Farmer’s Markets Farmer’s Markets
A series of data visualizations on Farmers’ Market data from data.gov. Dataset Properties Name Location (lat, long, city, state, address) Hours of operation... Farmer’s Markets

A series of data visualizations on Farmers’ Market data from data.gov.

Dataset Properties

  • Name
  • Location (lat, long, city, state, address)
  • Hours of operation
  • Different goods categories and a yes/no if they’re available at that market


I made the following graphic to understand the distribution of the 29 goods for each farmers market such as availability of honey, flowers, fruits, wine, etc. After filtering out some results that didn’t have attributes I was interested in, there were roughly 5.5k farmers markets left.

I was surprised to see how correlated location was with maple availability and that there are more farmers markets with baked goods, honey, and jam, than there are with fruits. Also fascinating to see that 18% of markets sell pet food.



A fun abstract example where each farmers market is represented by a series of circles depending on if it had juice (cyan), cheese (yellow), baked goods (magenta), or an empty circle if it didn’t have any of those goods.

Animating jus cuz.



The next version was to look at two different dimensions for the farmers markets and understand how the dimensions overlapped. I explored this with a bump chart that encoded the overlap as well as changed rank.

In this case you can see how seafood dominates the farmers markets on the west coast but then as you get into the midwest maple starts to take over and then dominates for most of the eastern side of the US. The interactive version allows you to pick which goods you’re comparing.


See the interactive version on bl.ocks.


This iteration was spurred by Elijah suggesting k-means clustering on the dataset. Thanks @emilbays for the kMeans Library.

The first pass was to understand when clustered, how did each cluster’s centroid fall from 0 (No) to 1 (Yes) on the scale for each dimension.


I decided it would be more interesting to show those distributions connected in a series. The x-axis is organized from most common to least common goods.


The biggest change in the final version was the addition of areas around the lines representing each cluster’s centroid values. The areas exaggerate that cluster when it deviates from the rest of the clusters. This attempts to visually answer the question “Which features in each cluster differentiate it from the rest?”

Creating the legend for this made me realize how much even a simple annotations framework would make it so much easier to create callouts and annotations for your visualizations. Sounds like my next project 🙂


See the interactive version on bl.locks and the original post location here.

Susie Lu

Susie Lu

Susie is an expert in Data Visualization with complementary skills in design and front end development. Susie has experience using HTML, CSS, Javascript, JQuery, d3.js, and backbone.js. She worked at SVDS as a Senior Data Visualization Developer telling data stories throughout the company for client needs and internal research projects. Prior to SVDS Susie worked at Accenture Technology Labs as a Senior R&D Developer. She played a major role in incorporating data visualization into the Labs’ research agenda. Susie has been the data visualization expert/developer on projects ranging from basketball insights, to the history of rock and roll, to the state of gerrymandering. Before Accenture Technology Labs, she graduated the University of Washington receiving Dean’s Medals in the School of Engineering and the College of Arts & Sciences. She uses her experience with art and engineering to bring a well-rounded perspective to data science.