What Can Population Density tell us About our State?

Group Members: Kevin Milne Darwin Lopez Michael Wong

Inspiration

To understand how location can affect finding jobs since we will be graduating and going to the working world soon. Additionally, we want to further explore the dynamics of our state. Washington is interesting in that there is a fairly urban western region of the state, but the rest of the state can be pretty rural. There's also preconceived notions about bigger cities versus smaller cities. Bigger cities are often characterized as being plagued by homelessness. In Seattle in particular, with rising housing prices, many assume that the percentage of the population housed must be lower than elsewhere. We want to explore whether there is a relationship between the population density of a region and the employment and housing rates.

What it does

Analyze factors in locations that affect employment and housing, specifically focused on population density.

How we built it

Downloaded databases on population and population density in locations, then matched the locations in other databases that stored information on employment, housing, and other factors such as income Used machine learning to try to predict and calculate the accuracy of how related each one were

Challenges we ran into

Converting the database into csv files that is readable and comparable to the other ones. We predicted that downloading the data and importing it into our IDE wouldn't take much time. However, cleaning the data in excel turned out to be one of the most arduous aspects of our project. Additionally, merging the databases once we had access to them in csv form also took far longer than anticipated.

Accomplishments that we're proud of

We're proud of how we were able to complete the project, especially in regards to overcoming the challenges we outlined above. We are also proud that we were able to accomplish an additional learning goal. We created visualizations for our regressions and the visualizations we chose really told a story with our data.

What we learned

We learned how to use and apply data science and python and apply it in the real world. It was so interesting to be able to take data from Washington -- a state we all grew up in -- and derive patterns from the immediate world around us. We learned that some of the preconceived notions regarding cities may be inaccurate; the total percentage of individuals housed in regions with a higher population density is higher than in areas with a lower population density. We were really surprised by this. Last but not least, we learned how useful running regression analyses can be, especially with big datasets

What's next for What Can Population Density tell us About our State?

We have two further goals: bold 1) Future research should aim at putting our variables with conversation with each other. Are our x variables independent of each other, or are they related? Would this hurt our analysis? Additionally, we should check whether there are any confounding variables that are impinging upon our results.

bold 2) This model should be applied to other states and mean squared errors should be calculated based on the predicted values based on our model and the actual data for that state. If our model is accurate, then we may have found a general relationship between our variables of interest. If our model is not accurate, then the relationship we observed in our analysis may be unique to Washington.

Built With

• python