Auto-theft Data Insights
We can provide geographic and other data on auto-theft in the city of Toronto over the past few years. Students can visualize the data and identify trends and anomalies in that data. By identifying relationships the students may make predictions or suggest strategies on how to prevent/combat auto theft in the future.
Public Service Announcement on Distracted Driving
Creating a PSA about distracted driving which can take could be any medium they choose. We shall review the top 10 deliverables and choose the top 3.
Segmentation & Analytics of Major Crime Indicators Data
Segmentation & Analytics of Major Crime Indicator (MCI) Data Students would examine the MCI data* available on the Toronto Police Service's Public Safety Data Portal (PSDP), in order to conduct: 1) High level review & exploratory analysis re segmentation and data cleaning 2) Hot-spot mapping of high volume MCI areas within the 140 neighborhoods by crime type / rate 3) Presentation of recommendations *Consideration to be given on the use of text mining of street names and free form notes MCI Data can be found @ http://data.torontopolice.on.ca/
Training & Development Plan for the Best-In-Class Crime Analyst Programme
The Analytics & Innovation unit at the Toronto Police Service is in the process of developing a structured Analyst programme and would like to supplement this work with a training and skills development plan for potential new hires as well as existing analysts - all possessing various skills, education, and background. Project goals include but not limited to those previously outlined as Project Examples such as: 1) detailed life cycle journey map, 2) training plan for new employees including the on-boarding and job-required skills development, 3) training plan for existing analysts for succession planning as well as rotational development throughout the various districts and squads, 4) high-level plan for leadership acting roles to develop motivated analysts for senior positions, 5) identification of key conferences, software solutions, periodical subscriptions, etc that would be beneficial to the programme, 6) identification of ways to stay up-to-date on legislative changes
Segmentation & Analytics of Killed and Seriously Injured Collision Data
Segmentation & Analytics of Killed and Seriously Injured (KSI) Collision Data Students would examine the KSI data* available on the Toronto Police Service's Public Safety Data Portal (PSDP), in order to conduct: 1) High level review & exploratory analysis re segmentation and data cleaning i.e. types of drivers and/or other road users 2) Hot-spot mapping of high volume KSI collision areas within the 140 neighborhoods by collision type 3) Presentation of recommendations in the context of current TPS policies *Consideration to be given on the use of text mining of street names and free form notes KSI Data can be found @ http://data.torontopolice.on.ca/
Business Plan for Business Intelligence and Analytics Unit
• Mission statement of the unit • Description of Business Intelligence and Analytics Unit (BIAU) • Description of how BIAU is different from other units and other organizations • Description of management team, including the experience of key team members and previous successes • How you plan to market the new initiatives, e.g. Innovation • Analysis of unit’s strengths, weaknesses, opportunities, and threat, which will show that we’re realistic and have considered opportunities and challenges • Develop a cash flow statement so we understand what our needs are now and will be in the future (a cash flow statement also can help us consider how cash flow could impact growth) • Revenue projections and • Summary/conclusion that wraps everything together (this also could be an executive summary at the beginning of the plan)
Managerial Analytics for Toronto Police Service
The project output is expected will promote the mandate of BIAU through any new insights produced. The format, length and style of the final report will be determined by the student teams based on the project objectives, as stated below. Themes: 1. KSI & Traffic Safety 2. Assaults 3. Break & Enter All teams must pick one additional Major Crime Indicator (MCI) or any other category (other than the above three) from the Public Safety Data Portal to add to their theme, and include in their analysis and report. Scope: Three Key Performance Indicators One Benchmark for each type of crime Insights from primary and secondary research Industry wide from other law enforcement agencies (police forces or otherwise) from anywhere in the world, that the team considers significant to be used for point (1) and (2) above. Build a forecasting model from historical data, to map the future of TPS in the context of the theme assigned to you. A Final Report of maximum 20 pages, with findings and insights; and conclusions and recommendations. The project scope and methodology must be well defined at the beginning, with the relevant analysis to be attached (even if as part of appendices) We are open to a Final Presentation of maximum 15-20 minutes, as part of the final class in December 2018, to succinctly provide an overview and highlight the key points from the project work. We will be happy to view the presentations at Concordia via video conference for this purpose and interact with the students as well. Overall Flow for Final Report: Background Research Methodology Findings & Insights Conclusions Recommendations NOTE: The above flow is meant to be only a guideline and is completely flexible to be revised and / or replaced.
TPS BIAU Podcast
1. Develop an overall format or structure for the podcast episode 2. Create all media like background music, etc 3. Recommend and produce the banners for the podcast for hosting on the TPS intranet 4. Provide the right guidelines for what technology may be required to integrate the final output with TPS ecosystem 5. Provide a clear list of inputs like recordings, etc required from TPS end for this 1. A podcast episode of reasonable length (current expectation: 15 minutes) 2. A summary report, very brief in nature, that lists: a. Findings & Insights (about TPS and/or BIAU) b. Conclusions & Recommendations (about TPS and/or BIAU) c. Risks & Disclaimers about the final podcast episode output d. Personal Learnings from the project (as a team)