Visualize your data

Use this guide to help you plan your visualizations in Gist. Select what you want to do below.

Year

Album

Artist

Genre

Subgenre

1967

Sgt. Pepper's Lonely Hearts Club Band

The Beatles

Rock

Psychedelic Rock

1959

Kind of Blue

Miles Davis

Jazz

Modal

1971

Blue

Joni Mitchell

Pop

Acoustic

1965

Bringing It All Back Home

Bob Dylan

Folk

Folk Rock

1975

Greatest Hits

Al Green

Funk/Soul

Soul

Compare categories
Use Gist to display categorical data. This is a dataset of the Rolling Stone Top 500 albums. The dataset has three categories—artist, genre, and subgenre. Data can be summed by any of these categories to make comparisons, for example showing how many rock albums there were, or how many albums the Beatles had in the Top 500.
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Compare as percent
The Pie Chart view divides a circle into segments with the length of each segment representing a proportion of each category to the total sum of all data. Pie charts are useful in visualizing the proportion of categories that make up a dataset as well as for comparing proportions of categories to one another. Here it is used to visualize the percentage breakdown of genres across the Rolling Stone dataset.
Compare across groups
The Bar Chart view is one of the most versatile views in Gist. It can be set to either horizontal or vertical bars. There are several main types of bar charts available, including a basic bar chart, histogram, stacked bar chart, and grouped bar chart. Here we are using the stacked bar chart to make comparisons across the genre and subgenre categories of the Rolling Stone dataset.
Compare as percent, across groups
The Pie Set view is a collection of pie charts. This view is useful for visualizing how a share of specific categories changes from one dataset to another. Here we are showing pies for each genre, with the segmentation inside each pie reflecting the subgenre category.
Compare as percent, across groups
The Treemap view is a collection of nested rectangles. This view is useful for showing hierarchical relations between two category fields of the dataset. It makes it possible to study your data on up to two levels. Here we are using it to map the number of albums in each genre category.

Year

Hotels

Liquor stores

Restaurants

1950

81000000

305000000

964000000

1951

219000000

1734000000

393000000

1952

236000000

2070000000

422000000

1953

274000000

2540000000

481000000

1954

272000000

2487000000

484000000

Show trends over time
Use Gist to display trends in your data over time. This is a dataset of alcohol expenditure in the U.S. across several venue types, including hotels, liquor stores, and restaurants. The data includes a year column, making it possible to show trends over a certain timespan.
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Measurements over time
The Line Graph view displays quantitative values over a continuous interval or time period. Line graphs are useful in visualizing trends and analyzing how the data for one or multiple categories changes over time. Here the graph shows the growth in alcohol consumption across each of the venue types.
Categories over time
The Area Chart view is similar to the Line Graph, with the exception that the area below each line is filled with a color. Simple area charts are used to represent overall trends of quantitative values for one or multiple categories over a time. Area charts are most useful when multiple categories are stacked on top of each other in order to represent the development each category over time as well as in relation to the total sum of all categories. Here the chart highlights the relative size of each venue category over time.
Show change over time
Gist can also show change over time via animation for specific objects in your data. This dataset tracks the income, population and life expectancy of countries in the world since 1800.
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Year

Country

Income

Population

Life Exp.

1800

United States

2128

6801854

39.41

1810

United States

2283

8294928

39.41

1820

United States

2242

10361646

39.41

1830

United States

2552

13480460

39.41

1840

United States

2792

17942443

39.41

Objects over time
The Bubble Chart view displays a set of bubbles plotted according to two variables, one on the horizontal and the other on the vertical axis of the chart. In addition, the size of the bubble can be set to a variable in your data, and a playback control at the bottom of the screen will play chronologically through a series of Date values. Here, the visualization shows all countries with income on the x-axis and life expectancy on the y-axis, with population mapped to the size of the bubble—animated over time.

Title

Artist

Date

Medium

Les Menines

Pablo Picasso

1959

Lithograph

10th Chicago International Film Festival

Saul Bass

1973

Poster

Poeme de L'Angle Droit

Le Corbusier

1955

Lithograph

Marseille

Henri Cartier-Bresson

1932

Photography

Tote

Gerhard Richter

1963

Painting

Browse collections
Gist makes it possible to browse objects in your dataset visually. This dataset is of the collection of the Museum of Modern Art in New York. It includes every object in the collection and its metadata, for example the title, artist, date and medium.
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Non-hierarchical collections
The Gallery view visualizes every data object in your dataset as a grid. Objects can be filtered by categories or ranges in your data, and sorted in alphabetical or numeric order. Galleries can display images in your data, as well as icons or colors assigned to categories for each object.
Grouped collections
The Grouped Gallery view is similar to the Gallery, but it groups data by category, numeric range or time range (e.g. Decade, Year, Month). Here it is used to group MoMA’s collection by decade.
Collection metadata
The Table view is useful for showing the raw data in a compact way, for seeing the data contained in a visualization, and when a more detailed and precise data comparison is required.

Rank

Name

Country

1

California Institute of Technology

United States

2

University of Oxford

United Kingdom

3

Stanford University

United States

4

University of Cambridge

United States

5

Massachusetts Institute of Technology

United States

Map country data
Gist can visualize geographic data on a global country map. This is a dataset of university rankings across the world, with universities categorized by country. Gist can sum data for each country to show the distribution on a map.
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Compare areas by country
The country map (included in the Map view) displays countries of the world with the color of the region corresponding to a value in the dataset. Also known as a choropleth map, this map is useful for understanding how data is distributed geographically as well as for relative country comparisons with respect to various factors such as demographics, economics, or other. Here, it is used to show the distribution of top universities by country.

Name

Type

Party

State

Abraham, Ralph

Representative

Republican

LA

Adams, Alma

Representative

Democrat

NC

Aderholt, Robert

Representative

Republican

AL

Aguilar, Pete

Representative

Democrat

CA

Alexander, Lamar

Senator

Republican

TN

Map regional data
In addition to countries, Gist can also visualize data by region—states, provinces or counties. This is a dataset of US legislators, including their type (senator or representative), party affiliation, and state.
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Compare areas by region
The Regional Map view colors geographic areas according values in the data, at the level of the state, county, province, or prefecture. Regional Maps are available for a variety of regions including US, China, India, Asia, ASEAN, and Europe. The Regional Map provides a way to visualize how the data varies across a specific geographic region and how its subregions parts compare to one another. Here, the Regional Map is used to show a comparison of states with the most legislators.

City

Latitude

Longitude

Population

Tokyo

35.6850

139.7514

22006299

Mumbai

19.0169

72.8569

15834918

Mexico City

19.4424

-99.1309

14919501

Shanghai

31.2164

121.4365

14797756

São Paulo

-23.5586

-46.6250

14433147

Map location data
It is also possible to plot location data geographically, as long as your data includes latitude and longitude fields. This is a dataset of cities across the world including the population as well as the latitude and longitude for each city.
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Simple coordinates
The pin map (included in the Map view) displays pins at the specific geographic location on the map based on latitude and longitude coordinates. The pin map is useful for getting a sense for how the data is distributed geographically. Here it shows the locations or cities around the world.
Coordinates with magnitude
The bubble map (included in the Map view) displays circles at the specific geographic location on the map based on latitude and longitude coordinates. The size of the circle can be set to correspond to a value in the data set. The Bubble Map is useful for getting a sense for both how the data is distributed geographically as well as for relative comparison of the values in the dataset. Here it shows the locations of cities around the world with bubbles sized by population.

Flight Nr

Origin Lat

Origin Lon

Dest Lat

Dest Lon

AAL051

35.685

139.751

32.897

-97.037

KLM1000

19.016

72.856

51.477

-0.461

VIR011

19.442

-99.130

38.748

-90.370

DLH923

31.216

121.436

32.897

-97.037

BAW247

-23.558

-46.625

51.477

-0.461

Map origin and destination
Finally, it is possible to map routes between two sets of latitude/longitude coordinates. This is a dataset of flight paths, including an origin and destination set of latitude/longitude values, as well as the flight number.
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Map routes between locations
The Globe view visualizes locations on the surface of a globe based on latitude and longitude coordinates. In addition, it is possible to specify an origin and a destination for coordinates. This will draw connecting lines between the two points. The View can be used to simply visualize points on a globe, as well as visualizing paths from an origin to a destination. Here it is used to show flight paths around the world.

Title

Speaker

Funny

Confusing

Inspiring

Ken Robinson: Do schools kill creativity?

Ken Robinson

19645

242

24924

Al Gore: Averting the climate crisis

Al Gore

544

62

413

David Pogue: Simplicity sells

David Pogue

964

27

230

Majora Carter: Greening the ghetto

Majora Carter

59

32

1070

Hans Rosling: The best stats you've ever seen

Hans Rosling

1390

72

2893

Show relationships in numeric fields
Gist makes it possible to show relationships based on numeric fields. This is a dataset of TED Talks, taken from the TED website. The talks were rated by users according to a set of attributes, including “funny”, “confusing”, and “inspiring,” and each field includes the sum total of these ratings.
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Compare two or more variables
The Scatterplot view displays a set of bubbles plotted according to two variables, one on the horizontal and the other on the vertical axis of the chart. In addition, the size of the bubble can be set to a variable in your data. The Scatterplot can be used to detect relationships that might exist between the variables as well as spotting outliers—values that don’t follow the same pattern as the others. Here, it is used to show the ranking of TED talks according to two attributes, to highlight the distribution of talks relative to the selection.

Name

TItle

Summary

Yemisi Adegoke

Fighting Misinformation and Defending the Open Web

"Yemisi Adegoke is a multimedia journalist at the BBC based in Lagos. …“

Peter Agh

Inclusive Development in Europe's Cities

"Peter Agh served as City Manager of Nove Zamky, Slovakia for 10 years before …”

Chitra Akileswaran

Hysteria No More: Data, Doctors and Women’s Health

"Chitra Akileswaran, MD, MBA is the Co-founder and Chief Medical Officer of ...”

David Albert

Next-Gen Technology Ignites Healthier Lifestyles

"David E. Albert, MD, an Oklahoma native, is a physician, inventor and ..."

Francine Anthony

Our Own Worst Enemy: Why Women Keep Each Other Down

"Francine Anthony is the head of Global Partner Marketing at Sitecore …”

Visualize word frequency
Use Gist to visualize how many times specific words appeared across a field or set of fields. This is a dataset of talks at the South-by-Southwest (SXSW) conference. Text fields like the “summary” field can be parsed to extract popular words and highlight them.
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Display word popularity
The Word Cloud view displays a set of words extracted from Text Fields in your dataset, organized by frequency. The Word Cloud can be used to show the words are used most often within your data. Here is it used to highlight the most popular words in the SXSW talk summaries.

Try this interactive visualization of the various views Gist has to offer.

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