ESSAY RESPONSE
-Positive and negative aspects of this dataset. - List 2 different paths you would like to (hypothetically) study about this dataset.
The dataset at hand is one that explores many different aspects relating to hate crimes, and is heavy with information. There are several variables within the data set that can be taken into account, and studied as well. There is the county of the hatecrime, the type of crime, the victim, and the frequency. All of these variable are thorough in describing the content of the data set, and leave little room for questions. Another thing to appreciate about this data set is its specifics. It does not have a limited or closed off amount of oppressed persons, and tackles many different kinds of groups. It also has several categories, and among everything else, is generally a rich dataset that is good to examine. That being said, however, the set has some weaknesses as well.
The first weakness can be seen with the the timeframe. It ranges from 2010 to 2016, a timeframe that isn’t very old, but isn’t very updated either.Having a longer timeframe can allow for better analysis, maybe to detect trends or several rises and falls over the years. Its short time frame might also be an explanation for one of the other flaws in the data set, a lot of the zeros that it has. To me, the zeros suggest perhaps that there’s been a case of underreporting. The crime could have happened, but just wasn’t reported.This notion can be harmful, because certain crimes might get overlooked. Personally, I also wish the dataset wasn’t restricted to one state. It isn’t exactly a flaw, but I think a wider range of countries would have more to look at. Aside from that, the data set is still pretty rich with content to be studied.
One of the paths I think would be interesting to look at would be examining the types of crimes, and who it was committed against. To compare the frequency of each crime across different groups. It isn’t necessarily a beneficial examination, but it would show what kind of incidents each group is subjected to. It could be helpful to see the severity of the crimes as well, and how harmful it can be. I would want to measure the frequency of each type of crime, like take for example, I feel like hate crimes that have to do with religious groups are usually linked to some type of vandalism. One of the most notable ways to hatecrime any religious institution is to vandalize it. Any place of worship has been subjected to those types of crimes. However, for other groups like ethnic groups or LGPTQ+ individuals, the hatecrimes they’re subjected to might be higher in personal attacks. I think it would be interesting to confirm or further look into this using the dataset, to come to a better understanding.
Another path I think would be interesting to explore would actually to look into all the zeros in the dataset. I feel that these zeros are a result of underreporting, meaning a crime happened, it just wasn’t reported. Studying this though would likely have to result in bringing in another dataset as well, to use it as a comparison of some sort. I think looking at these zeros can reveal biases in the data and underreporting issues across different oppressed groups. I just think it would be interesting to look at. Misinformation is a huge problem in today’s time, but I guess so is a lack of information, if that makes sense. I thought it was strange that the dataset contained so many zeros, especially in categories like anti-female, or anti-transgender. I have no doubt that these groups face a heavy amount of discrimination, so I thought it was strange that so much data was missing for them. But at the same time, I suppose it’s because the datset is so restricted.
