Story
Hartford Area Habitat for Humanity is a non profit Christain affiliated organization in Hartford which works towards providing housing for those in need. Habitat's mission is to put God's love into action by building homes for families who are not financially capable of doing so themselve. As it says on their website, “Habitat for Humanity was founded on the conviction that every man, woman and child should have a simple, durable place to live in dignity and safety, and that decent shelter in decent communities should be a matter of conscience and action for all.” From 1989 to present day, Habitat has built a total of 250 homes for families in need. We teamed up with Habitat to help them build data and map their houses so they can have a better understanding of who and where they are building these homes. Educational attainment and Home Ownership can be used to track what type of people are using Habitat and what locations have Habitat been building in Hartford County and Tolland County.
Research Question.
My build stage 4 question was, in what kinds of neighborhoods is Habitat building homes? This question matters because their buyers want to live in areas that are safe and reliable. The satisfaction of their homeowners is a point of emphasis for Habitat so understanding the neighborhoods they have built in and the demographic makeup of those areas is important for Habitat’s future success in creating more safe homes. I focused on educational attainment and homeownership demographic census data to track where Habitat has been building its homes. I chose Educational attainment as one of the demographics because in our society graduating from high school and college is crucial for finding a job and generating a stable income. Buying a home costs a lot of money and jobs that pay that amount often require a high school diploma and or a college degree. Tracking where Habitat has built homes in the past using this demographic allows for the organization to plan builds in the future, and gives them important information on the educational state of their neighborhoods. The second demographic I focused on was home ownership. This demographic is important for Habitat because their mission is to provide people with affordable housing in hopes that they are able to save money in the future because they are not spending every dollar on their home. In 2020 Hartford Habitat for Humanity published the Reflective Report on the Impact of Homeownership. In this report it is found that 63% of people who have used Habitat say it was a good investment for them. A quote found on their report states “Benefits of homeownership that were not available to me as a renter from simple things like being able to start a garden and having creative control over home decor to more substantial things like providing security for my children, and also a sense of awareness, gratitude, and accomplishment that are embodied through the program” Habitat Report. By understanding the percentage of home ownership and owner occupied housing, Habitat can use this to continue their search for neighborhoods that need more owner occupied housing. This will benefit the individual but also the neighborhood as whole because the increase in saved wealth will allow for new areas of spending, such as being able to put more of their income into their child's education or even their own.Educational Attainment
The United States Census Bureau defines educational attainment as “the highest level of education that an individual has completed. This is distinct from the level of schooling that an individual is attending." Habitat has been building a large majority of their homes in census tracts that have low educational attainment. 65% of Habitat homes are in census tracts with a low level of educational attainment. This is not a bad thing because finding lots to build affordable housing is not easy. These low educational attainment areas may be the only logical place to build homes from a money spending point of view. By building homes in these areas Habitat is doing their part to change these percentages and is working towards increasing education levels by providing owner occupied housing which takes financial stress off of families by removing monthly renting fees. This allows families to invest their money in things like education for their children which over time will help decrease the low educational attainment in those neighborhoods. Although it may be a challenge to build homes in higher educational attainment areas, looking into possible build sites in these areas could be a future goal for Habitat.
Figure 1: Explore the Interactive Chart. This column chart shows the percentage of educational attainment within the census tracts Habitat has been building homes in
Owner Occupancy
Habitat has been building a large majority of their homes in census tracts that have low Owner Occupied housing.“Home ownership or Owner occupancy is a form of housing tenure in which a person, called the owner-occupier, owner-occupant, or home owner, owns the home in which they live” Source. 89% of Habitat homes are in census tracts with a low level of Owner Occupancy. This may not be a surprise because these areas are ones that have many vacant lots to build in. This is not necessarily a bad thing because as Habitat continues to do this they are actively increasing Owner Occupancy rates with every home they build. Building in these locations may be intentional as Habitat may see an opportunity to create change in these census tracts. Habitat is battling this issue correctly because every home they build adds more than just an Owner Occupied house, it continues the journey of creating new communities that are safe and provide adequate living for families to start a life or for people who are in need of financial relief.
Figure 2: Explore the Interactive Chart. My column chart illustrates the relationship between the areas in which Habitat builds homes and the percentage of Owner Occupancy the given census tracts have.
Construction of Visualizations:
I was able to make my visualizations by finding data for both demographics using a site called Social Explorer. In Social Explorer I was able to use specific definitions to tailor the data to fit the demographics I chose. For educational attainment I selected “Highest educational attainment for population 25 years or older.” I chose this because I felt that people who are looking to buy homes are over the age of 25. I also chose this because it gave me data on the “some college or more” category. This category gave me the percentage of “some college or more” attainment for every census tract in Hartford and in Tolland. For the home ownership demographic the specific definition I used in Social Explorer was “Owner Occupied housing units”. This category gave me the number of owner occupied housing units for each census tract in Hartford and Tolland. I chose this definition because having owner occupied housing benefits home owners in the long run. The ability to own a home takes away the stress of spending money every month and adds the opportunity for people to make their home their own. Owning property is an investment and when people don’t do this they lose out on the ability to gain extra wealth for themselves and their kids. The other specific definition I used in Social Explorer was “households by household type” because it gave me the total number of housing units per census tract.
After I had collected the two data sets I downloaded and uploaded each into their own spreadsheet. Unlike the educational attainment data, the owner occupied housing units were given to me as numbers rather than percentages. To find the percentages I added a new column named “Total number of Occupied housing units per census tract” and pasted the numbers to correspond with the tracts. I then divided the owner occupied units by the total to find the percentage of owner occupied units for Hartford and Tolland census tracts. After this step I set my ranges using histograms for both demographics. Both histograms had 15 bins and I knew I had to split these into three categories. These categories were High, Middle, Low for both demographics. For the educational data I used the histogram I made to select the percentage range which corresponds with the High, Middle, Low levels. The ranges I chose for this data were, 95-75=High, 74-45=Middle, 44-11=Low. I chose these ranges because I sorted the percentage column from highest to lowest which gave me a good idea of the ranges I should select for each level. For the home ownership data I followed a similar process and sorted the percentage column from highest to lowest and using the histogram I was able to set me ranges as, 99-75=High, 74-45=Middle, 44-3=Low. Now that I had my ranges set I made a new column in both sheets named education level and owner occupancy level.
Figure 3:Educational Attainment Histogram.
Figure 4:Owner Occupancy Histogram.
I then used a site called Mapshaper to form a spatial join to count the number of Habitat homes in each census tract. Because not all of the census tracts pulled from Social Explorer were ones that Habitat was building in, I had to upload the spatial join from Mapshaper and create a new sheet to house this data. With the Habitat census tracts and count of homes per tract in my sheet I now could match the Habitat homes in tracts sheet to the sheet that contained the percentages for both demographics. Once this match was complete both data sets showed the census tract, percentage of demographic, level, and count of home. With this done in both sheets I now had the correct information to create a pivot table to show the SUM of Habitat homes in each High, Middle, and Low levels. With the SUM of Habitat homes for each level in my sheet I had to make a choice about what information I wanted to present in my visualizations. I felt displaying the percentage of homes in each group would be most informative and best understood by my viewers. I had to find this percentage myself which I did by taking the SUM of the count of Habitat homes in each level and dividing it by the total number of Habitat homes which was 243. Once I did this for both demographics I now was ready to create a visualization of the data.