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DATA4100
Data Visualisation Software
Lesson 9
Modelling, Data analysis and dashboards 1
Lesson Learning Outcomes

1 Construct and interpret map models and charts
in Power BI
2 Construct a basic dashboard
3 Exploring uses of dashboards
4 Note on integrating viz software with existing
systems

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‡ Today we will be using a cleaned detailed version of the
Melbourne Airbnb dataset called
³airbnb_detailed_listings_cleaned.csv´
‡ Note that you can also continue to use the uncleaned file
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This Photo by Unknown Author is licensed under CC BY-NC-ND
Maps in Power BI
‡ Start Power BI
‡ Load the Airbnb dataset

Maps in Power BI
‡ Click on the map icon in the visuals pane (it
looks like a globe)
‡ ‘UDJWKHµORQJLWXGH¶DQGµODWLWXGH¶YDULDEOHV
to the appropriate position on the left.
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summarise option ticked
‡ ‘UDJWKHµEHGURRPV¶YDULDEOHWRWKHVL]H
position. Notice it is summed, so change it
to
count of bedrooms
Basic Map in Power BI
‡ How can we change the
map to better see those
$LUEQE¶VWKDWKDYHPRUH
than one bedroom?

Filtering in Power BI
‡ Go to the Filters panel
‡ Click on Count of bedrooms
‡ Select is greater than or equal to
‡ 7SHµ¶LQWKHER[SURYLGHGWKHQ
SUHVVµ$SSOILOWHU¶
‡ Click on the map and zoom in
‡ Drag the map around to see the
suburbs that interest you
‡ Adjust the size of the map if you
want to place more charts on the
page provided

Add your Treemap
‡ Double click on the treemap
‡ ‘UDJWKHµhost_neighbourhood¶YDULDEOHWRWKH*URXSSRVLWLRQ
‡ Drag the Accommodates to the Values position and change it to
Average (not sum)

Filter out Accommodates
‡ Filter out the accommodates less than 4
Property type and Price
‡ Click on the clustered bar chart
‡ Drag property_type to the Axis position
‡ Drag price to the Values position, and change to average
What is the most expensive property/room on average?
Reflect and redo dashboard
‡ Reflect on what you learnt about Gestalt laws, preattentive attributes and cognitive load from last week
‡ Take another look at your charts and dashboard and see
if you can improve it in terms of the concepts above
Feel free to experiment and create a new dashboard
This Photo by Unknown Author is licensed under CC BY-NC-ND
Activity: Maps in Tableau
‡ 2SHQWKHGDWDILOHLQ7DEOHDXEVHOHFWLQJWKHµ7H[WILOH¶RSWLRQXQGHU
the Connect menu

Activity: Maps in Tableau
‡ 6HOHFWµ6KHHW¶WRVHHDOORIWKHYDULDEOHVRQWKHOHIWKDQGVLGH
‡ Convert the Longitude and Latitude to dimensions, drag them to
column and row position, and the map should appear (or click on it on
the RHS)
‡ ‘UDJWKHµ%HGURRPV¶YDULDEOHDFURVVWRWKHVL]HSDUWRIµ0DUNV¶PHQX
Also change it to a discrete variable and count calculation (by clicking
on the variable you dragged across and moving down the menu
‡ ‘UDJWKHµ%HGV¶YDULDEOHWRWKHColor SDUWRIWKHµ0DUNV¶PHQX
change it to a discrete variable and count calculation
‡ Before more calculations see next page for output

Basic Map model
You can start to interact with the map
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questions
‡ +RZFDQZHFKDQJHWKHPDSWREHWWHUVHHWKRVH$LUEQE¶VWKDWKDYHPRUHWKDQ
one bedroom in the case of Tableau
?
‡ &OLFNRQµ%HGURRPV¶DQGVHOHFWµILOWHU¶IURPWKHPHQX
‡ Unclick 1
‡ 6HOHFWµ$SSO¶
‡ Zoom in
‘UDJWKHµ+RVWORFDWLRQ¶YDULDEOHWRWKH0DUNVPHQX

Filtered map
Q1. How many beds do the largest accommodations have?
Q2. Interact with the map to see where they are located.

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‡ Open another sheet
‡ Drag Accommodates to columns, make sure it is average and Host
Neighbourhood to rows
‡ You will initially get a bar chart, so click on the tree map and answer the
following:
‡ Q: Which host neighbourhoods can accommodate the most visitors on
average? See next page
This Photo by Unknown Author is licensed under CC
BY-SA

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Exclude the neighbourhoods with
fewer accommodation
‡ Highlight the left hand corner, right click and select exclude to remove them
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And property type
‡ Drag property type to rows
‡ ‘UDJSULFHWRWKHVL]HSDUWRIWKHµ0DUNV¶
menu
‡ You can click on the title of the charts to
enter more informative titles
Q:What appears to be the cheapest type of
$LUEQE¶V”

Simple Dashboard Exploration
‡ Click on a Dashboard (along
same panel as sheets)
‡ You will see the three sheets
‡ You can experiment with the
layout size and position
‡ Replace the titles, Increase
font size or hide them
‡ Spend 15 minutes exploring
any charts you want and the
layout of a simple dashboard

Uses of data dashboards?
‡ Visualising data for a static report, e.g. company annual
report
‡ Tracking key performance indicators in real time
‡ Displaying the status of tasks on a project
‡ Smart City Statistics (traffic, weather, etc.) updated in near
real time
‡ Monitoring sensor data in real time
This Photo by Unknown
Author is licensed under
CC BY-SA
Examples: Social Capital in the USA
https://public.tableau.com/en-gb/gallery/?tab=viz-of-the-day&type=viz-of-the-day
Example: Typhoon Seasonality
https://public.tableau.com/en-gb/gallery/?tab=viz-of-the-day&type=viz-of-the-day
Example: Current versus Comparison
Periods
https://public.tableau.com/en-gb/gallery/?tab=viz-of-the-day&type=viz-of-the-day
Integrating viz software
‡ Suppose that you want to integrate your favourite visualisation software
with existing systems?
‡ Check compatibility with existing data storage and maintenance options, so
data can easily be accessed
‡ Chose visualisation software that can
± Run on a number of devices and accepts data in different formats
± It should be able to be networked
± Has simple code behind it
± Allows users to represent their data in many ways
‡ E.g. Tableau has connectors to integrate it with APIs, databases, webhooks
and more
https://jeffreymorgan.io/articles/visualization-software-requirements/
Webscraping
‡ Suppose you have visualised data from a webpage
‡ You can import a table from a website into Google sheets using
the ImportHTML function
‡ Then the Google Sheets connector in Tableau Public 10 makes
it possible to refresh the visualization every 24 hours
‡ If you have time, try the suggestions on the following webpage
‡
https://public.tableau.com/en-us/s/blog/2016/08/webscrapingeverybody-using-import-functions-google-sheets