LA Crime Rates and Racial Bias

Our group researched a dataset used by the LAPD that includes information regarding crimes dating from 2020 to 2025. The dataset reflects the details of the original crime reports. It contains twenty-eight columns and over one million rows. The information in these columns includes when the crime was committed, the sex and age of the victim, the location of the crime, weapon type, and more. The purpose of this dataset is to help reduce crime and recognize what areas need more policing. 

Using the information from the dataset, we asked two questions. The main question is in regards to the details present. We asked, "How do these variables (race, location, and crime type) indicate a possible racial hate crime against African Americans?" The secondary question relates to missing details. We asked, "What information is missing that could further prove or disprove that a hate crime took place?"

Before starting our research, we needed to cut down our dataset to a manageable amount. We decided to cut it down from one million rows to two hundred fifty. Next, we needed to define what a hate crime is. According to the FBI, it is "a traditional offense like murder, arson, or vandalism with an added element of bias.” To identify possible hate crimes in our dataset, we used Lindsay Poirier's “Reading Datasets” method. Utilizing both denotative and deconstructive reading, we researched how officials define each category of the dataset and how the exclusion of particular data might conceal or expose hate crimes. After looking at the dataset, we did some external research as well. This includes the population of African Americans in LA and areas of LA that have higher crime rates.

After conducting our research, we found that out of the two hundred fifty crimes documented, thirty-three of them had an African American victim. More than half of those crimes (55%) were assaults. Ten, or 30.3%, of those crimes involved some kind of weapon. Out of the victims of these crimes, 51% were women. We also found that a majority of the crimes were committed in areas of LA that have higher crime rates and more police presence. 

Our research also led us to the conclusion that there is valuable information missing that could help determine if the crime committed was due to racial bias. The dataset does not include the perpetrator's race. Having this information would make it easier to determine if the crime was committed due to the victim's race. For example, the perpetrator might be the same race as the victim, possibly ruling out racial bias. The motivation of the perpetrator is also missing. Though a crime might be committed against a person of color, there is no motivation listed to prove if it was executed out of racial bias. Hypothetically, the crime and the race could be unrelated. Another piece of information missing is the LA population. However, unlike the previously mentioned components, we were able to research this one. We found that only 8.5% of the LA population is African American, a very small percentage compared to the number of African American victims. 

Overall, the information provided in the dataset alone makes it hard to conclude if an act against an African American is out of racial bias. There is too much missing data to come to a concrete decision. It could be written off as someone being in the wrong place at the wrong time. However, it can be determined that the population of African Americans in LA, compared to the number of African American victims, is disproportionate and is something that should be investigated further by the LAPD.