A two-step process to increase successful geocoding in publicly available police stop data

ABSTRACT

Many police departments are meeting calls for transparency by releasing publicly accessible data. High-quality address locations are critical for successful and accurate geocoding, though the content and quality of that data can drastically vary across datasets. In this study, we showcase a two-step geocoding process that helps convert low-quality address locations into geo-locatable addresses using traditional geocoding and Jaro-Winkler edit distance methods with police stop data from the San Diego Police Department. For reference, only 83% of stops were geocoded when using traditional geocoding methods. By employing the Jaro-Winkler edit distance to clean the stop address strings, we were able to geocode 99% of stops. We further discuss data creation practices and solutions for data quality-related issues for police departments and researchers when using publicly available policing data.

KEYWORDS:

• Policing data
• geocoding
• publicly available data
• data quality
• Jaro-Winkler edit distance

Acknowledgments

We want to thank our anonymous reviewers for their valuable insights and suggestions. Additionally, this work was supported by the National Science Foundation under Award #2051226, “Constructing Race-Specific Driving Patterns to Address Racial Profiling.” Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the NSF [2051226].

Notes on contributors

Danielle Wallace

Danielle Wallace is an Associate Professor in the School of Criminology and Criminal Justice and an Associate Director at the Center for Violence Prevention and Community Safety at Arizona State University. Her research agenda includes neighborhoods and crime, policing, racial/ethnic and disability-related disparities in policing, and issues related to incarceration, re-entry and health.

Edward Helderop

Edward Helderop is a systems analyst and the associate director of the Center for Geospatial Sciences at the University of California, Riverside. His main interests include GIScience, big data, and network analytics (particularly as applied to urban infrastructure systems). His previous research explored turnover and resiliency in plant-pollinator networks and urban transportation modeling during disasters. Eddie received his B.S. in Biology from Hope College, his M.S. in Geography from Oregon State University, and his Ph.D. in Geography from Arizona State University.

Anthony Grubesic

Anthony H. Grubesic is a professor in the School of Public Policy at the University of California, Riverside. His research and teaching interests are in geocomputation, spatial analysis, regional development, and public policy evaluation.

Jason Walker

Jason Walker is a doctoral student in the School of Criminology and Criminal Justice at Arizona State University. His primary research interests focus on neighborhood crime and disorder, police, public health in the criminal justice system, and sentencing outcomes. Prior to attending the doctoral program at Arizona State University, Jason worked as an analyst for the United States Sentencing Commission.

Xiaoyue Cathy Liu

Xiaoyue Cathy Liu received her B.S. in electrical engineering from Beijing Jiaotong University, and Ph.D. from the University of Washington. She is currently an associate professor in the Civil & Environmental Engineering at the University of Utah. Her research interests include multimodal transportation system, electrified mobility, equity in transportation, and big data applications.

Ran Wei

Dr. Ran Wei is currently an Associate Professor in the School of Public Policy and a founding member of the Center for Geospatial Sciences at the University of California, Riverside. Her areas of emphasis include GIScience, urban and regional analysis, spatial analysis, optimization, geovisualization, high performance computing and location analysis. Substantively, she has focused on a range of national and international issues, including urban/regional growth, transportation, public health, crime, housing mobility, energy infrastructure, and environmental sustainability.

Yirong Zhou

Yirong Zhou (M’94) received B.S. in Statistics from the University of Science and Technology of China in 2017 and an M.S. in Statistics from George Washington University in 2019. He is a current Ph.D. Student in Civil & Environmental Engineering at the University of Utah under the supervision of Prof Xiaoyue Cathy Liu. His research focuses on data-driven transportation system modeling.

Connor Stewart

Connor Stewart is a graduate student at Arizona State University. His interests include the use of data science in criminology, networks, and domestic terrorism.