Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

Zhiyue Xia, Kathleen Stewart, Junchuan Fan

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. We focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

Original languageEnglish
Article number101599
JournalComputers, Environment and Urban Systems
Volume87
DOIs
StatePublished - May 2021

Funding

These analyses were funded through the National Drug Early Warning System (NDEWS) Coordinating Center at the University of Maryland's Center for Substance Abuse Research (CESAR). Authors are grateful for the support and encouragement received from NDEWS Coordinating Center staff, especially Dr. Eric Wish and Eleanor Artigiani. NDEWS is supported by the National Institute on Drug Abuse of the National Institutes of Health (NIH NIDA) under award number U01DA038360. This content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH NIDA. The authors thank the anonymous reviewers helped improve and clarify this manuscript.

Keywords

  • Heroin
  • Machine learning
  • Opioid crisis
  • Random forest
  • Spatiotemporal modeling
  • Synthetic drugs

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