My Research ...

Impact of SoFi Stadium on Inglewood

** Research judged as Runner Up for 2021 NABET Conference Best Paper Award
**Currently under peer-review as an invited submission to the Journal of Business, Education, and Technology

In February of 2016, Stan Kroenke – the owner of the Los Angeles Rams – argued that the construction of the team’s new stadium in Inglewood, California would, with a $5.5 billion price tag, create a “ripple effect so profound” that it would “boost the neighborhood’s subpar property values along the way.” Kroenke made his point while ignoring what an increase in property value can produce through such a gentrification process: marginal damage to the local education system as neighborhoods skew towards higher-income residents, the depletion in long-term viability and supply of “low-cost housing,” and the “deepening class polarization” within the neighboring urban housing markets are among just some of the chief concerns. To examine Kroenke’s claims and the underlying socio-economic issues, this paper uses the Zillow’s proprietary ZTRAX database to, first, construct a standard difference-in-differences model to explore whether the construction of SoFi Stadium did indeed boost the neighborhood’s property values. After, by using the `tidycensus` package in the R programming language, socio-economic factors will be explored at both the county subdivision and census tract levels.

Predicting the Impact of a New Stadium Using a k-means Clustering Unsupervised Algorithm

** Scheduled for presentation at the 2022 ASMA Conference in indianapolis, indiana
**currently preparing manuscript for submission to the journal of sport management

This paper showcases an applied predictive model that uses data from those neighborhoods previously impacted by the construction of stadiums to examine the potential impact on neighborhoods with similar attributes facing looming stadium construction – in this case, specifically, Arlington Heights. The creation of the model is a two-step process. First, I employ the use of a k-means clustering unsupervised machine learning algorithm and the ‘tidycensus’ package in RStudio to produce geodemographic classification based on census tracts. This results in coupling those census tracts with similar demographic characteristics to those tracts in Arlington Heights. Second, the model uses a standard difference-in-differences approach to suggest what may occur in Arlington Heights based on the recorded impact on similar neighborhoods by professional stadiums. Using data from Zillow’s proprietary ZTRAX database, along with data from the ‘tidycensus’ package, the model put forth in this research is able to predict the rate of growth of both home prices and average rent costs in the areas surrounding the new stadium, as well as the impact on racial demographics by constructing a multi-group segregation index and a diversity gradient. The implications of this proposed model for practitioners in the sport industry is significant, as being able to forecast the impact of a professional stadium on neighborhoods and/or communities will greatly assist in remedying, or easing, the impact of gentrification that often occurs in such scenarios

Los Angeles Olympics and Overpolicing Research

** Research published in the journal of olympic studiesACCESS IT HERE

To provide public oversight and documentation of policing efforts in the leadup to and hosting of the 2028 Los Angeles Olympic Summer Games, this research note uses publicly available arrests records data provided directly from the city of Los Angeles that, as of September 2020, contains more than 33,000,000 data points dating back to 2010. By utilizing the R programming language this research note displays how the dataset can be manipulated, organized, and visualized to observe arrests activity in Los Angeles. The result, after detailing how the data can be critically examined in micro and macro-levels in both graph and map form, is a reproducible, systematic, and data-driven approach to police oversight that allows researchers, journalists, and other concerned citizens the ability to provide the transparent and ongoing public documentation missing during previous Olympic hosts’ attempts to “sanitize” their city prior to the Games.