---
title: Day 1 – Points – U.S. Credit Unions
permalink: day-1-points-us-credit-unions
date: 2025-11-01T23:43:51-04:00
tags:
  - 30DayMapChallenge
  - R
  - DataViz
  - Maps
---

### Day 1 - Points
**[Challenge Description](https://30daymapchallenge.com/#:~:text=Challenge%20Classic%3A%20Map%20with%20point%20data%20(e.g.%2C%20individual%20locations%2C%20points%20of%20interest%2C%20clusters).%20Focus%20on%20effective%20symbolization%20and%20density%20visualization.):** Visualize point-based data such as individual locations, points of interest, or clusters. Focus on symbolization and density representation.


### My Submission

Maps visualizing **U.S. credit unions**, where each packed circle represents a credit union.  
- **Circle size** corresponds to **member size**.  
- **Circle color** indicates **member growth rate** — blue for higher growth, red for lower or negative growth.

![Member Size Map](../assets/basic.png)
[Click here to view high-quality PDF ⬆️](../assets/basic.pdf)

![Growth Rate Map](../assets/growth.png)
[Click here to view high-quality PDF ⬆️](../assets/growth.pdf)


### Local Competition Between Big and Small CUs?

It’s interesting to observe that within clusters of neighboring credit unions, **larger institutions (shown as bigger circles) tend to grow faster than smaller ones.** This pattern raises the question of whether local competition plays a role—**perhaps larger credit unions, with greater resources and brand recognition, are better positioned to attract members and gradually displace smaller competitors.** 

To explore this idea, I ran a simple **state-level fixed-effects regression**:

```{r}
felm(formula = log(growth_4q + 1) ~ log(members) | state | 0 | state, data = df)
```

and the results:
```
Coefficients:
             Estimate Cluster s.e. t value Pr(>|t|)    
log(members)   0.1234       0.0266   4.639 2.47e-05 ***

```

Regression results show that the elasticity of member growth with respect to credit union size is approximately **0.12**.

This means that, **holding state-level effects constant, a 10% increase in membership size is associated with about a 1.2% higher growth rate over the past four quarters.**

The coefficient is positive and **statistically significant** (p < 0.001), suggesting evidence of **a scale advantage among larger credit unions.**

**Should smaller credit unions consider adopting efficient marketing intelligence to stay competitive and survive?**


### References  
- Tutorial: [How to Make a Cartogram with Packed Circles in R](https://flowingdata.com/2024/04/17/how-to-make-a-cartogram-with-packed-circles-in-r/)  
- Source Data: [NCUA Credit Union Call Report Data](https://ncua.gov/analysis/credit-union-corporate-call-report-data)


*Made by [Matt Zhu](https://mattzhu.net) for the [#30DayMapChallenge](https://30daymapchallenge.com/).*