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Impossible Maps | Arts & Cultural Non-Profit Navigator

Last Week's Recap

What I wrote on the questions I planned to ask with the map:

  • Are there clusters of art institutions in Manhattan? What are the characteristics of these clusters?

  • How do art institutions interact with their localities?

  • How have private and public funding spread in Manhattan? Is there a relationship between the location of an institution and the funding it receives? Could we infer why?


Getting Data

Organizations' Financial Data

IRS requires non-profits to disclose their financials on a form called Form 990. Initially, I thought the financial disclosure would include the donors and the amount, but apparently the donor information is not made publicly available, although some organizations chose to do so, like Eyebeam.

So we had to make do with whatever data is available, and we decided to go with the API provided by Charity Navigator (free 30 days trial!). The API allows us to call summaries of the 990s of all the non-profit institutions in the states.

The body of data we use consists of the following:

"properties": { "charityName": charityName, "categoryName": categoryName, "causeName": causeName, "causeID": causeID, "nteeType": nteeType, "nteeClassification": nteeClassification, "streetAddress1": streetAddress1, "streetAddress2": streetAddress2, "programExpenses": programExpenses, "totalNetAssets": totalNetAssets, "totalExpenses": totalExpenses, "fundraisingExpenses": fundraisingExpenses, "administrativeExpenses": administrativeExpenses, "totalRevenue": totalRevenue, "primaryRevenueGrowth": primaryRevenueGrowth, "programExpensesRatio": programExpensesRatio, "fundraisingEfficiency": fundraisingEfficiency, "liabilitiesToAssetsRatio": liabilitiesToAssetsRatio, "administrationExpensesRatio": administrationExpensesRatio, "workingCapitalRatio": workingCapitalRatio, "programExpensesGrowth": programExpensesGrowth,

//For search functionality "isVisible" : true },


Getting the coordinates

Unfortunately, the API does not give us the lat & lng coordinates of the organizations. So I used a CSV file provided by Eric Hurst to match postal code with geo coordinates.


Combining the data

A call for arts & cultural organizations in New York returns 186 objects, and we used a scraper to manually call for these 186 organizations' financial data, one by one.

We then combine the different JSON files (organization general data, financial data, coordinates) using AJAX into a single geojson object.


Designing the Visualization

While the 2D view functions as a normal navigator, I chose to present the data as 3D object to communicate the disparity in the wealth that has been accumulated in different fields of arts & cultural organization in New York.

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