Data Visualisation, Info-Graphics and Google Public Data Explorer
Visualisation is the process of giving a visual form to information which is otherwise dry or impenetrable.
Visualisations/info-graphics are an important tool when presenting data, and can be used to show patterns, correlations and the ‘big picture’.
Info-graphic tools answer questions in a meaningful way that makes answers accessible to others. Traditionally stories have been conveyed through text, and visualisations have been used to display additional or supporting information. Recently, however, improved software helps create sophisticated narrative visualisations that are increasingly being used as standalone stories. These can be linear and interactive, inviting verification, new questions and alternative explanations.
Visualisations/info-graphics are an important tool when presenting data, and can be used to show patterns, correlations and the ‘big picture’.
Info-graphic tools answer questions in a meaningful way that makes answers accessible to others. Traditionally stories have been conveyed through text, and visualisations have been used to display additional or supporting information. Recently, however, improved software helps create sophisticated narrative visualisations that are increasingly being used as standalone stories. These can be linear and interactive, inviting verification, new questions and alternative explanations.
Seven steps in creating a good visualisation are:
1. Acquire
The first is to obtain the data. It is a good idea to have a specific question when collecting data and to know what it is that one is trying to show in his visualisation. The more specific the question, the clearer the visualisation will be.
2. Parse
Play around with the data to structure it into categories, columns and to give it meaning.
3. Filter
Get rid of any additional information that will only confuse the visualisation and the user and make it less understandable.
4. Scrape/Mine
Get the data into a form ready for visualisation.
5. Represent
It is important to use the right visualisation to present different sets of data. Every data set is unique and it is important that the visualisation used fits with the data.
For example, Bar Charts or Histograms are good at clearly showing the difference between one quantity and another. Line Graphs are suited for showing trend, acceleration or deceleration, and volatility, including sudden peaks or troughs. Scattergrams are similarly useful for this, and are also good at showing anomalies. Bubble Charts can depict three aspects of the data through their place on each axis and the size of the bubble itself. Treemaps are useful for depicting different parts of a whole and their relationship with each other. Different software can be used to create visualisations.
6. Refine
Check the visualisation to see how it has worked and how it could be improved. More attention may need to be drawn to a particular part of the data and attributes like the colour may need to be changed to improve readability.
7. Interaction
It is a good idea to add interactive elements to the visualisation to let the user control or explore the data. If the user can select a subset of data or change the viewpoint, then that added control will make their viewing experience more satisfactory.
Finally, one must remember that although visualisations can improve the users’ understanding and experience of a story in many cases, it is not a good idea to create a visualisation for the sake of it. One must make sure that it tells or enhances the story in a meaningful way.
The first is to obtain the data. It is a good idea to have a specific question when collecting data and to know what it is that one is trying to show in his visualisation. The more specific the question, the clearer the visualisation will be.
2. Parse
Play around with the data to structure it into categories, columns and to give it meaning.
3. Filter
Get rid of any additional information that will only confuse the visualisation and the user and make it less understandable.
4. Scrape/Mine
Get the data into a form ready for visualisation.
5. Represent
It is important to use the right visualisation to present different sets of data. Every data set is unique and it is important that the visualisation used fits with the data.
For example, Bar Charts or Histograms are good at clearly showing the difference between one quantity and another. Line Graphs are suited for showing trend, acceleration or deceleration, and volatility, including sudden peaks or troughs. Scattergrams are similarly useful for this, and are also good at showing anomalies. Bubble Charts can depict three aspects of the data through their place on each axis and the size of the bubble itself. Treemaps are useful for depicting different parts of a whole and their relationship with each other. Different software can be used to create visualisations.
6. Refine
Check the visualisation to see how it has worked and how it could be improved. More attention may need to be drawn to a particular part of the data and attributes like the colour may need to be changed to improve readability.
7. Interaction
It is a good idea to add interactive elements to the visualisation to let the user control or explore the data. If the user can select a subset of data or change the viewpoint, then that added control will make their viewing experience more satisfactory.
Finally, one must remember that although visualisations can improve the users’ understanding and experience of a story in many cases, it is not a good idea to create a visualisation for the sake of it. One must make sure that it tells or enhances the story in a meaningful way.
Google Public Data Explorer
The Google Public Data Explorer makes large, public-interest datasets easy to explore, visualize and communicate. As the charts and maps animate over time, the changes in the world become easier to understand. One does not have to be a data expert to navigate between different views, make comparisons, and share findings.
Students, journalists, policy
makers and everyone else can play with the tool to create visualizations of
public data, link to them, or embed them in their own webpages. Embedded charts
and links can update automatically so that one is always sharing the latest
available data.
The Public Data Explorer launched
in March, 2010.
Data Sources
All of the datasets in the Public Data Explorer are provided
by third-party data providers, such as international organizations, national
statistical offices, non-governmental organizations, and research institutions.
These providers, and not the Google Public Data Team, are responsible for
creating and maintaining all of the content that appears in the product
A subset of datasets from the Public Data Explorer are indexed in Google Web Search. Searching for metrics from these
datasets will generate a graph at the top of your search results, and clicking
on this graph will take you to the corresponding visualization in the Public
Data Explorer.
More information about each of
the searchable datasets and their associated triggering queries is shown in the
table below:
|
Dataset
(Provider)
|
Data
Details
|
Example
Queries
|
|
World Development Indicators
(World Bank) |
A variety of metrics related to
world growth and development, by year, by country.
|
|
|
Unemployment in the US
(US Bureau of Labour Statistics) |
Unemployment
rate by month, by state, county, and for the country as a whole.
|
|
|
Population in the US
(US Census Bureau) |
Population
by year, by state, county and for the country as a whole.
|
|
|
Unemployment in Europe
(Eurostat) |
Unemployment
by month, by country.
|
|
|
Minimum Wage in Europe
(Eurostat) |
Minimum
wage in Euros by half-year, by country.
|
|
|
Government Debt in Europe
(Eurostat) |
Government
debt as percentage of GDP by year, by country.
|
|
|
Broadband Penetration in Europe
(Eurostat) |
Broadband
connections per hundred inhabitants, by half-year, by country.
|
Visualizing the data in a dataset
User Interface Overview
The Public Data Explorer visualization
interface is laid out as shown in the following diagram.
Image Key
1.
Data
pane
2.
"Compare
by" pane
3.
Filters
pane
4.
Chart
area
5.
Play
button
6.
Chart
type selector
7.
Chart
settings
8.
Link
button
Creating Charts
To create a chart:
(1)
Select a chart type using the chart type
selector. There are four chart types to choose from:
1.
Lines
2.
Bars
3.
Map
4.
Bubbles
(2)
Select statistics to show in your chart by
clicking on the metric names in the data pane. If the current visualization is
a line chart, then the chosen metric will automatically be applied to the
y-axis. Otherwise, a drop-down menu will appear listing the chart features
(e.g., y-axis, color, size) that this metric can be applied to. Note that a
single metric can be applied to multiple features, if desired.
(3)
Choose entities to compare by clicking on the checkboxes
in the "Compare by" pane. In many datasets, you can also click on the
current comparison category (e.g., "Countries") to compare by another
dataset dimension.
(4)
Pick values to filter on by selecting entities
in the filter pane. Like the statistics chosen earlier, filter values are
associated with chart features via a drop-down menu. Note that, in some
datasets, there is nothing to filter on so this pane does not appear in the UI.
(5)
Click on the play button (in a bar, map, or
bubble chart) to animate the bars or bubbles in the chart over time;
alternatively, in a line chart, adjust the x-axis time range by sliding the
tabs in the timeline below the chart.
(6)
Enjoy your visualization!
Extra Features
- Move bar or bubble labels by clicking and dragging them in the chart area.
- Get data at a particular time point by hovering over a line, bar, or bubble in the chart area. In the case of a line chart, a pop-up will appear containing the metric value. For the other chart types, labels will appear on the axes and/or legend containing the values for the chosen bar or bubble.
- Select or deselect entities by clicking on individual bars and bubbles in the chart.
- Fine-tune your visualization using the options in the chart settings menu. These allow you to switch to a logarithmic scale, hide unselected bars or bubbles, or show bubble trails, among other things.
- Save a chart or embed it in a website by clicking on the link button.
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