Projects

The American Retail Crime, Shrink, & Security Initiative is an ongoing effort dedicated to collecting, analyzing, and reporting industry trends in loss and violence within stores. Data is gathered from participating retailers and standardized for consistency. Summary statistics are generated by zip code and retail sector. Eventually, ARCSS aims to enhance the insights obtained from the National Retail Security Survey, conducted annually in collaboration with the National Retail Federation (NRF). This initiative will equip policymakers with the necessary data to shape legislation aimed at safeguarding both workers and customers.

Research2Practice Articles

Research2Practice articles cover trending topics that also tie-in to larger projects being perused at the LPRC. The aim is to discuss approaches for addressing key concerns within the industry using analytics and knowledge from practitioners in the field. These articles also serve to institutionalize learning, creating an ongoing record of the most pertinent challenges faced by loss prevention and asset protection professionals.

Data Analytics Working Group

The LPRC’s Data Analytics Working Group (DAWG) enables retailers, manufacturers, and technology companies to exchange perspectives and recent developments in the utilization of data analytics for enhancing safety, minimizing theft, and optimizing operations. In addition, this working group includes demonstrations designed to instruct both managers and analysts on innovative approaches to better understand their data. These hour-long demonstrations cover a wide range of topics.

Crime Risk by ZIP Code

Ziprisk is a simple web app that allows users to easily view socio-demographic factors that correlate to crime risk at the ZIP code level. The data is taken from the 2021 American Community Survey conducted by the U.S. Census Bureau which is publicly available using the census API. On the back end this is a Python Flask application using SQLAlchemy for data retrieval.

LPRC SaferPlaces Dashboard

In collaboration with the Gainesville Police Department (GPD), University of Florida Police Department (UFPD), and Ersi inc., the Loss Prevention Research Council (LPRC) created an up-to-date crime incident map for the city to launch the SaferPlaces research initiative. Calls for service data are piped into ArcGIS online and visualized as a heat map along with LPRC StoreLab locations and field assets.

Organized Retail Crime Across the States (ORCATS) Survey

The Loss Prevention Research Council (LPRC), Loss Prevention Foundation (LPF), and Loss Prevention Magazine (LPM) conducted an industry-wide survey of investigators and multi-store loss prevention managers to understand the causes, consequences, and control of Organized Retail Crime (ORC) across the United States. I processed the survey response data, generated summary statistics, and developed a dashboard to communicate findings.

Inverse Distance Weighting

A common geostatstical technique used to interpolate values across a study area based on a number of explicit sample locations. I had trouble finding a package that implemented this technique in a simple way which proved to be a problem when using IDW to interpolate air pollution concentrations at millions of locations. To make it computationally feasible I coded IDW and an accuracy metric as c++ functions and included them in a package.

Health Accessibility Models

Health accessibility models have been used to identify regions that are medically under-served due to their population and distance from medical facilities. I used these models to identify neighborhoods in the city of Huaquillas that may lack access to vector control services. I also simulate how different placements of workers and resources could improve access and identify locations to optimally place a new facility.

UFMathGeo

UFMathGeo is a mechanistic model used to estimate the probability of finding two species of disease transmitting mosquito for a given month in a number of counties in the US. For archival purposes the code available on github.

Spatial Networks

There are many powerful R packages that allow users to perform network analyses. However there currently isn’t the built-in functionality to use these analyses on spatial datasets. To study a road network for example, you need to convert the shapefile GIS format into an object that is accepted by a package like ‘igraph’. I provide an example on how to perform these conversions and use network analysis to find the shortest route between two locations in the road network.