Parks & Rec

• 

  %Percent low income per CTA (from Kinder)

• 

  Number of parks per CTA (from Kinder)

• 

  These are our graphs calculated from the .csv files.

Inspiration

Houston ranks poorly in the relation to other largest cities in the US park access and quality. We wanted to explore the relationship between park access and income levels in different Houston neighborhoods.

What it does

Using .csv files from the Kinder website, we found a correlation between number of parks/CTA code and % low-income/CTA code.

How we built it

We used a combination of Python code and spreadsheet software to analyze and perform mathematical operations on our data.

Challenges we ran into

We were hindered by our lack of coding/previous hackathon experience. Additionally, our initial datasets did not give us a strong correlation to analyze.

Accomplishments that we're proud of

We are proud of the learning we were able to do, as this was the first Datathon we had all participated in. Our datasets had relevant social implications, something we considered important to our project.

What we learned

We learned that parks need to have more access to a wider community. In terms of technical skills, we learned how to use the basics of Github.

What's next for Parks & Rec

If given more time, we would try to find a stronger correlation in the datasets we were given. We would have attempted to use more code, such as data analysis in R with shapefiles, to create a map representation.

We would want to use our findings to analyze park use/density and determine specific neighborhood park deserts. Our data could have also compared park locations to other factors of wellbeing, including: Health and Wellness, Education opportunities/outcomes, Youth and community programs.

Built With

• google-spreadsheets
• python