Dataset Overview
The dataset comprises various columns, each providing specific details about crime incidents. The key columns we’ll focus on are:
- COMPNOS
- NatureCode
- INCIDENT_TYPE_DESCRIPTION
- MAIN_CRIMECODE
- REPTDISTRICT
- REPORTINGAREA
- FROMDATE
- WEAPONTYPE
- Shooting
- DOMESTIC
- SHIFT
- Year
- Month
- DAY_WEEK
- UCRPART
- X
- Y
- STREETNAME
- XSTREETNAME
- Location
Let’s start our exploration!
Exploratory Data Analysis (EDA)
- Incident Types and Crime Codes:
- What are the most common incident types?
- Which crime codes are prevalent?
- Distribution of Crime by District:
- How does the number of crimes vary across different districts?
- Are there districts with higher or lower crime rates?
- Time-based Analysis:
- How has the overall crime rate changed over the years?
- Is there a monthly or weekly pattern in crime incidents?
Insights from EDA
- Incident Types and Crime Codes:
- The dataset provides a diverse range of incident types, from thefts to assaults.
- Certain crime codes might be more common, indicating specific types of criminal activity.
- Distribution of Crime by District:
- Some districts may experience higher crime rates than others.
- Understanding the variations can help allocate resources effectively.
- Time-based Analysis:
- Over the years, there might be trends or fluctuations in crime rates.
- Analyzing monthly and weekly patterns can reveal when certain types of crimes are more likely to occur.
Questions for Further Exploration
- Spatial Analysis:
- Are there specific locations or streets with consistently high crime rates?
- How do crime rates vary between main streets and cross streets?
- Weapon Involvement:
- What types of weapons are most commonly involved in crimes?
- Is there a correlation between weapon type and the severity of incidents?
- Domestic Incidents:
- How prevalent are domestic incidents, and do they follow any patterns?
- Are there specific shifts or days of the week when domestic incidents are more likely?