Analyzing Crime Patterns in Montgomery County, USA: An Inclusive Study Based on NIBRS Data (2016-2022)

This study aims to present an analysis of crime pattern in Montgomery County, USA, with the data derived from National Incident-Based Reporting System (NIBRS) of the Criminal Justice Information Services (CJIS) Division Uniform Crime Reporting (UCR) Program. The given dataset covers the period from 2016 to 2022, including details on crimes based on Montgomery County’s UCR rules and provides details on reported crimes.

The primary objective is to analyse patterns in crime occurrences considering various factors in Montgomery County. By examining the dataset, aim is to identify the types of crimes, their frequencies, and potential categorizations.

The study utilizes statistical and data analysis techniques to visualise trends, categorization, and areas for potential intervention. Through these visualizations, the research seeks to provide actionable recommendations to Montgomery County's local police agencies, contributing to enhanced public safety.

Key Tasks

• Cleaned and preprocessed large datasets from the National Incident-Based Reporting System (NIBRS).

• Conducted exploratory data analysis (EDA) and created visualizations to identify crime trends and patterns.

• Performed statistical analysis to highlight high-crime areas and seasonal trends.

• Developed intuitive visualizations to effectively communicate findings to stakeholders.

Common Libraries used

• Numpy

• Pandas

• Seaborn

• Matplotlib

Most used functions and object

• Np.where() – For grouping and merging data.

• Value_counts() – to find the frequency of the item

• Countplot() – to plot the bar graph(X, Y axis and hue attributes changes according to the requirement).

• Container object – to present the precise count for each item and enhancing the granularity of the visualization.

• Color palette – is used for choosing the custom colour palette.

• Scatterplot() – to visualize the pictorial representation and maps.