Temporal and Spatial Analysis of Physical Check Theft and Darknet Sales Through Encrypted Messaging Platforms

• In Event: Trends in Online Fraud: An Analysis of Illicit Commodities over Digital Platforms

Abstract

Objective: Geospatially reveal patterns in thefts of physical checks sold in illicit darknet markets to create temporal and spatial theft patterns and predictive modeling.
Data/Methods: Stolen check images were collected from a darknet encrypted messaging platform using the Python-tesseract (pytesseract) optical character recognition (OCR) tool and the output address data were geocoded using the Esri geolocator service. The geocoded check address data, United States Census Bureau 2019 American Community Survey mean income data by available census tract, and geocoded address locations of all United States Postal Service mail collection boxes were used in ArcGIS Pro to create clustered heat maps and the initial spatial analysis.
Results: Preliminary analyses reveal physical check theft clustering patterns occurring in census tracts with higher mean income in densely populated areas. Further analyses are required to better understand the clustering patterns to mailbox collection sites and temporal variations from August 2021 throughout 2022.
Conclusion/Implications: Preliminary cluster and demographic analyses conclude further investigation is needed to better understand offender check theft patterns using victim demographics.

Authors

• Shelleen Akin, Georgia State University

• Cameron Hoffman, Georgia State University

• David Maimon, Georgia State University