Set M: Maryland Clean Energy Grant Awards

This dataset was collected from Maryland Open Data. The dataset contains historical data on total grant amount awarded to different clean energy projects. The projects are grouped first by county and then by zipcode. They are aggregated into four different technology types and were colored accordingly: solar hot water, solar PV, geothermal and wind. The colors were chosen to represent the Maryland flag. Each box represents the total amount grant awarded for a certain technology in certain zip code.

Visualization M1.1

The projects were filtered to show only values below $40,000 for grant award amount.
M1_1

Visualization M1.2

This is the same visualization as M1.1 but showing values between $10,000 and $40,000. The borders were removed here.
M1_2

Visualization M2.2

There the projects were first divided into two groups where one had states with more expenditure on solar and the other had states with more expenditure in other technologies. They were then further categorized into solar and non-solar group.
M2_2

Visualization M2.3

This is a variation of M2.2 with no border.
M2_3

Visualization M3.1

Here, no filter has been applied on the dataset and it’s presented in a slice-and-dice layout with no borders.
M3_1

Set L : Maryland County Data

Maryland county data was collected from the 2011 Maryland Statistical Handbook. This dataset contains land area, population, population density and per capita personal income (current USD) for each county of Maryland for year 2010. In the visualizations, the red, yellow, black and white were chosen to represent Maryland.

Visualization L1.1

In this visualization, each of the boxes represent the population of each county. The counties have been grouped by their region. The coloring was done based on four equal bins of population density.
L1_1

Visualization L2.1

Each box shows the land area of a county while the colors represent per capita personal income. The counties have been grouped by their region. The coloring was done in two equally dense bins with colors representing each end of both the bins.
L2_1

Visualization L2.2

This is the same visualization as above with the grouping removing. Coloring was also done in 4 equally dense bins.
L2_2

Set I: Global Air Traffic

This dataset was collected from openflights.org via visualizing.org. The dataset holds data about airports and flight route data.

Visualization I1.1

Each of the boxes represents the number of incoming international flights to an airport. The color varies according to the ratio of international flights to domestic flights. Grey boxes have only international flight and no domestic ones. Among the others, red ones tend to have more international flights while the yellow have more domestic flights. The most bright box in the visualization is Amman airport closely followed by Zurich, Prague, Dublin etc. The visualization was filtered to show airports that have at least one international flight. The airports have been grouped by country.

L_1_1

Visualization I2.1

The boxes represent the ratio of international to domestic flights for individual airports. Larger sizes indicate more international flights. The color represents total number of routes served by that airport. The coloring was done via two equally spaced bins highlighting the extremely busy international airports like London, Paris, Moscow and Chicago.

L_2_1

Visualization I2.2

This is the same visualization at I2.1 with a different binning and color scheme. Red airports are the ones with most routes followed by orange, yellow, grey and black.

L_2_2

Visualization I2.3

This is the same visualization at I2.1 with a different color scheme.

I_2_3

Visualization I2.5

This is the same visualization at I2.1 with a different color scheme and layout.
I_2_5

Set J: NBA Player data

This dataset of 441 players in the National Basketball Association is organized into 29 teams. For each player area indicates number of points scored and color indicates number of personal fouls. Darker, redder colors indicate more personal fouls. There is clearly a wide range of scoring and personal fouling, but the large green rectangles highlight those who manage to score many points while avoiding personal fouling.

Visualization J1.1

J1_1

Visualization J1.2

J1_2

Set H: US Business Dynamics

This dataset was collected from US Census Bureau. The dataset contains state-wise 2011 data on businesses and employment.

Visualization H1.1

The boxes represent each state’s job creation count. The boxes are colored according to net job creation rates – states below 1% are red, between 1% and 2% are white, and above 2% are blue. The states are grouped first by region then by division.

H1_1

Visualization H1.2

Same data as above with a slight different color scheme and aspect ratio.

H1_2

Visualization H1.3

Same data as above without any grouping or hierarchy.

H1_3

Visualization H2.1

The boxes represent number of firms in each state. The boxes are colored according to net job creation rates – states below 2.1% are red and above 2.1% are blue. The states are grouped into five manually chose clusters with all blue clusters in one group.

H2_1

Visualization H2.2

This uses same data from above visualization in a slice-and-dice format.

H2_2

Set G: US Population

US population data for 2012 was collected from Census.gov. This dataset contains state-wise total population, over 18 population and percentage of 18+ population for 2012. In the following visualizations, the box size represents total population of a state and the color varies as per the percentage of 18+ population for that individual state.

Visualization G.1.1

G1_1

Visualization G.1.2

G1_2

Visualization G.1.3

G1_3

Visualization G.1.4

G1_4

Visualization G.1.5

G1_5

Set F: Doing Business Across the World

Doing Business data was collected from World Bank’s Doing Business website. The dataset contains new firm count and new firm density for all countries for individual years from 2004 to 2011. For the following visualizations, the box size represents new firm count and the colors represent various degrees of firm density. The countries are grouped into major economic groups. There are 864 records in this dataset.

Visualization F.1.1

F1_1

Visualization F.1.2

F1_2

Visualization F.1.3

F1_3

Set A: World urban population (Part 2)

About the dataset

The dataset was collected from World Bank’s website. This contains population density (people per sq. km of land area), urban population count, urban population as a percentage of of total population and annual urban population growth percentage for all countries for the year 2010. There are 210 records in total.

Visualization A.1.1

The box sizes represent urban population count for individual country. We filtered the data to include countries with urban population of 20 million or more. This resulted in 36 records. Countries with negative urban population growth are colored in pink – here we find only Ukraine under this criteria. Other countries are colored in a black to blue scale where black represents zero urban population growth and blue represents the highest amoung this countries (6.25%).

A1_1

Visualization A.1.2

The box sizes represent urban population count for individual country. We filtered the data to include countries with population density of 42 or more people per sq. km of land area. This resulted in 148 records. The coloring was done based on urban population as a percentage of total population. The lowest percentage (10.9%) is colored green, the highest percentage (100)%) is colored red and the midpoint (55.4%) is colored white.

A1_2

Visualization A.1.3

The box sizes and color both represent population density of people per sq. km of land area. The lowest value(0) is colored black and the highest(944) is yellow. The dataset was filtered to show countries with population density of 1000 or less people per sq. km of land area. This resulted in 201 records.

A1_3

Visualization A.1.4

This visualization is similar to the previous one (A.1.3). However, it was further filtered to show countries with urban population of 5.37 million or less resulting in 123 records. The blue color represents lowest (0) population density and white represents highest (944).

A1_4

Visualization A.1.5

In this strip treemap, the box sizes represent population density of people per sq. km of land area. The color represents annual urban population growth percentage. Negative values are colored in less darker brown. Positive values are colored from yellowish brown (0%) to dark brown (6.25%). The data is filtered to have countries with population density of 100 or more people per sq. km of land area (91 records). Countries are grouped into two equally dense bins for population density.

A1_5

Visualization A.1.6

This slice and dice visualization further filters the previous visualization to view countries with population density between 100 to 6631 people per sq. km of land area (87 records). Countries are grouped into 10 equally dense bins for population density.

A1_6

Visualization A.1.7

9 countries that have more than 1000 people per sq. km of land area are displayed in this visualization where the box sizes represent urban population. The colors are done arbitrarily to provide unique colors to countries.

A1_7

Visualization A.1.8

This is the same filtered data as the previous visualization (A.1.7) used in a striped format. The color represents population density with green being lowest (1066) and red being highest (19,847).

A1_8

Visualization A.1.9

This visualization shows countries with 23.3 million or more urban population. The box sizes are urban population count. The color represents population density with the highest being greenish yellow (19,847) and the lowest being purple(4). The coloring was done in 4 equally dense bin and the scale was logarithmic.

A1_9

Visualization A.9.1

A9_1

Visualization A.9.2

A9_2

Visualization A.9.3

A9_3

Set D: Last.fm’s top 20 Artists in 10 years of scrobbling

About the dataset

This data set contains artist data from Last.fm. Last.fm, on their 10 year anniversary, published a list of top 100 artists based on their popularity as per Last.fm’s user data. From that list, we have taken the top 20 artists, the total number of times their tracks were ‘scrobbled’ or played, and the number of unique listeners for each artists. We also identified board genres of the artists based on their most popular tags.

Visualization D.1.1

These visualizations were inspired by Piet Mondrian‘s Composition with Yellow, Blue and Red.

The boxes represent individual artists where the size of the box is the number of times their tracks were played while the color represents the genre of the artist.

D1_1

Visualization D.1.1.1

This is the same visualization as the earlier (D1.1) with different aspect ratio and without borders.

D1_1_1

Visualization D.1.1.2

This is the same visualization as the earlier (D1.1) with different aspect ratio.

D1_1_2

Visualization D1.3

This color palette was inspired by Gene Davis’s Firebox. The boxes represent individual artists where the size of the box is the number of unique listeners. The coloring was done by dividing the listeners count into 4 equally dense bins to provide unique colors to artists.

D1_3

Visualization D1.4

This is the same treemap as the earlier one (D1.3) with a slice-and-dice layout – inspired by Gene Davis’s Firebox.

D1_4

Visualization D1.5

Another visualization inspired by Gene Davis. The boxes represent individual artists where the size of the box is the number of times their tracks were played. The coloring was done by dividing the listeners count into 4 equally dense bins to provide unique colors to artists.

D1_5

Set B: World energy consumption and CO2 emission (Part 2)

The dataset was collected from US Energy Information Administration. It contains 2010 data on total oil supply (thousand barrels per day), CO2 Emission (million metric tons), per capita CO2 emission (metric tons of CO2 per person), energy production (quadrillion Btu), and energy consumption (quadrillion Btu) for all countries categorized into 7 continents. There are 224 records in total.

Visualization B.1.1

The size of the boxes represent CO2 emission for countries while the color varies by per capita CO2 emission. The coloring was done via categorizing countries into 6 equally dense bins for per capita CO2 emission.

B1_1

Visualization B.1.2

This is the same visualization as above (B.1.1) with the top 5 CO2 emitting countries removed. This has a slice and dice layout without any borders.

B1_2

Visualization B.1.3

The data in visualization B.1.2 was sorted by per capita CO2 emission values for this visualization.

B1_3

Visualization B.1.4

This is the same data as visualization B.1.2 laid out in squarified layout. However, the original continent categorization was removed and a hierarchy based on 6 equally dense bins of per capita CO2 emission was used.

B1_4

Visualization B.1.5

B1_5

Visualization B.2.1

This dataset does not use any hierarchy. The size of boxes represent energy consumption of countries. The boxes are colored based on continents.

B2_1

Visualization B.3.1

This visualization shows the top 4 CO2 emitting countries. The color theme was inspired by Joseph Albers’ Homage to the Square – Glow.

B3_1