localities_LNI

Technical Specifications Document: Technical Specifications Link

Description: This segmentation aims to classify funding organizations by geographic scope based on their grantmaking behavior into three categories:

• Local/Regional: Orgs operating primarily in one state or a few localities
• National: Orgs operating across many U.S. states
• International: Orgs with notable international grantmaking activity

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Non-Profit Mapping Project Documentation: Local vs National vs International Segmentation

Author: Edward Moore - GivingTuesday Data Commons
Note for external readers: Some Databricks links in this document point to internal notebooks and may not be accessible to people outside GivingTuesday.

1. Approach

Definitions

This segmentation aims to classify funding organizations by geographic scope based on their grantmaking behavior into three categories:

1. Local/Regional: Orgs operating primarily in one state or a few localities
2. National: Orgs operating across many U.S. states
3. International: Orgs with notable international grantmaking activity

Variables

The variables that may be used in the classification approach include:

• FILERNAME1
• FILEREIN
• TAXYEAR
• FILERUSSTATE (990PF Basic Fields Data Mart: Header A - Charity Location - Domestic State)
• SIGOCPYRFAPC (990PF Current Grants Data Mart: Part 15 Table 3A - Col A - Row 2 - Address Zip or Postal Code)
• SIGOCAFFRFACO (990PF Current Grants Data Mart: Part 15 Table 3A - Col A - Row 2 - Address Country)
• SIGOCPYRFAPO (990PF Current Grants Data Mart: Part 15 Table 3A - Col A - Row 2 - Address State or Province)
• SIGOCPYAMOUN (990PF Current Grants Data Mart: Part 15 Table 3A - Col E - Row 2 - Amount)
• SIGOCPYRFSTA (990PF Current Grants Data Mart: Part 15 Table 3A - Col C - Row 2 - Foundation Status of Recipient)
• SIGOCPYRFACI (990PF Current Grants Data Mart: Part 15 Table 3A - Col A - Row 2 - Address City)
• RECTABADDSTA (990 Schedule I Grants Domestic Orgs & Gov Data Mart: Schedule I Part 2 Table Col A Row 2 - Address US - State)
• RETAAMOFCAGR (990 Schedule I Grants Domestic Orgs & Gov Data Mart: Schedule I Part 2 Table Col D - Amount of Cash)
• RECTABADDCIT (990 Schedule I Grants Domestic Orgs & Gov Data Mart: Schedule I Part 2 Table Col A Row 2 - Address US - City)
• Schedule F Variables TBD

Approach

1. Identify funding orgs from Form 990 and 990PF (Part XIV-3a), including Schedules I and F
2. Standardize and enrich location data using state/country lookups and geocoding via Geopy
3. Calculate foreign grant percentage, max state concentration, number of distinct U.S. recipient states, and a composite score combining all three features
4. Apply KMeans to group orgs into 3 clusters reflecting geographic scope
   OR
   Choose thresholds for the features (foreign grant percentage, max state concentration, number of states) to define the 3 geographic categories (local, national, international)

2. Code documentation

Notebooks

1. Geopy Lookups
   Geopy Lookups (Databricks)
   This notebook runs geolocation lookups for missing or ambiguous country/state data.
2. 990PF Cleaning
   990PF Cleaning (Databricks)
   This notebook cleans and standardizes grants data from Form 990-PF.
3. 990PF Aggregation
   990PF Aggregation (Databricks)
   This notebook aggregates grant data by state and calculates key features.
4. 990 Aggregation
   990 Aggregation (Databricks)
   This notebook performs similar aggregations for Form 990 Schedule I and F filers.
5. Classification
   Classification (Databricks)
   This notebook combines datasets, scales features, and runs KMeans clustering

3. Outputs and Results

K-means Cluster Definitions

• Cluster 0: Local/Regional
  • High concentration in one state
  • Low foreign activity
• Cluster 1: International
  • High percentage of international grants
• Cluster 2: National
  • Wide U.S. state distribution
  • Moderate to low international share

4. List of approaches that did not work

5. Plan for Deploy, Scale, Archive, and Expose-data steps

• Easily extendable to additional years and EINs
• Output is an assigned geographic scope (local, national, or international) for each EIN per tax year

6. Future work

• Incorporate Schedule F data once available
• Potentially explore labeling and supervised learning using curated examples rather than clustering
• Review k-means clustering approach and decide if it should be scaled or if a definitive feature-threshold approach should be used instead to define the 3 clusters