Data visualization improvement
The Toronto Police Service (TPS) is undergoing continuous improvement efforts to enhance confidence and strengthen ties with our society by providing access to open data for public safety in Toronto.The Service would like students to recommend potential improvements of data visualization available on the Toronto Police Service’s Public Safety Data Portal (PSDP). Students can choose at least one visualization (map or dashboard) to :1) Derive insights for the development of new visualizations for PSDP such as analytic dashboards, interactive visual exploration, heat maps that would enhance the “analytics journey” of users2) Development of mock-up solutions that would expand the usage of data visualization by the public3) Thorough ability to conduct and recommend ways to visualize analytics and tell a story would be a plus4) Delivery of a final report and/or presentation of recommendationsAll maps can be found here: http://data.torontopolice.on.ca/pages/mapsAll data analytics visualizations can be found here: http://data.torontopolice.on.ca/pages/data-analytics
Insights and recommendations to decrease an MCI
The Toronto Police Service (TPS) is undergoing continuous improvement efforts to enhance confidence and strengthen ties with our society by providing access to open data for public safety in Toronto. The Service would like students to examine Major Crime Indicators (MCI) data* available on the Toronto Police Service’s Public Safety Data Portal (PSDP), in order to conduct:1) Exploratory Analysis of one particular crime type from the MCI dataset (Homicide, Robbery, Assault, Theft Over, Auto Theft, Break & Enter)2) Insights of main crime tendencies and correlations with demographics, other MCIs, temporal data (time of day, day of week), and other factors3) Final report and/or presentation of recommendations to decrease the crime rate and enhance crime prevention of the selected MCI*Consideration to be given on the use of text mining of street names and free form notesMCI Data can be found at http://data.torontopolice.on.ca/datasets/mci-2014-to-2019
Data visualization improvement
The Toronto Police Service (TPS) is undergoing continuous improvement efforts to enhance confidence and strengthen ties with our society by providing access to open data for public safety in Toronto.The Service would like students to recommend potential improvements of data visualization available on the Toronto Police Service’s Public Safety Data Portal (PSDP). Students can choose at least one visualization (map or dashboard) to :1) Derive insights for the development of new visualizations for PSDP such as analytic dashboards, interactive visual exploration, heat maps that would enhance the “analytics journey” of users2) Development of mock-up solutions that would expand the usage of data visualization by the public3) Thorough ability to conduct and recommend ways to visualize analytics and tell a story would be a plus4) Delivery of a final report and/or presentation of recommendationsAll maps can be found here: http://data.torontopolice.on.ca/pages/mapsAll data analytics visualizations can be found here: http://data.torontopolice.on.ca/pages/data-analytics
How can police enforce covid-19 measures effectively?
One of the challenges faced by law enforcement is to have a clear understanding of COVID-19 related measures in order to enforce them properly. Moreover, the public needs a clear understanding of what measures are currently in place in order to comply. Not all measures are clearly communicated through written media releases.. Many measure announcements have been issued at press conferences and Twitter briefings on behalf of government officials, or in other video formats.As the COVID-19 pandemic continues, government measures and restrictions may have changed over time, rendering any comparison of rates of infection between different jurisdictions and geographies very challenging.The Toronto Police Service (TPS) would like students to research and qualify the government and public health measures put in place by City of Toronto, Government of Ontario, City of New York, State of New York, and federal measures on behalf of Canada and the US. Students can also investigate other cities or states as they see fit (i.e. British Columbia, California), as well as possible correlated crimes leveraging search trends platforms. We ask them to:1) Review government and public health sources of information for confirmed measures put in place in response to the COVID-19 pandemic.2) Compare confirmed measures in one jurisdiction/municipality/province/state with others3) Identify the changes made in policies or measures over time, since the beginning of the pandemic4) Review of reports in media regarding COVID-19 policy enforcement for accuracy and potential for misinformationa. Recommendations for the public as well as officers to follow in order to work with the best quality information and comply with legislation5) Prepare a final report and/or presentation of findings with recommendations for future pandemics
Machine Learning applications to understand tendencies in Fatal Collisions
The Toronto Police Service (TPS) is undergoing continuous improvement efforts to enhance confidence and strengthen ties with our society by providing access to open data for public safety in Toronto.The Service would like students to identify artificial intelligence and machine learning opportunities for the Fatal Collisions data* available on the Toronto Police Service’s Public Safety Data Portal (PSDP), in order to:1) Derive insights and patterns, especially among potential relationships between variables, such as demographics, temporal data, impaired driving, DUI, and others2) Build predictive analytics models3) Create hot-spot mapping4) Deliver a final report and/or presentation of findings and recommendations*Consideration to be given on the use of text mining of street names and free form notesLeveraging other open data sets, such as City of Toronto, weather-related data, or others to be identified by students, is recommended.Fatal Collisions and other traffic related data can be found at http://data.torontopolice.on.ca/datasets/fatal-collisions
Can Major Crime Indicators be Predicted?
The Toronto Police Service (TPS) is undergoing continuous improvement efforts to enhance confidence and strengthen ties with our society by providing access to open data for public safety in Toronto. The Service would like students to research and develop correlation models for the Homicides and Assault data* available on the Toronto Police Service’s Public Safety Data Portal (PSDP) and a comparable city’s open data portal in Canada or US, in order to conduct:The Service would like students to explore and derive insights from the Major Crime Indicators (MCI) dataset in conjunction with at least one other open data set of their choice, for instance 311, City of Toronto, or other open dataset they find interesting and relevant. Ideally, students would:1) Derive insights and patterns, especially regarding potential relationships between variables, such as demographics, unemployment rates, period of the day and year, other crimes and socio-economic trends, and the comparison of tendencies between cities2) Build predictive analytics models3) Perform hot-spot mapping4) Deliver a final report and/or presentation of findings and recommendationsPotential Open Datasets:https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2https://data.cityofnewyork.us/Public-Safety/NYC-crime/qb7u-rbmrhttps://www.toronto.ca/city-government/data-research-maps/open-data/
Correlation between Major Crime Indicators and Twitter trends
The Toronto Police Service (TPS) is undergoing continuous improvement efforts to enhance confidence and strengthen ties with our society by providing access to open data for public safety in Toronto.The Service would like students to research and develop correlation models for the one of the MCI data sets (Homicide, Robbery, Assault, Theft Over, Auto Theft, Break & Enter) available on the Toronto Police Service’s Public Safety Data Portal (PSDP) and Twitter Streaming API, in order to :1) Investigate potential relationships between that MCI and historical tweets and trending hashtags2) Derive insights and patterns3) Build predictive analytics models4) Deliver a final presentation and/or report of findings and recommendationsMCI Data: http://data.torontopolice.on.ca/pages/major-crime-indicatorsHomicide Data: http://data.torontopolice.on.ca/datasets/homicide-1
Machine Learning Applications for Break and Enter Data
The Toronto Police Service (TPS) is undergoing continuous improvement efforts to enhance confidence and strengthen ties with our society by providing access to open data for public safety in Toronto. The Service would like students to identify artificial intelligence and machine learning applications for the Break & Enter data* available on the Toronto Police Service’s Public Safety Data Portal (PSDP), in order to:1) Investigate potential relationships between Break & Enters and other factors, such as temporal data (time of day, day of week), neighbourhood, demographics, tree coverage, etc.2) Derive insights and patterns3) Build predictive analytics models4) Create hot-spot mapping of increasing volume B&E areas within the 140 neighbourhoods and identify commonalities in the premise types5) Deliver a final report and/or presentation of recommendationsLeveraging other open data sets, such as 311, City of Toronto, or others to be identified by students, is recommended.B&E Data can be found at http://data.torontopolice.on.ca/datasets/break-and-enter-2014-to-2019
Gang Prevention PR Campaign - Toronto Police Service
The Gang Prevention Town Hall Meetings are an initiative of the Toronto Police Service geared towards educating local communities on preventing youth gang involvement. Community attendance at these events tend to fluctuate, with the lowest attendance in places with high gang activity. The aim of the Town Halls are:To raise awareness of the Toronto Police Service's gang prevention initiativeTo provide awareness and education on objective youth gang risk factors that are evidence and academically basedTo connect the local community with social service agencies to mitigate youth gang risk factors with protective factorsTo identify and empower community leaders with the tools, networks, and support to foster community success from the inside outTo gain insight into perceptions, issues and lived experiences of community members impacted by gangs and gang violenceTo identify gaps in service to provide optimal policingThe outcome of this project should be a Public Relations Campaign that can be implemented immediately, and increase community attendance especially in the areas with high gang activity, and generally to increase public awareness of gang prevention strategies.
Content Creation for Recruitment Campaign
The Toronto Police Service (the Service) is undergoing continuous recruitment efforts to strengthen it's workforce by hiring police constables, special constables, parking enforcement officers, court officers, as well as many civilian roles including data analysts, records management and human resources personnel. The Service would like to create a portfolio of stock imagery, video shorts, and/or other relevant digital content for use on social media and digital recruitment channels. We would like students to create these assets. Students from photography studies, visual arts, film studies, etc. would be well suited to this project.
Toronto Police Service Social Media Strategy
Analyze the current social media strategy of Toronto Police Service, examining growth and reach. Measuring its effectiveness in engaging with the community, and devising recommendations to be implemented.
Gang Prevention Town Hall PR Campaign - Toronto Police Service
The Gang Prevention Town Hall Meetings are an initiative of the Toronto Police Service geared towards educating local communities on preventing youth gang involvement. Community attendance at these events tend to fluctuate, with the lowest attendance in places with high gang activity. The aim of the Town Halls are:To raise awareness of the Toronto Police Service's gang prevention initiativeTo provide awareness and education on objective youth gang risk factors that are evidence and academically basedTo connect the local community with social service agencies to mitigate youth gang risk factors with protective factorsTo identify and empower community leaders with the tools, networks, and support to foster community success from the inside outTo gain insight into perceptions, issues and lived experiences of community members impacted by gangs and gang violenceTo identify gaps in service to provide optimal policingThe outcome of this project should be a Public Relations Campaign that can be implemented immediately, and increase community attendance especially in the areas with high gang activity.
Toronto Police Service - Community Engagement Framework
The goal of this project is to deliver a framework that will be used by TPS to successfully engage with new immigrants to the City of Toronto. The project outcome should be centered around how TPS should go about educating new immigrants about its services, the role TPS plays in the community and how policing works in Toronto.
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.
Global Search Tool - Implementation Plan
Global Search is a web-based content search tool that is designed for front-line, investigative, analytics and administrative members to access all of the organizations data/information in a seamless search. The search functions are based on access to information which is consistent with each member’s respective role and permissions and provides timely and insightful access to information which is currently inaccessible or available only through multiple searches. This partnership would look to create an implementation plan which can provide a smooth transition from pilot project to final roll out. The final report can serve as a template for other similar initiatives.
Segmentation & Analytics of Major Crime Indicators Data
Segmentation & Analytics of Major Crime Indicator (MCI) DataStudents 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 / rate3) Presentation of recommendations *Consideration to be given on the use of text mining of street names and free form notesMCI 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
Home-sharing Services Data Analysis for Detection of Possible Human Trafficking
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 neighborhood crime data
Home-sharing Services Data Analysis for Detection of Possible Human Trafficking
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
Intranet website for employees only
The goals for this project are to deliver a robust, data-driven website hosted locally on our servers which is easy to navigate and designed with the end-user in mind. The main consumers of this website are analysts, police officers, senior officers and administrators. The focus of the website is to deliver an engaging and dynamic experience through the use and integration of technologies such as chatbots, dynamic charting tools etc.
Segmentation & Analytics of Killed and Seriously Injured Collision Data
Segmentation & Analytics of Killed and Seriously Injured (KSI) Collision DataStudents 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 users2) Hot-spot mapping of high volume KSI collision areas within the 140 neighborhoods by collision type3) 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 notesKSI 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 TPS - Group 2
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: 1. Three Key Performance Indicators 2. One Benchmark for each type of crime 3. 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. 4. Build a forecasting model from historical data, to map the future of TPS in the context of the theme assigned to you. 1. 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) 2. 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 vis video conference for this purpose and inteact with the students as well. Overall Flow for Final Report: 1. Background 2. Research Methodology 3. Findings & Insights 4. Conclusions 5. Recommendations The above flow is meant to be only a guideline and is completely flexible to be revised and / or replaced.
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 crimeInsights 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: BackgroundResearch MethodologyFindings & InsightsConclusions RecommendationsNOTE: The above flow is meant to be only a guideline and is completely flexible to be revised and / or replaced.
TPS Social Media Audience Analysis
To follow all the 3 Phases listed in the Riipen Project Summary, exactly how it is mentioned with the following objectives in mind: 1. To conduct analytics on the corporate social media accounts and the individual accounts (especially of the TPS leadership) to identify influencers, success of campaigns (will be specified later) and overall public sentiment. as well as the TPS member sentiment. 2. To identify current weaknesses in our social media efforts, and identify opportunities for improvements. 3. To check for alignment of the TPS social media content the Service level organisational priorities. 1. A Final Report of maximum 20 pages, with findings and insights (mandatory) and conclusions and recommendations (not mandatory). 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) 2. 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 visit the class at Seneca for this purpose and meet with the students as well.
Information Management Working Group - Internal Branding
The Information Management Working Group (IMWG), with representatives from all pillars, was created in 2017 by the Enterprise Business Intelligence (EBI) Steering Committee to provide direction and oversight on information management and governance with the vision of Service-wide expansion. As part of this project, the students are expected to: 1. Identify the requirements of the IMWG agenda and the BIAU objectvies and apply to the style, colours, templates of the output 2. Identify technological requirements expected from the TPS to integrate with their current technology infrastructure, wherever applicable 3. Keep in mind the core values, maintain professionalism and work in line with current TPS policies and philosophy 4. Provide their work in multiple formats for ease of use at TPS (eg. PNG, JPEG, etc) 5. Accept feedback from IMWG group and make revisions where needed to arrive at final version of output 1. Multiple logo proofs with three variations of your log: a primary logo (text with graphic), a text-only logo, and a graphic-only logo 2. Multiple banner proofs with three variations (as extensions of the logos) 3. A multi-page PDF Visual Branding Guide including the chosen logo, with components explained, rationale behind the design, and the story supporting it 4. A completed sample letterhead, business card, badge
The Toronto Police Service Social Media Strategy
To follow all the 3 Phases listed in the Riipen Project Summary, excatly how it is mentioned with the following objectives in mind:1. To conduct analytics on the corproate social media accounts and the individual accounts (especially of the TPS leadership) to indentify influencers, successful projects and campaigns2. To identify current weaknesses in our social media efforts, and identify opportunities for improvements3. To make recommendations for improvement and in doing so, align the strategy and recommendations such that it complements the Service level organisational priorities 1. A Final Report of maximum 20 pages, with findings, insights, 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)2. A FInal Presentation of maximum 15-20 minutes, as part of the final class in November 2018, to succinctly provide an overview and highlight the key points from the project work.
The Toronto Police Service Social Media Strategy
To follow all the 3 Phases listed in the Riipen Project Summary, excatly how it is mentioned with the following objectives in mind:1. To conduct analytics on the corproate social media accounts and the individual accounts (especially of the TPS leadership) to indentify influencers, successful projects and campaigns 2. To identify current weaknesses in our social media efforts, and identify opportunities for improvements 3. To make recommendations for improvement and in doing so, align the strategy and recommendations such that it complements the Service level organisational priorities1. A Final Report of maximum 20 pages, with findings, insights, 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) 2. A FInal Presentation of maximum 15-20 minutes, as part of the final class in November 2018, to succinctly provide an overview and highlight the key points from the project work.
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)