using gis for data visualization presentation€¦ · data in a few different spreadsheet formats;...
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The first application we will look at is Google Fusion Tables. Fusion tables support data in a few different spreadsheet formats; we will be using CSV in our case.
The first thing that we must do is to decide how we want to map out our data. Google Map provides a number of geocodes each day for free so if you have street addresses or postal codes you can map your data that way.
However the best way is to use Latitude and Longitude as I am using here, it is the most accurate and fastest method.
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It is best to use a single variable that holds the Latitude and Longitude, and then import this into Google Drive as a Fusion Table. Just follow the wizard making sure to select the appropriate options, also make sure to deselect the checkbox that says allow export if your data is still in a draft format. As if the checkbox is ticked it will allow other users to export your data as a CSV.
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Once formatted we must make sure that our variables are the right type, to do this select the drop down arrow on the variables name and click “Change” this will open a pop‐up allowing you to verify that each variable is the correct format, especially that the Lat Long variable is a “Location” type.
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Next we will go to the Map tab to actually visualize our data. Click on the tab and in the “Select location” panel make sure that Lat_Long is selected. Doing so will ensure that the lat long variable is providing the spatial information for your map.
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Next we will format the data points. Click on the map tab again and select “Change map styles” this makes a pop‐up window appear which allows us to format our data points. Select the bucket tab and divide the data into 3, using the LOI score as the variable. The result is a map showing LOI broken down into 3 ranges, of high, medium, and low.
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The next tool we will look at is ArcGIS Online, this is a web tool that is completely focused on maps so as you would expect it has more functionality than Google Fusion Tables, and furthermore it supports shapefiles natively, which makes finding base data easier.
The only downside being that it is not a free, it works through a token system. A user purchases a number of tokens and different actions (page loads, geocoding, etc.) consume a different number of tokens.
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Now if you go to the table of contents of your map and hover over a layer a little arrow will appear to the right of the layer name. Click on that arrow and then select “change symbol”. Then set use to “colour” and you can see a number of new drop down boxes appear.
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We can also easily customize the pop‐up that appears when a user clicks a feature on the map so that only certain attributes of our data are shown. Furthermore we can customize the pop‐up so that it looks more presentable.
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A customized pop‐up window.
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ArcMap is the most powerful of the tools that we will look at today, it is a fully fledged GIS program with the ability to make maps and export them into a variety of different formats. It is made by ESRI, who also make ArcGIS Online, and it is also a paid for application.
In the example below we will demonstrate how to convert point data to polygon data and visualize this so that it still appears to be points. There are two very good reasons why we would want to do this and these are…
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The problem with points occurs when the location of the point is based on the centroid of a polygon, as in the case of postal codes. In the image above you can see a number of different dots each representing a student living in a home on a street. Each one is coloured differently to represent some variable, and all are visible.
However if we geocoded these students based on their postal code, all of their locations would be the same, and they would overlap. Making it appear as if their location is in the centre of the postal code. Furthermore only 1 of the students will be visible and which one is visible is totally randomized.
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The second issue with point data is privacy. In our case we are dealing with students and although just by itself postal code is not considered to be personal data when it is combined with other information it can become personal. For example if we mapped out students who were in grade 6, white, and male by postal code this would be considered personal data.
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We Start with a file that has the variable we want to map, in this case race, and postal codes so we can map the data.
We don’t need to use postal codes, we could use actual addresses, or Lattitude and longitude but normally postal codes are more readily available.
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We convert the variable we want to map into a number of binary (true/false) variables. In the example we originally had 1 variable with 9 different categories, and each student was assigned a category depending on their race (e.g. if the student was aboriginal they received a “1”, if black they received a “2” etc.).
Now we will have 9 different variables, and if a student is of that race they will receive a “1” for that variable. So a white student would receive a “1” for the white variable, and nothing for all other race variables.
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The result.
This is done so that we can aggregate the points later on.
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Next we will geocode the student locations by postal code.
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The result of the geocode.
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Rotating the data frame and adding in the Toronto city boundary to make everything more recognizable.
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We will remove all the students living outside the city of Toronto.
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Next we will conduct a spatial join in order to join polygons to points. We are doing this because our student information (points) will need some sort of unique identifier to be grouped by, and this will be our polygons, which in our case is census tracts.
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Now we will take our newly created merged points, and dissolve them based on the Census tract ID which is a unique identifier for each census tract. We will also sum each of the 9 race variables we created earlier.
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The result will be a multipoint file that contains the number of students that are in each given race.
As can be seen here, only one record is selected but a number of points are highlighted on the map.
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Next we will join the dissolved points to the Census tract file (join point to polygon as opposed to last time which was polygon to point). This join is based on attributes from a table, whereas last time we joined based on spatial location.
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Export the data as a new file.Unjoin the data from the original in order to keep things neat and tidy.
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The result.
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By using graduated colours we can map out a single variable at a time in order to see the distribution of each individual race.
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Race using graduated colours.
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Or we can use dot density to look at a number of different races at once. If we keep a dot value of 1 then each point represents a student of a given race.
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The points are randomly drawn inside their given polygon, in this case census tracts, which helps to protect the privacy of each student.
The points will also not overlap each other which helps us see a pattern more clearly than if we had been using points, which do overlap if a point has the same location as another point.
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