LaPlante, Rita. “Next Stop Eastie”, Boston College, 2022. http://hdl.handle.net/2345/bc-ir:109511.
This paper examines neighborhood socioeconomic ascent in both Boston and the Greater Boston metropolitan statistical area. Using random forests, a supervised machine learning algorithm, and a collection of physical and demographic neighborhood characteristics gathered from the American Community Survey, I model changes in neighborhood socioeconomic status and identify neighborhoods in my study area that experienced relative socioeconomic ascent or relative socioeconomic decline between 2010 and 2019. In order to gain a better understanding of future socioeconomic change throughout my study area, I use a random forests model to predict neighborhood socioeconomic status in 2028. I find that my best random forests model offers an improvement over traditional linear modeling techniques and, through mapping results for Boston specifically, that change in Boston is occurring in minority, working class neighborhoods, especially along the city’s waterfront. These findings, in combination with qualitative community data, can be used to inform policy concerning matters ranging from housing to transportation in the years to come.