Assignment 2 : Mahima Singh

All data is interesting. Choosing one dataset to visualize took a little longer than I thought it would. Ultimately I decided on the “Hate Crime” data because it has so many levels. The data dictionary is adequality explains the fields and I didn’t have to clean any of the data myself. Some other datasets that involved age groups required me to convert 0ct-10-2019 to 10-19 because the file had that column set to date type.

There are two kinds of crimes in the dataset: “Crimes Against Persons” and “Property Crimes”. While these two are choices under the “Crime Type” column, the type of bias isn’t classified into groups. This data was found in the “Hate Crime Overview” document in the “about” tab on the site.

To find the total number of crimes per type for each bias, I filtered on that particular crime type and summed all the columns. Then I just took these numbers and pasted them into different sheets of the chart making sure that the correct columns came under the correct bias type.

I have worked with before and my personal favorite theme for a chart has always been “Tokyo.” It fit well with the nature of the data. Even though the area charts are giving us an idea of the number of crimes across the different biases, having information on the total number of incidents and especially the number of offenders and the number of victims adds value to the visualization.

Hate Crimes by Bias Type: 2010 -2015
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