The_Effectiveness_of_Big_Data-Driven_Predictive_Policing_Systematic_Review.pdf

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Status Citation (Found) Matched Data / Notes
Hallucination A geostatistical analysis of crime in seattle considering infrastructure and datamined colocation
Delts, R. G. (2020)
Raw: Delts, R. G. (2020). A geostatistical analysis of crime in seattle considering infrastructure and datamined colocation [Doctoral dissertation]. George Mason University.
The citation is a fabrication that blends a real institution (George Mason University) and a real research topic associated with it (Seattle crime studies by David Weisburd) into a non-existent dissertation. No records were found in the Mason Archival Repository Service (MARS) or ProQuest.
Hallucination Predicting spatial patterns of identity theft victimization using overlay mapping
Reed, M. S. (2015)
Raw: Reed, M. S. (2015). Predicting spatial patterns of identity theft victimization using overlay mapping [Doctoral dissertation]. San Diego State University.
The citation is a metadata chimera. The title is fabricated, likely inspired by G. S. Lane's work on geographies of identity theft, and incorrectly attributed to Mark S. Reed and San Diego State University.
Minor Error Hot spots policing effects on crime
Braga, A.; Papachristos, A.; Hureau, D. (2012)
Campbell Systematic Reviews
DOI: 10.4073/csr.2012.6
Raw: Braga, A., Papachristos, A., & Hureau, D. (2012). Hot spots policing effects on crime. Campbell Systematic Reviews, 8(1), 1-90. https://doi.org/10.4073/csr.2012.6
Match: The Effects of “Pulling Levers” Focused Deterrence Strategies on Crime
Authors: Anthony A. Braga; David L. Weisburd
Venue: Campbell Systematic Reviews
DOI: 10.4073/csr.2012.6
URL: https://doi.org/10.4073/csr.2012.6
DOI resolves to a real paper, but the cited title is substantially different from the official title (similarity 0.35) — classic metadata chimera (real coordinates + wrong title).
Minor Error The logic of data bias and its impact on place-based predictive policing
Brantingham, P. J. (2017)
Ohio State Journal of Criminal Law
Raw: Brantingham, P. J. (2017). The logic of data bias and its impact on place-based predictive policing. Ohio State Journal of Criminal Law, 15, 473.
Match: The Logic of Data Bias and Its Impact on Place-Based Predictive Policing
Authors: P. Jeffrey Brantingham
Venue: Ohio State Journal of Criminal Law
The citation is correct in title, author, journal, volume, and page. However, the publication year for Volume 15, Issue 2 of the Ohio State Journal of Criminal Law is 2018, not 2017. Following the hard rule for journal serials, a mismatch between the citation year (2017) and the volume/issue year (2018) is a minor_error.
Minor Error Policing predictive policing
Ferguson, A. G. (2016)
Washington University Law Review
Raw: Ferguson, A. G. (2016). Policing predictive policing. Washington University Law Review, 94(5), 1109-1190.
Match: Policing Predictive Policing
Authors: Andrew Guthrie Ferguson
Venue: Washington University Law Review
URL: https://openscholarship.wustl.edu/law_lawreview/vol94/iss5/5/
The citation correctly identifies the article and its volume/issue coordinates (94:5, p. 1109). However, Volume 94, Issue 5 of the Washington University Law Review was officially published in 2017, not 2016 (which was the SSRN/preprint date). The page range ends at 1189 rather than 1190.
Minor Error An experimental study of classification algorithms for crime prediction
Iqbal, R.; Murad, M. A. A.; Mustapha, A.; Panahy, P. H. S.; Khanahmadliravi, N. (2013)
Indian Journal of Science and Technology
DOI: 10.17485/ijst/2013/v6i3.6
Raw: Iqbal, R., Murad, M. A. A., Mustapha, A., Panahy, P. H. S., & Khanahmadliravi, N. (2013). An experimental study of classification algorithms for crime prediction. Indian Journal of Science and Technology, 6(3), 1-7. https://doi.org/10.17485/ijst/2013/v6i3.6
Match: An Experimental Study of Classification Algorithms for Crime Prediction
Authors: Rizwan Iqbal; M. A. A. Murad; A. Mustapha; P. H. S. Panahy; N. Khanahmadliravi
Venue: Indian Journal of Science and Technology
DOI: 10.17485/ijst/2013/v6i3.6
URL: https://doi.org/10.17485/ijst/2013/v6i3.6
The citation provides the page range as 1-7 (likely the PDF internal page count) instead of the official journal pagination of 4219-4225. All other metadata (title, authors, journal, volume, issue, year, and DOI) is correct.
Minor Error Prediction of hourly effect of land use on crime
Matijosaitiene, I.; Zhao, P.; Jaume, S.; Gilkey, J. W. Jr. (2018)
ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi8010016
Raw: Matijosaitiene, I., Zhao, P., Jaume, S., & Gilkey, J. W. Jr. (2018). Prediction of hourly effect of land use on crime. ISPRS International Journal of Geo-Information, 8(1), 16. https://doi.org/10.3390/ijgi8010016
Match: Prediction of Hourly Effect of Land Use on Crime
Authors: Irina Matijosaitiene; Peng Zhao; Sylvain Jaume; Joseph W. Gilkey Jr.
Venue: ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi8010016
URL: https://www.mdpi.com/2220-9964/8/1/16
The citation uses the online-first publication year (2018) instead of the official volume/issue year (2019). Per the policy, for traditional journals with volume/issue coordinates, citing the online-first year instead of the volume year is a minor_error.
Minor Error Mayor de Blasio announces partnership with Crime Lab New York to advance evidence-driven, cost-effective public safety strategies
NYC. (2015)
Raw: NYC. (2015). Mayor de Blasio announces partnership with Crime Lab New York to advance evidence-driven, cost-effective public safety strategies. Retrieved from https://www.nyc.gov/officeof-the-mayor/news/039-15/mayor-de-blasio-partnership-crime-lab-new-york-advance-evidencedrivenNationalPoliceChiefs’Council(NPCC) and Association of Police and Crime Commissioners (APCC). (2016). Policing vision 2025. Retrieved from https://www.npcc.police.uk/documents.
Match: Mayor de Blasio Announces Partnership With Crime Lab New York To Advance Evidence-Driven, Cost-Effective Public Safety Strategies
Authors: NYC
Venue: City of New York (NYC.gov)
URL: https://www.nyc.gov/office-of-the-mayor/news/039-15/mayor-de-blasio-announces-partnership-with-crime-lab-new-york-to-advance-evidence-driven-
The citation is a 'chimera' where two separate bibliographic entries (the NYC press release and the NPCC Policing Vision 2025 document) have been fused together in the raw_text and URL fields. While the first work is correctly identified, the garbled metadata noise from the second citation warrants a minor_error status.
Minor Error What is the future… of predictive policing
Ratcliffe, J. (2015)
Translational Criminology
Raw: Ratcliffe, J. (2015). What is the future… of predictive policing. Translational Criminology, 6(2), 151- 166.
Match: What Is the Future…of Predictive Policing?
Authors: Jerry Ratcliffe
Venue: Translational Criminology
The citation has several factual errors. The work was published in the Spring 2014 issue (Issue 6), not 2015 Vol. 6(2). The correct pages are 4–5. The page range 151–166 in the citation is incorrect for this publication.
Minor Error Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice
Richardson, R.; Schultz, J. M.; Crawford, K. (2019)
NYUL Review Online
Raw: Richardson, R., Schultz, J. M., & Crawford, K. (2019). Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice. NYUL Review Online, 94, 15.
Match: Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice
Authors: Rashida Richardson; Jason M. Schultz; Kate Crawford
Venue: N.Y.U. Law Review Online
DOI: 10.2139/ssrn.3333423
The citation correctly identifies the authors, title, venue, volume, and year. However, the page number provided (15) is incorrect; the article begins on page 192 of Volume 94 of the N.Y.U. Law Review Online.
Minor Error Utilizing geographic information systems to analyze emerging hotspots and cold spots of violent and non-violent crime
Schaffter, C. (2020)
Raw: Schaffter, C. (2020). Utilizing geographic information systems to analyze emerging hotspots and cold spots of violent and non-violent crime. [Doctoral dissertation]. Utica College.
Match: Utilizing geographic information systems to analyze emerging hotspots and cold spots of violent and non-violent crime
Authors: Caleb Schaffter
Venue: Utica College (Master's Thesis)
The work is a real Master's thesis by Caleb Schaffter from 2020 at Utica College. The citation incorrectly labels it as a doctoral dissertation, which is a minor metadata error.
Minor Error National Data Analytics Solution - Violent Crime
West Midlands Police. (2022)
Raw: West Midlands Police. (2022). National Data Analytics Solution - Violent Crime. Retrieved from https://www.westmidlands-pcc.gov.uk/wp-content/uploads/2022/01/2021-11-03-EC-AgendaItem-2.1-NDAS-VC-National-Data-Analytics-Solution.pdf
Match: National Data Analytics Solution – Violent Crime
Authors: West Midlands Police
Venue: West Midlands Police and Crime Commissioner
URL: https://www.westmidlands-pcc.gov.uk/wp-content/uploads/2022/01/2021-11-03-EC-Agenda-Item-2.1-NDAS-VC-National-Data-Analytics-Solution.pdf
The document exists and is a submission to the West Midlands Police Ethics Committee. The cited URL contains a typographical error (missing hyphen between 'Agenda' and 'Item'); the live document is found at a nearly identical URL.
Minor Error Utilization of street view and satellite imagery data for crime prediction
Wu, J.; Hui, J.; Xian, R. (2017)
Presentation CS230 at Stanford University
Raw: Wu, J., Hui, J., & Xian, R. (2017). Utilization of street view and satellite imagery data for crime prediction. Presentation CS230 at Stanford University.
Match: Utilization of Street View and Satellite Imagery Data for Crime Prediction
Authors: Jeffery Wu; Jonathan Hui; Rui Xian
Venue: Stanford University CS230: Deep Learning (Project Report)
URL: http://cs230.stanford.edu/projects_winter_2020/reports/32644967.pdf
The citation correctly identifies the authors, title, and course. The primary error is the year (cited as 2017; actual is 2020). Additionally, the document is a course project report rather than a formal conference presentation.
Verified A crime prediction model based on spatial and temporal data
Ait El Bour, H.; Ounacer, S.; Elghomari, Y.; Jihal, H.; Azzouazi, M. (2018)
Periodicals of Engineering and Natural Sciences (PEN)
DOI: 10.21533/pen.v6i2.524
Raw: Ait El Bour, H., Ounacer, S., Elghomari, Y., Jihal, H., & Azzouazi, M. (2018). A crime prediction model based on spatial and temporal data. Periodicals of Engineering and Natural Sciences (PEN), 6(2), 360-364. https://doi.org/10.21533/pen.v6i2.524
Match: A crime prediction model based on spatial and temporal data
Authors: Hicham Ait El Bour; Soumaya Ounacer; Yassine Elghomari; Houda Jihal; Mohamed Azzouazi
Venue: Periodicals of Engineering and Natural Sciences (PEN)
DOI: 10.21533/pen.v6i2.524
URL: https://doi.org/10.21533/pen.v6i2.524
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Localized crime prediction methods
Al Boni, M. (2017)
Raw: Al Boni, M. (2017). Localized crime prediction methods [Doctoral dissertation]. University of Virginia.
Match: Localized crime prediction methods
Authors: Mohammad Al Boni
Venue: University of Virginia
The work is a 2017 PhD dissertation from the University of Virginia. While a similar conference paper exists from 2016 ("Area-Specific Crime Prediction Models"), the cited title belongs to the dissertation.
Verified Predicting crime with routine activity patterns inferred from social media
Al Boni, M.; Gerber, M. S. (2017)
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016) - Conference Proceedings
Raw: Al Boni, M., & Gerber, M. S. (2017). Predicting crime with routine activity patterns inferred from social media. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016) - Conference Proceedings.
Match: Predicting crime with routine activity patterns inferred from social media
Authors: Mohammad Al Boni; Matthew S. Gerber
Venue: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
DOI: 10.1109/smc.2016.7844410
URL: https://doi.org/10.1109/smc.2016.7844410
Verified via static CrossRef title search (score: 1.00)
Verified Crime prediction based on crime types and using spatial and temporal criminal hotspots
Almanie, T.; Mirza, R.; Lor, E. (2015)
International Journal of Data Mining & Knowledge Management Process
DOI: 10.5121/ijdkp.2015.5401
Raw: Almanie, T., Mirza, R., & Lor, E. (2015). Crime prediction based on crime types and using spatial and temporal criminal hotspots. International Journal of Data Mining & Knowledge Management Process, 5(4), 01-19. https://doi.org/10.5121/ijdkp.2015.5401
Match: Crime Prediction Based on Crime Types and Using Spatial and Temporal Criminal Hotspots
Authors: Tahani Almanie; Rsha Mirza; Elizabeth Lor
Venue: International Journal of Data Mining & Knowledge Management Process
DOI: 10.5121/ijdkp.2015.5401
URL: https://doi.org/10.5121/ijdkp.2015.5401
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Crime prediction through urban metrics and statistical learning
Alves, L. G. A.; Ribeiro, H. V.; Rodrigues, F. A. (2018)
Physica A: Statistical Mechanics and Its Applications
DOI: 10.1016/j.physa.2018.03.084
Raw: Alves, L. G. A., Ribeiro, H. V., & Rodrigues, F. A. (2018). Crime prediction through urban metrics and statistical learning. Physica A: Statistical Mechanics and Its Applications, 505, 435-443. https://doi.org/10.1016/j.physa.2018.03.084
Match: Crime prediction through urban metrics and statistical learning
Authors: Luiz G.A. Alves; Haroldo V. Ribeiro; Francisco A. Rodrigues
Venue: Physica A: Statistical Mechanics and its Applications
DOI: 10.1016/j.physa.2018.03.084
URL: https://doi.org/10.1016/j.physa.2018.03.084
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Testing a geospatial predictive policing strategy: Application of ArcGIS 3D analyst tools for forecasting commission of residential burglaries
Amiri, S. (2014)
Raw: Amiri, S. (2014). Testing a geospatial predictive policing strategy: Application of ArcGIS 3D analyst tools for forecasting commission of residential burglaries [Doctoral dissertation]. Washington State University.
Match: Testing a geospatial predictive policing strategy: Application of ArcGIS 3D analyst tools for forecasting commission of residential burglaries
Authors: Solmaz Amiri
Venue: Washington State University
URL: https://www.ojp.gov/pdffiles1/nij/grants/248642.pdf
The work is a Doctor of Design (D.Des.) dissertation submitted to Washington State University in December 2014. It is also indexed by the NIJ as Grant No. 2012-R2-CX-0004.
Verified Classifying crime places by neighborhood visual appearance and police geonarratives: A machine learning approach
Amiruzzaman, M.; Curtis, A.; Zhao, Y.; Jamonnak, S.; Ye, X. (2021)
Journal of Computational Social Science
DOI: 10.1007/s42001-021-00107-x
Raw: Amiruzzaman, M., Curtis, A., Zhao, Y., Jamonnak, S., & Ye, X. (2021). Classifying crime places by neighborhood visual appearance and police geonarratives: A machine learning approach. Journal of Computational Social Science, 4(2), 813-837. https://doi.org/10.1007/s42001-021-00107-x
Match: Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach
Authors: Md Amiruzzaman; Andrew Curtis; Ye Zhao; Suphanut Jamonnak; Xinyue Ye
Venue: Journal of Computational Social Science
DOI: 10.1007/s42001-021-00107-x
URL: https://doi.org/10.1007/s42001-021-00107-x
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Predicting local crime clusters using (multinomial) logistic regression
Andresen, M. A. (2015)
Cityscape
Raw: Andresen, M. A. (2015). Predicting local crime clusters using (multinomial) logistic regression. Cityscape, 17, 249-262.
Match: Predicting Local Crime Clusters Using (Multinomial) Logistic Regression
Authors: Martin A. Andresen
Venue: Cityscape: A Journal of Policy Development and Research
The citation is verified. The minor end-page discrepancy (262 vs 261) is considered a cosmetic or trailing page variation and does not trigger a minor_error status.
Verified Predicting property crime risk: an application of risk terrain modeling in Vancouver, Canada
Andresen, M. A.; Hodgkinson, T. (2018)
European Journal on Criminal Policy and Research
DOI: 10.1007/s10610-018-9386-1
Raw: Andresen, M. A., & Hodgkinson, T. (2018). Predicting property crime risk: an application of risk terrain modeling in Vancouver, Canada. European Journal on Criminal Policy and Research, 24(4), 373-392. https://doi.org/10.1007/s10610-018-9386-1
Match: Predicting Property Crime Risk: an Application of Risk Terrain Modeling in Vancouver, Canada
Authors: Martin A. Andresen; Tarah Hodgkinson
Venue: European Journal on Criminal Policy and Research
DOI: 10.1007/s10610-018-9386-1
URL: https://doi.org/10.1007/s10610-018-9386-1
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Towards a Crime Hotspot Detection Framework for Patrol Planning
Araujo, A.; Cacho, N.; Bezerra, L.; Vieira, C.; Borges, J. (2019)
HPCC/SmartCity/DSS 2018 (Proceedings)
Raw: Araujo, A., Cacho, N., Bezerra, L., Vieira, C., & Borges, J. (2019). Towards a Crime Hotspot Detection Framework for Patrol Planning. In Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018.
Match: Towards a Crime Hotspot Detection Framework for Patrol Planning
Authors: Adelson Araujo; Nelio Cacho; Leonardo Bezerra; Carlos Vieira; Julio Borges
Venue: 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
DOI: 10.1109/hpcc/smartcity/dss.2018.00211
URL: https://doi.org/10.1109/hpcc/smartcity/dss.2018.00211
Verified via static CrossRef title search (score: 1.00)
Verified Can routinely collected ambulance data about assaults contribute to reduction in community violence?
Ariel, B.; Weinborn, C.; Boyle, A. (2015)
Emergency Medicine Journal: EMJ
DOI: 10.1136/emermed-2013-203133
Raw: Ariel, B., Weinborn, C., & Boyle, A. (2015). Can routinely collected ambulance data about assaults contribute to reduction in community violence? Emergency Medicine Journal: EMJ, 32(4), 308- 313. https://doi.org/10.1136/emermed-2013-203133
Match: Can routinely collected ambulance data about assaults contribute to reduction in community violence?
Authors: Barak Ariel; Cristobal Weinborn; Adrian Boyle
Venue: Emergency Medicine Journal
DOI: 10.1136/emermed-2013-203133
URL: https://doi.org/10.1136/emermed-2013-203133
Verified via static CrossRef DOI lookup (score: 0.95)
Verified Crime prediction using patterns and context
Baloian, N.; Bassaletti, E.; Fern�andez, M.; Figueroa, O.; Fuentes, P.; Manasevich, R.; Vergara, M. (2017)
Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD 2017)
Raw: Baloian, N., Bassaletti, E., Fern�andez, M., Figueroa, O., Fuentes, P., Manasevich, R., … Vergara, M. (2017). Crime prediction using patterns and context. In Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD 2017).
Match: Crime prediction using patterns and context
Authors: Nelson Baloian; Col. Enrique Bassaletti; Mario Fernandez; Oscar Figueroa; Pablo Fuentes; Raul Manasevich; Marcos Orchard; Sergio Penafiel; Jose A. Pino; Mario Vergara
Venue: 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD)
DOI: 10.1109/cscwd.2017.8066662
URL: https://doi.org/10.1109/cscwd.2017.8066662
Verified via static CrossRef title search (score: 0.99)
Verified Analyzing the impact of foursquare and streetlight data with human demographics on future crime prediction
Bappee, F. K.; Petry, L. M.; Soares, A.; Matwin, S. (2021)
Springer
Raw: Bappee, F. K., Petry, L. M., Soares, A., & Matwin, S. (2021). Analyzing the impact of foursquare and streetlight data with human demographics on future crime prediction. In Advances in data science and information engineering (pp. 435-449). Springer.
Match: Analyzing the Impact of Foursquare and Streetlight Data with Human Demographics on Future Crime Prediction
Authors: Fateha Khanam Bappee; Lucas May Petry; Amilcar Soares; Stan Matwin
Venue: Transactions on Computational Science and Computational Intelligence
DOI: 10.1007/978-3-030-71704-9_29
URL: https://doi.org/10.1007/978-3-030-71704-9_29
Verified via static CrossRef title search (score: 1.00)
Verified Examining the impact of cross-domain learning on crime prediction
Bappee, F. K.; Soares, A.; Petry, L. M.; Matwin, S. (2021)
Journal of Big Data
DOI: 10.1186/s40537-021-00489-9
Raw: Bappee, F. K., Soares, A., Petry, L. M., & Matwin, S. (2021). Examining the impact of cross-domain learning on crime prediction. Journal of Big Data, 8(1), 96. https://doi.org/10.1186/s40537-021-00489-9
Match: Examining the impact of cross-domain learning on crime prediction
Authors: Fateha Khanam Bappee; Amilcar Soares; Lucas May Petry; Stan Matwin
Venue: Journal of Big Data
DOI: 10.1186/s40537-021-00489-9
URL: https://doi.org/10.1186/s40537-021-00489-9
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Predictive policing: What can we learn from Wal-Mart and Amazon about fighting crime in a recession?
Beck, C.; McCue, C. (2009)
Police Chief
Raw: Beck, C., & McCue, C. (2009). Predictive policing: What can we learn from Wal-Mart and Amazon about fighting crime in a recession? Police Chief, 76(11), 18.
Match: Predictive Policing: What Can We Learn from Wal-Mart and Amazon about Fighting Crime in a Recession?
Authors: Charlie Beck; Colleen McCue
Venue: The Police Chief
The citation is accurate in all metadata fields. Citing the start page (18) when the full range is 18-24 is acceptable and verified.
Verified Analyzing and predicting spatial crime distribution using crowdsourced and open data
Belesiotis, A.; Papadakis, G.; Skoutas, D. (2018)
ACM Transactions on Spatial Algorithms and Systems
DOI: 10.1145/3190345
Raw: Belesiotis, A., Papadakis, G., & Skoutas, D. (2018). Analyzing and predicting spatial crime distribution using crowdsourced and open data. ACM Transactions on Spatial Algorithms and Systems, 3(4), 1-31. https://doi.org/10.1145/3190345
Match: Analyzing and Predicting Spatial Crime Distribution Using Crowdsourced and Open Data
Authors: Alexandros Belesiotis; George Papadakis; Dimitrios Skoutas
Venue: ACM Transactions on Spatial Algorithms and Systems
DOI: 10.1145/3190345
URL: https://doi.org/10.1145/3190345
Verified via static CrossRef DOI lookup (score: 1.00)
Verified To predict and to manage. Predictive policing in the United States
Benbouzid, B. (2019)
Big Data & Society
Raw: Benbouzid, B. (2019). To predict and to manage. Predictive policing in the United States. Big Data & Society, 6(1), 2053951719861703
Match: To predict and to manage. Predictive policing in the United States
Authors: Bilel Benbouzid
Venue: Big Data & Society
DOI: 10.1177/2053951719861703
URL: https://doi.org/10.1177/2053951719861703
Verified via static CrossRef title search (score: 1.00)
Verified THE STATE OF SURVEILLANCE IN 2018
Big Brother Watch (BBW) (2018)
Raw: Big Brother Watch (BBW). (2018). THE STATE OF SURVEILLANCE IN 2018. Retrieved from https:// bigbrotherwatch.org.uk/wp-content/uploads/2018/09/The-State-of-Surveillance-in-2018.pdf.
Match: The State of Surveillance in 2018
Authors: Big Brother Watch
Venue: Big Brother Watch
URL: https://bigbrotherwatch.org.uk/wp-content/uploads/2018/09/The-State-of-Surveillance-in-2018.pdf
The citation is verified. The report 'The State of Surveillance in 2018' was published by Big Brother Watch in September 2018 and is available at the cited URL.
Verified Moves on the street: Classifying crime hotspots using aggregated anonymized data on people dynamics
Bogomolov, A.; Lepri, B.; Staiano, J.; Letouz� e, E.; Oliver, N.; Pianesi, F.; Pentland, A. (2015)
Big Data
DOI: 10.1089/big.2014.0054
Raw: Bogomolov, A., Lepri, B., Staiano, J., Letouz� e, E., Oliver, N., Pianesi, F., & Pentland, A. (2015). Moves on the street: Classifying crime hotspots using aggregated anonymized data on people dynamics. Big Data, 3(3), 148-158. https://doi.org/10.1089/big.2014.0054
Match: Moves on the Street: Classifying Crime Hotspots Using Aggregated Anonymized Data on People Dynamics
Authors: Andrey Bogomolov; Bruno Lepri; Jacopo Staiano; Emmanuel Letouzé; Nuria Oliver; Fabio Pianesi; Alex Pentland
Venue: Big Data
DOI: 10.1089/big.2014.0054
URL: https://doi.org/10.1089/big.2014.0054
All bibliographic fields (title, authors, journal, volume, issue, year, pages, and DOI) match the official record from Mary Ann Liebert, Inc. The encoding artifact in 'Letouzé' is disregarded per instructions.
Verified Once upon a crime: Towards crime prediction from demographics and mobile data
Bogomolov, A.; Lepri, B.; Staiano, J.; Oliver, N.; Pianesi, F.; Pentland, A. (2014)
Proceedings of the 16th International Conference on Multimodal Interaction
Raw: Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., & Pentland, A. (2014, November). Once upon a crime: Towards crime prediction from demographics and mobile data. In Proceedings of the 16th International Conference on Multimodal Interaction (pp. 427-434).
Match: Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data
Authors: Andrey Bogomolov; Bruno Lepri; Jacopo Staiano; Nuria Oliver; Fabio Pianesi; Alex Pentland
Venue: Proceedings of the 16th International Conference on Multimodal Interaction
DOI: 10.1145/2663204.2663254
URL: https://doi.org/10.1145/2663204.2663254
Verified via static CrossRef title search (score: 1.00)
Verified Time series analysis for crime forecasting
Borowik, G.; Wawrzyniak, Z. M.; Cichosz, P. (2018)
IEEE
DOI: 10.1109/ICSENG.2018.8638179
Raw: Borowik, G., Wawrzyniak, Z. M., & Cichosz, P. (2018). December). Time series analysis for crime forecasting. In 2018 26th International Conference on Systems Engineering (ICSEng) (pp. 1- 10). IEEE. https://doi.org/10.1109/ICSENG.2018.8638179
Match: Time series analysis for crime forecasting
Authors: Grzegorz Borowik; Zbigniew M. Wawrzyniak; Pawel Cichosz
Venue: 2018 26th International Conference on Systems Engineering (ICSEng)
DOI: 10.1109/icseng.2018.8638179
URL: https://doi.org/10.1109/icseng.2018.8638179
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Ability of crime, demographic and business data to forecast areas of increased violence
Bowen, D. A.; Mercer Kollar, L. M.; Wu, D. T.; Fraser, D. A.; Flood, C. E.; Moore, J. C.; Sumner, S. A. (2018)
International Journal of Injury Control and Safety Promotion
DOI: 10.1080/17457300.2018.1467461
Raw: Bowen, D. A., Mercer Kollar, L. M., Wu, D. T., Fraser, D. A., Flood, C. E., Moore, J. C., … Sumner, S. A. (2018). Ability of crime, demographic and business data to forecast areas of increased JUSTICE EVALUATION JOURNAL 147 violence. International Journal of Injury Control and Safety Promotion, 25(4), 443-448. https:// doi.org/10.1080/17457300.2018.1467461
Match: Ability of crime, demographic and business data to forecast areas of increased violence
Authors: Daniel A. Bowen; Laura M. Mercer Kollar; Daniel T. Wu; David A. Fraser; Charles E. Flood; Jasmine C. Moore; Elizabeth W. Mays; Steven A. Sumner
Venue: International Journal of Injury Control and Safety Promotion
DOI: 10.1080/17457300.2018.1467461
URL: https://doi.org/10.1080/17457300.2018.1467461
Verified via static CrossRef DOI lookup (score: 0.99)
Verified Measuring the geographical displacement and diffusion of benefit effects of crime prevention activity
Bowers, K. J.; Johnson, S. D. (2003)
Journal of Quantitative Criminology
DOI: 10.1023/A:1024909009240
Raw: Bowers, K. J., & Johnson, S. D. (2003). Measuring the geographical displacement and diffusion of benefit effects of crime prevention activity. Journal of Quantitative Criminology, 19(3), 275- 301. https://doi.org/10.1023/A:1024909009240
Match: Measuring the Geographical Displacement and Diffusion of Benefit Effects of Crime Prevention Activity
Authors: Kate J. Bowers; Shane D. Johnson
Venue: Journal of Quantitative Criminology
DOI: 10.1023/a:1024909009240
URL: https://doi.org/10.1023/a:1024909009240
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Policing crime and disorder hot spots: A randomized controlled trial
Braga, A. A.; Bond, B. J. (2008)
Criminology
DOI: 10.1111/j.1745-9125.2008.00124.x
Raw: Braga, A. A., & Bond, B. J. (2008). Policing crime and disorder hot spots: A randomized controlled trial. Criminology, 46(3), 577-607. https://doi.org/10.1111/j.1745-9125.2008.00124.x
Match: POLICING CRIME AND DISORDER HOT SPOTS: A RANDOMIZED CONTROLLED TRIAL*
Authors: ANTHONY A. BRAGA; BRENDA J. BOND
Venue: Criminology
DOI: 10.1111/j.1745-9125.2008.00124.x
URL: https://doi.org/10.1111/j.1745-9125.2008.00124.x
Verified via static CrossRef DOI lookup (score: 1.00)
Verified A conceptual model for anticipating crime displacement
Brantingham, P. L.; Brantingham, P. J. (2000)
American Society of Criminology Annual Meeting
Raw: Brantingham, P. L., & Brantingham, P. J. (2000, November). A conceptual model for anticipating crime displacement. In American Society of Criminology Annual Meeting, San Francisco, CA.
Match: A Conceptual Model for Anticipating Crime Displacement: Rational Choices in Context
Authors: Patricia L. Brantingham
Venue: American Society of Criminology Annual Meeting, San Francisco, CA
The presentation title in the official ASC 2000 meeting abstracts includes the subtitle ': Rational Choices in Context'. The paper was later published in more detail as 'Anticipating the Displacement of Crime Using the Principles of Environmental Criminology' in Crime Prevention Studies, Vol. 16 (2003).
Verified Big data surveillance: The case of policing
Brayne, S. (2017)
American Sociological Review
DOI: 10.1177/0003122417725865
Raw: Brayne, S. (2017). Big data surveillance: The case of policing. American Sociological Review, 82(5), 977-1008. https://doi.org/10.1177/0003122417725865
Match: Big Data Surveillance: The Case of Policing
Authors: Sarah Brayne
Venue: American Sociological Review
DOI: 10.1177/0003122417725865
URL: https://doi.org/10.1177/0003122417725865
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Crime data forecasting using machine learning and big data
Brindha, R.; Thillaikarasi, M. (2021)
Webology
Raw: Brindha, R., & Thillaikarasi, M. (2021). Crime data forecasting using machine learning and big data. Analytics. Special Issue on Computing Technology and Information Management. Webology, 18. 591-606.
Match: Crime data forecasting using machine learning and big data analytics
Authors: Brindha, R.; Thillaikarasi, M.
Venue: Webology
The citation is verified. The word 'Analytics' in the raw text refers to the final word of the full title ('...big data analytics') and the specific Special Issue of Webology Volume 18.
Verified Modeling the influence of places on crime risk through a non-linear effects model: A comparison with risk terrain modeling
Briz-Redon, � A.; � Mateu, J.; Montes, F. (2022)
Applied Spatial Analysis and Policy
DOI: 10.1007/s12061-021-09410-6
Raw: Briz-Redon, � A., � Mateu, J., & Montes, F. (2022). Modeling the influence of places on crime risk through a non-linear effects model: A comparison with risk terrain modeling. Applied Spatial Analysis and Policy, 15(2), 507-527. https://doi.org/10.1007/s12061-021-09410-6
Match: Modeling the Influence of Places on Crime Risk Through a Non-Linear Effects Model: a Comparison with Risk Terrain Modeling
Authors: Álvaro Briz-Redón; Jorge Mateu; Francisco Montes
Venue: Applied Spatial Analysis and Policy
DOI: 10.1007/s12061-021-09410-6
URL: https://doi.org/10.1007/s12061-021-09410-6
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Risk terrain modeling for spatial risk assessment
Caplan, J. M.; Kennedy, L. W.; Barnum, J. D.; Piza, E. L. (2015)
Cityscape: A Journal of Policy Development and Research
Raw: Caplan, J. M., Kennedy, L. W., Barnum, J. D., & Piza, E. L. (2015). Risk terrain modeling for spatial risk assessment. Cityscape: A Journal of Policy Development and Research, 17(1), 7-16.
Match: Risk Terrain Modeling for Spatial Risk Assessment
Authors: Joel M. Caplan; Leslie W. Kennedy; Jeremy D. Barnum; Eric L. Piza
Venue: Cityscape: A Journal of Policy Development and Research
The citation aligns perfectly with the official record in Volume 17, Issue 1, pages 7-16 of Cityscape (2015).
Verified Using Vulnerability and Exposure to Improve Robbery Prediction and Target Area Selection
Caplan, J. M.; Kennedy, L. W.; Piza, E. L.; Barnum, J. D. (2020)
Applied Spatial Analysis and Policy
DOI: 10.1007/s12061-019-09294-7
Raw: Caplan, J. M., Kennedy, L. W., Piza, E. L., & Barnum, J. D. (2020). Using Vulnerability and Exposure to Improve Robbery Prediction and Target Area Selection. Applied Spatial Analysis and Policy, 13(1), 113-136. https://doi.org/10.1007/s12061-019-09294-7
Match: Using Vulnerability and Exposure to Improve Robbery Prediction and Target Area Selection
Authors: Joel M. Caplan; Leslie W. Kennedy; Eric L. Piza; Jeremy D. Barnum
Venue: Applied Spatial Analysis and Policy
DOI: 10.1007/s12061-019-09294-7
URL: https://doi.org/10.1007/s12061-019-09294-7
Verified via static CrossRef DOI lookup (score: 1.00)
Verified The Indianapolis harmspot policing experiment
Carter, J. G.; Mohler, G.; Raje, R.; Chowdhury, N.; Pandey, S. (2021)
Journal of Criminal Justice
DOI: 10.1016/j.jcrimjus.2021.101814
Raw: Carter, J. G., Mohler, G., Raje, R., Chowdhury, N., & Pandey, S. (2021). The Indianapolis harmspot policing experiment. Journal of Criminal Justice, 74(c), 101814. https://doi.org/10.1016/j.jcrimjus.2021.101814
Match: The Indianapolis harmspot policing experiment
Authors: Jeremy G. Carter; George Mohler; Rajeev Raje; Nahida Chowdhury; Saurabh Pandey
Venue: Journal of Criminal Justice
DOI: 10.1016/j.jcrimjus.2021.101814
URL: https://doi.org/10.1016/j.jcrimjus.2021.101814
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Predicting crime by exploiting supervised learning on heterogeneous data
Castro, U. R.; Rodrigues, M. W.; Brandao, W. C. (2020)
ICEIS (1)
Raw: Castro, U. R., Rodrigues, M. W., & Brandao, W. C. (2020). Predicting crime by exploiting supervised learning on heterogeneous data. In ICEIS (1) (pp. 524-531).
Match: Predicting Crime by Exploiting Supervised Learning on Heterogeneous Data
Authors: Úrsula Castro; Marcos Rodrigues; Wladmir Brandão
Venue: Proceedings of the 22nd International Conference on Enterprise Information Systems
DOI: 10.5220/0009392005240531
URL: https://doi.org/10.5220/0009392005240531
Verified via static CrossRef title search (score: 1.00)
Verified Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments
Catlett, C.; Cesario, E.; Talia, D.; Vinci, A. (2019)
Pervasive and Mobile Computing
DOI: 10.1016/j.pmcj.2019.01.003
Raw: Catlett, C., Cesario, E., Talia, D., & Vinci, A. (2019). Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments. Pervasive and Mobile Computing, 53, 62-74. https://doi.org/10.1016/j.pmcj.2019.01.003
Match: Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments
Authors: Charlie Catlett; Eugenio Cesario; Domenico Talia; Andrea Vinci
Venue: Pervasive and Mobile Computing
DOI: 10.1016/j.pmcj.2019.01.003
URL: https://doi.org/10.1016/j.pmcj.2019.01.003
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Improving law enforcement daily deployment through machine learning-informed optimization under uncertainty
Chase, J. D.; Nguyen, D. T.; Sun, H.; Lau, H. C. (2019)
Proceedings of the TwentyEighth International Joint Conference on Artificial Intelligence (IJCAI-19)
Raw: Chase, J. D., Nguyen, D. T., Sun, H., & Lau, H. C. (2019). Improving law enforcement daily deployment through machine learning-informed optimization under uncertainty. In Proceedings of the TwentyEighth International Joint Conference on Artificial Intelligence (IJCAI-19): Macau, August (pp. 10-16).
Match: Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization under Uncertainty
Authors: Jonathan Chase; Duc Thien Nguyen; Haiyang Sun; Hoong Chuin Lau
Venue: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
DOI: 10.24963/ijcai.2019/806
URL: https://doi.org/10.24963/ijcai.2019/806
Verified via static CrossRef title search (score: 1.00)
Verified Learning deep representation from big and heterogeneous data for traffic accident inference
Chen, Q. J.; Song, X.; Yamada, H.; Shibasaki, R. (2016)
AAAI Conference on Artificial Intelligence
DOI: 10.1609/aaai.v30i1.10011
Raw: Chen, Q. J., Song, X., Yamada, H., & Shibasaki, R. (2016, Feb 12-17). Learning deep representation from big and heterogeneous data for traffic accident inference. Paper presented at the 30th Association-for-the-Advancement-of-Artificial-Intelligence (AAAI) Conference on Artificial Intelligence, Phoenix, AZ. https://doi.org/10.1609/aaai.v30i1.10011
Match: Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference
Authors: Quanjun Chen; Xuan Song; Harutoshi Yamada; Ryosuke Shibasaki
Venue: Proceedings of the AAAI Conference on Artificial Intelligence
DOI: 10.1609/aaai.v30i1.10011
URL: https://doi.org/10.1609/aaai.v30i1.10011
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Crime prediction using Twitter sentiment and weather
Chen, X.; Cho, Y.; Jang, S. Y. (2015)
IEEE
DOI: 10.1109/SIEDS.2015.7117012
Raw: Chen, X., Cho, Y., & Jang, S. Y. (2015, April). Crime prediction using Twitter sentiment and weather. In 2015 Systems and Information Engineering Design Symposium (pp. 63-68). IEEE. https:// doi.org/10.1109/SIEDS.2015.7117012
Match: Crime prediction using Twitter sentiment and weather
Authors: Xinyu Chen; Youngwoon Cho; Suk Young Jang
Venue: 2015 Systems and Information Engineering Design Symposium
DOI: 10.1109/sieds.2015.7117012
URL: https://doi.org/10.1109/sieds.2015.7117012
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Crime mapping powered by machine learning and web GIS
Chen, Y. (2019)
Raw: Chen, Y. (2019). Crime mapping powered by machine learning and web GIS [Doctoral dissertation]. California State University, Northridge.
Match: Crime Mapping Powered by Machine Learning and Web GIS
Authors: Yixin Chen
Venue: California State University, Northridge
URL: https://scholarworks.calstate.edu/handle/10211.3/211516
The work is officially a Master's thesis, not a doctoral dissertation as stated in the bracketed text, but all other metadata (title, author, year, institution) is an exact match. Per instructions to be lenient with dissertation/thesis metadata and versioning for the same underlying document, it is marked as verified.
Verified Urban crime risk prediction using point of interest data
Cichosz, P. (2020)
ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi9070459
Raw: Cichosz, P. (2020). Urban crime risk prediction using point of interest data. ISPRS International Journal of Geo-Information, 9(7), 459. https://doi.org/10.3390/ijgi9070459
Match: Urban Crime Risk Prediction Using Point of Interest Data
Authors: Paweł Cichosz
Venue: ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi9070459
URL: https://doi.org/10.3390/ijgi9070459
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Understanding the predictors of street robbery hot spots: A matched pairs analysis and systematic social observation
Connealy, N. T. (2021)
Crime & Delinquency
DOI: 10.1177/0011128720926116
Raw: Connealy, N. T. (2021). Understanding the predictors of street robbery hot spots: A matched pairs analysis and systematic social observation. Crime & Delinquency, 67(9), 1319-1352. https://doi.org/10.1177/0011128720926116
Match: Understanding the Predictors of Street Robbery Hot Spots: A Matched Pairs Analysis and Systematic Social Observation
Authors: Nathan T. Connealy
Venue: Crime & Delinquency
DOI: 10.1177/0011128720926116
URL: https://doi.org/10.1177/0011128720926116
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Risk factor and high-risk place variations across different robbery targets in Denver, Colorado
Connealy, N. T.; Piza, E. L. (2019)
Journal of Criminal Justice
DOI: 10.1016/j.jcrimjus.2018.11.003
Raw: Connealy, N. T., & Piza, E. L. (2019). Risk factor and high-risk place variations across different robbery targets in Denver, Colorado. Journal of Criminal Justice, 60, 47-56. https://doi.org/10. 1016/j.jcrimjus.2018.11.003
Match: Risk factor and high-risk place variations across different robbery targets in Denver, Colorado
Authors: Nathan T. Connealy; Eric L. Piza
Venue: Journal of Criminal Justice
DOI: 10.1016/j.jcrimjus.2018.11.003
URL: https://doi.org/10.1016/j.jcrimjus.2018.11.003
Verified via static CrossRef DOI lookup (score: 1.00)
Verified A three-part exploration linking social media, big data, and GIS: A case of predictive crime analysis
Corso, A. (2015)
Raw: Corso, A. (2015). A three-part exploration linking social media, big data, and GIS: A case of predictive crime analysis [Doctoral dissertation]. The Claremont Graduate University.
Match: A three-part exploration linking social media, big data, and GIS: A case of predictive crime analysis
Authors: Anthony Corso
Venue: The Claremont Graduate University (Dissertation)
The dissertation was completed under the Center for Information Systems & Technology (CISAT) at Claremont Graduate University. The Google Scholar record from the author's profile and ESRI proceedings corroborating the specific framing provide sufficient evidence for verification.
Verified The prediction of criminal recidivism in juveniles: A metaanalysis
Cottle, C. C.; Lee, R. J.; Heilbrun, K. (2001)
Criminal Justice and Behavior
DOI: 10.1177/0093854801028003005
Raw: Cottle, C. C., Lee, R. J., & Heilbrun, K. (2001). The prediction of criminal recidivism in juveniles: A metaanalysis. Criminal Justice and Behavior, 28(3), 367-394. https://doi.org/10.1177/0093854801028003005
Match: The Prediction of Criminal Recidivism in Juveniles: A Meta-Analysis
Authors: CINDY C. COTTLE; RIA J. LEE; KIRK HEILBRUN
Venue: Criminal Justice and Behavior
DOI: 10.1177/0093854801028003005
URL: https://doi.org/10.1177/0093854801028003005
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Understanding police culture
Crank, J. P. (2014)
Routledge
ISBN: 978-1-317-52142-6
Raw: Crank, J. P. (2014). Understanding police culture. Routledge.
Match: Understanding Police Culture
Authors: John P. Crank
Venue: None
DOI: 10.4324/9781315721255
ISBN: 978-1-317-52142-6
Verified via static CrossRef title search (score: 1.00)
Verified Predicting the next location for trajectories from stolen vehicles
Da Silva Neto, J. S.; Coelho Da Silva, T. L.; Cruz, L. A.; Monteiro De Lira, V.; De MacEdo, J. A. F.; Pires Magalhaes, R.; Peres, L. G. (2021)
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Raw: Da Silva Neto, J. S., Coelho Da Silva, T. L., Cruz, L. A., Monteiro De Lira, V., De MacEdo, J. A. F., Pires Magalhaes, R., & Peres, L. G. (2021). Predicting the next location for trajectories from stolen vehicles. In Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI.
Match: Predicting the Next Location for Trajectories From Stolen Vehicles
Authors: Jose S. Da Silva Neto; Ticiana L. Coelho Da Silva; Livia Almada Cruz; Vinicius Monteiro de Lira; Jose Antonio F. de Macedo; Regis Pires Magalhaes; Lucas Gaspar Peres
Venue: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)
DOI: 10.1109/ictai52525.2021.00073
URL: https://doi.org/10.1109/ictai52525.2021.00073
Verified via static CrossRef title search (score: 1.00)
Verified Risk terrain modeling predicts child maltreatment
Daley, D.; Bachmann, M.; Bachmann, B. A.; Pedigo, C.; Bui, M. T.; Coffman, J. (2016)
Child Abuse & Neglect
DOI: 10.1016/j.chiabu.2016.09.014
Raw: Daley, D., Bachmann, M., Bachmann, B. A., Pedigo, C., Bui, M. T., & Coffman, J. (2016). Risk terrain modeling predicts child maltreatment. Child Abuse & Neglect, 62, 29-38. https://doi.org/10. 1016/j.chiabu.2016.09.014
Match: Risk terrain modeling predicts child maltreatment
Authors: Dyann Daley; Michael Bachmann; Brittany A. Bachmann; Christian Pedigo; Minh-Thuy Bui; Jamye Coffman
Venue: Child Abuse & Neglect
DOI: 10.1016/j.chiabu.2016.09.014
URL: https://doi.org/10.1016/j.chiabu.2016.09.014
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Spatio-temporal prediction of crimes using network analytic approach
Dash, S. K.; Safro, I.; Srinivasamurthy, R. S. (2018)
IEEE
DOI: 10.1109/BigData.2018.8622041
Raw: Dash, S. K., Safro, I., & Srinivasamurthy, R. S. (2018, December). Spatio-temporal prediction of crimes using network analytic approach. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 1912-1917). https://doi.org/10.1109/BigData.2018.8622041
Match: Spatio-temporal prediction of crimes using network analytic approach
Authors: Saroj Kumar Dash; Ilya Safro; Ravisutha Sakrepatna Srinivasamurthy
Venue: 2018 IEEE International Conference on Big Data (Big Data)
DOI: 10.1109/bigdata.2018.8622041
URL: https://doi.org/10.1109/bigdata.2018.8622041
Verified via static CrossRef DOI lookup (score: 1.00)
Verified What is wrong about Robocops as consultants? A technology-centric critique of predictive policing
Degeling, M.; Berendt, B. (2018)
AI & Society
DOI: 10.1007/s00146-017-0730-7
Raw: Degeling, M., & Berendt, B. (2018). What is wrong about Robocops as consultants? A technology-centric critique of predictive policing. AI & Society, 33(3), 347-356. https://doi.org/10.1007/s00146-017-0730-7
Match: What is wrong about Robocops as consultants? A technology-centric critique of predictive policing
Authors: Martin Degeling; Bettina Berendt
Venue: AI & SOCIETY
DOI: 10.1007/s00146-017-0730-7
URL: https://doi.org/10.1007/s00146-017-0730-7
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Safety app: Crime prediction using GIS
Deshmukh, A.; Banka, S.; Dcruz, S. B.; Shaikh, S.; Tripathy, A. K. (2020)
2020 3rd International Conference on Communication Systems, Computing and IT Applications (CSCITA 2020) - Proceedings
Raw: Deshmukh, A., Banka, S., Dcruz, S. B., Shaikh, S., & Tripathy, A. K. (2020). Safety app: Crime prediction using GIS. In 2020 3rd International Conference on Communication Systems, Computing and IT Applications (CSCITA 2020) - Proceedings.
Match: Safety App: Crime Prediction Using GIS
Authors: Atharva Deshmukh; Sourab Banka; Sean Bruno Dcruz; Sana Shaikh; Amiya Kumar Tripathy
Venue: 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA)
DOI: 10.1109/cscita47329.2020.9137772
URL: https://doi.org/10.1109/cscita47329.2020.9137772
Verified via static CrossRef title search (score: 1.00)
Verified Exploiting points of interest for predictive policing
Do R^ ego, L. G. C.; Da Silva, T. L. C.; Magalh~aes, R. P.; De Mac^Edo, J. A. F.; Silva, W. C. P. (2020)
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities (ARIC 2020)
DOI: 10.1145/3423455.3430319
Raw: Do R^ ego, L. G. C., Da Silva, T. L. C., Magalh~aes, R. P., De Mac^Edo, J. A. F., & Silva, W. C. P. (2020). Exploiting points of interest for predictive policing. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities (ARIC 2020). https://doi. org/10.1145/3423455.3430319
Match: Exploiting points of interest for predictive policing
Authors: Luís Gustavo Coutinho do Rêgo; Ticiana Linhares Coelho da Silva; Regis Pires Magalhães; José Antônio Fernandes de Macêdo; Wellington Clay Porcino Silva
Venue: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities
DOI: 10.1145/3423455.3430319
The citation contains LaTeX-style/OCR representations for diacritics in author names (e.g., ^e, ~a) which is a notation rendering issue and remains verified. The DOI contains a line-break/space artifact that resolves correctly once normalized.
Verified Predictability of gun crimes: A comparison of hot spot and risk terrain modelling techniques
Drawve, G.; Moak, S. C.; Berthelot, E. R. (2016)
Policing and Society
DOI: 10.1080/10439463.2014.942851
Raw: Drawve, G., Moak, S. C., & Berthelot, E. R. (2016). Predictability of gun crimes: A comparison of hot spot and risk terrain modelling techniques. Policing and Society, 26(3), 312-331. https:// doi.org/10.1080/10439463.2014.942851
Match: Predictability of gun crimes: a comparison of hot spot and risk terrain modelling techniques
Authors: Grant Drawve; Stacy C. Moak; Emily R. Berthelot
Venue: Policing and Society
DOI: 10.1080/10439463.2014.942851
URL: https://doi.org/10.1080/10439463.2014.942851
Verified via static CrossRef DOI lookup (score: 0.95)
Verified The accuracy, fairness, and limits of predicting recidivism
Dressel, J.; Farid, H. (2018)
Science Advances
DOI: 10.1126/sciadv.aao5580
Raw: Dressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4(1), eaao5580. https://doi.org/10.1126/sciadv.aao5580
Match: The accuracy, fairness, and limits of predicting recidivism
Authors: Julia Dressel; Hany Farid
Venue: Science Advances
DOI: 10.1126/sciadv.aao5580
URL: https://doi.org/10.1126/sciadv.aao5580
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Deep convolutional neural networks for spatiotemporal crime prediction
Duan, L.; Hu, T.; Cheng, E.; Zhu, J.; Gao, C. (2017)
International Conference on Information and Knowledge Engineering (IKE)
Raw: Duan, L., Hu, T., Cheng, E., Zhu, J., & Gao, C. (2017). Deep convolutional neural networks for spatiotemporal crime prediction. In Proceedings of the International Conference on Information and Knowledge Engineering (IKE) (pp. 61-67).
Match: Deep Convolutional Neural Networks for Spatiotemporal Crime Prediction
Authors: Lian Duan; Tao Hu; En Cheng; Jianfeng Zhu; Chao Gao
Venue: Proceedings of the International Conference on Information and Knowledge Engineering (IKE)
All bibliographic fields (title, authors, year, conference venue, and page range) match the records found in Semantic Scholar and OUCI.
Verified Isolating target and neighbourhood vulnerabilities in crime forecasting
Dugato, M.; Favarin, S.; Bosisio, A. (2018)
European Journal on Criminal Policy and Research
DOI: 10.1007/s10610-018-9385-2
Raw: Dugato, M., Favarin, S., & Bosisio, A. (2018). Isolating target and neighbourhood vulnerabilities in crime forecasting. European Journal on Criminal Policy and Research, 24(4), 393-415. https:// doi.org/10.1007/s10610-018-9385-2
Match: Isolating Target And Neighbourhood Vulnerabilities In Crime Forecasting
Authors: Marco Dugato; Serena Favarin; Antonio Bosisio
Venue: European Journal on Criminal Policy and Research
DOI: 10.1007/s10610-018-9385-2
URL: https://doi.org/10.1007/s10610-018-9385-2
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Predictive policing: Not yet, but soon preemptive?
Egbert, S.; Krasmann, S. (2020)
Policing and Society
DOI: 10.1080/10439463.2019.1611821
Raw: Egbert, S., & Krasmann, S. (2020). Predictive policing: Not yet, but soon preemptive? Policing and Society, 30(8), 905-919. https://doi.org/10.1080/10439463.2019.1611821
Match: Predictive policing: not yet, but soon preemptive?
Authors: Simon Egbert; Susanne Krasmann
Venue: Policing and Society
DOI: 10.1080/10439463.2019.1611821
URL: https://doi.org/10.1080/10439463.2019.1611821
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Understanding policing demand and deployment through the lens of the city and with the application of big data
Ellison, M.; Bannister, J.; Lee, W. D.; Haleem, M. S. (2021)
Urban Studies
DOI: 10.1177/0042098020981007
Raw: Ellison, M., Bannister, J., Lee, W. D., & Haleem, M. S. (2021). Understanding policing demand and deployment through the lens of the city and with the application of big data. Urban Studies, 58(15), 3157-3175. https://doi.org/10.1177/0042098020981007
Match: Understanding policing demand and deployment through the lens of the city and with the application of big data
Authors: Mark Ellison; Jon Bannister; Won Do Lee; Muhammad Salman Haleem
Venue: Urban Studies
DOI: 10.1177/0042098020981007
URL: https://doi.org/10.1177/0042098020981007
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Developing machine learning based predictive models for smart policing
Elluri, L.; Mandalapu, V.; Roy, N. (2019)
IEEE
DOI: 10.1109/SMARTCOMP.2019.00053
Raw: Elluri, L., Mandalapu, V., & Roy, N. (2019, June). Developing machine learning based predictive models for smart policing. In 2019 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 198-204). https://doi.org/10.1109/SMARTCOMP.2019.00053
Match: Developing Machine Learning Based Predictive Models for Smart Policing
Authors: Lavanya Elluri; Varun Mandalapu; Nirmalya Roy
Venue: 2019 IEEE International Conference on Smart Computing (SMARTCOMP)
DOI: 10.1109/smartcomp.2019.00053
URL: https://doi.org/10.1109/smartcomp.2019.00053
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Correlated impulses: Using Facebook interests to improve predictions of crime rates in urban areas
Fatehkia, M.; O’Brien, D.; Weber, I. (2019)
PloS One
DOI: 10.1371/journal.pone.0211350
Raw: Fatehkia, M., O’Brien, D., & Weber, I. (2019). Correlated impulses: Using Facebook interests to improve predictions of crime rates in urban areas. PloS One, 14(2), e0211350. https://doi.org/10.1371/journal.pone.0211350
Match: Correlated impulses: Using Facebook interests to improve predictions of crime rates in urban areas
Authors: Masoomali Fatehkia; Dan O’Brien; Ingmar Weber
Venue: PLOS ONE
DOI: 10.1371/journal.pone.0211350
URL: https://doi.org/10.1371/journal.pone.0211350
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Predictive crime mapping
Fitterer, J.; Nelson, T. A.; Nathoo, F. (2015)
Police Practice and Research
DOI: 10.1080/15614263.2014.972618
Raw: Fitterer, J., Nelson, T. A., & Nathoo, F. (2015). Predictive crime mapping. Police Practice and Research, 16(2), 121-135. https://doi.org/10.1080/15614263.2014.972618
Match: Predictive crime mapping
Authors: J. Fitterer; T.A. Nelson; F. Nathoo
Venue: Police Practice and Research
DOI: 10.1080/15614263.2014.972618
URL: https://doi.org/10.1080/15614263.2014.972618
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Keeping score: Predictive analytics in policing
Fitzpatrick, D. J.; Gorr, W. L.; Neill, D. B. (2019)
Annual Review of Criminology
DOI: 10.1146/annurev-criminol-011518-024534
Raw: Fitzpatrick, D. J., Gorr, W. L., & Neill, D. B. (2019). Keeping score: Predictive analytics in policing. Annual Review of Criminology, 2(1), 473-491. https://doi.org/10.1146/annurev-criminol-011518-024534
Match: Keeping Score: Predictive Analytics in Policing
Authors: Dylan J. Fitzpatrick; Wilpen L. Gorr; Daniel B. Neill
Venue: Annual Review of Criminology
DOI: 10.1146/annurev-criminol-011518-024534
URL: https://doi.org/10.1146/annurev-criminol-011518-024534
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Effectiveness of anonymised information sharing and use in health service, police, and local government partnership for preventing violence related injury: Experimental study and time series analysis
Florence, C.; Shepherd, J. P.; Brennan, I.; Simon, T. (2011)
British Medical Journal
DOI: 10.1136/bmj.d3313
Raw: Florence, C., Shepherd, J. P., Brennan, I., & Simon, T. (2011). Effectiveness of anonymised information sharing and use in health service, police, and local government partnership for JUSTICE EVALUATION JOURNAL 149 preventing violence related injury: Experimental study and time series analysis. British Medical Journal, 342, d3313. https://doi.org/10.1136/bmj.d3313.
Match: Effectiveness of anonymised information sharing and use in health service, police, and local government partnership for preventing violence related injury: experimental study and time series analysis
Authors: C. Florence; J. Shepherd; I. Brennan; T. Simon
Venue: BMJ
DOI: 10.1136/bmj.d3313
URL: https://doi.org/10.1136/bmj.d3313
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Modeling and prediction of criminal activity based on spatio-temporal probabilistic risk functions
Flores, P.; Vergara, M.; Fuentes, P.; Jaramillo, F.; Acun~a, D.; Perez, A.; Orchard, M. (2015)
Annual Conference of the PHM Society
DOI: 10.36001/phmconf.2015.v7i1.2676
Raw: Flores, P., Vergara, M., Fuentes, P., Jaramillo, F., Acun~a, D., Perez, A., & Orchard, M. (2015). Modeling and prediction of criminal activity based on spatio-temporal probabilistic risk functions. In Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2676
Match: Modeling and Prediction of Criminal Activity Based on Spatio-Temporal Probabilistic Risk Functions
Authors: Paulina Flores; Mario Vergara; Pablo Fuentes; Francisco Jaramillo; David Acuña; Aramis Perez; Marcos Orchard
Venue: Annual Conference of the PHM Society
DOI: 10.36001/phmconf.2015.v7i1.2676
URL: https://papers.phmsociety.org/index.php/phmconf/article/view/2676
The citation is exactly correct. The DOI resolves to the official publisher page of the PHM Society, which confirms all metadata fields including title, authors, volume, and issue. The citation 'Acun~a' for 'Acuña' is a standard ASCII representation of the tilde character and is not an error.
Verified Suspects prediction towards terrorist attacks based on machine learning
Gao, Y.; Wang, X.; Chen, Q.; Guo, Y.; Yang, Q.; Yang, K.; Fang, T. (2019)
IEEE
DOI: 10.1109/BigDIA.2019.8802726
Raw: Gao, Y., Wang, X., Chen, Q., Guo, Y., Yang, Q., Yang, K., & Fang, T. (2019, July). Suspects prediction towards terrorist attacks based on machine learning. In 2019 5th International Conference on Big Data and Information Analytics (BigDIA) (pp. 126-131). IEEE. https://doi.org/10.1109/BigDIA.2019. 8802726
Match: Suspects Prediction towards Terrorist Attacks Based on Machine Learning
Authors: Yingying Gao; Xianghan Wang; Qiuyu Chen; Yu Guo; Qingqing Yang; Kewei Yang; Taosheng Fang
Venue: 2019 5th International Conference on Big Data and Information Analytics (BigDIA)
DOI: 10.1109/bigdia.2019.8802726
URL: https://doi.org/10.1109/bigdia.2019.8802726
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Predicting dynamical crime distribution from environmental and social influences
Garnier, S.; Caplan, J. M.; Kennedy, L. W. (2018)
Frontiers in Applied Mathematics and Statistics
DOI: 10.3389/fams.2018.00013
Raw: Garnier, S., Caplan, J. M., & Kennedy, L. W. (2018). Predicting dynamical crime distribution from environmental and social influences. Frontiers in Applied Mathematics and Statistics, 4, 13. https://doi.org/10.3389/fams.2018.00013
Match: Predicting Dynamical Crime Distribution From Environmental and Social Influences
Authors: Simon Garnier; Joel M. Caplan; Leslie W. Kennedy
Venue: Frontiers in Applied Mathematics and Statistics
DOI: 10.3389/fams.2018.00013
URL: https://doi.org/10.3389/fams.2018.00013
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Bus stops and violence, are risky places really risky?
Gerell, M. (2018)
European Journal on Criminal Policy and Research
DOI: 10.1007/s10610-018-9382-5
Raw: Gerell, M. (2018). Bus stops and violence, are risky places really risky? European Journal on Criminal Policy and Research, 24(4), 351-371. https://doi.org/10.1007/s10610-018-9382-5
Match: Bus Stops and Violence, Are Risky Places Really Risky?
Authors: Manne Gerell
Venue: European Journal on Criminal Policy and Research
DOI: 10.1007/s10610-018-9382-5
URL: https://doi.org/10.1007/s10610-018-9382-5
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Spatio-temporal modeling of the US college crime data
Gezer, F. (2017)
Raw: Gezer, F. (2017). Spatio-temporal modeling of the US college crime data [Doctoral dissertation]. University of Delaware.
Match: Spatio-temporal modeling of the US college crime data
Authors: Gezer, F.
Venue: University of Delaware
The work is a doctoral dissertation from the University of Delaware. A related conference paper with a similar title was also published by the same author in 2021.
Verified Risk terrain modeling for road safety: Identifying crash-related environmental factors in the province of C�adiz, Spain
Gim� enez-Santana, A.; Medina-Sarmiento, J. E.; Miro�-Llinares, F. (2018)
European Journal on Criminal Policy and Research
DOI: 10.1007/s10610-018-9398-x
Raw: Gim� enez-Santana, A., Medina-Sarmiento, J. E., & Miro�-Llinares, F. (2018). Risk terrain modeling for road safety: Identifying crash-related environmental factors in the province of C�adiz, Spain. European Journal on Criminal Policy and Research, 24(4), 451-467. https://doi.org/10.1007/s10610-018-9398-x
Match: Risk terrain modeling for road safety: identifying crash-related environmental factors in the province of Cádiz, Spain
Authors: Alejandro Giménez-Santana; José E. Medina-Sarmiento; Fernando Miró-Llinares
Venue: European Journal on Criminal Policy and Research
DOI: 10.1007/s10610-018-9398-x
URL: https://link.springer.com/article/10.1007/s10610-018-9398-x
The citation is verified. The discrepancies noted are cosmetic encoding artifacts for Spanish diacritics and minor capitalization differences.
Verified Taking police culture seriously: Police discretion and the limits of law
Goldsmith, A. (1990)
Policing and Society
DOI: 10.1080/10439463.1990.9964608
Raw: Goldsmith, A. (1990). Taking police culture seriously: Police discretion and the limits of law. Policing and Society, 1(2), 91-114. https://doi.org/10.1080/10439463.1990.9964608
Match: Taking police culture seriously: Police discretion and the limits of law†
Authors: Andrew Goldsmith
Venue: Policing and Society
DOI: 10.1080/10439463.1990.9964608
URL: https://doi.org/10.1080/10439463.1990.9964608
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Crime hotspot prediction based on dynamic spatial analysis
Hajela, G.; Chawla, M.; Rasool, A. (2021)
ETRI Journal
DOI: 10.4218/etrij.2020-0220
Raw: Hajela, G., Chawla, M., & Rasool, A. (2021). Crime hotspot prediction based on dynamic spatial analysis. ETRI Journal, 43(6), 1058-1080. https://doi.org/10.4218/etrij.2020-0220
Match: Crime hotspot prediction based on dynamic spatial analysis
Authors: Gaurav Hajela; Meenu Chawla; Akhtar Rasool
Venue: ETRI Journal
DOI: 10.4218/etrij.2020-0220
URL: https://doi.org/10.4218/etrij.2020-0220
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Risk prediction of theft crimes in urban communities: An integrated model of LSTM and ST-GCN
Han, X.; Hu, X.; Wu, H.; Shen, B.; Wu, J. (2020)
IEEE Access
DOI: 10.1109/ACCESS.2020.3041924
Raw: Han, X., Hu, X., Wu, H., Shen, B., & Wu, J. (2020). Risk prediction of theft crimes in urban communities: An integrated model of LSTM and ST-GCN. IEEE Access, 8, 217222-217230. https:// doi.org/10.1109/ACCESS.2020.3041924
Match: Risk Prediction of Theft Crimes in Urban Communities: An Integrated Model of LSTM and ST-GCN
Authors: Xinge Han; Xiaofeng Hu; Huanggang Wu; Bing Shen; Jiansong Wu
Venue: IEEE Access
DOI: 10.1109/access.2020.3041924
URL: https://doi.org/10.1109/access.2020.3041924
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Precision of actuarial risk assessment instruments: Evaluating the ‘margins of error’ of group vs. individual predictions of violence
Hart, S. D.; Michie, C.; Cooke, D. J. (2007)
The British Journal of Psychiatry. Supplement
DOI: 10.1192/bjp.190.5.s60
Raw: Hart, S. D., Michie, C., & Cooke, D. J. (2007). Precision of actuarial risk assessment instruments: Evaluating the ‘margins of error’ of group vs. individual predictions of violence. The British Journal of Psychiatry. Supplement, (49), s60-s65. https://doi.org/10.1192/bjp.190.5.s60
Match: Precision of actuarial risk assessment instruments: Evaluating the ‘margins of error’ of group v. individual predictions of violence
Authors: Stephen D. Hart; Christine Michie; David J. Cooke
Venue: British Journal of Psychiatry
DOI: 10.1192/bjp.190.5.s60
URL: https://doi.org/10.1192/bjp.190.5.s60
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Ambient population and larceny-theft: A spatial analysis using mobile phone data
He, L.; P�aez, A.; Jiao, J.; An, P.; Lu, C.; Mao, W.; Long, D. (2020)
ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi9060342
Raw: He, L., P�aez, A., Jiao, J., An, P., Lu, C., Mao, W., & Long, D. (2020). Ambient population and larceny-theft: A spatial analysis using mobile phone data. ISPRS International Journal of GeoInformation, 9(6), 342. https://doi.org/10.3390/ijgi9060342
Match: Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data
Authors: Li He; Antonio Páez; Jianmin Jiao; Ping An; Chuntian Lu; Wen Mao; Dongping Long
Venue: ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi9060342
URL: https://doi.org/10.3390/ijgi9060342
Verified via static CrossRef DOI lookup (score: 1.00)
Verified What you find depends on where you look: Using emergency medical services call data to target illicit drug use hot spots
Hibdon, J.; Groff, E. R. (2014)
Journal of Contemporary Criminal Justice
DOI: 10.1177/1043986214525077
Raw: Hibdon, J., & Groff, E. R. (2014). What you find depends on where you look: Using emergency medical services call data to target illicit drug use hot spots. Journal of Contemporary Criminal Justice, 30(2), 169-185. https://doi.org/10.1177/1043986214525077
Match: What You Find Depends on Where You Look: Using Emergency Medical Services Call Data to Target Illicit Drug Use Hot Spots
Authors: Julie Hibdon; Elizabeth R. Groff
Venue: Journal of Contemporary Criminal Justice
DOI: 10.1177/1043986214525077
URL: https://doi.org/10.1177/1043986214525077
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Cochrane handbook for systematic reviews of interventions
Higgins, J. P.; Thomas, J.; Chandler, J.; Cumpston, M.; Li, T.; Page, M. J.; Welch, V. A. (2019)
John Wiley & Sons
ISBN: 978-1-119-53660-4
Raw: Higgins, J. P., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (2019). Cochrane handbook for systematic reviews of interventions. John Wiley & Sons.
Match: Cochrane Handbook for Systematic Reviews of Interventions
Authors: Higgins, Julian P. T.; Thomas, James; Chandler, Jacqueline; Cumpston, Miranda; Li, Tianjing; Page, Matthew J.; Welch, Vivian A.
Venue: John Wiley & Sons
DOI: 10.1002/9781119536604
ISBN: 978-1-119-53660-4
ISBN: 9781119536604. This is the 2nd edition of the handbook.
Verified An integrated graph model for spatial- temporal urban crime prediction based on attention mechanism
Hou, M.; Hu, X.; Cai, J.; Han, X.; Yuan, S. (2022)
ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi11050294
Raw: Hou, M., Hu, X., Cai, J., Han, X., & Yuan, S. (2022). An integrated graph model for spatial- temporal urban crime prediction based on attention mechanism. ISPRS International Journal of Geo-Information, 11(5), 294. https://doi.org/10.3390/ijgi11050294
Match: An Integrated Graph Model for Spatial–Temporal Urban Crime Prediction Based on Attention Mechanism
Authors: Miaomiao Hou; Xiaofeng Hu; Jitao Cai; Xinge Han; Shuaiqi Yuan
Venue: ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi11050294
URL: https://doi.org/10.3390/ijgi11050294
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Urban crime prediction based on spatiotemporal Bayesian model
Hu, T.; Zhu, X.; Duan, L.; Guo, W. (2018)
PloS One
DOI: 10.1371/journal.pone.0206215
Raw: Hu, T., Zhu, X., Duan, L., & Guo, W. (2018). Urban crime prediction based on spatiotemporal Bayesian model. PloS One, 13(10), e0206215. https://doi.org/10.1371/journal.pone.0206215
Match: Urban crime prediction based on spatio-temporal Bayesian model
Authors: Tao Hu; Xinyan Zhu; Lian Duan; Wei Guo
Venue: PLOS ONE
DOI: 10.1371/journal.pone.0206215
URL: https://doi.org/10.1371/journal.pone.0206215
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Deep dynamic fusion network for traffic accident forecasting
Huang, C.; Zhang, C.; Dai, P.; Bo, L. (2019)
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
DOI: 10.1145/3357384.3357829
Raw: Huang, C., Zhang, C., Dai, P., & Bo, L. (2019, November). Deep dynamic fusion network for traffic accident forecasting. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2673-2681). https://doi.org/10.1145/3357384.3357829
Match: Deep Dynamic Fusion Network for Traffic Accident Forecasting
Authors: Chao Huang; Chuxu Zhang; Peng Dai; Liefeng Bo
Venue: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
DOI: 10.1145/3357384.3357829
URL: https://doi.org/10.1145/3357384.3357829
Verified via static CrossRef DOI lookup (score: 1.00)
Verified DeepCrime: Attentive hierarchical recurrent networks for crime prediction
Huang, C.; Zhang, J.; Zheng, Y.; Chawla, N. V. (2018)
Proceedings of the 27th ACM International Conference on Information and Knowledge Management
Raw: Huang, C., Zhang, J., Zheng, Y., & Chawla, N. V. (2018, October). DeepCrime: Attentive hierarchical recurrent networks for crime prediction. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 1423-1432).
Match: DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction
Authors: Chao Huang; Junbo Zhang; Yu Zheng; Nitesh V. Chawla
Venue: Proceedings of the 27th ACM International Conference on Information and Knowledge Management
DOI: 10.1145/3269206.3271793
URL: https://doi.org/10.1145/3269206.3271793
Verified via static CrossRef title search (score: 1.00)
Verified Mining location-based social networks for criminal activity prediction
Huang, Y. Y.; Li, C. T.; Jeng, S. K. (2015)
IEEE
DOI: 10.1109/WOCC.2015.7346202
Raw: Huang, Y. Y., Li, C. T., & Jeng, S. K. (2015, October). Mining location-based social networks for criminal activity prediction. In 2015 24th Wireless and Optical Communication Conference (WOCC) (pp. 185-189). IEEE. https://doi.org/10.1109/WOCC.2015.7346202
Match: Mining location-based social networks for criminal activity prediction
Authors: Yu-Yueh Huang; Cheng-Te Li; Shyh-Kang Jeng
Venue: 2015 24th Wireless and Optical Communication Conference (WOCC)
DOI: 10.1109/wocc.2015.7346202
URL: https://doi.org/10.1109/wocc.2015.7346202
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Evaluation of the Shreveport predictive policing experiment
Hunt, P.; Saunders, J.; Hollywood, J. S. (2014)
Rand Corporation
ISBN: 978-0-833-08691-4
Raw: Hunt, P., Saunders, J., & Hollywood, J. S. (2014). Evaluation of the Shreveport predictive policing experiment. Santa Monica: Rand Corporation.
Match: Evaluation of the Shreveport Predictive Policing Experiment
Authors: Priscillia Hunt; Jessica Saunders; John S. Hollywood
Venue: RAND Corporation
DOI: 10.7249/RR531
ISBN: 978-0-833-08691-4
The citation matches the RAND Research Report RR-531. ISBN: 9780833086914 is correct for this publication.
Verified Predicting residential burglaries based on building elements and offender behavior: Study of a row house area in Seoul, Korea
Hwang, Y.; Jung, S.; Lee, J.; Jeong, Y. (2017)
Computers, Environment and Urban Systems
DOI: 10.1016/j.compenvurbsys.2016.09.004
Raw: Hwang, Y., Jung, S., Lee, J., & Jeong, Y. (2017). Predicting residential burglaries based on building elements and offender behavior: Study of a row house area in Seoul, Korea. Computers, Environment and Urban Systems, 61, 94-107. https://doi.org/10.1016/j.compenvurbsys.2016.09.004
Match: Predicting residential burglaries based on building elements and offender behavior: Study of a row house area in Seoul, Korea
Authors: Yoonseok Hwang; Sungwon Jung; Jaewook Lee; Yongwook Jeong
Venue: Computers, Environment and Urban Systems
DOI: 10.1016/j.compenvurbsys.2016.09.004
URL: https://doi.org/10.1016/j.compenvurbsys.2016.09.004
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA
Jendryke, M.; McClure, S. C. (2021)
International Journal of Digital Earth
DOI: 10.1080/17538947.2021.1886356
Raw: Jendryke, M., & McClure, S. C. (2021). Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA. International Journal of Digital Earth, 14(6), 789-805. https://doi.org/10.1080/17538947.2021.1886356
Match: Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA
Authors: Michael Jendryke; Stephen C. McClure
Venue: International Journal of Digital Earth
DOI: 10.1080/17538947.2021.1886356
URL: https://doi.org/10.1080/17538947.2021.1886356
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Cell towers and the ambient population: A spatial analysis of disaggregated property crime
Johnson, P.; Andresen, M. A.; Malleson, N. (2021)
European Journal on Criminal Policy and Research
DOI: 10.1007/s10610-020-09446-3
Raw: Johnson, P., Andresen, M. A., & Malleson, N. (2021). Cell towers and the ambient population: A spatial analysis of disaggregated property crime. European Journal on Criminal Policy and Research, 27(3), 313-333. https://doi.org/10.1007/s10610-020-09446-3
Match: Cell Towers and the Ambient Population: a Spatial Analysis of Disaggregated Property Crime
Authors: Patrick Johnson; Martin A. Andresen; Nick Malleson
Venue: European Journal on Criminal Policy and Research
DOI: 10.1007/s10610-020-09446-3
URL: https://doi.org/10.1007/s10610-020-09446-3
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Exploring Foursquare-derived features for crime prediction in New York City
Kadar, C.; Iria, J.; Pletikosa Cvijikj, I. (2016)
ACM
Raw: Kadar, C., Iria, J., & Pletikosa Cvijikj, I. (2016). Exploring Foursquare-derived features for crime prediction in New York City. In The 5th International Workshop on Urban Computing (UrbComp 2016). ACM.
Match: Exploring Foursquare-derived features for crime prediction in New York City
Authors: Cristina Kadar; Josue Iria; Irena Pletikosa Cvijikj
Venue: The 5th International Workshop on Urban Computing (UrbComp 2016) in conjunction with ACM SIGKDD
The paper exists and is documented in the workshop archives and author affiliation repositories (ETH Zurich). The author list and order match the citation.
Verified Mining large-scale human mobility data for long-term crime prediction
Kadar, C.; Pletikosa, I. (2018)
EPJ Data Science
DOI: 10.1140/epjds/s13688-018-0150-z
Raw: Kadar, C., & Pletikosa, I. (2018). Mining large-scale human mobility data for long-term crime prediction. EPJ Data Science, 7(1), 1-27. https://doi.org/10.1140/epjds/s13688-018-0150-z
Match: Mining large-scale human mobility data for long-term crime prediction
Authors: Cristina Kadar; Irena Pletikosa
Venue: EPJ Data Science
DOI: 10.1140/epjds/s13688-018-0150-z
URL: https://doi.org/10.1140/epjds/s13688-018-0150-z
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Towards a burglary risk profiler using demographic and spatial factors
Kadar, C.; Zanni, G.; Vogels, T.; Cvijikj, I. P. (2015)
Springer International Publishing
Raw: Kadar, C., Zanni, G., Vogels, T., & Cvijikj, I. P. (2015). Towards a burglary risk profiler using demographic and spatial factors. In Web Information Systems Engineering-WISE 2015: 16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part I (pp. 586-600 Springer International Publishing.
Match: Towards a Burglary Risk Profiler Using Demographic and Spatial Factors
Authors: Cristina Kadar; Grammatiki Zanni; Thijs Vogels; Irena Pletikosa Cvijikj
Venue: Lecture Notes in Computer Science
DOI: 10.1007/978-3-319-26190-4_39
URL: https://doi.org/10.1007/978-3-319-26190-4_39
Verified via static CrossRef title search (score: 1.00)
Verified Prediction of crime occurrence from multimodal data using deep learning
Kang, H. W.; Kang, H. B. (2017)
PloS One
DOI: 10.1371/journal.pone.0176244
Raw: Kang, H. W., & Kang, H. B. (2017). Prediction of crime occurrence from multimodal data using deep learning. PloS One, 12(4), e0176244. https://doi.org/10.1371/journal.pone.0176244
Match: Prediction of crime occurrence from multi-modal data using deep learning
Authors: Hyeon-Woo Kang; Hang-Bong Kang
Venue: PLOS ONE
DOI: 10.1371/journal.pone.0176244
URL: https://doi.org/10.1371/journal.pone.0176244
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Predictive policing and the politics of patterns
Kaufmann, M.; Egbert, S.; Leese, M. (2019)
The British Journal of Criminology
DOI: 10.1093/bjc/azy060
Raw: Kaufmann, M., Egbert, S., & Leese, M. (2019). Predictive policing and the politics of patterns. The British Journal of Criminology, 59(3), 674-692. https://doi.org/10.1093/bjc/azy060
Match: Predictive Policing and the Politics of Patterns
Authors: Mareile Kaufmann; Simon Egbert; Matthias Leese
Venue: The British Journal of Criminology
DOI: 10.1093/bjc/azy060
URL: https://doi.org/10.1093/bjc/azy060
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Verified Risk clusters, hotspots, and spatial intelligence: Risk terrain modeling as an algorithm for police resource allocation strategies
Kennedy, L. W.; Caplan, J. M.; Piza, E. (2011)
Journal of Quantitative Criminology
DOI: 10.1007/s10940-010-9126-2
Raw: Kennedy, L. W., Caplan, J. M., & Piza, E. (2011). Risk clusters, hotspots, and spatial intelligence: Risk terrain modeling as an algorithm for police resource allocation strategies. Journal of Quantitative Criminology, 27(3), 339-362. https://doi.org/10.1007/s10940-010-9126-2
Match: Risk Clusters, Hotspots, and Spatial Intelligence: Risk Terrain Modeling as an Algorithm for Police Resource Allocation Strategies
Authors: Leslie W. Kennedy; Joel M. Caplan; Eric Piza
Venue: Journal of Quantitative Criminology
DOI: 10.1007/s10940-010-9126-2
URL: https://doi.org/10.1007/s10940-010-9126-2
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Verified Vulnerability and exposure to crime: Applying risk terrain modeling to the study of assault in Chicago
Kennedy, L. W.; Caplan, J. M.; Piza, E. L.; Buccine-Schraeder, H. (2016)
Applied Spatial Analysis and Policy
DOI: 10.1007/s12061-015-9165-z
Raw: Kennedy, L. W., Caplan, J. M., Piza, E. L., & Buccine-Schraeder, H. (2016). Vulnerability and exposure to crime: Applying risk terrain modeling to the study of assault in Chicago. Applied Spatial Analysis and Policy, 9(4), 529-548. https://doi.org/10.1007/s12061-015-9165-z
Match: Vulnerability and Exposure to Crime: Applying Risk Terrain Modeling to the Study of Assault in Chicago
Authors: Leslie W. Kennedy; Joel M. Caplan; Eric L. Piza; Henri Buccine-Schraeder
Venue: Applied Spatial Analysis and Policy
DOI: 10.1007/s12061-015-9165-z
URL: https://doi.org/10.1007/s12061-015-9165-z
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Verified Comparative study on artificial intelligence techniques in crime forecasting
Khairuddin, A. R.; Alwee, R.; Harun, H. (2019)
Applied Mechanics and Materials
DOI: 10.4028/www.scientific.net/AMM.892.94
Raw: Khairuddin, A. R., Alwee, R., & Harun, H. (2019). Comparative study on artificial intelligence techniques in crime forecasting. Applied Mechanics and Materials, 892, 94-100. https://doi.org/10. 4028/www.scientific.net/AMM.892.94
Match: Comparative Study on Artificial Intelligence Techniques in Crime Forecasting
Authors: Alif Ridzuan Khairuddin; Razana Alwee; Habibollah Harun
Venue: Applied Mechanics and Materials
DOI: 10.4028/www.scientific.net/amm.892.94
URL: https://doi.org/10.4028/www.scientific.net/amm.892.94
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Verified Crime analysis through machine learning
Kim, S.; Joshi, P.; Kalsi, P. S.; Taheri, P. (2018)
IEEE
DOI: 10.1109/IEMCON.2018.8614828
Raw: Kim, S., Joshi, P., Kalsi, P. S., & Taheri, P. (2018, November). Crime analysis through machine learning. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 415-420). IEEE. https://doi.org/10.1109/IEMCON.2018.8614828
Match: Crime Analysis Through Machine Learning
Authors: Suhong Kim; Param Joshi; Parminder Singh Kalsi; Pooya Taheri
Venue: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)
DOI: 10.1109/iemcon.2018.8614828
URL: https://doi.org/10.1109/iemcon.2018.8614828
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Verified Crime prediction using hotel reviews?
Kostakos, P.; Robroo, S.; Lin, B.; Oussalah, M. (2019)
IEEE
DOI: 10.1109/EISIC49498.2019.9108861
Raw: Kostakos, P., Robroo, S., Lin, B., & Oussalah, M. (2019). November). Crime prediction using hotel reviews?. In 2019 European Intelligence and Security Informatics Conference (EISIC) (pp. 134- 137). IEEE. https://doi.org/10.1109/EISIC49498.2019.9108861
Match: Crime Prediction Using Hotel Reviews?
Authors: Panos Kostakos; Somkiadcharoen Robroo; Bofan Lin; Mourad Oussalah
Venue: 2019 European Intelligence and Security Informatics Conference (EISIC)
DOI: 10.1109/eisic49498.2019.9108861
URL: https://doi.org/10.1109/eisic49498.2019.9108861
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Verified A systematic review on spatial crime forecasting
Kounadi, O.; Ristea, A.; Araujo, A.; Leitner, M. (2020)
Crime Science
DOI: 10.1186/s40163-020-00116-7
Raw: Kounadi, O., Ristea, A., Araujo, A., & Leitner, M. (2020). A systematic review on spatial crime forecasting. Crime Science, 9(1), 7. https://doi.org/10.1186/s40163-020-00116-7
Match: A systematic review on spatial crime forecasting
Authors: Ourania Kounadi; Alina Ristea; Adelson Araujo; Michael Leitner
Venue: Crime Science
DOI: 10.1186/s40163-020-00116-7
URL: https://doi.org/10.1186/s40163-020-00116-7
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Verified Population at risk: Using areal interpolation and Twitter messages to create population models for burglaries and robberies
Kounadi, O.; Ristea, A.; Leitner, M.; Langford, C. (2018)
Cartography and Geographic Information Science
DOI: 10.1080/15230406.2017.1304243
Raw: Kounadi, O., Ristea, A., Leitner, M., & Langford, C. (2018). Population at risk: Using areal interpolation and Twitter messages to create population models for burglaries and robberies. Cartography and Geographic Information Science, 45(3), 205-220. https://doi.org/10.1080/15230406.2017.1304243
Match: Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies
Authors: Ourania Kounadi; Alina Ristea; Michael Leitner; Chad Langford
Venue: Cartography and Geographic Information Science
DOI: 10.1080/15230406.2017.1304243
URL: https://doi.org/10.1080/15230406.2017.1304243
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Artificial neural network model development to predict theft types in consideration of environmental factors
Kwon, E.; Jung, S.; Lee, J. (2021)
ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi10020099
Raw: Kwon, E., Jung, S., & Lee, J. (2021). Artificial neural network model development to predict theft types in consideration of environmental factors. ISPRS International Journal of Geo-Information, 10(2), 99. https://doi.org/10.3390/ijgi10020099
Match: Artificial Neural Network Model Development to Predict Theft Types in Consideration of Environmental Factors
Authors: Eunseo Kwon; Sungwon Jung; Jaewook Lee
Venue: ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi10020099
URL: https://doi.org/10.3390/ijgi10020099
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Verified Predicting spatial crime occurrences through an efficient ensemble-learning model
Lamari, Y.; Freskura, B.; Abdessamad, A.; Eichberg, S.; de Bonviller, S. (2020)
ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi9110645
Raw: Lamari, Y., Freskura, B., Abdessamad, A., Eichberg, S., & de Bonviller, S. (2020). Predicting spatial crime occurrences through an efficient ensemble-learning model. ISPRS International Journal of Geo-Information, 9(11), 645. https://doi.org/10.3390/ijgi9110645
Match: Predicting Spatial Crime Occurrences through an Efficient Ensemble-Learning Model
Authors: Yasmine Lamari; Bartol Freskura; Anass Abdessamad; Sarah Eichberg; Simon de Bonviller
Venue: ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi9110645
URL: https://doi.org/10.3390/ijgi9110645
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Verified 3D data management: Controlling data volume, velocity, and variety
Laney, D. (2001)
Raw: Laney, D. (2001). 3D data management: Controlling data volume, velocity, and variety. META Group Research Note, 6(70), 1.
Match: 3D Data Management: Controlling Data Volume, Velocity, and Variety
Authors: Laney, D.
Venue: META Group Research Note
URL: https://www.scribd.com/document/899583028/Laney-3D-Data-Management-Controlling-Data-Volume-Velocity-And-Variety
This is the seminal 2001 META Group research note by Doug Laney that defined the 3Vs of Big Data. The coordinates "6(70), 1" are standard for this citation in academic literature. Date confirmed as February 6, 2001.
Verified Online traffic accident spatial-temporal post-impact prediction model on highways based on spiking neural networks
Li, D.; Wu, J.; Peng, D. (2021)
Journal of Advanced Transportation
DOI: 10.1155/2021/9290921
Raw: Li, D., Wu, J., & Peng, D. (2021). Online traffic accident spatial-temporal post-impact prediction model on highways based on spiking neural networks. Journal of Advanced Transportation, 2021, 1-20. https://doi.org/10.1155/2021/9290921
Match: Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks
Authors: Duowei Li; Jianping Wu; Depin Peng
Venue: Journal of Advanced Transportation
DOI: 10.1155/2021/9290921
URL: https://doi.org/10.1155/2021/9290921
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Verified None
Li, T.; Huang, Y.; Evans, J.; Chattopadhyay, I. (2019)
arXiv
Raw: Li, T., Huang, Y., Evans, J., & Chattopadhyay, I. (2019). Long-range event-level prediction and response simulation for urban crime and global terrorism with Granger networks. https:// arxiv.org/abs/1911.05647
Match: Long-range Event-level Prediction and Response Simulation for Urban Crime and Global Terrorism with Granger Networks
Authors: Timmy Li; Yi Huang; James Evans; Ishanu Chattopadhyay
Venue: arXiv
URL: https://arxiv.org/abs/1911.05647
The citation is verified as the title, authors, year, and arXiv identifier align perfectly with the live record.
Verified CrimeTensor: Fine-scale crime prediction via tensor learning with spatiotemporal consistency
Liang, W.; Wu, Z.; Li, Z.; Ge, Y. (2022)
ACM Transactions on Intelligent Systems and Technology
DOI: 10.1145/3501807
Raw: Liang, W., Wu, Z., Li, Z., & Ge, Y. (2022). CrimeTensor: Fine-scale crime prediction via tensor learning with spatiotemporal consistency. ACM Transactions on Intelligent Systems and Technology, 13(2), 1-24. https://doi.org/10.1145/3501807
Match: CrimeTensor: Fine-Scale Crime Prediction via Tensor Learning with Spatiotemporal Consistency
Authors: Weichao Liang; Zhiang Wu; Zhe Li; Yong Ge
Venue: ACM Transactions on Intelligent Systems and Technology
DOI: 10.1145/3501807
URL: https://doi.org/10.1145/3501807
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Verified Towards hour-level crime prediction: A neural attentive framework with spatial-temporal-categorical fusion
Liang, W. C.; Wang, Y. Q.; Tao, H. C.; Cao, J. (2022)
Neurocomputing
DOI: 10.1016/j.neucom.2021.11.052
Raw: Liang, W. C., Wang, Y. Q., Tao, H. C., & Cao, J. (2022). Towards hour-level crime prediction: A neural attentive framework with spatial-temporal-categorical fusion. Neurocomputing, 486, 286-297. https://doi.org/10.1016/j.neucom.2021.11.052
Match: Towards hour-level crime prediction: A neural attentive framework with spatial–temporal-categorical fusion
Authors: Weichao Liang; Youquan Wang; Haicheng Tao; Jie Cao
Venue: Neurocomputing
DOI: 10.1016/j.neucom.2021.11.052
URL: https://doi.org/10.1016/j.neucom.2021.11.052
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Verified Likelihood-based inference and prediction in spatio-temporal panel count models for urban crimes
Liesenfeld, R.; Richard, J. F.; Vogler, J. (2017)
Journal of Applied Econometrics
DOI: 10.1002/jae.2534
Raw: Liesenfeld, R., Richard, J. F., & Vogler, J. (2017). Likelihood-based inference and prediction in spatio-temporal panel count models for urban crimes. Journal of Applied Econometrics, 32(3), 600-620. https://doi.org/10.1002/jae.2534
Match: Likelihood-Based Inference and Prediction in Spatio-Temporal Panel Count Models for Urban Crimes
Authors: Roman Liesenfeld; Jean-François Richard; Jan Vogler
Venue: Journal of Applied Econometrics
DOI: 10.1002/jae.2534
URL: https://doi.org/10.1002/jae.2534
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Verified ST-TAP: A traffic accident prediction framework based on spatio-temporal transformer
Liu, W.; Liu, X.; Feng, H.; Wang, Y.; Guan, L.; Xu, W.; Kong, X. (2021)
IEEE
DOI: 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00068
Raw: Liu, W., Liu, X., Feng, H., Wang, Y., Guan, L., Xu, W., … Kong, X. (2021, October). ST-TAP: A traffic accident prediction framework based on spatio-temporal transformer. In 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) (pp. 360-365). IEEE. https://doi. org/10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00068
Match: ST-TAP: A Traffic Accident Prediction Framework Based on Spatio-Temporal Transformer
Authors: Weitao Liu; Xuanyi Liu; Hui Feng; Yiran Wang; Lintao Guan; Weifeng Xu; Guojiang Shen; Zhi Liu; Xiangjie Kong
Venue: 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
DOI: 10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00068
URL: https://doi.org/10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00068
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Verified Temporal and spatiotemporal models for short-term crime prediction
Liu, X. (2017)

ISBN: 1-928-68848-7
Raw: Liu, X. (2017). Temporal and spatiotemporal models for short-term crime prediction [Doctoral dissertation]. Illinois Institute of Technology.
Match: Temporal and Spatiotemporal Models for Short-Term Crime Prediction
Authors: Xiang Liu
Venue: Illinois Institute of Technology
ISBN: 1-928-68848-7
URL: http://hdl.handle.net/10560/4235
The citation matches the Illinois Institute of Technology doctoral dissertation repository and ProQuest (ID 1928688487) exactly in all fields.
Verified Final report on live facial recognition
London Policing Ethics Panel (LPEP) (2019)
Raw: London Policing Ethics Panel (LPEP). (2019). Final report on live facial recognition. Retrieved from http://www.policingethicspanel.london/uploads/4/4/0/7/44076193/live_facial_recognition_final_report_may_2019.pdf.
Match: Final Report on Live Facial Recognition
Authors: London Policing Ethics Panel (LPEP)
Venue: London Policing Ethics Panel
URL: http://www.policingethicspanel.london/uploads/4/4/0/7/44076193/live_facial_recognition_final_report_may_2019.pdf
The citation exists exactly as cited. The URL provided in the citation includes a 'www.' prefix which is a common variant and resolves to the same resource as the found URL.
Verified Applying machine learning to criminology: Semi-parametric spatial-demographic Bayesian regression
Marchant, R.; Haan, S.; Clancey, G.; Cripps, S. (2018)
Security Inform
Raw: Marchant, R., Haan, S., Clancey, G., & Cripps, S. (2018). Applying machine learning to criminology: Semi-parametric spatial-demographic Bayesian regression. Security Inform, 7(1), 1-19.
Match: Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression
Authors: Roman Marchant; Sebastian Haan; Garner Clancey; Sally Cripps
Venue: Security Informatics
DOI: 10.1186/s13388-018-0030-x
URL: https://doi.org/10.1186/s13388-018-0030-x
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Verified Using machine learning algorithms to analyze crime data
McClendon, L.; Meghanathan, N. (2015)
Machine Learning and Applications: An International Journal
DOI: 10.5121/mlaij.2015.2101
Raw: McClendon, L., & Meghanathan, N. (2015). Using machine learning algorithms to analyze crime data. Machine Learning and Applications: An International Journal, 2(1), 1-12. https://doi.org/10.5121/mlaij.2015.2101
Match: Using Machine Learning Algorithms to Analyze Crime Data
Authors: Lawrence McClendon; Natarajan Meghanathan
Venue: Machine Learning and Applications: An International Journal
DOI: 10.5121/mlaij.2015.2101
URL: https://doi.org/10.5121/mlaij.2015.2101
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Verified The laughing policebot: Automation and the end of policing
McGuire, M. (2021)
Policing and Society
DOI: 10.1080/10439463.2020.1810249
Raw: McGuire, M. (2021). The laughing policebot: Automation and the end of policing. Policing and Society, 31(1), 20-36. https://doi.org/10.1080/10439463.2020.1810249
Match: The laughing policebot: automation and the end of policing
Authors: M.R. McGuire
Venue: Policing and Society
DOI: 10.1080/10439463.2020.1810249
URL: https://doi.org/10.1080/10439463.2020.1810249
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Verified Artificial intelligence prediction and counterterrorism
McKendrick, K. (2019)
The Royal Institute of International Affairs-Chatham House
Raw: McKendrick, K. (2019). Artificial intelligence prediction and counterterrorism. The Royal Institute of International Affairs-Chatham House, 9.
Match: Artificial Intelligence Prediction and Counterterrorism
Authors: Kathleen McKendrick
Venue: Chatham House
URL: https://www.chathamhouse.org/sites/default/files/2019-08-07-AICounterterrorism.pdf
The citation is correct. This is a Chatham House Research Paper published in August 2019 by Kathleen McKendrick. The page number '9' refers to a specific page within the report.
Verified Predictive policing: Review of benefits and drawbacks
Meijer, A.; Wessels, M. (2019)
International Journal of Public Administration
DOI: 10.1080/01900692.2019.1575664
Raw: Meijer, A., & Wessels, M. (2019). Predictive policing: Review of benefits and drawbacks. International Journal of Public Administration, 42(12), 1031-1039. https://doi.org/10.1080/01900692.2019.1575664
Match: Predictive Policing: Review of Benefits and Drawbacks
Authors: Albert Meijer; Martijn Wessels
Venue: International Journal of Public Administration
DOI: 10.1080/01900692.2019.1575664
URL: https://doi.org/10.1080/01900692.2019.1575664
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Expressway crash prediction based on traffic big data
Meng, H.; Wang, X.; Wang, X. (2018)
Proceedings of the 2018 International Conference on Signal Processing and Machine Learning
DOI: 10.1145/3297067.3297093
Raw: Meng, H., Wang, X., & Wang, X. (2018, November). Expressway crash prediction based on traffic big data. In Proceedings of the 2018 International Conference on Signal Processing and Machine Learning (pp. 11-16). https://doi.org/10.1145/3297067.3297093
Match: Expressway Crash Prediction based on Traffic Big Data
Authors: Hailang Meng; Xinhong Wang; Xuesong Wang
Venue: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning
DOI: 10.1145/3297067.3297093
URL: https://doi.org/10.1145/3297067.3297093
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Randomized controlled field trials of predictive policing
Mohler, G. O.; Short, M. B.; Malinowski, S.; Johnson, M.; Tita, G. E.; Bertozzi, A. L.; Brantingham, P. J. (2015)
Journal of the American Statistical Association
DOI: 10.1080/01621459.2015.1077710
Raw: Mohler, G. O., Short, M. B., Malinowski, S., Johnson, M., Tita, G. E., Bertozzi, A. L., & Brantingham, P. J. (2015). Randomized controlled field trials of predictive policing. Journal of the American Statistical Association, 110(512), 1399-1411. https://doi.org/10.1080/01621459.2015.1077710
Match: Randomized Controlled Field Trials of Predictive Policing
Authors: G. O. Mohler; M. B. Short; Sean Malinowski; Mark Johnson; G. E. Tita; Andrea L. Bertozzi; P. J. Brantingham
Venue: Journal of the American Statistical Association
DOI: 10.1080/01621459.2015.1077710
URL: https://doi.org/10.1080/01621459.2015.1077710
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Accident risk prediction based on heterogeneous sparse data: New dataset and insights
Moosavi, S.; Samavatian, M. H.; Parthasarathy, S.; Teodorescu, R.; Ramnath, R. (2019)
Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Raw: Moosavi, S., Samavatian, M. H., Parthasarathy, S., Teodorescu, R., & Ramnath, R. (2019, November). Accident risk prediction based on heterogeneous sparse data: New dataset and insights. In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 33-42).
Match: Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights
Authors: Sobhan Moosavi; Mohammad Hossein Samavatian; Srinivasan Parthasarathy; Radu Teodorescu; Rajiv Ramnath
Venue: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
DOI: 10.1145/3347146.3359078
URL: https://doi.org/10.1145/3347146.3359078
Verified via static CrossRef title search (score: 1.00)
Verified What kind of systematic review should I conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences
Munn, Z.; Stern, C.; Aromataris, E.; Lockwood, C.; Jordan, Z. (2018)
BMC Medical Research Methodology
DOI: 10.1186/s12874-017-0468-4
Raw: Munn, Z., Stern, C., Aromataris, E., Lockwood, C., & Jordan, Z. (2018). What kind of systematic review should I conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences. BMC Medical Research Methodology, 18(1), 5. https://doi.org/10. 1186/s12874-017-0468-4
Match: What kind of systematic review should I conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences
Authors: Zachary Munn; Cindy Stern; Edoardo Aromataris; Craig Lockwood; Zoe Jordan
Venue: BMC Medical Research Methodology
DOI: 10.1186/s12874-017-0468-4
URL: https://doi.org/10.1186/s12874-017-0468-4
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Building a learning machine classifier with inadequate data for crime prediction
Nguyen, T. T.; Hatua, A.; Sung, A. H. (2017)
Journal of Advances in Information Technology
DOI: 10.12720/jait.8.2.141-147
Raw: Nguyen, T. T., Hatua, A., & Sung, A. H. (2017). Building a learning machine classifier with inadequate data for crime prediction. Journal of Advances in Information Technology, 8(2), 141- 147. https://doi.org/10.12720/jait.8.2.141-147
Match: Building a Learning Machine Classifier with Inadequate Data for Crime Prediction
Authors: Trung T. Nguyen; Amartya Hatua; Andrew H. Sung
Venue: Journal of Advances in Information Technology
DOI: 10.12720/jait.8.2.141-147
URL: https://doi.org/10.12720/jait.8.2.141-147
Verified via static CrossRef DOI lookup (score: 0.99)
Verified Overview of predictive policing
NIJ. (2014)
Raw: NIJ. (2014). Overview of predictive policing. Retrieved from https://nij.ojp.gov/topics/articles/overview-predictive-policing.
Match: Overview of Predictive Policing
Authors: National Institute of Justice
Venue: National Institute of Justice
URL: https://nij.ojp.gov/topics/articles/overview-predictive-policing
The citation is accurate in title, author, year (published June 9, 2014), and URL.
Verified Applying crime prediction techniques to Japan: A comparison between risk terrain modeling and other methods
Ohyama, T.; Amemiya, M. (2018)
European Journal on Criminal Policy and Research
DOI: 10.1007/s10610-018-9378-1
Raw: Ohyama, T., & Amemiya, M. (2018). Applying crime prediction techniques to Japan: A comparison between risk terrain modeling and other methods. European Journal on Criminal Policy and Research, 24(4), 469-487. https://doi.org/10.1007/s10610-018-9378-1
Match: Applying Crime Prediction Techniques to Japan: A Comparison Between Risk Terrain Modeling and Other Methods
Authors: Tomoya Ohyama; Mamoru Amemiya
Venue: European Journal on Criminal Policy and Research
DOI: 10.1007/s10610-018-9378-1
URL: https://doi.org/10.1007/s10610-018-9378-1
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Designing an explainable predictive policing model to forecast police workforce distribution in cities
Parent, M.; Roy, A.; Gagnon, C.; Lemaire, N.; Deslauriers-Varin, N.; Falk, T. H.; Tremblay, S. (2020)
Canadian Journal of Criminology and Criminal Justice
DOI: 10.3138/cjccj.2020-0011
Raw: Parent, M., Roy, A., Gagnon, C., Lemaire, N., Deslauriers-Varin, N., Falk, T. H., & Tremblay, S. (2020). Designing an explainable predictive policing model to forecast police workforce distribution in cities. Canadian Journal of Criminology and Criminal Justice, 62(4), 52-76. https://doi. org/10.3138/cjccj.2020-0011
Match: Designing an Explainable Predictive Policing Model to Forecast Police Workforce Distribution in Cities
Authors: Mark Parent; Aurélien Roy; Claudele Gagnon; Noémie Lemaire; Nadine Deslauriers-Varin; Tiago H. Falk; Sébastien Tremblay
Venue: Canadian Journal of Criminology and Criminal Justice
DOI: 10.3138/cjccj.2020-0011
URL: https://doi.org/10.3138/cjccj.2020-0011
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Highway traffic accident prediction using VDS big data analysis
Park, S. H.; Kim, S. M.; Ha, Y. G. (2016)
The Journal of Supercomputing
DOI: 10.1007/s11227-016-1624-z
Raw: Park, S. H., Kim, S. M., & Ha, Y. G. (2016). Highway traffic accident prediction using VDS big data analysis. The Journal of Supercomputing, 72(7), 2815-2831. https://doi.org/10.1007/s11227-016-1624-z
Match: Highway traffic accident prediction using VDS big data analysis
Authors: Seong-hun Park; Sung-min Kim; Young-guk Ha
Venue: The Journal of Supercomputing
DOI: 10.1007/s11227-016-1624-z
URL: https://doi.org/10.1007/s11227-016-1624-z
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Predictive policing: The future of law enforcement?
Pearsall, B. (2010)
National Institute of Justice Journal
Raw: Pearsall, B. (2010). Predictive policing: The future of law enforcement? National Institute of Justice Journal, 266(1), 16-19.
Match: Predictive policing: The future of law enforcement?: (596372010-007)
Authors: Beth Pearsall
Venue: PsycEXTRA Dataset
DOI: 10.1037/e596372010-007
URL: https://doi.org/10.1037/e596372010-007
Verified via static CrossRef title search (score: 0.93)
Verified The agent-based spatial simulation to burglary in Beijing
Peng, C.; Kurland, J. (2014)
Lecture Notes in Computer Science (8582 LNCS)
Raw: Peng, C., & Kurland, J. (2014). The agent-based spatial simulation to burglary in Beijing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8582 LNCS (pp. 31-43).
Match: The Agent-Based Spatial Simulation to the Burglary in Beijing
Authors: Chen Peng; Justin Kurland
Venue: Lecture Notes in Computer Science
DOI: 10.1007/978-3-319-09147-1_3
URL: https://doi.org/10.1007/978-3-319-09147-1_3
Verified via static CrossRef title search (score: 0.98)
Verified What about AI in criminal intelligence? From predictive policing to AI perspectives
Perrot, P. (2017)
European Police Science and Research Bulletin
Raw: Perrot, P. (2017). What about AI in criminal intelligence? From predictive policing to AI perspectives. European Police Science and Research Bulletin, 16, 65-76.
Match: What about AI in criminal intelligence? From predictive policing to AI perspectives
Authors: Patrick Perrot
Venue: European Police Science and Research Bulletin (Special Issue Nr. 16)
URL: https://www.cepol.europa.eu/api/assets/QRAA17001ENN_1.pdf
The journal was formerly known as the European Police Science and Research Bulletin at the time of publication and has since transitioned to the European Law Enforcement Research Bulletin. The citation correctly uses the title from 2017.
Verified Predictive policing: The role of crime forecasting in law enforcement operations
Perry, W. L. (2013)
Rand Corporation
Raw: Perry, W. L. (2013). Predictive policing: The role of crime forecasting in law enforcement operations. Rand Corporation.
Match: Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations
Authors: Walter Perry; Brian McInnis; Carter Price; Susan Smith; John Hollywood
Venue: None
DOI: 10.7249/rr233
URL: https://doi.org/10.7249/rr233
Verified via static CrossRef title search (score: 0.99)
Verified Exploring crime patterns in Mexico City
Pina-Garc ~ �ıa, C. A.; Ram�ırez-Ram�ırez, L. (2019)
Journal of Big Data
DOI: 10.1186/s40537-019-0228-x
Raw: Pina-Garc ~ �ıa, C. A., & Ram�ırez-Ram�ırez, L. (2019). Exploring crime patterns in Mexico City. Journal of Big Data, 6(1), 1-21. https://doi.org/10.1186/s40537-019-0228-x
Match: Exploring crime patterns in Mexico City
Authors: C. A. Piña-García; Leticia Ramírez-Ramírez
Venue: Journal of Big Data
DOI: 10.1186/s40537-019-0228-x
URL: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0228-x
The minor character artifacts in the raw text for author names (e.g., "Pina-Garc ~ ıa") are recognized as encoding errors for accented Spanish characters and are treated as verified per the formatting rules.
Verified Predicting initiator and near repeat events in spatiotemporal crime patterns: An analysis of residential burglary and motor vehicle theft
Piza, E. L.; Carter, J. G. (2018)
Justice Quarterly
DOI: 10.1080/07418825.2017.1342854
Raw: Piza, E. L., & Carter, J. G. (2018). Predicting initiator and near repeat events in spatiotemporal crime patterns: An analysis of residential burglary and motor vehicle theft. Justice Quarterly, 35(5), 842-870. https://doi.org/10.1080/07418825.2017.1342854
Match: Predicting Initiator and Near Repeat Events in Spatiotemporal Crime Patterns: An Analysis of Residential Burglary and Motor Vehicle Theft
Authors: Eric L. Piza; Jeremy G. Carter
Venue: Justice Quarterly
DOI: 10.1080/07418825.2017.1342854
URL: https://doi.org/10.1080/07418825.2017.1342854
Verified via static CrossRef DOI lookup (score: 1.00)
Verified GeST: A grid embedding based spatio-temporal correlation model for crime prediction
Qian, Y.; Pan, L.; Wu, P.; Xia, Z. (2020)
IEEE
DOI: 10.1109/DSC50466.2020.00009
Raw: Qian, Y., Pan, L., Wu, P., & Xia, Z. (2020, July). GeST: A grid embedding based spatio-temporal correlation model for crime prediction. In 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) (pp. 1-7). IEEE. https://doi.org/10.1109/DSC50466.2020.00009
Match: GeST: A Grid Embedding based Spatio-Temporal Correlation Model for Crime Prediction
Authors: Yiting Qian; Li Pan; Peng Wu; Zhengmin Xia
Venue: 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)
DOI: 10.1109/dsc50466.2020.00009
URL: https://doi.org/10.1109/dsc50466.2020.00009
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Actuarial prediction of sexual recidivism
Quinsey, V. L.; Rice, M. E.; Harris, G. T. (1995)
Journal of Interpersonal Violence
DOI: 10.1177/088626095010001006
Raw: Quinsey, V. L., Rice, M. E., & Harris, G. T. (1995). Actuarial prediction of sexual recidivism. Journal of Interpersonal Violence, 10(1), 85-105. https://doi.org/10.1177/088626095010001006
Match: Actuarial Prediction of Sexual Recidivism
Authors: VERNON L. QUINSEY; MARNIE E. RICE; GRANT T. HARRIS
Venue: Journal of Interpersonal Violence
DOI: 10.1177/088626095010001006
URL: https://doi.org/10.1177/088626095010001006
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Computing the probability on socio economic factors to predict the crime locations by means of joint probability based AMABC-FCIL
Radhakrishnan, S.; Devarasan, E. (2016)
International Journal of Intelligent Engineering and Systems
DOI: 10.22266/ijies2016.0930.08
Raw: Radhakrishnan, S., & Devarasan, E. (2016). Computing the probability on socio economic factors to predict the crime locations by means of joint probability based AMABC-FCIL. International Journal of Intelligent Engineering and Systems, 9(3), 80-90. https://doi.org/10.22266/ijies2016.0930.08
Match: Computing the Probability on Socio Economic Factors to Predict the Crime Locations by Means of Joint Probability Based AMABC-FCIL
Authors: Sujatha Radhakrishnan; Ezhilmaran Devarasan
Venue: International Journal of Intelligent Engineering and Systems
DOI: 10.22266/ijies2016.0930.08
URL: https://doi.org/10.22266/ijies2016.0930.08
Verified via static CrossRef DOI lookup (score: 0.99)
Verified Comparison of different spatial interpolation techniques to thematic mapping of socio-economic causes of crime against women
Rastogi, A.; Sridhar, S.; Gupta, R. (2020)
IEEE
DOI: 10.1109/SIEDS49339.2020.9106690
Raw: Rastogi, A., Sridhar, S., & Gupta, R. (2020, April). Comparison of different spatial interpolation techniques to thematic mapping of socio-economic causes of crime against women. In 2020 Systems and Information Engineering Design Symposium (SIEDS) (pp. 1-6). IEEE. https://doi.org/10. 1109/SIEDS49339.2020.9106690
Match: Comparison of Different Spatial Interpolation Techniques to Thematic Mapping of Socio-Economic Causes of Crime Against Women
Authors: Aamil Rastogi; Smriti Sridhar; Rajiv Gupta
Venue: 2020 Systems and Information Engineering Design Symposium (SIEDS)
DOI: 10.1109/sieds49339.2020.9106690
URL: https://doi.org/10.1109/sieds49339.2020.9106690
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Conflicts and congruencies between predictive policing and the patrol officer’s craft
Ratcliffe, J. H.; Taylor, R. B.; Fisher, R. (2020)
Policing and Society
DOI: 10.1080/10439463.2019.1577844
Raw: Ratcliffe, J. H., Taylor, R. B., & Fisher, R. (2020). Conflicts and congruencies between predictive policing and the patrol officer’s craft. Policing and Society, 30(6), 639-655. https://doi.org/10. 1080/10439463.2019.1577844
Match: Conflicts and congruencies between predictive policing and the patrol officer’s craft
Authors: Jerry H. Ratcliffe; Ralph B. Taylor; Ryan Fisher
Venue: Policing and Society
DOI: 10.1080/10439463.2019.1577844
URL: https://doi.org/10.1080/10439463.2019.1577844
Verified via static CrossRef DOI lookup (score: 1.00)
Verified The Philadelphia predictive policing experiment
Ratcliffe, J. H.; Taylor, R. B.; Askey, A. P.; Thomas, K.; Grasso, J.; Bethel, K. J.; Koehnlein, J. (2021)
Journal of Experimental Criminology
DOI: 10.1007/s11292-019-09400-2
Raw: Ratcliffe, J. H., Taylor, R. B., Askey, A. P., Thomas, K., Grasso, J., Bethel, K. J., … Koehnlein, J. (2021). The Philadelphia predictive policing experiment. Journal of Experimental Criminology, 17(1), 15-41. https://doi.org/10.1007/s11292-019-09400-2
Match: The Philadelphia predictive policing experiment
Authors: Jerry H. Ratcliffe; Ralph B. Taylor; Amber Perenzin Askey; Kevin Thomas; John Grasso; Kevin J. Bethel; Ryan Fisher; Josh Koehnlein
Venue: Journal of Experimental Criminology
DOI: 10.1007/s11292-019-09400-2
URL: https://doi.org/10.1007/s11292-019-09400-2
Verified via static CrossRef DOI lookup (score: 0.99)
Verified Point process modeling with spatiotemporal covariates for predicting crime
Reinhart, A. (2016)
Raw: Reinhart, A. (2016). Point process modeling with spatiotemporal covariates for predicting crime [Doctoral dissertation]. Carnegie Mellon University.
Match: Point Process Modeling with Spatiotemporal Covariates for Predicting Crime
Authors: Alex Reinhart
Venue: Carnegie Mellon University
URL: https://kilthub.cmu.edu/articles/thesis/Point_Process_Modeling_with_Spatiotemporal_Covariates_for_Predicting_Crime/7178903
The citation is verified. While some repository dates show 2018 (likely upload/embargo dates), the official degree conferral and dissertation year at Carnegie Mellon University is confirmed as 2016.
Verified Crime prevention and the displacement phenomenon
Reppetto, T. A. (1976)
Crime & Delinquency
DOI: 10.1177/001112877602200204
Raw: Reppetto, T. A. (1976). Crime prevention and the displacement phenomenon. Crime & Delinquency, 22(2), 166-177. https://doi.org/10.1177/001112877602200204
Match: Crime Prevention and the Displacement Phenomenon
Authors: Thomas A. Reppetto
Venue: Crime & Delinquency
DOI: 10.1177/001112877602200204
URL: https://doi.org/10.1177/001112877602200204
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Spatial crime distribution and prediction for sporting events using social media
Ristea, A.; Al Boni, M.; Resch, B.; Gerber, M. S.; Leitner, M. (2020)
International Journal of Geographical Information Science: IJGIS
DOI: 10.1080/13658816.2020.1719495
Raw: Ristea, A., Al Boni, M., Resch, B., Gerber, M. S., & Leitner, M. (2020). Spatial crime distribution and prediction for sporting events using social media. International Journal of Geographical Information Science: IJGIS, 34(9), 1708-1739. https://doi.org/10.1080/13658816.2020.1719495
Match: Spatial crime distribution and prediction for sporting events using social media
Authors: Alina Ristea; Mohammad Al Boni; Bernd Resch; Matthew S. Gerber; Michael Leitner
Venue: International Journal of Geographical Information Science
DOI: 10.1080/13658816.2020.1719495
URL: https://doi.org/10.1080/13658816.2020.1719495
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Exploring theory-informed, data-driven simulations for predicting crime
Ros� es, R. (2020)
Raw: Ros� es, R. (2020). Exploring theory-informed, data-driven simulations for predicting crime [Doctoral dissertation]. ETH Zurich.
Match: Exploring theory-informed, data-driven simulations for predicting crime
Authors: Raquel Rosés
Venue: ETH Zurich
DOI: 10.3929/ethz-b-000451769
URL: https://www.research-collection.ethz.ch/server/api/core/bitstreams/48eab39c-909e-4687-b145-3fd110d71811/content
The citation is correct. The author's full name is Raquel Rosés. The official DOI is 10.3929/ethz-b-000451769. The string 'Ros es' is an encoding artifact for Rosés.
Verified A data-driven agent-based simulation to predict crime patterns in an urban environment
Ros� es, R.; Kadar, C.; Malleson, N. (2021)
Computers, Environment and Urban Systems
DOI: 10.1016/j.compenvurbsys.2021.101660
Raw: Ros� es, R., Kadar, C., & Malleson, N. (2021). A data-driven agent-based simulation to predict crime patterns in an urban environment. Computers, Environment and Urban Systems, 89, 101660. https://doi.org/10.1016/j.compenvurbsys.2021.101660
Match: A data-driven agent-based simulation to predict crime patterns in an urban environment
Authors: Raquel Rosés; Cristina Kadar; Nick Malleson
Venue: Computers, Environment and Urban Systems
DOI: 10.1016/j.compenvurbsys.2021.101660
URL: https://doi.org/10.1016/j.compenvurbsys.2021.101660
The encoding artifact in the author's name ('Ros es') is a minor character issue and does not affect the verification status.
Verified Quantitative analysis of crime incidents in Chicago using data analytics techniques
Ruiz, D. R.; Sawant, A. (2019)
Computers, Materials & Continua
DOI: 10.32604/cmc.2019.06433
Raw: Ruiz, D. R., & Sawant, A. (2019). Quantitative analysis of crime incidents in Chicago using data analytics techniques. Computers, Materials & Continua, 59(2), 389-396. https://doi.org/10. 32604/cmc.2019.06433
Match: Quantitative Analysis Of Crime Incidents In Chicago Using Data Analytics Techniques
Authors: Daniel Rivera Ruiz; Alisha Sawant
Venue: Computers, Materials & Continua
DOI: 10.32604/cmc.2019.06433
URL: https://doi.org/10.32604/cmc.2019.06433
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Theft prediction with individual risk factor of visitors
Rumi, S. K.; Deng, K.; Salim, F. D. (2018)
Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
DOI: 10.1145/3274895.3274994
Raw: Rumi, S. K., Deng, K., & Salim, F. D. (2018, November). Theft prediction with individual risk factor of visitors. In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 552-555). https://doi.org/10.1145/3274895.3274994
Match: Theft prediction with individual risk factor of visitors
Authors: Shakila Khan Rumi; Ke Deng; Flora D. Salim
Venue: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
DOI: 10.1145/3274895.3274994
URL: https://doi.org/10.1145/3274895.3274994
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Modelling regional crime risk using directed graph of check-ins
Rumi, S. K.; Salim, F. D. (2020)
Proceedings of the 29th ACM International Conference on Information & Knowledge Management
DOI: 10.1145/3340531.3412065
Raw: Rumi, S. K., & Salim, F. D. (2020, October). Modelling regional crime risk using directed graph of check-ins. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 2201-2204). https://doi.org/10.1145/3340531.3412065
Match: Modelling Regional Crime Risk using Directed Graph of Check-ins
Authors: Shakila Khan Rumi; Flora D. Salim
Venue: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
DOI: 10.1145/3340531.3412065
URL: https://doi.org/10.1145/3340531.3412065
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Comparison of near-repeat, machine learning, and risk terrain modeling for making spatiotemporal predictions of crime
Rummens, A.; Hardyns, W. (2020)
Applied Spatial Analysis and Policy
DOI: 10.1007/s12061-020-09339-2
Raw: Rummens, A., & Hardyns, W. (2020). Comparison of near-repeat, machine learning, and risk terrain modeling for making spatiotemporal predictions of crime. Applied Spatial Analysis and Policy, 13(4), 1035-1053. https://doi.org/10.1007/s12061-020-09339-2
Match: Comparison of near-Repeat, Machine Learning and Risk Terrain Modeling for Making Spatiotemporal Predictions of Crime
Authors: Anneleen Rummens; Wim Hardyns
Venue: Applied Spatial Analysis and Policy
DOI: 10.1007/s12061-020-09339-2
URL: https://doi.org/10.1007/s12061-020-09339-2
Verified via static CrossRef DOI lookup (score: 1.00)
Verified The effect of spatiotemporal resolution on predictive policing model performance
Rummens, A.; Hardyns, W. (2021)
International Journal of Forecasting
DOI: 10.1016/j.ijforecast.2020.03.006
Raw: Rummens, A., & Hardyns, W. (2021). The effect of spatiotemporal resolution on predictive policing model performance. International Journal of Forecasting, 37(1), 125-133. https://doi.org/10. 1016/j.ijforecast.2020.03.006
Match: The effect of spatiotemporal resolution on predictive policing model performance
Authors: Anneleen Rummens; Wim Hardyns
Venue: International Journal of Forecasting
DOI: 10.1016/j.ijforecast.2020.03.006
URL: https://doi.org/10.1016/j.ijforecast.2020.03.006
Verified via static CrossRef DOI lookup (score: 1.00)
Verified The use of predictive analysis in spatiotemporal crime forecasting: Building and testing a model in an urban context
Rummens, A.; Hardyns, W.; Pauwels, L. (2017)
Applied Geography
DOI: 10.1016/j.apgeog.2017.06.011
Raw: Rummens, A., Hardyns, W., & Pauwels, L. (2017). The use of predictive analysis in spatiotemporal crime forecasting: Building and testing a model in an urban context. Applied Geography, 86, 255-261. https://doi.org/10.1016/j.apgeog.2017.06.011
Match: The use of predictive analysis in spatiotemporal crime forecasting: Building and testing a model in an urban context
Authors: Anneleen Rummens; Wim Hardyns; Lieven Pauwels
Venue: Applied Geography
DOI: 10.1016/j.apgeog.2017.06.011
URL: https://doi.org/10.1016/j.apgeog.2017.06.011
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Do mobile phone data provide a better denominator in crime rates and improve spatiotemporal predictions of crime?
Rummens, A.; Snaphaan, T.; Van de Weghe, N.; Van den Poel, D.; Pauwels, L. J. R.; Hardyns, W. (2021)
ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi10060369
Raw: Rummens, A., Snaphaan, T., Van de Weghe, N., Van den Poel, D., Pauwels, L. J. R., & Hardyns, W. (2021). Do mobile phone data provide a better denominator in crime rates and improve spatiotemporal predictions of crime? ISPRS International Journal of Geo-Information, 10(6), 369. https://doi.org/10.3390/ijgi10060369
Match: Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime?
Authors: Anneleen Rummens; Thom Snaphaan; Nico Van de Weghe; Dirk Van den Poel; Lieven J. R. Pauwels; Wim Hardyns
Venue: ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi10060369
URL: https://doi.org/10.3390/ijgi10060369
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Deep multi-view spatio-temporal network for urban crime prediction
Salama, U.; Chen, X.; Yao, L.; Paik, H. Y.; Wang, X. (2021)
Springer International Publishing
Raw: Salama, U., Chen, X., Yao, L., Paik, H. Y., & Wang, X. (2021). Deep multi-view spatio-temporal network for urban crime prediction. In Databases theory and applications: 32nd Australasian Database Conference, ADC 2021, Dunedin, New Zealand, January 29-February 5, 2021, Proceedings 32 (pp. 50-61). Springer International Publishing.
Match: Deep Multi-view Spatio-Temporal Network for Urban Crime Prediction
Authors: Usama Salama; Xiaocong Chen; Lina Yao; Hye-Young Paik; Xianzhi Wang
Venue: Lecture Notes in Computer Science
DOI: 10.1007/978-3-030-69377-0_5
URL: https://doi.org/10.1007/978-3-030-69377-0_5
Verified via static CrossRef title search (score: 1.00)
Verified The ‘uberization of policing’? How police negotiate and operationalise predictive policing technology
Sandhu, A.; Fussey, P. (2021)
Policing and Society
DOI: 10.1080/10439463.2020.1803315
Raw: Sandhu, A., & Fussey, P. (2021). The ‘uberization of policing’? How police negotiate and operationalise predictive policing technology. Policing and Society, 31(1), 66-81. https://doi.org/10. 1080/10439463.2020.1803315
Match: The ‘uberization of policing’? How police negotiate and operationalise predictive policing technology
Authors: Ajay Sandhu; Peter Fussey
Venue: Policing and Society
DOI: 10.1080/10439463.2020.1803315
URL: https://doi.org/10.1080/10439463.2020.1803315
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Reform predictive policing
Shapiro, A. (2017)
Nature
DOI: 10.1038/541458a
Raw: Shapiro, A. (2017). Reform predictive policing. Nature, 541(7638), 458-460. https://doi.org/10. 1038/541458a
Match: Reform predictive policing
Authors: Aaron Shapiro
Venue: Nature
DOI: 10.1038/541458a
URL: https://doi.org/10.1038/541458a
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Criminal combat: Crime analysis and prediction using machine learning
Shukla, A.; Katal, A.; Raghuvanshi, S.; Sharma, S. (2021)
IEEE
DOI: 10.1109/CONIT51480.2021.9498397
Raw: Shukla, A., Katal, A., Raghuvanshi, S., & Sharma, S. (2021, June). Criminal combat: Crime analysis and prediction using machine learning. In 2021 International Conference on Intelligent Technologies (CONIT) (pp. 1-5). IEEE. https://doi.org/10.1109/CONIT51480.2021.9498397
Match: Criminal Combat: Crime Analysis and Prediction Using Machine Learning
Authors: Amar Shukla; Avita Katal; Saurav Raghuvanshi; Shivam Sharma
Venue: 2021 International Conference on Intelligent Technologies (CONIT)
DOI: 10.1109/conit51480.2021.9498397
URL: https://doi.org/10.1109/conit51480.2021.9498397
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Terrorism prediction using artificial neural network
Soliman, G. M.; Abou-El-Enien, T. H. (2019)
Revue d’Intelligence Artificielle
DOI: 10.18280/ria.330201
Raw: Soliman, G. M., & Abou-El-Enien, T. H. (2019). Terrorism prediction using artificial neural network. Revue d’Intelligence Artificielle, 33(2), 81-87. https://doi.org/10.18280/ria.330201
Match: Terrorism Prediction Using Artificial Neural Network
Authors: Ghada Soliman; Tarek Abou-El-Enien
Venue: Revue d'Intelligence Artificielle
DOI: 10.18280/ria.330201
URL: https://doi.org/10.18280/ria.330201
Verified via static CrossRef DOI lookup (score: 1.00)
Verified A deep learning framework for predicting burglaries based on multiple contextual factors
Solomon, A.; Kertis, M.; Shapira, B.; Rokach, L. (2022)
Expert Systems with Applications
DOI: 10.1016/j.eswa.2022.117042
Raw: Solomon, A., Kertis, M., Shapira, B., & Rokach, L. (2022). A deep learning framework for predicting burglaries based on multiple contextual factors. Expert Systems with Applications, 199, 117042. https://doi.org/10.1016/j.eswa.2022.117042
Match: A deep learning framework for predicting burglaries based on multiple contextual factors
Authors: Adir Solomon; Mor Kertis; Bracha Shapira; Lior Rokach
Venue: Expert Systems with Applications
DOI: 10.1016/j.eswa.2022.117042
URL: https://doi.org/10.1016/j.eswa.2022.117042
Verified via static CrossRef DOI lookup (score: 1.00)
Verified An adaptive method for analyzing and predicting the crime locations by means of AMABC and ARM
Sujatha, R.; Ezhilmaran, D. (2014)
Journal of Theoretical and Applied Information Technology
Raw: Sujatha, R., & Ezhilmaran, D. (2014). An adaptive method for analyzing and predicting the crime locations by means of AMABC and ARM. Journal of Theoretical and Applied Information Technology, 59(1), 45-56.
Match: An adaptive method for analyzing and predicting the crime locations by means of AMABC and ARM
Authors: Sujatha, R.; Ezhilmaran, D.
Venue: Journal of Theoretical and Applied Information Technology
URL: http://www.jatit.org/volumes/Vol59No1/5Vol59No1.pdf
The citation matches the publisher record exactly in terms of authors, title, journal, volume, issue, pages, and year.
Verified A new efficient SIF-based FCIL (SIF-FCIL) mining algorithm in predicting the crime locations
Sujatha, R.; Ezhilmaran, D. (2016)
Journal of Experimental & Theoretical Artificial Intelligence
DOI: 10.1080/0952813X.2015.1020573
Raw: Sujatha, R., & Ezhilmaran, D. (2016). A new efficient SIF-based FCIL (SIF-FCIL) mining algorithm in predicting the crime locations. Journal of Experimental & Theoretical Artificial Intelligence, 28(3), 561-579. https://doi.org/10.1080/0952813X.2015.1020573
Match: A new efficient SIF-based FCIL (SIF–FCIL) mining algorithm in predicting the crime locations
Authors: R. Sujatha; D. Ezhilmaran
Venue: Journal of Experimental & Theoretical Artificial Intelligence
DOI: 10.1080/0952813x.2015.1020573
URL: https://doi.org/10.1080/0952813x.2015.1020573
Verified via static CrossRef DOI lookup (score: 1.00)
Verified CrimeForecaster: Crime prediction by exploiting the geographical neighborhoods’ spatiotemporal dependencies
Sun, J.; Yue, M.; Lin, Z.; Yang, X.; Nocera, L.; Kahn, G.; Shahabi, C. (2021)
Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Raw: Sun, J., Yue, M., Lin, Z., Yang, X., Nocera, L., Kahn, G., & Shahabi, C. (2021). CrimeForecaster: Crime prediction by exploiting the geographical neighborhoods’ spatiotemporal dependencies. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 52-67).
Match: CrimeForecaster: Crime Prediction by Exploiting the Geographical Neighborhoods’ Spatiotemporal Dependencies
Authors: Jiao Sun; Mingxuan Yue; Zongyu Lin; Xiaochen Yang; Luciano Nocera; Gabriel Kahn; Cyrus Shahabi
Venue: Lecture Notes in Computer Science
DOI: 10.1007/978-3-030-67670-4_4
URL: https://doi.org/10.1007/978-3-030-67670-4_4
Verified via static CrossRef title search (score: 1.00)
Verified Anisotropic diffusion for improved crime prediction in urban China
Tang, Y.; Zhu, X.; Guo, W.; Wu, L.; Fan, Y. (2019)
ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi8050234
Raw: Tang, Y., Zhu, X., Guo, W., Wu, L., & Fan, Y. (2019). Anisotropic diffusion for improved crime prediction in urban China. ISPRS International Journal of Geo-Information, 8(5), 234. https://doi. org/10.3390/ijgi8050234
Match: Anisotropic Diffusion for Improved Crime Prediction in Urban China
Authors: Yicheng Tang; Xinyan Zhu; Wei Guo; Ling Wu; Yaxin Fan
Venue: ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi8050234
URL: https://doi.org/10.3390/ijgi8050234
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Verified Geographical crime rate prediction system
Tarlekar, S.; Bhosle, R.; D’souza, E.; Sheikh, S. (2021)
IEEE
DOI: 10.1109/INDISCON53343.2021.9582218
Raw: Tarlekar, S., Bhosle, R., D’souza, E., & Sheikh, S. (2021). August). Geographical crime rate prediction system. 2021 IEEE India Council International Subsections Conference (INDISCON) (pp. 1-6). IEEE. https://doi.org/10.1109/INDISCON53343.2021.9582218
Match: Geographical Crime Rate Prediction System
Authors: Sai Tarlekar; Rucha Bhosle; Elysia D'souza; Sana Sheikh
Venue: 2021 IEEE India Council International Subsections Conference (INDISCON)
DOI: 10.1109/indiscon53343.2021.9582218
URL: https://doi.org/10.1109/indiscon53343.2021.9582218
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Verified Spatial modeling and analysis of the determinants of property crime in Portugal
Tavares, J. P.; Costa, A. C. (2021)
ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi10110731
Raw: Tavares, J. P., & Costa, A. C. (2021). Spatial modeling and analysis of the determinants of property crime in Portugal. ISPRS International Journal of Geo-Information, 10(11), 731. https://doi.org/10.3390/ijgi10110731
Match: Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
Authors: Joana Paulo Tavares; Ana Cristina Costa
Venue: ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi10110731
URL: https://doi.org/10.3390/ijgi10110731
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Verified Application of big data optimized clustering algorithm in cloud computing environment in traffic accident forecast
Tian, Z.; Zhang, S. (2021)
Peer-to-Peer Networking and Applications
DOI: 10.1007/s12083-020-00994-3
Raw: Tian, Z., & Zhang, S. (2021). Application of big data optimized clustering algorithm in cloud computing environment in traffic accident forecast. Peer-to-Peer Networking and Applications, 14(4), 2511-2523. https://doi.org/10.1007/s12083-020-00994-3
Match: Application of big data optimized clustering algorithm in cloud computing environment in traffic accident forecast
Authors: Zhun Tian; Shengrui Zhang
Venue: Peer-to-Peer Networking and Applications
DOI: 10.1007/s12083-020-00994-3
URL: https://doi.org/10.1007/s12083-020-00994-3
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Verified Legal and ethical issues in the prediction of recidivism
Tonry, M. (2014)
Federal Sentencing Reporter
DOI: 10.1525/fsr.2014.26.3.167
Raw: Tonry, M. (2014). Legal and ethical issues in the prediction of recidivism. Federal Sentencing Reporter, 26(3), 167-176. https://doi.org/10.1525/fsr.2014.26.3.167
Match: Legal and Ethical Issues in the Prediction of Recidivism
Authors: Michael Tonry
Venue: Federal Sentencing Reporter
DOI: 10.1525/fsr.2014.26.3.167
URL: https://doi.org/10.1525/fsr.2014.26.3.167
Verified via static CrossRef DOI lookup (score: 1.00)
Verified A national discussion on predictive policing: Defining our terms and mapping successful implementation strategies
Uchida, C. D. (2009)
National Institute of Justice Los Angeles
Raw: Uchida, C. D. (2009). A national discussion on predictive policing: Defining our terms and mapping successful implementation strategies. National Institute of Justice Los Angeles.
Match: A National Discussion on Predictive Policing: Defining Our Terms and Mapping Successful Implementation Strategies
Authors: Uchida, Craig D.
Venue: National Institute of Justice
URL: https://www.ojp.gov/pdffiles1/nij/grants/230404.pdf
The citation is correct. The document is a report (NCJ 230404) summarizing the First Predictive Policing Symposium held in Los Angeles in 2009, published by the National Institute of Justice.
Verified Future risks of frontier AI
UK Government Office for Science. (2023)
Raw: UK Government Office for Science. (2023). Future risks of frontier AI. Retrieved from https://assets.publishing.service.gov.uk/media/653bc393d10f3500139a6ac5/future-risks-of-frontier-ai-annex-a.pdf.
Match: Future risks of frontier AI (Annex A)
Authors: UK Government Office for Science
Venue: GOV.UK / Government Office for Science
URL: https://assets.publishing.service.gov.uk/media/653bc393d10f3500139a6ac5/future-risks-of-frontier-ai-annex-a.pdf
The citation is verified as it correctly identifies the author, year, and title of a real report. The provided URL is the official CDN link for the UK government document.
Verified Big data, crime and security
UK Houses of Parliament. (2014)
Raw: UK Houses of Parliament. (2014). Big data, crime and security. Retrieved from https://post.parliament.uk/research-briefings/post-pn-470/.
Match: Big data, crime and security
Authors: UK Houses of Parliament; Parliamentary Office of Science and Technology
Venue: UK Parliamentary Office of Science and Technology (POSTnote 470)
URL: https://post.parliament.uk/research-briefings/post-pn-470/
The citation refers to a research briefing published by the Parliamentary Office of Science and Technology (POST), an office of the UK Houses of Parliament. The specific briefing mentioned in the URL path (post-pn-470) and title is 'Big Data, Crime and Security', published in July 2014.
Verified Big data
US Census Bureau. (2021)
Raw: US Census Bureau. (2021). Big data. Retrieved from https://www.census.gov/topics/research/big-data. html.
Match: Big Data
Authors: U.S. Census Bureau
Venue: U.S. Census Bureau
URL: https://www.census.gov/topics/research/big-data.html
The citation is a valid match for a live topic page on the Census Bureau website. The year 2021 is a plausible access or publication year for this resource.
Verified Big data: Seizing opportunities, preserving values
US Executive Office of the President. (2014)
White House, Executive Office of the President
Raw: US Executive Office of the President. (2014). Big data: Seizing opportunities, preserving values. White House, Executive Office of the President.
Match: Big Data: Seizing Opportunities, Preserving Values
Authors: Executive Office of the President
Venue: White House, Executive Office of the President
URL: https://obamawhitehouse.archives.gov/sites/default/files/docs/big_data_privacy_report_may_1_2014.pdf
The citation is for an official 2014 White House report. All fields (title, author, year) match the official document found in the Obama White House archives.
Verified Crimes prediction using spatio-temporal data and kernel density estimation
Vinnia Kemala Putri; Felix, I. K. (2019)
2019 Asia Pacific Conference on Research in Industrial and Systems Engineering (APCoRISE)
DOI: 10.1109/APCoRISE46197.2019.9318972
Raw: Vinnia Kemala Putri, & Felix, I. K. (2019). Crimes prediction using spatio-temporal data and kernel density estimation. 2019 Asia Pacific Conference on Research in Industrial and Systems Engineering (APCoRISE), Depok, Indonesia (pp. 1-6). https://doi.org/10.1109/APCoRISE46197.2019.9318972.
Match: Crimes Prediction Using Spatio-Temporal Data and Kernel Density Estimation
Authors: Vinnia Kemala Putri; Felix I. Kurniadi
Venue: 2019 Asia Pacific Conference on Research in Industrial and Systems Engineering (APCoRISE)
DOI: 10.1109/apcorise46197.2019.9318972
The citation is verified. The DOI 10.1109/APCoRISE46197.2019.9318972 resolves to the paper 'Crimes Prediction Using Spatio-Temporal Data and Kernel Density Estimation' presented at the 2019 APCoRISE conference. The authors are Vinnia Kemala Putri and Felix I. Kurniadi (cited as Felix, I. K.).
Verified Improving crime count forecasts using Twitter and taxi data
Vomfell, L.; H€ardle, W. K.; Lessmann, S. (2018)
Decision Support Systems
DOI: 10.1016/j.dss.2018.07.003
Raw: Vomfell, L., H€ardle, W. K., & Lessmann, S. (2018). Improving crime count forecasts using Twitter and taxi data. Decision Support Systems, 113, 73-85. https://doi.org/10.1016/j.dss.2018.07.003
Match: Improving crime count forecasts using Twitter and taxi data
Authors: Lara Vomfell; Wolfgang Karl Härdle; Stefan Lessmann
Venue: Decision Support Systems
DOI: 10.1016/j.dss.2018.07.003
URL: https://doi.org/10.1016/j.dss.2018.07.003
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Deep learning for real-time crime forecasting and its ternarization
Wang, B.; Yin, P.; Bertozzi, A. L.; Brantingham, P. J.; Osher, S. J.; Xin, J. (2019)
Chinese Annals of Mathematics, Series B
DOI: 10.1007/s11401-019-0168-y
Raw: Wang, B., Yin, P., Bertozzi, A. L., Brantingham, P. J., Osher, S. J., & Xin, J. (2019). Deep learning for real-time crime forecasting and its ternarization. Chinese Annals of Mathematics, Series B, 40(6), 949-966. https://doi.org/10.1007/s11401-019-0168-y
Match: Deep Learning for Real-Time Crime Forecasting and Its Ternarization
Authors: Bao Wang; Penghang Yin; Andrea Louise Bertozzi; P. Jeffrey Brantingham; Stanley Joel Osher; Jack Xin
Venue: Chinese Annals of Mathematics, Series B
DOI: 10.1007/s11401-019-0168-y
URL: https://doi.org/10.1007/s11401-019-0168-y
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Verified Understanding the spatial distribution of crime based on its related variables using geospatial discriminative patterns
Wang, D.; Ding, W.; Lo, H.; Morabito, M.; Chen, P.; Salazar, J.; Stepinski, T. (2013)
Computers, Environment and Urban Systems
DOI: 10.1016/j.compenvurbsys.2013.01.008
Raw: Wang, D., Ding, W., Lo, H., Morabito, M., Chen, P., Salazar, J., & Stepinski, T. (2013). Understanding the spatial distribution of crime based on its related variables using geospatial discriminative patterns. Computers, Environment and Urban Systems, 39, 93-106. https://doi. org/10.1016/j.compenvurbsys.2013.01.008
Match: Understanding the spatial distribution of crime based on its related variables using geospatial discriminative patterns
Authors: Dawei Wang; Wei Ding; Henry Lo; Melissa Morabito; Ping Chen; Josue Salazar; Tomasz Stepinski
Venue: Computers, Environment and Urban Systems
DOI: 10.1016/j.compenvurbsys.2013.01.008
URL: https://doi.org/10.1016/j.compenvurbsys.2013.01.008
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Non-stationary model for crime rate inference using modern urban data
Wang, H.; Yao, H.; Kifer, D.; Graif, C.; Li, Z. (2017)
IEEE Transactions on Big Data
DOI: 10.1109/TBDATA.2017.2786405
Raw: Wang, H., Yao, H., Kifer, D., Graif, C., & Li, Z. (2017). Non-stationary model for crime rate inference using modern urban data. IEEE Transactions on Big Data, 5(2), 180-194. https://doi.org/10. 1109/TBDATA.2017.2786405
Match: Non-Stationary Model for Crime Rate Inference Using Modern Urban Data
Authors: Hongjian Wang; Huaxiu Yao; Daniel Kifer; Corina Graif; Zhenhui Li
Venue: IEEE Transactions on Big Data
DOI: 10.1109/tbdata.2017.2786405
URL: https://doi.org/10.1109/tbdata.2017.2786405
Verified via static CrossRef DOI lookup (score: 0.95)
Verified Crime risk analysis through big data algorithm with urban metrics
Wang, J.; Hu, J.; Shen, S.; Zhuang, J.; Ni, S. (2020)
Physica A: Statistical Mechanics and Its Applications
DOI: 10.1016/j.physa.2019.123627
Raw: Wang, J., Hu, J., Shen, S., Zhuang, J., & Ni, S. (2020). Crime risk analysis through big data algorithm with urban metrics. Physica A: Statistical Mechanics and Its Applications, 545, 123627. https://doi.org/10.1016/j.physa.2019.123627
Match: Crime risk analysis through big data algorithm with urban metrics
Authors: Jia Wang; Jun Hu; Shifei Shen; Jun Zhuang; Shunjiang Ni
Venue: Physica A: Statistical Mechanics and its Applications
DOI: 10.1016/j.physa.2019.123627
URL: https://doi.org/10.1016/j.physa.2019.123627
Verified via static CrossRef DOI lookup (score: 1.00)
Verified The spatial and social patterning of property and violent crime in Toronto neighbourhoods: A spatial-quantitative approach
Wang, L.; Lee, G.; Williams, I. (2019)
ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi8010051
Raw: Wang, L., Lee, G., & Williams, I. (2019). The spatial and social patterning of property and violent crime in Toronto neighbourhoods: A spatial-quantitative approach. ISPRS International Journal of Geo-Information, 8(1), 51. https://doi.org/10.3390/ijgi8010051
Match: The Spatial and Social Patterning of Property and Violent Crime in Toronto Neighbourhoods: A Spatial-Quantitative Approach
Authors: Lu Wang; Gabby Lee; Ian Williams
Venue: ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi8010051
URL: https://doi.org/10.3390/ijgi8010051
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Verified The spatio-temporal modeling for criminal incidents
Wang, X.; Brown, D. E. (2012)
Security Informatics
DOI: 10.1186/2190-8532-1-2
Raw: Wang, X., & Brown, D. E. (2012). The spatio-temporal modeling for criminal incidents. Security Informatics, 1(1), 1-17. https://doi.org/10.1186/2190-8532-1-2
Match: The spatio-temporal modeling for criminal incidents
Authors: Xiaofeng Wang; Donald E Brown
Venue: Security Informatics
DOI: 10.1186/2190-8532-1-2
URL: https://doi.org/10.1186/2190-8532-1-2
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Spatio-temporal modeling of criminal incidents using geographic, demographic, and Twitter-derived information
Wang, X.; Brown, D. E.; Gerber, M. S. (2012)
2012 IEEE International Conference on Intelligence and Security Informatics: Cyberspace, Border, and Immigration Securities
Raw: Wang, X., Brown, D. E., & Gerber, M. S. (2012). Spatio-temporal modeling of criminal incidents using geographic, demographic, and Twitter-derived information. Paper presented at the ISI 2012 - 2012 IEEE International Conference on Intelligence and Security Informatics: Cyberspace, Border, and Immigration Securities.
Match: Spatio-temporal modeling of criminal incidents using geographic, demographic, and twitter-derived information
Authors: Xiaofeng Wang; Donald E. Brown; Matthew S. Gerber
Venue: 2012 IEEE International Conference on Intelligence and Security Informatics
DOI: 10.1109/isi.2012.6284088
URL: https://doi.org/10.1109/isi.2012.6284088
Verified via static CrossRef title search (score: 1.00)
Verified Deep temporal multi-graph convolutional network for crime prediction
Wang, Y.; Ge, L.; Li, S.; Chang, F. (2020)
Springer International Publishing
Raw: Wang, Y., Ge, L., Li, S., & Chang, F. (2020). Deep temporal multi-graph convolutional network for crime prediction. In Conceptual Modeling: 39th International Conference, ER 2020, Vienna, Austria, November 3-6, 2020, Proceedings (pp. 525-538). Springer International Publishing.
Match: Deep Temporal Multi-Graph Convolutional Network for Crime Prediction
Authors: Yaqian Wang; Liang Ge; Siyu Li; Feng Chang
Venue: Lecture Notes in Computer Science
DOI: 10.1007/978-3-030-62522-1_39
URL: https://doi.org/10.1007/978-3-030-62522-1_39
Verified via static CrossRef title search (score: 1.00)
Verified Relationships between crime and everyday factors
Wawrzyniak, Z. M.; Borowik, G.; Szczechla, E.; Michalak, P.; Pytlak, R.; Cichosz, P.; Perkowski, E. (2018)
INES 2018 - IEEE 22nd International Conference on Intelligent Engineering Systems, Proceedings
Raw: Wawrzyniak, Z. M., Borowik, G., Szczechla, E., Michalak, P., Pytlak, R., Cichosz, P., … Perkowski, E. (2018). Relationships between crime and everyday factors. Paper presented at the INES 2018 - IEEE 22nd International Conference on Intelligent Engineering Systems, Proceedings.
Match: Relationships between crime and everyday factors
Authors: Zbigniew Wawrzyniak; Grzegorz Borowik; Eliza Szczechla; Paweł Michalak; Radosław Pytlak; Paweł Cichosz; Dobiesław Ircha; Wojciech Olszewski; Emilian Perkowski
Venue: EasyChair Preprints
DOI: 10.29007/c8sd
URL: https://doi.org/10.29007/c8sd
Verified via static CrossRef title search (score: 0.99)
Verified Economy and society: An outline of interpretive sociology
Weber, M. (1978)
University of California Press
ISBN: 0-520-02824-4
Raw: Weber, M. (1978). Economy and society: An outline of interpretive sociology. University of California Press.
Match: Economy and Society: An Outline of Interpretive Sociology
Authors: Max Weber
Venue: University of California Press
ISBN: 0-520-02824-4
This is the first complete English edition (reprinting the 1968 Bedminster Press version). ISBN-10: 0520028244; ISBN-13: 978-0520028241.
Verified CrimeSTC: A deep spatial-temporal-categorical network for citywide crime prediction
Wei, Y.; Liang, W.; Wang, Y.; Cao, J. (2020)
Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems
DOI: 10.1145/3440840.3440850
Raw: Wei, Y., Liang, W., Wang, Y., & Cao, J. (2020, November). CrimeSTC: A deep spatial-temporal-categorical network for citywide crime prediction. In Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems (pp. 75-79). https://doi.org/10.1145/3440840.3440850
Match: CrimeSTC: A Deep Spatial-Temporal-Categorical Network for Citywide Crime Prediction
Authors: Yue Wei; Weichao Liang; Youquan Wang; Jie Cao
Venue: Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems
DOI: 10.1145/3440840.3440850
URL: https://doi.org/10.1145/3440840.3440850
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Verified Mapping the risk terrain for crime using machine learning
Wheeler, A. P.; Steenbeek, W. (2021)
Journal of Quantitative Criminology
Raw: Wheeler, A. P., & Steenbeek, W. (2021). Mapping the risk terrain for crime using machine learning. Journal of Quantitative Criminology, 37(2), 445-480.
Match: Mapping the Risk Terrain for Crime Using Machine Learning
Authors: Wheeler, A. P.; Steenbeek, W.
Venue: Journal of Quantitative Criminology
DOI: 10.1007/s10940-020-09457-7
The citation matches the official publication record. Although published online-first in 2020, it appeared in Volume 37, Issue 2 in 2021, making the cited year correct for the journal version.
Verified CAS: Criminaliteits Anticipatie Systeem: Predictive policing in Amsterdam
Willems, D. (2014)
1st International Workshop on Planning of Emergency Services, Theory and Practice
Raw: Willems, D. (2014, June). CAS: Criminaliteits Anticipatie Systeem: Predictive policing in Amsterdam. In 1st International Workshop on Planning of Emergency Services, Theory and Practice, Amsterdam, Netherlands (pp. 25-27).
Match: CAS: Criminaliteits Anticipatie Systeem: Predictive policing in Amsterdam
Authors: Willems, D.
Venue: 1st International Workshop on Planning of Emergency Services, Theory and Practice
URL: https://event.cwi.nl/mtw2014/media/files/Willems,%20Dick%20-%20CAS%20Crime%20anticipation%20system%20_%20predicting%20policing%20in%20Amsterdam.pdf
The work exists and was presented by Dick Willems (Amsterdam Police) at the workshop held at CWI in Amsterdam in June 2014. The title in the citation uses the Dutch expansion of the 'CAS' acronym, which is the official name of the system.
Verified Integrating crime and traffic crash data in Nashville
Wyatt, J.; Alexander, M. (2010)
Geography & Public Safety
Raw: Wyatt, J., & Alexander, M. (2010). Integrating crime and traffic crash data in Nashville. Geography & Public Safety, 2(3), 9-11.
Match: Integrating Crime and Traffic Crash Data in Nashville: (506742011-006)
Authors: Jason Wyatt; Michael Alexander
Venue: PsycEXTRA Dataset
DOI: 10.1037/e506742011-006
URL: https://doi.org/10.1037/e506742011-006
Verified via static CrossRef title search (score: 0.93)
Verified Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area
Xia, Z.; Stewart, K.; Fan, J. (2021)
Computers, Environment and Urban Systems
DOI: 10.1016/j.compenvurbsys.2021.101599
Raw: Xia, Z., Stewart, K., & Fan, J. (2021). Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area. Computers, Environment and Urban Systems, 87, 101599. https://doi.org/10.1016/j.compenvurbsys.2021.101599
Match: Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area
Authors: Zhiyue Xia; Kathleen Stewart; Junchuan Fan
Venue: Computers, Environment and Urban Systems
DOI: 10.1016/j.compenvurbsys.2021.101599
URL: https://doi.org/10.1016/j.compenvurbsys.2021.101599
Verified via static CrossRef DOI lookup (score: 1.00)
Verified A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery
Yang, B.; Liu, L.; Lan, M.; Wang, Z.; Zhou, H.; Yu, H. (2020)
International Journal of Geographical Information Science
DOI: 10.1080/13658816.2020.1737701
Raw: Yang, B., Liu, L., Lan, M., Wang, Z., Zhou, H., & Yu, H. (2020). A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery. International Journal of Geographical Information Science, 34(9), 1740-1764. https:// doi.org/10.1080/13658816.2020.1737701
Match: A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery
Authors: Bo Yang; Lin Liu; Minxuan Lan; Zengli Wang; Hanlin Zhou; Hongjie Yu
Venue: International Journal of Geographical Information Science
DOI: 10.1080/13658816.2020.1737701
URL: https://doi.org/10.1080/13658816.2020.1737701
Verified via static CrossRef DOI lookup (score: 1.00)
Verified CrimeTelescope: Crime hotspot prediction based on urban and social media data fusion
Yang, D.; Heaney, T.; Tonon, A.; Wang, L.; Cudre-Mauroux, � P. (2018)
World Wide Web
DOI: 10.1007/s11280-017-0515-4
Raw: Yang, D., Heaney, T., Tonon, A., Wang, L., & Cudre-Mauroux, � P. (2018). CrimeTelescope: Crime hotspot prediction based on urban and social media data fusion. World Wide Web, 21(5), 1323-1347. https://doi.org/10.1007/s11280-017-0515-4
Match: CrimeTelescope: crime hotspot prediction based on urban and social media data fusion
Authors: Dingqi Yang; Terence Heaney; Alberto Tonon; Leye Wang; Philippe Cudré-Mauroux
Venue: World Wide Web
DOI: 10.1007/s11280-017-0515-4
URL: https://doi.org/10.1007/s11280-017-0515-4
Verified via static CrossRef DOI lookup (score: 1.00)
Verified Prediction of crime hotspots based on spatial factors of random forest
Yao, S.; Wei, M.; Yan, L.; Wang, C.; Dong, X.; Liu, F.; Xiong, Y. (2020)
IEEE
DOI: 10.1109/ICCSE49874.2020.9201899
Raw: Yao, S., Wei, M., Yan, L., Wang, C., Dong, X., Liu, F., & Xiong, Y. (2020, August). Prediction of crime hotspots based on spatial factors of random forest. In 2020 15th International Conference on Computer Science & Education (ICCSE) (pp. 811-815). IEEE. https://doi.org/10. 1109/ICCSE49874.2020.9201899
Match: Prediction of Crime Hotspots based on Spatial Factors of Random Forest
Authors: Shuyu Yao; Ming Wei; Lingyu Yan; Chunzhi Wang; Xinhua Dong; Fangrui Liu; Ying Xiong
Venue: 2020 15th International Conference on Computer Science & Education (ICCSE)
DOI: 10.1109/iccse49874.2020.9201899
URL: https://doi.org/10.1109/iccse49874.2020.9201899
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Verified Spatiotemporal prediction of theft risk with deep inceptionresidual networks
Ye, X.; Duan, L.; Peng, Q. (2021)
Smart Cities
DOI: 10.3390/smartcities4010013
Raw: Ye, X., Duan, L., & Peng, Q. (2021). Spatiotemporal prediction of theft risk with deep inceptionresidual networks. Smart Cities, 4(1), 204-216. https://doi.org/10.3390/smartcities4010013
Match: Spatiotemporal Prediction of Theft Risk with Deep Inception-Residual Networks
Authors: Xinyue Ye; Lian Duan; Qiong Peng
Venue: Smart Cities
DOI: 10.3390/smartcities4010013
URL: https://doi.org/10.3390/smartcities4010013
Verified via static CrossRef DOI lookup (score: 1.00)
Verified An integrated model for crime prediction using temporal and spatial factors
Yi, F.; Yu, Z.; Zhuang, F.; Zhang, X.; Xiong, H. (2018)
IEEE
DOI: 10.1109/ICDM.2018.00190
Raw: Yi, F., Yu, Z., Zhuang, F., Zhang, X., & Xiong, H. (2018, November). An integrated model for crime prediction using temporal and spatial factors. In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 1386-1391). IEEE. https://doi.org/10.1109/ICDM.2018.00190
Match: An Integrated Model for Crime Prediction Using Temporal and Spatial Factors
Authors: Fei Yi; Zhiwen Yu; Fuzhen Zhuang; Xiao Zhang; Hui Xiong
Venue: 2018 IEEE International Conference on Data Mining (ICDM)
DOI: 10.1109/icdm.2018.00190
URL: https://doi.org/10.1109/icdm.2018.00190
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Verified Using risk terrain modeling to predict homeless related crime in Los Angeles, California
Yoo, Y.; Wheeler, A. P. (2019)
Applied Geography
Raw: Yoo, Y., & Wheeler, A. P. (2019). Using risk terrain modeling to predict homeless related crime in Los Angeles, California. Applied Geography, 109, 102039.
Match: Using Risk Terrain Modeling to Predict Homeless Related Crime in Los Angeles, California
Authors: Youngmin Yoo; Andrew Palmer Wheeler
Venue: None
DOI: 10.31235/osf.io/swfpn
URL: https://doi.org/10.31235/osf.io/swfpn
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Verified Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data
Yuan, Z.; Zhou, X.; Yang, T. (2018)
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Raw: Yuan, Z., Zhou, X., & Yang, T. (2018, July) Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 984-992).
Match: Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data
Authors: Zhuoning Yuan; Xun Zhou; Tianbao Yang
Venue: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
DOI: 10.1145/3219819.3219922
URL: https://doi.org/10.1145/3219819.3219922
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Verified Interpretable classification models for recidivism prediction
Zeng, J.; Ustun, B.; Rudin, C. (2017)
Journal of the Royal Statistical Society Series A: Statistics in Society
DOI: 10.1111/rssa.12227
Raw: Zeng, J., Ustun, B., & Rudin, C. (2017). Interpretable classification models for recidivism prediction. Journal of the Royal Statistical Society Series A: Statistics in Society, 180(3), 689-722. https://doi.org/10.1111/rssa.12227
Match: Interpretable Classification Models for Recidivism Prediction
Authors: Jiaming Zeng; Berk Ustun; Cynthia Rudin
Venue: Journal of the Royal Statistical Society Series A: Statistics in Society
DOI: 10.1111/rssa.12227
URL: https://doi.org/10.1111/rssa.12227
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Verified An adaptive spatial resolution method based on the ST-ResNet model for hourly property crime prediction
Zhang, H.; Zhang, J.; Wang, Z.; Yin, H. (2021)
ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi10050314
Raw: Zhang, H., Zhang, J., Wang, Z., & Yin, H. (2021). An adaptive spatial resolution method based on the ST-ResNet model for hourly property crime prediction. ISPRS International Journal of GeoInformation, 10(5), 314. https://doi.org/10.3390/ijgi10050314
Match: An Adaptive Spatial Resolution Method Based on the ST-ResNet Model for Hourly Property Crime Prediction
Authors: Hong Zhang; Jie Zhang; Zengli Wang; Hao Yin
Venue: ISPRS International Journal of Geo-Information
DOI: 10.3390/ijgi10050314
URL: https://doi.org/10.3390/ijgi10050314
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Verified Interpretable machine learning models for crime prediction
Zhang, X.; Liu, L.; Lan, M.; Song, G.; Xiao, L.; Chen, J. (2022)
Computers, Environment and Urban Systems
DOI: 10.1016/j.compenvurbsys.2022.101789
Raw: Zhang, X., Liu, L., Lan, M., Song, G., Xiao, L., & Chen, J. (2022). Interpretable machine learning models for crime prediction. Computers, Environment and Urban Systems, 94, 101789. https:// doi.org/10.1016/j.compenvurbsys.2022.101789
Match: Interpretable machine learning models for crime prediction
Authors: Xu Zhang; Lin Liu; Minxuan Lan; Guangwen Song; Luzi Xiao; Jianguo Chen
Venue: Computers, Environment and Urban Systems
DOI: 10.1016/j.compenvurbsys.2022.101789
URL: https://doi.org/10.1016/j.compenvurbsys.2022.101789
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Verified Time and location recommendation for crime prevention
Zhang, Y.; Siriaraya, P.; Kawai, Y.; Jatowt, A. (2019)
Lecture Notes in Computer Science (11496 LNCS)
Raw: Zhang, Y., Siriaraya, P., Kawai, Y., & Jatowt, A. (2019). Time and location recommendation for crime prevention. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11496 LNCS (pp. 47-62).
Match: Time and Location Recommendation for Crime Prevention
Authors: Yihong Zhang; Panote Siriaraya; Yukiko Kawai; Adam Jatowt
Venue: Lecture Notes in Computer Science
DOI: 10.1007/978-3-030-19274-7_4
URL: https://doi.org/10.1007/978-3-030-19274-7_4
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Verified Analysis of street crime predictors in web open data
Zhang, Y.; Siriaraya, P.; Kawai, Y.; Jatowt, A. (2020)
Journal of Intelligent Information Systems
DOI: 10.1007/s10844-019-00587-4
Raw: Zhang, Y., Siriaraya, P., Kawai, Y., & Jatowt, A. (2020a). Analysis of street crime predictors in web open data. Journal of Intelligent Information Systems, 55(3), 535-559. https://doi.org/10.1007/s10844-019-00587-4
Match: Analysis of street crime predictors in web open data
Authors: Yihong Zhang; Panote Siriaraya; Yukiko Kawai; Adam Jatowt
Venue: Journal of Intelligent Information Systems
DOI: 10.1007/s10844-019-00587-4
URL: https://doi.org/10.1007/s10844-019-00587-4
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Verified Predicting time and location of future crimes with recommendation methods
Zhang, Y.; Siriaraya, P.; Kawai, Y.; Jatowt, A. (2020)
Knowledge-Based Systems
DOI: 10.1016/j.knosys.2020.106503
Raw: Zhang, Y., Siriaraya, P., Kawai, Y., & Jatowt, A. (2020b). Predicting time and location of future crimes with recommendation methods. Knowledge-Based Systems, 210, 106503. https://doi. org/10.1016/j.knosys.2020.106503
Match: Predicting time and location of future crimes with recommendation methods
Authors: Yihong Zhang; Panote Siriaraya; Yukiko Kawai; Adam Jatowt
Venue: Knowledge-Based Systems
DOI: 10.1016/j.knosys.2020.106503
URL: https://doi.org/10.1016/j.knosys.2020.106503
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Verified Modeling temporal-spatial correlations for crime prediction
Zhao, X.; Tang, J. (2017)
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
DOI: 10.1145/3132847.3133024
Raw: Zhao, X., & Tang, J. (2017, November). Modeling temporal-spatial correlations for crime prediction. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 497-506). https://doi.org/10.1145/3132847.3133024
Match: Modeling Temporal-Spatial Correlations for Crime Prediction
Authors: Xiangyu Zhao; Jiliang Tang
Venue: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
DOI: 10.1145/3132847.3133024
URL: https://doi.org/10.1145/3132847.3133024
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Verified Security alert: Generalized deep multi-view representation learning for crime forecasting
Zheng, Z.; Xia, Y.; Chen, X.; Yao, J. (2023)
Computational Intelligence
DOI: 10.1111/coin.12504
Raw: Zheng, Z., Xia, Y., Chen, X., & Yao, J. (2023). Security alert: Generalized deep multi-view representation learning for crime forecasting. Computational Intelligence, 39(1), 4-17. https://doi.org/10.1111/coin.12504
Match: Security alert: Generalized deep multi-view representation learning for crime forecasting
Authors: Ziwan Zheng; Yu Xia; Xiaocong Chen; Junwei Yao
Venue: Computational Intelligence
DOI: 10.1111/coin.12504
URL: https://doi.org/10.1111/coin.12504
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Verified ESCORT: Finegrained urban crime risk inference leveraging heterogeneous open data
Zhou, B. B.; Chen, L. B.; Zhou, F. X.; Li, S. J.; Zhao, S.; Das, S. K.; Pan, G. (2021)
IEEE Systems Journal
DOI: 10.1109/JSYST.2020.3023762
Raw: Zhou, B. B., Chen, L. B., Zhou, F. X., Li, S. J., Zhao, S., Das, S. K., & Pan, G. (2021). ESCORT: Finegrained urban crime risk inference leveraging heterogeneous open data. IEEE Systems Journal, 15(3), 4656-4667. https://doi.org/10.1109/JSYST.2020.3023762
Match: ESCORT: Fine-Grained Urban Crime Risk Inference Leveraging Heterogeneous Open Data
Authors: Binbin Zhou; Longbiao Chen; Fangxun Zhou; Shijian Li; Sha Zhao; Sajal K. Das; Gang Pan
Venue: IEEE Systems Journal
DOI: 10.1109/jsyst.2020.3023762
URL: https://doi.org/10.1109/jsyst.2020.3023762
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Verified Dynamic road crime risk prediction with urban open data
Zhou, B. B.; Chen, L. B.; Zhou, F. X.; Li, S. J.; Zhao, S.; Pan, G. (2022)
Frontiers of Computer Science
DOI: 10.1007/s11704-021-0136-z
Raw: Zhou, B. B., Chen, L. B., Zhou, F. X., Li, S. J., Zhao, S., & Pan, G. (2022). Dynamic road crime risk prediction with urban open data. Frontiers of Computer Science, 16(1), 1-13. https://doi.org/10. 1007/s11704-021-0136-z
Match: Dynamic road crime risk prediction with urban open data
Authors: Binbin Zhou; Longbiao Chen; Fangxun Zhou; Shijian Li; Sha Zhao; Gang Pan
Venue: Frontiers of Computer Science
DOI: 10.1007/s11704-021-0136-z
URL: https://doi.org/10.1007/s11704-021-0136-z
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Verified Exploration of the hidden influential factors on crime activities: A big data approach
Zhou, J.; Li, Z.; Ma, J. J.; Jiang, F. (2020)
IEEE Access
DOI: 10.1109/ACCESS.2020.3009969
Raw: Zhou, J., Li, Z., Ma, J. J., & Jiang, F. (2020). Exploration of the hidden influential factors on crime activities: A big data approach. IEEE Access, 8, 141033-141045. https://doi.org/10.1109/ ACCESS.2020.3009969
Match: Exploration of the Hidden Influential Factors on Crime Activities: A Big Data Approach
Authors: Jianming Zhou; Zheng Li; Jack J. Ma; Feifeng Jiang
Venue: IEEE Access
DOI: 10.1109/access.2020.3009969
URL: https://doi.org/10.1109/access.2020.3009969
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Verified Comparison of model performance for basic and advanced modeling approaches to crime prediction
Zhu, Y. (2018)
Intelligent Information Management
DOI: 10.4236/iim.2018.106011
Raw: Zhu, Y. (2018). Comparison of model performance for basic and advanced modeling approaches to crime prediction. Intelligent Information Management, 10(06), 123-132. https://doi.org/10. 4236/iim.2018.106011
Match: Comparison of Model Performance for Basic and Advanced Modeling Approaches to Crime Prediction
Authors: Yuezhexuan Zhu
Venue: Intelligent Information Management
DOI: 10.4236/iim.2018.106011
URL: https://doi.org/10.4236/iim.2018.106011
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Verified The age of surveillance capitalism: The fight for a human future at the new frontier of power
Zuboff, S. (2019)
PublicAffairs
ISBN: 978-1-610-39569-4
Raw: Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. Newyork: PublicAffairs.
Match: The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power
Authors: Shoshana Zuboff
Venue: PublicAffairs
ISBN: 978-1-610-39569-4
ISBN: 978-1610395694. Minor spacing/capitalization differences (Newyork vs New York) are cosmetic.
Verified Predicting pedestrian crashes in Texas’ intersections and midblock segments
Zuniga-Garcia, N.; Perrine, K. A.; Kockelman, K. M. (2022)
Sustainability (Switzerland)
DOI: 10.3390/su14127164
Raw: Zuniga-Garcia, N., Perrine, K. A., & Kockelman, K. M. (2022). Predicting pedestrian crashes in Texas’ intersections and midblock segments. Sustainability (Switzerland), 14(12), 7164. https:// doi.org/10.3390/su14127164
Match: Predicting Pedestrian Crashes in Texas’ Intersections and Midblock Segments
Authors: Natalia Zuniga-Garcia; Kenneth A. Perrine; Kara M. Kockelman
Venue: Sustainability
DOI: 10.3390/su14127164
URL: https://doi.org/10.3390/su14127164
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