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ORCID

https://orcid.org/0000-0002-1209-2067

Access Type

Open Access Thesis

Document Type

thesis

Degree Program

Civil Engineering

Degree Type

Master of Science (M.S.)

Year Degree Awarded

2022

Month Degree Awarded

May

Abstract

Food accessibility has lately been of primary interest given its impact on public health outcomes. This thesis illustrates the gaps in food access by applying spatial analysis in Massachusetts accounting for a variety of demographic and socioeconomic factors. The number of grocery stores, farmers markets, and convenience stores within 1/4 and 1 mile of the Census tracts’ centroids are the two accessibility metrics used in the spatial analysis. In addition, a regression model is developed using the Gradient Boosting machine learning method to show the relationship between the socioeconomic factors and the number of grocery stores within 1 mile of the Census tracts’ centroids. Percent of minority population, population in poverty, vehicle ownership, and population density are the factors used as explanatory variables. The results include histograms and maps for the spatial analysis, which show that access to food services is higher in urban high-density areas regardless of high poverty and minority percent values. The regression model results include partial dependence plots, variable importance plot as well as a map that illustrates the standard deviation of the residual values. This research can inform that when the vehicle ownership, the minority and poverty percent increase, the number of grocery stores increases, as these values are high in big cities where there is high availability of food retailers.

DOI

https://doi.org/10.7275/28632885

First Advisor

Eleni Christofa

Second Advisor

Eric Gonzales

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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