This project is analyzing the correlation between crime and weather in Atlanta. We hypothesize that weather conditions impact the level of crime. We suggest that the impact weather has on crime is not consistent amongst all types of crime, with some crime increasing when weather conditions encourages individuals to stay home(domestic violence) and other crime decreasing when weather conditions encourage individuals to leave the home (home burglary).
When looking at the historical crime and weather data from 2009 – 2018, we find that on average, if temperature increases by 1 degree, we estimate crimes to increase by .82. Likewise, for every additional percent of rain there is a day, we estimate crimes to increase by 5.62 crimes. Both variables are highly significant, and our model has an adjusted R-square of .96. The closer R-square is to 1, the more of the relationship our model explains, this model explains a large portion of the relationship of crime to weather
Using our model, we are able to forecast the number of crimes that will be committed in Atlanta based on temperature and rain predicted.
Our crime counter looks at all reported crime in the city of Atlanta in 2020, as of the latest data provided by the city of Atlanta.
Choose a date from the dropdown below to see a week of actual crimes commited vs forecasted crimes based on our model.
To see actual locations of specific crimes committed in Atlanta on a day 2020, choose a date from the dropdown. An icon will appear in the top right of the graph to indicate if it was more sunny or more rainy on that day.
The graph below shows crimes committed vs a specific weather metric over the span of 10 years. Choose a metric from the Crime and Weather dropdowns to see the correlation over time of weather and crime
The Atlanta Police Department collects and distributes data on several different kinds of crime that is recorded on a weekly basis. For the purposes of this exercise, we have simplified the data into 5 separate categories of crime, though there is a fair amount of overlap which may occur for these different types of crime. Below is a high level overview of the different types of crime which are included in this analysis, as well as some background on the regressions used to calculate the crime forecast.
The diagram below gives a high level overview of the data sources we used, and how we connected them together within the project flow. The application is completely hosted on the cloud, connecting the web server (nginx), API Server (Flask), Postgres DB, and Mongo DB. Apart from real-time updating weather data, we also used large historical databases for crime data (349,030 entries) and weather (98,592 entries), which we loaded into Postgres DB. We then loaded the processed data into Mongo DB to use for the visualizations above.
Initially, our team was excited to work with data related to movie going patrons, and how much more likely people were to go to movies based on the weather. Does a rainy day typically increase the relative spending at local theaters? Unfortunately, due to COVID-19, movie-going data has experienced some significant variability of late. This led to the natural transition of our team from “The Rotten Tomatoes” to “The Rotten Apples”, showing how weather impacts crime rates.