As someone who moves across cities recent years, crime data visualization is a helpful resource for me to understand the safeness of the city by areas. For this week, I’ve decided to look into “Crime Data from 2010 to Present.”
This dataset contains information regarding crime happenings in the Los Angeles area and the information is subdivided into smaller categorized data. There are around 26 columns of data entries which include the crime’s ID number itself, the date reported and time occurred. In addition, there are several specific categories for the geographical data, including an area name associated with an area code (i.e. 01 for Central, 06 for Hollywood), as well as a 4-digit code for the reporting district (a specific district of the area reported). The actual address and coordinate of the crime scene are also included for further specificity. On the other hand, the actual crime is coded and described in both a quantitative and simplified qualitative way. Each crime category was given an associated number and a standardized description (i.e. 510 for vehicle-stolen, 354 for theft of identity), in which the lower the number is, the more severe the crime should be interpreted as. Lastly, there are also data regarding the victim’s age, gender, and ethnicity, premises as well as weapons and crime status.
Crime dataset, in general, are useful information for the public, especially those who are new to a city such that they can effectively use the data set to better understand the safety in different areas. I believe this dataset makes the most sense to the local police department and are particularly useful for them to keep the city safe. For instance, they can assign forces to different district according to the dataset and thus, can effectively monitor and regulate the city’s safety.
Overall, the dataset has presented adequate information to have a surface understanding of the crime scene in Los Angeles as the dataset is encompassed of mostly hard facts. However, possible relevant information is left out in this dataset. One of the major data that could be left out is the crime that are not reported. Additionally, although difficult to collect or might not be collectible for certain cases, there is a lack of information regarding those that have committed the crime as well as the motive of criminal action. Thus, If I were to collect data in a different way with a different ontology, I would try and include the criminal’s age, race, ethnicity (also there might be a stereotyping issue) here, and record whether a certain crime events are directly harmful and pose risk to the general public or are there a certain relationship between criminal and the victim (in another word, the motive behind the action). This way, those who are studying the dataset will have a better understanding of the patterns and causes of the crime.
This was a great post to help illustrate the purpose and ontology of the dataset for LA’s Crime Data since 2010. The dataset I looked at was Arrest Data from 2010, which host fewer details than the dataset for Crime that you looked into, so I really enjoyed reading about this blog post and then clicking through the recordings of your dataset. I think that the general structure of both datasets are quite similar! We made similar observations that the point of view for which this ontology makes the most sense is the trained police force who is on the recording and receiving end of this data. Great job!