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Figure 1: What opportunities can Smart Cities offer? |
How does a Smart City address these challenges?
An example:
Figure 2: Examples of applications like Google Play that measure the levels of pollen. |
II. The Problem
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Figure 3: Image of London Data Store. |
London was one of the first European cities to opt for an Open Data Initiative, and in January 2010 they launched the London Data Store, where data sets were published covering a wide range of topics including: economy, employment, transport, the environment, security, housing and health. This Data Store contains more than 500 datasets and its website is visited every month by 50,000 people. In 2015 they received the ODI Open Data Publisher Award for pioneering work in publishing open data at both local and regional level.
What challenge faced the London firemen?
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Figure 3: Firemen in Tottenham who rescued a pregnant cat. |
The data
The result of the analysis shows a low level of risk. The “exposed” information is inadvertently related to the printer and creator of the document. However, it is always advisable to check.
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We will work with a tool that allows us to perform visual analysis very intuitively and create reports that will highlight the patterns that characterize the data. This is the Power BI Desktop tool.
Figure 7: The initial installation screen for Power BI Desktop, simply choose the correct format for your desktop.
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Format the Data
Figure 8: Screenshot for the selection of the data source. Show all possibilities “data source”.
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Figure 9: Selection screen. It allows us to select which pages from the Excel we wish to load. It then offers a partial visualization followed by the ability to edit the data. |
As these things never go well at first, we get a nice error message. We have no less that 2853 errors. We can investigate there origin directly from the “View Errors” section.
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Figure 10: Error message that appeared when we loaded our example.
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Figure 11: Yellow highlights the fields that were not able to load correctly.
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As this data does not give us any relevant information (due to the services only lasting hours instead of days and as we already have the start date for the service these then becomes redundant. For this reason we can simply delete this column. Highlight the entire column and in the menu select “Remove“.
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Figure 12: Deleting one of the columns
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Figure 13: Instead of deleting the data we can change the form of viewing it and use the data for the year. |
The format of the “DateTimeofCall” field also doesn’t seem sufficient. This time we opt for the “Change Type” option from the same menu however we convert it to include a decimal point.
Despire reducing the number of errors through reloading the data there continues to be errors in some of the fields. Now we are dealing with the “IncidentNumber” field, the format that should be applied is the “Text” format. “Close and Apply“.
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Figure 15: Change the format from Incident Number to the text format
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Visualizing the data with Power BI, basic concepts.
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Figure 16: By choosing a visualization, a canvas appears in the corresponding bookmark.
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The end result of the visualization depends on how we are formatting the data. The type of visualization also affects how the changes can automatically become updated through the process.
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Figure 17: Making a selection from the list of fields that we want to add and then drag them to the canvas of under the corresponding label under the window “Visualizations” (Axis, Legend etc.).
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Finally, we can change the size of the display on the canvas, reposition it, edit it, add labels and modify colours etc. As we hover over sections of the visualization we can see information about tools that contain detauls about that segment, such as tags and their total value.
Returning to our first example, we dediced to create a table visualization of the data. The bookmark is created and from this point we select the fields of data that we are interested in (highlighted in yellow), these are then added to the table.
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Figure 19:
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The selected fields (highlighted in yellow) appear under the “Values” column. Using this method you can add the selected fields directly from the list. In other visualizations, it is advisable to drag the field under the column with the correct label that we want (Legend, Shared Axis, Column values, values etc).
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Figure 20: In our example, through added the “AnimalGroupParent” field automatically allows us to filter for each type of animal that appears in the group.
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