FINDINGS

Our team broke down the dataset to extract detailed insights on the arrests for each borough. Click on a button below to explore the findings specific to each borough. Scroll below to see how we performed this data analysis.

DATA PREPARATION

To prepare the data, our team first queried the correct date range for arrests on NYC Open Data. We first queried all the arrest data between June 1st, 2019 and June 30th, 2019, then exported the results as a CSV file. Next we queried arrest data between June 1st, 2020 and June 30th, 2020, and exported that as well as to a CSV.

We then used Excel to create pivot tables that allowed us to break down the number of arrests by demographic group in each borough. Using the pivot tables, we were then able to calculate the proportions that were used to create our pie chart and bar charts.

CONCLUSION

After both a quantitative and qualitative analysis of our data, there are a few conclusions we can make.

From our map, it is evident that as we click through the different demographic groups, the Black and Hispanic layers are significantly more concentrated with arrest markers across all five boroughs, despite the overall population proportion for these demographic groups being rather low in some of these boroughs.

Likewise, our quantative analysis highlighted the exact percentages by which the arrest proportion and population proportion differed for these demographic groups. More often than not, Black and Hispanic individuals were disproportionately arrested in NYC boroughs. Even though they may have comprised only a small percentage of the residential population in the area, they made up the majority of the arrested population.

When considering these trends in the context of police violence and racial profiling, it is reasonble to conclude that black and brown individuals are unfairly targeted by NYPD. With this knowledge in hand, it is important efforts are made to combat the profiling of marginalized communities.

As we chose to focus our analysis on June of 2020, a month of rising protests in the fight against police brutality against Black communities, it is important to connect our data findings with historical context of the political atmosphere during this month. With the rising protests that came from the Black Lives Matter Movement, anti-protest bills with broad, sweeping criminalization language rose through legislation, giving harsh felony charges to protesters who were in a certain proximity of property damage. According to the NYPD news article written on July 6th, by June 7, a total of 1,126 arrests had been made during the protests in New York, all but 39 of which were for non-violent offenses. Many of these arrested protesters are people of color, which swayed our team to predict that arrests of Black and Hispanic communities would be higher in June 2020 compared to July 2020. After creating our arrest proportion comparison charts of the two years, we found that arrest numbers were fairly consistent both years, with the exception of Manhattan which had a 9% increase in the Black demographic group's proportion of the arrested population. This implies that there are many other factors that could affect the amount of arrests of each demographic group, which will be discussed further in our limitations.

LIMITATIONS

There are a few limitations to note about our mapping analysis and conclusion. Since we only used data from a single month to generate our map, it is possible that the patterns indicated are not overall trends, but rather instances that occured by random chance. Furthermore, it is possible that our arrest proportions calculated in June 2019 and June 2020 are not consistent with other months. As a result, it is important to take care when extrapolating from our findings.

Additionally, arrest demographic statistics are not only affected by racial profiling, but the lack of resources available to communities, leading to higher crime rates within marginalized groups. With more time, it would be beneficial to collect data on wealth inequality by demographic group in order to potentially expose a correlation between high number of arrests and demographic groups disproportionately affected by poverty.

Nevertheless, we hope that through our project, these inconsistencies in arrest versus population comparison can spark thought about the ways in which increased arrests amongst marginalized groups is not only an issue of racial profiling, but one of wealth and income inequality.