Editing Organized crime (section)

=== Computational approach ===
While most of the conceptual frameworks used to model organized crime emphasize the role of ''actors'' or ''activities'', computational approaches built on the foundations of [[data science]] and [[artificial intelligence]] are focusing on deriving new insights on organized crime from [[big data]]. For example, novel [[machine learning]] models have been applied to study and detect urban crime<ref>{{cite web|url=https://www.takedownproject.eu/|title=TAKEDOWN {{!}} Organized Crime and Terrorist Networks|access-date=2019-09-20}}</ref><ref>{{cite journal | last1 = Tundis | first1 = Andrea | last2 = Kaleem | first2 = Humayun | last3 = Muhlhauser | first3 = Max  |url=https://www.researchgate.net/publication/333320647|title=Tracking Criminal Events through IoT devices and an Edge Computing approach {{!}} Request PDF|website=ResearchGate|access-date=2019-09-20|doi=10.1109/ICCCN.2019.8846956| s2cid = 199002437 }}</ref> and online prostitution networks.<ref>{{Cite book |doi=10.1109/asonam.2018.8508276|isbn=9781538660515|chapter=Covert Online Ethnography and Machine Learning for Detecting Individuals at Risk of Being Drawn into Online Sex Work|title=2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)|pages=1096–1099 |date=2018 |last1=Kostakos|first1=Panos |last2=Sprachalova|first2=Lucie |last3=Pandya|first3=Abhinay |last4=Aboeleinen|first4=Mohamed |last5=Oussalah|first5=Mourad |s2cid=53080972|chapter-url=http://urn.fi/urn:nbn:fi-fe201901222703}}</ref><ref>{{cite web|url=https://onezero.medium.com/how-artificial-intelligence-is-tracking-sex-traffickers-276dcc025ecd|title=How Artificial Intelligence Is Tracking Sex Traffickers|last=Brody|first=Liz|date=2019-05-08|website=Medium|access-date=2019-09-20}}</ref> Big data has also been used to develop online tools predicting the risk for an individual to be a victim of online sex trade or getting drawn into online sex work.<ref>{{cite web|url=https://medium.com/rightmesh/girls-in-tech-x-ubc-hack-for-humanity-hackathon-recap-656131e51b9f|title=Girls in Tech x UBC: Hack for Humanity Hackathon Recap|last=MacNeil|first=Brianna|date=2018-02-13|website=Medium|access-date=2019-09-20}}</ref><ref>{{cite web|url=http://devpost.com/software/online-sex-work-risk-prediction|title=Online Sex-work Risk Prediction|website=Devpost|date=11 February 2018 |access-date=2019-09-20}}</ref> In addition, data from Twitter<ref>{{cite book | last1 = S Al Dhanhani | first1 = Safaa | title = Computer Science & Information Technology |chapter-url=https://www.researchgate.net/publication/322336420|chapter=Framework for Analyzing Twitter to Detect Community Suspicious Crime Activity|via=ResearchGate| date = 2018 | pages = 41–60 |access-date=2019-09-20|doi=10.5121/csit.2018.80104| isbn = 9781921987793 }}</ref> and [[Google Trends]]<ref>{{cite journal |first=Panos |last=Kostakos |date=2018 |title=Public Perceptions on Organised Crime, Mafia, and Terrorism: A Big Data Analysis based on Twitter and Google Trends |journal=International Journal of Cyber Criminology |volume=12 |issue=1 |pages=282–299 |url=https://www.cybercrimejournal.com/KostakosVol12Issue1IJCC2018.pdf |doi=10.5281/zenodo.1467919 |issn=0974-2891 |access-date=2019-09-20 |archive-date=2019-08-01 |archive-url=https://web.archive.org/web/20190801120121/http://www.cybercrimejournal.com/KostakosVol12Issue1IJCC2018.pdf |url-status=dead }}</ref> have been used to study the public perceptions of organized crime.

{| class="wikitable"
|-
! Model type || Environment || Group || Processes || Impacts
|-
| National || Historical or cultural basis || Family or hierarchy || Secrecy/bonds. Links to insurgents || Local corruption/influence. Fearful community.
|-
| Transnational || Politically and economically unstable || Vertical integration || Legitimate cover || Stable supply of illicit goods. High-level corruption.
|-
| Transnational/transactional || Any || Flexible. Small size. || Violent. Opportunistic. Risk taking || Unstable supply of range of illicit goods. Exploits young local offenders.
|-
| Entrepreneurial/transactional || Developed/high technology regions || Individuals or pairs. || Operating through legitimate enterprise || Provision of illicit services, e.g., money laundering, fraud, criminal networks.
|}