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.

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.

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.

Visualization M2.3

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

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.


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.

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.

Visualization L2.2

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

Set I: Global Air Traffic

This dataset was collected from via 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.


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.


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.


Visualization I2.3

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


Visualization I2.5

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

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


Visualization 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.


Visualization H1.2

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


Visualization H1.3

Same data as above without any grouping or hierarchy.


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.


Visualization H2.2

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


Set G: US Population

US population data for 2012 was collected from 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


Visualization G.1.2


Visualization G.1.3


Visualization G.1.4


Visualization G.1.5


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


Visualization F.1.2


Visualization F.1.3