Montreal Crime Analysis 2015 - 2020
Completion Date:
Project Overview
Montreal popularly known for bonjour Hi greetings, a blend of English and French word for good morning is a safe place to leave. Among Canada’s larger cities, it has one of the lowest crimes rates, with 4,100 incidents per 100,000 people in 2015. Since January 2015, Montreal police has published detailed data on certain, but not all, types of crimes. This project is inspired to show a breakdown of crimes reported by city and tracks its evolution through time, specifically from 2015 – 2020.
Role:
Problem Statement
The number of car thefts in Montreal jumped last year and some models, like the Honda CR-V, have become frequent targets. For the second year in a row, the Japanese manufacturer\'s SUV has been the most stolen vehicle in Quebec, according to the 2021 rankings of Équité Association, an organization that helps insurers counter theft and fraud. In 2021, the Montreal police (SPVM) counted 6,572 vehicle thefts in the city, an increase of 37% compared to the previous year.
Executive Summary
1. One of the biggest challenges i encountered was trying to source for dataset. I had previously worked on 3 different datasets before I finally settled for this dataset. I was almost done on visuals of one of them before a colleague told me that was his project. Although precious time was wasted on those datasets notwithstanding, I learnt different thing I wouldn’t have learnt from my present project.
2. Another challenge I had was how to create waterfall chart for my list of KPIs. At first it looks challenging however after proper searching through the internet I hit the right solution. I created a detached table and switched it with a measure.
3. Dividing my Cities into Cardinal, this is a big deal as it does not exist for the cities I worked with. My aim was to create something that stakeholders outside Colaberry especially folks from the cities on my dataset can relate with and accept even though it does not exist. I dug into Montreal police archived and tweak the way the cities were grouped based on Crime.
Data Dictionary
Key Takeaways
Next Steps
Suggestion
1. The data shows that most crime were carried out on Monday, which coincidentally is the first day of the week where most people will leave their home for business appointment and meetings. I would recommend that the police should put more personnel on patrol on this day. Research has shown that expanding police personnel leads to reductions in serious crime.
2. I would also recommend that Crime stopper activity should be promoted within the city. The data shows that bulk of the crime are committed within big cities where there is large population. Chances are that some resident may knows these criminals but are afraid to report to police for the fear of reprisal. So having a crime stopper who will serve as the middleman for members of the public who want to report criminal activity, but do not want to share this information directly to police will go a long way in reducing the crime rate in the city. Most especially when the informant knows that they will never give their name or testify in court.
3. The data revealed that 32% of the reported crime is Home Invasion. So, to curb this menace I would recommend that resident get a home security system in form of Closed-Circuit Television (CCTV) installed in their home. A home that has a security camera installed, is 300 % safer from break-ins than a home without a camera. This is because Criminals don\'t want to be watched or recorded.
4. Next to Home Invasion is Theft In MV with a share of 26% of total crime reported. In order to drastically reduced this evil, I would recommend that all potential targets are kept away from clear view. Target such as Laptops and bags should be kept inside the trunk when the occupant is not in. And in addition to that the city should improve on street lighting.
