Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations
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In The Last Decade
doi.org/10.7249/rr233 →Countries where authors are citing Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations
This map shows the geographic impact of Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations more than expected).
Fields of papers citing Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations
This network shows the impact of Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations.
About Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations
This paper, published in 2013, received 346 indexed citations . Written by Brian McInnis, Carter C. Price, Susan Smith and John S. Hollywood covering the research area of Artificial Intelligence and Sociology and Political Science. It is primarily cited by scholars working on Sociology and Political Science (222 citations), Political Science and International Relations (69 citations) and Safety Research (69 citations). Published in RAND Corporation eBooks.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.7249/rr233.