Author: gabrielleastrid

I examined Revilna, a site dedicated to “Exploring the Vilnius Ghetto.” Upon entering the site, it was pretty difficult for me to readily understand what the site’s purpose was. I personally had no idea what the Vilna Ghetto was on opening the site, and the site’s landing page didn’t lend me any insight. The copy only refers to the Lithuanian ghetto area as “the Ghetto” and offered no real further clarification, even upon browsing through the site’s various pages.

I ventured into the mapping narrative and found it also to be a tad confusing. I couldn’t readily identify the data that each map meant to present. Some presented types of institutions, some presented data in a more timeline-esque map visualization, others presented sites of events. When browsing through the different maps and pages, these shifted data types weren’t addressed, which made it hard to fully comprehend the narrative the site meant to present.

I also couldn’t find the source of their presented data, making it hard to understand and analyze the point of view of the narrative. Not knowing the credibility or purpose of the images/location data/general information really prohibited me from being able to pick out the biases and perspectival weaknesses of the narrative. The map itself, though, seems to leave out a lot of data, since there are a lot of spaces between the data points. It leads me to wonder what the spaces are between these institutions and event locations. Are they neighborhoods, are they government buildings, are they empty lots? Contextually, I’m not sure what’s going on there. In saying that, though, there is a good breadth of data addressing the various types of institutions that were in this ghetto, giving insight to what structures were in place for people residing in the area to engage with. The interactions of the residents with these various institutions are somewhat well represented in the brief descriptions and image galleries presented alongside the mapped data. I wonder, though, who had access to these various institutions and if there was a stark difference in access based on socioeconomic status.

Perhaps a better map of the area would present data about the people who were actually living in the Vilnius Ghetto (economic status/age/occupation/marital status), since the landing page does claim to describe “how the residents lived.” I think that presenting a clear legend/key to the data points and keeping it consistent throughout the site would have also been very helpful. A more layered map with all various “stories” and narratives as toggle options would have been helpful as well, in order to get a more holistic picture while having access to the details of the data points.

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This data set within the L.A. Controller’s office compiles Los Angeles County’s street pavement conditions according to a Pavement Condition Index. Each record is a numerical rating of the street conditions, with ranges going from good, fair, to poor. Each record contains a street name, location, and date. The data is arranged on an interactive map, showing green, yellow, and red areas for good, fair, and poor road conditions, respectively. It also highlights the neighborhood councils and council districts partitioning the city and allows a viewer to see the varying road repair plans across time by toggling by year.

This dataset’s ontology organizes data with an aim to understand where and when street conditions have been suffering and where they have been improved. The options to toggle between time periods and view district boundaries implies that those are pieces of information that provide contrast depending on spatial and temporal context. This ontology would benefit any worker under the Bureau of Street Services, which is where this dataset and interactive site originates from. It would be helpful in understanding the terrain of Los Angeles, the current conditions of the roads, and what work has been done in the past to remedy problem areas. It also goes to show what work needs to be done further regarding road conditions in certain areas, as there are certain districts with predominantly green (good) conditions, while others are overwhelmingly red (poor). It can also give insight to where funds are being allocated within those districts, specifically what amount of funds are being invested in street pavement repair.

This dataset gives a lot of insight into what the road conditions are like in Los Angeles, and also accurately shows the road repair plans as well. It communicates this data effectively through visual assets that only strengthen the narrative it sets out to convey. With regards to what’s left out of this dataset, it does seem like the data paints an incomplete picture. Many of the roads in the San Fernando Valley are documented and categorized, but lots of areas are lacking in representation. West LA and mid-city, for example, are sparse in data. Understanding the street conditions form the data presented in those areas would be more difficult and possibly misleading, as one may not be able to reach legitimate conclusions from just this data.

I think an interesting ontology to present this data in would be one that demonstrates some cultural/political information along with the street conditions. Incorporating information about the different council districts and their financial brackets or their budget breakdown would be interesting, so one would be able to make conclusions about the causes of the varying road conditions throughout the LA area.