| 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.
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| 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.
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| 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).
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| 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.
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| 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.
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| 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.
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| 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.
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| 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.
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| 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.
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| 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.
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| 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.
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| 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.
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| 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.
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| 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)
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| 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.
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| 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)
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| 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)
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| 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)
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| 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.
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| 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)
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| 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.
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| 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)
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| 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)
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| 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)
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| 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)
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| 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)
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| 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)
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| 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.
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| 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)
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| 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)
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| 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.
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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.
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| 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)
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| 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)
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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
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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)
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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)
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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).
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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
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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.
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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)
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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).
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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.
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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
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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)
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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)
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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.
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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
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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)
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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)
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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)
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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)
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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)
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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)
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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.
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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)
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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)
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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.
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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)
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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)
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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.
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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.
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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.
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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
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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)
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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
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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.
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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
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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
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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
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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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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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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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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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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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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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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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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)
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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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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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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.
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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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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.
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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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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
Verified via static CrossRef DOI lookup (score: 1.00)
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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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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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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.
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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.
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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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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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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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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.
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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
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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)
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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
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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
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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
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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)
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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.
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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
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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
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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)
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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)
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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)
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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.
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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)
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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.
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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)
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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)
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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)
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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)
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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)
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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)
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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)
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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.
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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)
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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)
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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.
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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.
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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)
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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)
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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)
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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)
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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)
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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
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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
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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)
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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
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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
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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
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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
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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
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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.
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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
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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
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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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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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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
Verified via static CrossRef DOI lookup (score: 1.00)
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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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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)
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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.
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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.
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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.
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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.
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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.
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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.).
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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)
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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
Verified via static CrossRef DOI lookup (score: 1.00)
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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)
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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)
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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)
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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
Verified via static CrossRef DOI lookup (score: 1.00)
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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)
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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)
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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
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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)
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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.
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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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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.
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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.
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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)
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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)
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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)
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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)
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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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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)
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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
Verified via static CrossRef DOI lookup (score: 1.00)
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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
Verified via static CrossRef title search (score: 1.00)
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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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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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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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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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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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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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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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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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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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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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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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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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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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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.
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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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