Embarking on Dashboard Week’s journey, we were given the crime data of Los Angeles on day one. As the data came from an unfamiliar area, I understood the critical importance of acquainting myself thoroughly with the dataset to ensure a successful analysis. Consequently, I dedicated almost fourth of my time to understanding what each column is about.

Upon examination, I organized the data into four distinct parts: time, place, victim, and crime details. The time aspect encompassed reported date, occurred date, and occurred time, providing essential temporal context. The place section revealed valuable information about crime areas, premises, and locations, adding geographical insights. The victim part furnished basic details about the victims, such as age, sex, and descent, contributing to a better understanding of the impacted individuals. Lastly, the crime segment offered vital insights into crime codes, descriptions, mocodes, weapons, and statuses, unraveling the intricacies of each criminal incident.

To streamline data preparation, I utilized Alteryx, implementing crucial transformations. Firstly, I standardized the date format for reported date, occurred date, and occurred time, ensuring consistency for further analysis. Secondly, I replaced the mocodes with descriptive words, enhancing clarity in the dataset. Furthermore, through meticulous research from official sources, I introduced a new column that represented crime categories derived from crime code descriptions, facilitating comprehensive categorization. Additionally, I expanded victim descent abbreviations into detailed information, making the victim demographics more insightful.

A fascinating observation surfaced from the crime mocodes description column, explicitly highlighting whether the suspect knew the victim. This unique perspective captivated my interest, compelling me to construct an insightful dashboard that analyzes the likelihood of crimes committed by strangers or acquaintances.

Guided by the dashboard’s theme, I swiftly harnessed the power of Tableau to create a diverse array of compelling charts. These visualizations offered captivating insights into the involvement of strangers and known suspects across various crime events, crime times, and their impact on victims.

As the journey has just begun, I am filled with anticipation to unearth further insights from this extensive crime data. Stay tuned for more thrilling updates as I progress through Dashboard Week!

The Data School
Author: The Data School