James Martin
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Researcher and data analyst using big data, machine learning, and statistical methods. I consult with organizations to help minimize risk through prevention science and actualize their strategy through data flows and endpoints. Always seeking opportunities to improve operations and learn new skills along the way. If my abilities can be of value to your organization or project let’s get in touch.
Skills
- Python
- (
- scikit-learn
- matplotlib
- keras
- flask
- )
- R
- (
- caret
- dplyr
- MASS
- sf
- ggplot2
- )
- SQL
- (
- mySQL
- PostgreSQL
- )
- C++
American Retail Crime, Shrink, and Security (ARCSS) Initiative
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.
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.
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Publications
Academic papers, preprints, and talks
Paper - Raising the Watchtower: Results from the ACCESS Task Force
Paper - Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy
Talk - The Big Picture: The Role of Mapping in Retail Crime Analysis and Risk Estimation
Paper - Household and climate factors influence Aedes aegypti presence in the arid city of Huaquillas, Ecuador
Paper - A time series analysis of the ecologic relationship between acute and intermediate PM2.5 exposure duration on neonatal intensive care unit admissions in Florida
Talk - UF MathGeo Model: A Mechanistic Approach Using Vector Traits and Georeferenced Climate Data
Experience
Research Scientist
I work with major retailers and solution providers to conduct basic and applied research in retail loss prevention. I advise retailers on how best to collect and analyze their data to improve processes. I also facilitate networking and information sharing among professionals in the loss prevention community. Some types of projects I’ve worked on include shrink modeling using location attributes, store level risk profiling, brand sentiment analysis utilizing social media, pre-post outcome analysis for deterrents, and forecasting of key performance indicators.
Research Assistant
My main tasks involved acquiring and processing large environmental datasets, often drawing upon my GIS abilities. I implemented machine learning models using ensemble methods to forecast environmental hazards like ambient air pollution. Projects I worked on include investigating the effects of air pollution on neonatal intensive care unit admissions, exploring the social and environmental dimensions of COVID-19 related mortality in Northern Italy, and characterizing the occupational health challenges of gulf coast seafood workers.
Graduate Research Assistant
I carried out a social-ecological study of Aedes aegypti risk in southern coastal Ecuador which required extensive data cleaning and analysis. I used hierarchical models within a multi-model, information criterion approach to investigate risk factors. Other projects I worked also concerned Ecuador and arboviral disease. These include identifying spatial-temporal clusters of Dengue fever using point pattern analyses and using health access models to simulate alternative vector control regiments.
Undergraduate Research Assistant
I was involved in a project which aimed to forecast the future range of African Trypanosomiasis under future climate conditions using a mechanistic model based on the biology of the Tsetse fly vector. I searched the literature for laboratory studies to inform model parameters and also wrote code to process these data and fit non-linear functions to them. The Quantitative Dynamics Lab was originally based at USF before moving to Virginia Tech.