Home-sharing Services Data Analysis for Detection of Possible Human Trafficking
Preferred learners
- Anywhere
- Academic experience
Categories
Skills
Project scope
- What is the main goal for this project?
-
We are looking to gain insight into any emerging trends regarding suspicious activity in home-sharing listings related to fraud, human sex trafficking or any associated illegal activity in Toronto.
Airbnb data is the largest repository of home-sharing data currently available. It is posted publicly at this site: http://insideairbnb.com/get-the-data.html Please use only the Toronto data. You may use any amount of years and any / all files provided including any other relevant open data sources.
Additionally, you may reach out to the website contributors at this email: murray@murraycox.com to get information on deleted postings, or other information which may also be of interest. A useful open data source to consider incorporating in analysis may be the Major Crime Indicators data, available on the Toronto Police Open Data Portal: http://data.torontopolice.on.ca/
Please post all of your code to a GitHub page and provide us with the link to both the consolidated data source(s) you use and any code you create. All of your results must be reproducible and code must run without errors. Please provide us with a 1 page executive summary of key findings and detailed report of up to 10 pages outlining your findings, recommendations, and next steps for further investigation.
Some possible starting points are:
- NLP/ text classification of reviews to flag any concerning comments, especially recurring ones
- Detecting / predicting fraudulent postings
- GIS analysis of locations of postings, in relation to Toronto neighbourhood crime data
- What is the main goal for this project?
-
We are looking to gain insight into any emerging trends regarding suspicious activity in home-sharing listings related to fraud, human sex trafficking or any associated illegal activity in Toronto.
Airbnb data is the largest repository of home-sharing data currently available. It is posted publicly at this site: http://insideairbnb.com/get-the-data.html Please use only the Toronto data. You may use any amount of years and any / all files provided including any other relevant open data sources.
Additionally, you may reach out to the website contributors at this email: murray@murraycox.com to get information on deleted postings, or other information which may also be of interest. A useful open data source to consider incorporating in analysis may be the Major Crime Indicators data, available on the Toronto Police Open Data Portal: http://data.torontopolice.on.ca/
Please post all of your code to a GitHub page and provide us with the link to both the consolidated data source(s) you use and any code you create. All of your results must be reproducible and code must run without errors. Please provide us with a 1 page executive summary of key findings and detailed report of up to 10 pages outlining your findings, recommendations, and next steps for further investigation.
Some possible starting points are:
- NLP/ text classification of reviews to flag any concerning comments, especially recurring ones
- Detecting / predicting fraudulent postings
- GIS analysis of locations of postings, in relation to Toronto neighbourhood crime data
- What tasks will learners need to complete to achieve the project goal?
-
No deliverables exist for this project.
About the company
- http://www.torontopolice.on.ca/
- Government, Defense & security
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