Visualization Of Crime At Austin

Technologies Used:Python,MongoDB,D3.js,BootStrap,Flask

This project takes up the task of solving a real world problem which involves crims commited at Austin.The crime record dataset compiled by the Austin Police department for the year of 2015 is taken as the source of this project. The main aim is to identify the various types of crimes committed in the Austin, Texas area and create an interactive visual medium to help viewers towards making sense of the data: Identifying the areas of Austin with the most crime, Crimes most prevalent in various Austin localities, Austin PD’s crime solving rate etc.The entire project was done using Python , MongoDB and D3.js and Flask

This was a group project and we were a group of 2 working on this project. thanks to Shyam Sundar my project partner for helping me succesfully complete this project.

An Interactive Map of Austin was drwan with the help of d3.map and using the GeoJSON dataset of Austin’s 10 Councils,using Mercator Projection.





Clicking on each council would fade out the other councils and make the selected council prominent. A Bubble chart was also made using the capabilities of D3.js. The purpose of the bubble chart was to show the how many cries were commited at each location which is determined by the ZIP code. The Bubbles are color coded per council.

Each time a request is made a query is made to the backend. Python handles the query and then it queries the mongodb to get teh required data.A complete utilization of features of ongoDB queries and Python Processing were utilized in this project.

The Bar graph gives information about the crimes reported in a chosen council.Ear bar gets highlighed on mouse hover.

A timeline chart is also plotted which displays crime occurrences of each crime for each month over the 12 months of 2015.These are color coded by crie Types. This timeline chart brings out time/seasonal relationships between different crimes.

An Additional feature that this chart possess is the ability to zoom in. That is selecting a range exapnads chart to focus more on the selected region as shown in an example below




The final chart that was plotted is the Hierarchical Circle Packing. This is one of the greatest chart that can be developed using D3. The reason being the depth of infomation that can be attained using this chart.Using this chart The information about the various types of crimes commited can be attained at great lengths.It can be classified based on the councils and also it can give infomarion on the number of days it took to solve the crime.The ability to extract such amount of information makes it a great accomplishment . An Example is shown below

This brings us to the end of this project. I am sure when we feed in the newest data a lot of insights can be obtined and might be helpul for combating crimes.