Type of Thesis
Daniel C Jones
Neighborhood analysis is the inquiry into how neighborhoods are constructed and implemented for applications in human-centered studies. This is a relevant field of research given the impact that these units have on the analysis of spatial data. As neighborhood-based research continues to develop, it is necessary to examine how perceived neighborhoods, which are neighborhoods defined by individuals, are being constructed over time and what factors are contributing to their delineation.
There is a lack of inquiry into why perceived neighborhoods take on specific dimensions. This study proposes that the dimensions assumed by these neighborhoods are influenced by the demographic characteristics of individuals, and the historical housing policies known as redlining which have shaped neighborhoods. The data used in this study are perceived neighborhoods and demographic characteristics from survey respondents in New York City and historical Home Owner’s Loan Corporation (HOLC) mortgage risk-assessment grades. Our methods involved developing a procedure for geocoding perceived neighborhood data from a telephone survey, integrating perceived neighborhood data from both telephone and online surveys, and conducting a statistical analysis to look for trends in the neighborhood polygon characteristics of area, perimeter and compactness in relation to survey methodology, demographic data and HOLC grades.
Our results include a novel methodology for geocoding perceived neighborhoods, as well as salient relationships between neighborhood polygon characteristics (area, perimeter and compactness) and demographic variables (age, gender, Hispanic identity, racial identity, employment status, education attainment, marriage status, and length of residency in neighborhood). Mean neighborhood perimeters were significantly different between male and female survey participants. Neighborhood area and perimeter were significantly different between categories within our Hispanic and employment status demographic variables. Area and compactness showed statistically significant differences between our racial identity and education variables. Groups within our income variable showed differences in neighborhood compactness. Lastly, we found that categories within demographic variables of race, income and education showed statistically significant associations with our HOLC data.
Kresek, Kai, "Are perceived neighborhoods palimpsests? Analyzing self-defined neighborhoods in the context of historical redlining" (2018). Undergraduate Honors Theses. 1630.