Data-driven Access of Philanthropy

Posted on Jan 13, 2022

The skills the authors demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Does data visualization provide any shortcuts for aligning “doers” with “funders”?

Philanthropy has been funding change-makers to improve the lives of countless people, stimulate innovation, and foster healing relationships within communities across the globe for centuries according to data collected. 

Despite the phenomenal wealth amassed by private foundations, applying for these funds is highly competitive, demanding dedicated resources from already “stretched” non-profit organizations. Even more discouraging, many of these foundations accept applications by invitation only.

Like the for-profit world, decisions to invest are established and fostered through trusted relationships. While social media enables anyone “access”, it doesn’t substitute for the assurance of mutually-aligned goals or values that comes from meaningful dialog and time invested.

So what, if any, value do large datasets of past transactions provide the nonprofit? I was curious to find out.

The Dataset

The dataset I evaluated was sourced from Foundation Directory Online (FDO), a subscription-based grants database of more than 24 million grant records from over 240,000 private foundations. I created the dataset by exporting all grant transactions from 2015 through October 2021 that include the keyword, “climate”. 

It captures over 10,000 records and 11 attributes which include:

  • Grantmaker name, most commonly a Foundation name
  • Grantmaker state
  • Recipient name, the non-profit organization or university awarded the grant
  • Recipient city, state, and country
  • Primary subject, categorizing the grant’s primary focus
  • Year in which the grant was authorized
  • Support strategies, describing how the award will be used (capital building, general program, unrestricted, etc.)
  • Description, describing the program being funded; varies greatly in length and in some instances is absent

Observations of data

Unlike my previous spreadsheet tool of choice, Python pandas enabled me to manipulate the dataset quickly by filtering, reshaping, and aggregating. Matplotlib was my plotting tool of choice. 

One meaningful view is histograms reflecting transactions by year as shown below (one caveat: note the scales!). Keep in mind that this dataset is limited to transactions that include the keyword “climate”, and fluctuations are heavily influenced by other current events (e.g., the pandemic and social unrest of 2020).

data

Reasoning about data

While simple queries and endless pivot tables can help a nonprofit stay current with grantmakers aligned to their operations, the results are not novel nor do they point to a meaningful path in terms of how to connect.

It was at this point that I nearly decided to pack this dataset up when I recognized the significance of the “inner” join: that I could tease out a discrete list of re-granters. This is a nuance not easily wrangled by typical spreadsheet software (or at least, I never set about trying). I was excited to pursue it!

Re-granters are organizations that both receive foundation support as well as distribute their own. Think of these organizations as intermediaries for the larger, exclusive entities with a deeper reach into the communities their recipients serve. In this regard, they are multiple networking steps closer than say, Bill or Melinda Gates, Michael Bloomberg, or any other executive of a major foundation.

With the assistance of NetworkX, I went to work filtering my dataframe with a list of re-granters and plotting the results. I couldn’t have been more excited.

data

Act!

These types of plots trigger action: who do I need to know?

Clearly, someone associated with a “hub” would be a great place to start!

And while those connections may seem to be too many degrees of separation away, why not try a spoke? I’m excited to think of how these types of visuals can spawn further inquiry (e.g., what are the networks associated with each of the spokes?). 

With this in mind, a Python-based NetworkX graph can provide meaningful direction and focus to the non-profit professional charged with securing funds (not to mention maintaining operations, volunteer management, fundraising campaigns, and any other number of unexpected surprises).

Of course, the output this method generates only becomes more meaningful with transaction histories based on specific programs (e.g., reforestation, youth activism, etc.)--I'm looking forward to maturing this workflow for the organizations I am partnering with!

Questions or comments? Let me know!

About Author

Jennifer Anderson

I've spent the last twenty years integrating and interpreting subsurface data sets as a petroleum geologist in California. I've recently left the industry and am excited to add more rigorous data analysis and visualization techniques to my skillset...
View all posts by Jennifer Anderson >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI