Project Type:
Individual project completed over 5 weeks. Primary tools used are Python and Streamlit API implementing Javascript and HTML/CSS programming
Project Goal:
- Utilize crime data provided by the LAPD to do a comprehensive review of different distributions of victims and areas where crimes are occurring the most. To integrate analysis, design and interactive features into a single application.
Awards
- It’s Nice That
- AIGA
- Fonts In Use
- The Dieline
Contact
- email@domain.com
Problem Discovery:
Since the COVID-19 pandemic there has been an increase in crimes throughout the city of Los Angeles. As crime increases and more people are being affected it is important to look at the types of crimes being committed and its circumstances to understand what is going on.
Problem Statement:
What kind of crimes are being committed and who are the victims?
RESEARCH
Before starting to create my application, I looked at various sources to see how crime trends are in LA and to see what kind of crimes are being reported on media platforms.
According to the Public Policy Institute of California (PPIC) there has been an upward trend in crime in Los Angeles.
- Homicides were up by about 17%
- Car break-ins and auto theft have seen an increase as the pandemic has gone on
Pulling from various news sources below are some of the types of crimes that are being broadcasted on the news.
LA gangs have sent out crews to follow and rob LA’s wealthy citizens following people with high-end cars and expensive jewelry
30 suspects wanted for violent crimes arrested in operation led by LAPD, federal agents. These suspects were convicted of murder, attempted murder, armed robbery and sexual assaults
Local meat market robbery caught on video
Suspect in the murder of a 34 year old in the Boyle Heights, East LA area
Project Information
The project is a data analysis of crime data for the City of Los Angeles collected from 2020 – Present.
In this analysis I created:
- - A map model to view where crimes are being committed throughout the different police sectors of Los Angeles.
- - An analysis on the distribution of victims of crimes determined by age and gender.
- - An analysis on the distribution of crime codes that are reported and their frequency of occurrence.
- - An interactive feature that allows a user to find out how many of a specific crime was committed towards an individual of a certain race.
All of the information is reported accurately from the database provided by the Los Angeles Police Department (LAPD).
This app was created using Python 3.10 within a Pipenv environment. The packages I used for this app were Streamlit, Pandas and Plotly. I created the app with functionality that would support updated data that is provided by the LAPD so that we are able to see how the statistics change in the future.
Application Features
The map model allows users to see the geographical coordinates of where crimes are being committed that can be filtered by the different police division areas of LA
The different graphical distributions examine the victims of crimes and the types of crimes. Looking at this data we can obtain information about who is being affected the most and if there is any statistical significance in the data
This part of the application allows users to select a crime and specified race of the victim to see the total number of crimes that were committed
Takeaways
From doing this project I learned a lot about how to pull from a dataset to obtain useful information. Using the crime dataset I was able to examine information about the location of crimes, the types of crimes and who the victims were.
Future Implications
We can use this information to help control crimes in certain areas and to also protect those that would be most likely the victim of certain crimes. Additionally, we can conduct research on how and why these trends are occurring in certain areas amongst certain people. Is there a relationship between the two?