.idvsolutions.com\/2011\/10\/telling-truth.html<\/a>>, [Accessed: October 31, 2018]<\/p>\nImporting Data<\/h3>\n
![\"\"](\"https:\/\/sites.temple.edu\/tudsc\/files\/2018\/11\/Add_Layer-300x80.png\")
<\/p>\n![\"\"](\"https:\/\/sites.temple.edu\/tudsc\/files\/2018\/11\/Joining_Layers-212x300.png\")
<\/p>\nCreating Styles<\/h3>\n
![\"\"](\"https:\/\/sites.temple.edu\/tudsc\/files\/2018\/11\/Map_one_and_two_complete-300x253.png\")
<\/p>\nFinal Adjustments<\/h3>\n
![\"\"](\"https:\/\/sites.temple.edu\/tudsc\/files\/2018\/11\/complete_zoom_B-300x186.png\")
<\/p>\n
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