-
-
Precinct wise data
-
Time of the day data
-
Heatmap of time of the day and precinct data
-
Yearly data
-
Monthly data
-
Top 10 precincts with most crime data
-
Date wise crime data
-
Scatterplot of categorie, precinct and time of the day data
-
Map of montreal
-
Heatmap of type of crime and precinct data
-
Heatmap of type of crime and time of day data
-
Top 5 precincts with the most crime
-
Type of crime data
Inspiration
We have always been fascinated about solving crimes. We like watching crime thriller. Even though our career paths went in different directions, we still wanted to contribute to reducing and solving crimes somehow. We believe that the root-cause analysis of the Montreal Crime Data would provide us with some insights of why the crime is happening and how it can be stopped or reduced
What it does
It analyses the crime report data of Montreal city and tries to gather some insights of "What crime is happening the most?", "Why is it happening?", "Is there any pattern to it?". And we did get some insights which we have shown in our project and demo video.
How we built it
We first cleaned the data by dropping duplicates and dropping the rows containing No values. The we tried to find the correlation between month of the year and crime statistics. We also found correlation between days of the month and crime statistics, between time of the day and crime statistics and between precinct and crime statistics. We also tried to find which kind of crime is most in which area and what time of the day. We then used our Geodata.json file to check the location of the precincts in the map and tried to understand why crime is more in some precincts.
Challenges we ran into
We did not have enough data to verify our findings. For example, in the data it was not mentioned that if the culprit was caught, what was the reason of him/her committing the crime, what is the population density of each precinct. If we had those data, we would have been able to gather more insights.
Accomplishments that we're proud of
Everyone of us team members being beginners in this field, we are proud of everything we could do to get all possible insights that we could get.
What we learned
We learned that we know too less and there is lot to learn and study as the data can be present anywhere, and as long as we have the data, we can play around with it and get any insights we can. We are still to learn too many things and we will be make sure to participate in more Datathons like this and keep expanding out knowledge horizon.
What's next for Montreal Crime Analysis
We would like to get more data which contains the reason for crimes, the background of culprits, how many people were involved in each crime, etc. Then we would like to explore the data more to come up with more insights on what motivated the individual to commit the crime and we might propose the solution to the government to stop the crimes.
Log in or sign up for Devpost to join the conversation.