New York: Factors In Crime
INTRODUCTION
The Division of Criminal Justice Services (DCJS) plays a vital role in the complex web of criminal justice data. With over 500 police and sheriff’s departments in New York State completely connected, the DCJS operates a web-based network collecting and compiling crime reports. This infrastructure serves as the backbone for New York’s official crime statistics, seamlessly feeding into the National Uniform Crime Reporting (UCR) Program led by the FBI. This network not only facilitates monthly crime reporting but also ensures uniformity in gauging overall crime volumes, underpinning law enforcement efforts, and shaping policies.
Insights from this question will help inform decisions regarding resource allocation, crime prevention strategies, community outreach programs, and legislative measures aimed at reducing crime and improving public safety. Understanding the primary drivers of crime can guide targeted interventions and initiatives to address underlying issues and contribute to a safer and more secure environment for residents.
Main Question
How do firearm rates correlate with overall crime rates in different counties of New York State, and what implications does this correlation have for law enforcement strategies and policy-making?
Ideal Data
The ideal data for determining the primary factor influencing crime in New York City would include detailed crime types and rates, demographic information, geographical data, historical trends, socioeconomic indicators, and law enforcement statistics like arrest and clearance rates. This type of data is relevant as it provides a comprehensive understanding of crime dynamics, aiding in targeted resource allocation, and effective crime prevention strategies.
METHODS
DATA COLLECTION
To answer the question of what influences crime rates in New York, I will use a dataset provided by the Division of Criminal Justice Services. The dataset in question comprises information related to crime statistics, including the county, year, population, and various counts and rates such as Index Count, Violent Count, Property Count, and Firearm Count. These fields provide a comprehensive snapshot, detailing the population size, total reported crimes (Index Count), and specific breakdowns for violent and property crimes. The inclusion of firearm-related counts offers insights into the role of firearms in crime. Analyzing these variables is crucial to understanding the factors influencing crime rates and formulating targeted strategies for crime prevention and law enforcement resource allocation.
The data was loaded into a Pandas DataFrame for analysis and visualization. No specific libraries like requests or BeautifulSoup were used for data collection, as the data was obtained from a pre-existing dataset.
To answer my question, I made a network graph that analyzed specific details that could help in answering my question. This network graph visually captures relationships among counties with the highest average crime rates in 2022. The code initializes a directed graph using NetworkX, adding nodes for the top 10 counties with high crime rates and edges based on firearm rates. Visualization with arrows shows relationships between counties. Node labels include population and firearm rate details, providing a concise representation of these connections.
DATA ANALYSIS
“Pop” refers to the population, and then you’ll see that the county is in a black bold text. “FR” stands for the firearm rate for the specific county. This network uses nodes to depict the top 10 counties with the highest 2022 crime rates. Each node represents a county and is sized based on its population. Information like population and firearm rate are displayed within the node. Edges connect nodes with higher firearm rates to those with lower ones, highlighting potential disparities. This visual representation helps us understand population context and potential connections between counties through nodes and edges.
In this situation, the “importance of a node depends on the degree (i.e. number of connections) of the node. The arrows(i.e. edges) are drawn from counties with higher firearm rates to counties with lower firearm rates. Counties with fewer arrows (or edges) pointing toward them are counties with the highest firearm rates. Vice versa, counties with the most arrows (or edges) pointing toward them are counties with the lowest firearm rates. Some important nodes in the network graph would be Tompkins, Bronx, and Broome. Tompkins has the most arrows or edges pointed towards it which means it's the county with the lowest firearm rate(19.9). Bronx on the other hand is the only county with no arrows pointing toward it, hence it's the county with the highest firearm rate (214.2). Broome County is the second county with the most edges towards it which means it is second as the second county with the lowest firearm rate (40.5)
Attached are more graphs I formulated based on the data in the CSV file.
Analyzing New York State crime data from the Division of Criminal Justice Services, the prominent factor influencing crime rates is identified: firearm rates. We all know New York and it probably comes as no surprise that firearms are a main factor in crimes as gang-on gang violence is extreme in that area. The network graph highlights counties with higher firearm rates facing more challenges. To address this issue, targeted interventions are crucial. Allocating resources for community engagement programs and advocating for stricter firearm regulations is considered a strategic approach. By focusing efforts on these key areas, law enforcement and policymakers can effectively reduce crime, fostering a safer environment in New York.
Data cleanup wasn’t needed as the CSV had no missing or invalid values. The main challenge was graph visualization, requiring adjustments for clarity. Others might face similar challenges in presenting complex data visually — necessary repetition is needed for meaningful representations.
LIMITATIONS
The analysis has a few limitations. First, it relies on reported crime data, which might not include all incidents. Second, it mainly looks at numbers, overlooking some important reasons behind crimes. There could be bias due to underreported crimes, affecting firearm rate accuracy. The data lacks socio-economic info and community details, limiting a complete understanding. Updates happen yearly, possibly missing real-time fluctuations. Also, creating a direct link between firearm rates and crime oversimplifies the issue. Societal, cultural, and individual factors aren’t fully considered. To truly address crime, a more comprehensive approach that considers various aspects is needed.
Github: https://github.com/elmantador45/Module-2-Assignment.git
