Reading Data Sets
Digital collection of annotated data sets.
Digital collection of annotated data sets.
Research update by the COVID-19 Data Working Group.
I am interested in the Macro scale and the macro effects evident at a city-scale level. I remember visiting New Orleans in 2016 and vividly remember seeing several signs with a large 'No' symbol drawn and the text "neighbors not tourists" printed on the sign. Recently, as part of my research into New Orleans, I stumbled on this piece by the Guardian on how short-term rentals through platforms such as Airbnb are leading to gentrification in New Orleans. Highlighted in the article is how several Airbnb hosts do not reside on the listed premises. I remember the place we stayed, as we were a large party, having a 617 prefix number. The prefix stood out as I knew the code 617 represented Boston and was curious what someone with ties to Boston doing in New Orleans as a host. In a similar vein, the article also highlights the problem of absentee hosts, hosts who acquire property for the sole purpose of setting up the property as an Airbnb site.
To tackle the problem, one councilwoman passed a law that required any Airbnb hosts in residential zones to have a homestead exemption verifying they live on site. In this case, a city-wide measure was taken and passed into law affecting the micro. It is common to have one host having several properties in different residential areas in New Orleans. From a technical standpoint, it could be viewed that Airbnb as technology is developed and presented as a scalable product. With no limits to reproducibility. Meanwhile, real-life discontinuities exist in the form of such homestead laws. It is impossible to live in more than one homestead at the same time. In other words, the concept of the human is not scalable.
Likewise, neither is cultural heritage. The city of New Orleans positions its self as a city with great cultural heritage. It is through this heritage that they seek to draw more and more tourists. How do cities think of scaling up successful initiatives and how do they navigate the political, social, ecological, or economic entanglements. At what point is downscaling necessary? Is culture scalable?
[1]https://www.theguardian.com/us-news/2019/mar/13/new-orleans-airbnb-trem…
I am currently a Ph.D. student interested in exploring the entanglements of scale, especially in the context of environmental sensing. My primary research seeks to engage in discourse around the value of scalability that is presented as inherent in computation. While the term scale-up is almost synonymous with computation, sustainability; on the other hand, is known as a problem of scale. Take for example, the discourse on climate change where the actions required to combat climate change requires interventions at different scales. In this context, demanding changes at individual scales while no corresponding changes happen at larger scales would not yield much.
In looking at New Orleans, I came across a video on IoT cameras developed by Cisco, the networking giant. What struck me other than the apparent rise of surveillance capitalism was the narrative of one of the police officers highlighted in the video. The officer mentions that it is not feasible for the city to place police officers on every corner. In the context of scale, the police officer is implying that cameras are useful as they extend the police officer's ability to surveil the city. In other words, cameras and the networks help scale up the police officer, making it possible for them to cover a larger scale than before.
One of the police officers, in the video, also mentions that New Orleans is a tourist and hospitable town. Which brings up the question at any given period, what scale of visitors can New Orleans support without stretching the city's resources? Several other cities in the world have made efforts to limit visitors, in order not stretch city resources. The recent crisis at Mount Everest is an excellent example of what happens when resources are stretched to accommodate the increasing number of local visitors. How could something of this nature similarly impact New Orleans?
At the communication center where the video feed is analyzed, the IT manager provides reasons as to why they chose Cisco as their vendor. One of the reasons he gives was that the system is easily expandable, allowing the ability to scale out/up the network.
I found the part where the healthcare worker relates to the difficulty of his position most compelling and persuasive. A man on the burial team talks about some of the challenges he faced. He says that they are in denial about the disease. For example, a man’s wife died from the disease. They took the body and marked the room with the health tattoo, do not enter and barricaded the door. A health team was tasked to disinfect the building but the moment they left the husband bust the door down and went inside. He died as well. “You see the challenges? You tell people, don’t do this, they pass behind you go do it, don’t do this, they say we are eating free money, the government is lying”.
I was probably influenced by the fact that I am a healthcare worker and while not the same situation, I can relate to his dilemma.
Dr. Knowles points out the structural failures of the World Trade Center due to steel beams and poor fireproofing material. Dr. Knowles connects the burning of the Capitol Building in 1814, the 1850 Hague Street boiler explosion in NYC, and Chicago’s Iroquois Theater Fire of 1903 to convey the different aspects of a structural disaster. The Capital Building focused on the investigation, the importance of the sentimental value of the building, and rebuild it as well as the difficulties involved with doing so. The Hague Street Explosion investigation attempted to pinpoint the root cause of the disaster, but after thorough investigation there were many failures at many different levels which led to the ultimate failure. The Iroquois theater fire revealed issues with public policy, regulation compliance, and public perception in addition to its investigation.
This is a list of analytics by the COVID-19 Data Group.