Dashboard in Google Data Studio -> Click on it and feel free to play with the data.
The purpose of this project is to showcase my analysis of arrests made by the New York Police Department from 01/2021 to 09/2021 through a dashboard created with "Google Data Studio". The data used in this project is from the dataset published by "NYC open data". (source: https://opendata.cityofnewyork.us/data/).
In order to perform the data analysis on this dataset, It was necessary first to stablish the main big question to understand more the data collection and to see if there is any pattern to be discover related to arrests: Most of the arrests might be identified by a combination of: age, group, sex and/or race?
In order to perform the data analysis on this dataset, It was necessary first to stablish the main big question to understand more the data collection and to see if there is any pattern to be discover related to arrests: Most of the arrests might be identified by a combination of: age, group, sex and/or race?
With the question established, (1) I prepared and cleaned the data using the .csv file as data source (source: https://data.cityofnewyork.us/Public-Safety/NYPD-Arrest-Data-Year-to-Date-/uip8-fykc) and (2) worked with "Google Data Studio" to elaborate the Dashboard to finally after this prior steps (3) analyse the data.
Upon examining the data, it became apparent that the majority of crimes were committed by individuals aged 25-44 (57.1%), males (83.1%), and those belonging to either the Black (49.4%) or White Hispanic (24.6%) race. In order to answer the question "What are the factors that most commonly contribute to arrests?" I needed to filter the data by these categories.
As a result of the data filtering, it can be seen that male individuals belonging to either the Black or White Hispanic race accounted for 61.4% (70,800 arrests), while the age group 25-44 years old accounted for 34.7% of the total arrests. The data suggests that the next crime is statistically more likely to be committed by a male (Black or White Hispanic race) between the ages of 25-44.
Upon reaching this conclusion, I felt that I had gained a deeper understanding of the dataset and became curious to learn more. Specifically, I wanted to know if the 70,800 arrests were concentrated in tourist areas or not.
Using the visualization, I can conclude there aren't a main concentration of arrests in the tourist zone or hot spot tourist zone perimeter (black= tourist zone, red= hot spot tourist zone) in comparision with anothers zones.
Dashboard in Google Data Studio -> Click on it and feel free to play with the data!
###Next Step on the project: Make statistical analysis and representation with Python (Pandas, Numpy, Matplotlib / Seaborn)
