Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations
2013346 citationsBrian McInnis, Carter C. Price et al.RAND Corporation eBooksprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by John S. Hollywood
Since
Specialization
Citations
This map shows the geographic impact of John S. Hollywood's research. 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 John S. Hollywood with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John S. Hollywood more than expected).
Fields of papers citing papers by John S. Hollywood
This network shows the impact of papers produced by John S. Hollywood. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John S. Hollywood. The network helps show where John S. Hollywood may publish in the future.
Co-authorship network of co-authors of John S. Hollywood
This figure shows the co-authorship network connecting the top 25 collaborators of John S. Hollywood.
A scholar is included among the top collaborators of John S. Hollywood based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with John S. Hollywood. John S. Hollywood is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hollywood, John S. & Zev Winkelman. (2015). Improving Information-Sharing Across Law Enforcement: Why Can't We Know?:. Kagoshima Kenritsu Tanki Daigaku Chiiki Kenkyūjo kenkyū nenpō.3 indexed citations
Silberglitt, Richard, et al.. (2015). Visions of Law Enforcement Technology in the Period 2024-2034: Report of the Law Enforcement Futuring Workshop.1 indexed citations
Hollywood, John S., et al.. (2011). Building on Clues: Methods to Help State and Local Law Enforcement Detect and Characterize Terrorist Activity (Final Report).1 indexed citations
16.
Hollywood, John S., et al.. (2007). Networked Forces in Stability Operations.
Hollywood, John S., et al.. (2005). Network-centric operations case study : air-to-air combat with and without Link 16. Defense Technical Information Center (DTIC).12 indexed citations
19.
Hollywood, John S., et al.. (2004). Out of the Ordinary.1 indexed citations
20.
Hollywood, John S., et al.. (2001). Virtual food service systems: technology transforming university food service structure. College student journal. 35(1). 122.
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.