In this week’s blog post, I am going to explore the dataset Immigration Workshops from the City of LA. According to the Duarte article, Ontology refers to an organizational strategy for dividing information into data. In the dataset Immigration Workshops, information is given and sorted into categories that provide easy access to the community, I will describe its ontology in the following paragraphs.
The ontology of the dataset showcase the available workshops related to immigration information. The dataset is first show up as a calendar, with days of the week (Sunday-Saturday), since the data appears in a calendar-like image, it provides the people an idea that the information may be distributed by dates and time. The datasets provide information such as the workshop location, date and time, they are categorized into different squares of the calendar to provide a clear place of where/when the workshop is going to happen. The content model of the dataset includes date of events, organization, site name, location, start time, end time, language, notes, Free, and contact.
I believe the dataset is useful for the immigrant community and people who are interested in requesting an immigration. Since the workshops contain a variety of information about immigration, it is beneficial for everyone, at different levels of the immigration process. For example, a person that is thinking about immigrating could attend the financial literacy course and citizenship, and for people who have already immigrated, they can attend the workshops about the requirements to pass the citizenship test..etc. In addition, the dataset also shows the language for each different workshop session which embraces the diversity of this topic.
When I first click the dataset link, the calendar- like presentation of the dataset appears right away, although it provided me an idea of what the schedule of the workshops will look like, there are not enough categories of information in each square to show clear information about one specific workshop. For example, on October 4th, there are 3 workshops, one of them is the library connection at Adams square that contains the information of citizenship and financial literacy classes. However, on the calendar provided, only library connection at adams square was shown, causing confusion. They could make bigger squares for the calendar to show more information about the workshop, not just the website name.
If I were to start over with this data-collection, I would combine or even get rid of some of the model content. Having too many model content will create confusion and is visually overwhelming. For example, I would combine the start time and end time into one category, labeling it as “time.” Other than that, I do think this data provide clear information of resources and is beneficial to the city of Los Angeles resident, especially the immigrant community.
I wrote about this dataset as well but didn’t talk about the calendar, I liked that you did though. I agree that too much model content can create confusion for the person looking at it. Combining some on the information would make the dataset more manageable to navigate. Great post!
It is interesting how this dataset shows up as a calendar since I am so used to seeing datasets in the form of spreadsheets in the past few weeks. I can see how a calendar would be much more useful since the target audience is for potential immigrants. I can see how the formal of boxes of information instead of columns could be a problem though since there is a lot of information they need to give.
The calendar is a unique perspective for the graph. While spreadsheets tend to be the most commn, it is interesting to see the data laid out in a more visually appealing way. It definitely makes it easier for the viewer to understand the information presented.