An Analysis of Terry Stops in Seattle, Washington, USA

Introduction

On June 10, 1968, the United States Supreme Court issued a decision on Terry v. Ohio case, in which an officer in plain clothes had conducted a stop-and-frisk search on three men he believed were about to rob a store. The officer found weapons on two of the men and convicted them of carrying concealed weapons. The two men had appealed the case, saying that the evidence used to convict them in the trial was found during an unreasonable search. The U.S. Supreme Court decided that police stop-and-frisks were not in violation of the Fourth Amendment of the U.S. Constitution, which prohibits unreasonable searches and seizures. This case introduced the notion of “reasonable suspicion,” which allows officers to detain a suspect without any clear evidence required for an arrest.

Exploratory Data Analysis

The following are graphics obtained after data scrubbing the dataset and conducting some exploratory data analysis using Numpy, Pandas, Matplotlib, and Seaborn Python packages/libraries. I proposed some questions to analyze the dataset better.

Which Race is Most Stopped?

The graph above displays Terry Stops according to the subjects’ race. The chart shows that White subjects most stopped, followed by black/African-American subjects. However, some subject demographics were missing from the data, so further data collection would be necessary. A majority of Terry Stop subjects were White, immediately followed by Black/African-Americans. According to Seattle’s government website, Seattle’s African-American population is 6.8% of the whole population compared to Seattle’s White population, making up 68% of the whole. However, African-Americans make about 1/3 of Terry Stops.

What demographic of officers perform the most stops?

The first graphic above displays the Terry Stops officer race demographic. The officers who performed the most stops were white. However, some officer demographics were missing from the data, so further data collection would be necessary. The second graphic shows the genders of the officers who performed Terry Stops. A large majority of officers are white men.

What is the Distribution of the Officer’s Ages?

The graph above shows the subjects’ age-group, according to race. The majority of subjects stopped were within the 26–35 age group across all races/ethnicities.
The graph above shows the subjects’ age-group, according to race. The majority of subjects stopped were within the 26–35 age group across all races/ethnicities.
The graph above displays the Terry Stops subjects’ age groups and whether they were carrying weapons.

What Subject Races are Most Stopped and are they Carry Weapons?

The graph above displays the Terry Stops subjects’ races and whether they were carrying weapons.

Model

Conclusions

A majority of Terry Stop subjects were White, immediately followed by Black/African-Americans. According to Seattle’s government website, Seattle’s African-American population is 6.8% of the whole population compared to Seattle’s White population that make up 68% of the whole. However, African-Americans make about 1/3 of Terry Stops. Across all races, the most stopped subjects were those belonging to the age group 26–35. This age group made up a third of the dataset and are also more likely to carry weapons on their person. As for officer demographics, a majority of officers were white men.

Recommendations

For the government of Seattle and the Seattle Police Department to better serve their citizens, I would recommend the following. First Seattle Police department should be hiring more officers belonging to minority communities and women as well. A majority of the Seattle PD are white men, which could cause problems in policing minority communities. Women officers can bring a new perspective to the police force, which would prove to be a good change. I would recommend Seattle PD to reallocate their weapons budget to advancing health care plans to include mental health services like therapy so that officers can better understand themselves and any biases they may have towards specific communities. Most Terry Stop subjects do not have weapons, so there is no need for officers to have so much military aid. With more focus on bettering police practices and the police themselves, there will be fewer lawsuits against officers for abuse of power, which saves Seattle a lot of money.

Future Work

This dataset contained a lot of NaN or incomplete information, so more data collection should be made before this analysis can be 100% approved. Many data columns had a majority of the data missing, so I did not use them in my study even though they could have provided some interesting insights. I would want more data collection to occur in minority communities because I am sure the Terry Stop data would be a lot different from this dataset.

Data Science Student