I’m currently working on starting a nonprofit organization addressing “period poverty” in my area. But before I could begin taking action for my project, I had to find some data to fuel my organization. I started by collecting data on my county and the county neighboring mine, looking for statistics on poverty. More specifically, who is affected most by poverty? This process is what some would call data mining. Which is defined by Merriam Webster as “The practice of searching through large amounts of computerized data to find useful patterns or trends.”
For more information on the background of my data mining process, I started with a three-step plan. The first step is gathering any data regarding the issue at hand (in this case: poverty). Then, go through your findings. Keep what is important and remove what isn’t helpful. In that same step, also categorize your data using whatever makes more sense for what you’re researching. I divided my data into two categories: who and where. Finally, the last step is action. Using the data that you have collected, address the issue at hand while keeping the data behind it in mind.
I started my research by looking at data from the Minnesota Department of Health. They provided statistics on where poverty is most prevalent in counties and neighborhoods. I used their map and discovered that the county I live in is over the state average poverty rate with almost 128,000 people of all ages suffering from poverty. Making my county’s poverty rate 10.4%. Also, the county directly east of mine suffers from even more poverty, with a rate of 14%. With this information, I knew I had to take action in my area and other areas around me because those were the places most affected.
Using more data from the MNDH, I discovered more about who is affected by poverty. The communities with the most poverty in Minnesota are American Indian or Alaskan Native, Black or African American, and those of Hispanic ethnicities. In addition, according to an organization called MN Compass, 10% of females in Minnesota live below the poverty line, compared to 8% of males. This data covered who is most affected by the issue I’m addressing.
Data mining can help any organization or corporation, not just nonprofits. Because with any data, you will have a better understanding of how to address an issue or grow as a business. The information I found from data mining helped confirm that I was focusing on a real problem. Because I’m concentrating on poverty regarding women’s health (feminine products) and females face poverty more than men. Likewise, I am working in an area with a higher poverty rate compared to many counties in the surrounding area. So, if there is one thing to take away from this post, it should be that even the smallest form of data science can make a big difference in any project.
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