If there is one thing that I gathered from taking this digital humanities course, it is that technology and data, does not seek to benefit everyone. From machine learning and prediction models with racist biases ingrained into them to digital maps being effectively useless to lands that aren’t inhabited by the western white man, everyday use technology can be a prime example of the legacies of colonialism. The article “Dividing Lines” by Mayukh Sen looks into the Google Earth platform and the difference in how it is used in real life between the western world and the global south, as well as how the portrayal of the platform are inaccurate.

The story of Saroo Brierly getting split from his family on a train and ending up 900 miles away forcing him into an orphanage only to find his family many years later, is no doubt an amazing story. I could only imagine the feeling of knowing your family is somewhere out in the world but, having no contact with them or truly knowing where they are. His use of Google Maps and vague memories together ended up reuniting him and his family in the end. The author of the article said the movie depicted the reunion as a win for Brierley and more of a win for Google Maps. As if the entire movie was one big advertisement for platform. The movie left out key realities about using Google Maps in the Global South. Those realities are that Google Maps does not care about these places. From the authors experience and my own, finding our hometowns in the United States come with no visual hiccups. No outdated or grainy maps and basically accurate depictions of what we would see there on a daily basis. Although when the author looks for his mother’s hometown in India, that he can’t even write in English. There are different results and to make things worse, zooming in on some of the results are basically visually useless. These problems did not arise in the movie Lion instead, the only problem with Brierley had to overcome was his lack of memory from his past. I find this the most interesting part about the article because we see this in different context all the time. As westerners, we love to romanticize adversity and struggle and turn it into a story about overcoming it. Oftentimes, there is a white man savior complex being buried into the message and here, we see that complex on display in the form of Google Maps. Basically, we have the immigrant foreign brown kid being saved by an all-knowing western technology that is portrayed to have no issues and not a hint of the legacies of colonialism. Yet clearly, if you open the platform on your own and look around at some of these global south communities, your results may vary. For starters, in my opinion, I think we need to point this out more in Hollywood because the white savior complex runs rapid there. Also, we need to question companies such as Google about some of their decisions they make with their platforms. Why do you neglect the global south? Why do you use these places as testing grounds? When satellite images are clearly available,  why are some parts of your maps so outdated and in such poor quality? The legacies of colonialism will continue to prevail unless we demand those who benefit the most from colonization to level the playing field.

            I’ve always found it amazing how retail stores can accurately predict to the hour of the day when a package you have ordered from them will arrive. With so much human activity Involved it blew my mind that it’s almost always spot on. Although for this to happen though, I knew there was so much exploitation in the works. Amazon drivers and workers working insane hours just to drop off your package on time is something that has sickened me for a long time but, “See No Evil” by Miriam Posner, opened my eyes to a deeper problem. This article dives into the tech infrastructure and also the human infrastructure of what makes up a supply chain. This article is a huge realization to me that there is a level of deception and exploitation that realistically nobody can comprehend.

For most companies, supply chain activities are sorted and managed through a software suite called SAP (Systems, Applications, and Products).  SAP is a massive software that companies can purchase to take care of all your supply chain needs and much more. Without the need to create and code software of their own, this cuts out a massive project that would take an absurd amount of time to do themselves. Products like these exist all over the business world, Salesforce and AWS for example do different things but, create a backbone to your business needs. Yet, companies are using these prewritten software’s without any real knowledge of what is going on behind the scenes. With SAP handling everything for you in this massive waterway of suppliers in your supply chain,  it becomes almost impossible to know where every product is coming from or how it’s being produced. Leonardo Bonanni put it like this, “If you’re a small apparel company, then you still might have 50,000 suppliers in your supply chain. You’ll have a personal relationship with about 200 to 500 agents or intermediaries. If you had to be in touch with everybody who made everything, you would either have a very small selection of products you could sell or an incredible margin that would give you the extra staff to do that.” Thus, making it nearly impossible with the current system to verify the working conditions of those creating the raw product or even verify that it is coming from where your company says it is coming from. To think that even companies are mostly in the dark when it comes to how workers are being treated while making their product at some point in the supply chain, is a scary thought and makes you wonder just how bad it can get.

So what can we do to fix this? Some ideas like putting it on the blockchain or using machine learning to stay clear of red flagged suppliers are some of the ideas the author talks about. In my personal opinion, blockchain tagging does seem to be the most reliable and ethical option. While I am a skeptic of blockchain and crypto with where it is at now, I do believe that one day we will head to a more online world and these will be the reliable way we operate as a society. With a Blockchain based supply chain, we would be able to tag and reliable tell where product is coming from and where it currently is in the process. With machine learning, I fear that rather than helping the problem, it will help businesses effectively make more money by only working with suppliers that do exploitation really well and can move product fast without hiccups from internal or external events. Rather than making working conditions better and verifiable, it could quickly lead to the complete opposite.

With supply chain methods and practices being so well established in today’s world, is it even possible to just start over or would that lead to total chaos?

            On Wednesday, November 17^th, I attended the Intro to GIS workshop that was hosted by GC Digital Initiatives. Through this workshop, they hoped to teach us three main things. The first being how spatial data is formatted, the second being how to look for spatial data, and the third how to use that spatial data to create a map.

            To start, they asked us to install QGIS if we have not already. QGIS is a mapping software that is great for creating static maps (maps that don’t move). While QGIS is just a software, the GIS in the name means Geographic Information Systems. They defined this as a framework to capture and analyze spatial and geographic data. In the real world, everything you see is all one layer- the roads, the mountains the buildings are all on the same layer but in the GIS world, every feature is a different layer. The waterways, the state lines, and elevation are all on their own layer and stacked on one another. These layers are created by spatial data. Spatial data has at least two dimensions, XY, and sometimes a third, which is Z. There are a few different forms of spatial data. Two very popular versions are vector and raster data. Vector data often represents points, tracks and roads, and land boundaries. Raster data is often used as classification data and changing values through space such as altitude, temperature, precipitation and population density. As the hands on part of the workshop, we were asked to load in the New York City zip codes. Once we did that, the next goal was to highlight the zip code you live in. For me, I live in the financial district so, the zip code I highlighted was 10005.

            As someone who is just getting into the GIS world but hasn’t done much with it since last spring semester, attending this workshop was a nice refresher and it made me want to go back and relearn some things. Such as map projections and understanding the many different ways spatial data is stored. These are very important concepts to comprehend if you want to truly grasp how GIS works.

As someone who has never done any text analysis, it took me awhile to come up with exactly what I wanted to do for this assignment. After thinking of all the different categories I could choose from such as something sports related, movie related, politics related, etc., I decided I’d stick to something to do with politics. My first (and favorite) idea, was to find a compilation of all of Donald Trump’s tweets and see if there were any words or phrases that he used most often. While this would have been a pretty funny and entertaining idea, it didn’t work out. On a more serious note, my next idea, and the one I went with, was to use the transcripts from presidential debates and see if we can spot some of the popular topics that were on their minds during that time period. I decided to look at three different presidential debates across 60 years. The debates I chose were Kennedy v Nixon (1960), bush v Clinton v Perot (1992), and Trump v Biden (2020).

Here’s what I found:

Kennedy v Nixon (1960)

In the 1960 presidential debate, we had the young John kennedy and then the not so young and charming Richard Nixon. Just looking at the word cloud above, we can see words like debate, transcript, and october. I noticed when putting a link into the site I used for this project, Voyant Tools, it collects just about every word it can find on the webpage, not just what’s in the actual transcript you are interested in. This trend will carry on through the other debate word clouds that are included in the blog post. Other common words like, mr, Kennedy, Nixon, president, vice, and senator, are because names and titles are used in the transcript to show who is talking and also, they are also obviously said often throughout the debate to address one another. Looking deeper than that though, amongst a bunch of random words, we can see words like communists, war, soviet, union, islands. These debates were during a time of uncertainty and the U.S. was locked in a Cold War with the Soviet Union. On top of that, the United States feared Fidel Castro and the possibility of his regime spreading Communism across the Western Hemisphere. In the second debate, the two candidates argued about two islands islands a few miles off the coast of the Chinese mainland. Kennedy argued that the line of denese should be drawn at Taiwan while Nixon believed they should draw the line where the West has drawn the line against Communism. Nixon ran with the idea that Kennedy would allow the Communists to take the islands. Kennedy ended up just sliding by Nixon and winning the presidential election in what became one of the most remembered presidential debates in history.

Bush v Clinton v Perot (1992)

32 years later, the 1992 debate looked a little different. This was the first presidential debate that included three candidates all on the same stage. Questions were formatted differently and would now involve real voters allowing them to ask pressing questions. In this word cloud, it isn’t as easy to tell what the topics were as the 1960 debate. Although, three words I do see are tax, people and jobs. In the debates, Clinton stressed that America has not invested in its people. He said that when people lose their jobs in his state, he’d probably learn their names. He said people now are working harder for less money than they were making ten years ago. Stating 12 years of trickle-down economics is the result of this. On the topic of taxes, Clintons goal was to have the government stimulate the economy and reduce the deficit by 50 percent over his term. Bush’s proposal was for a balanced-budget amendment, a line-item veto and a taxpayer checkoff rule. Taxes were a heated argument throughout this debate. With Clinton pushing for taxing the rich and leaving the middle class out of it, Bush was weary and “warned” those to keep an eye on their wallets.

Biden v Trump (2020)

That brings us to our most recent debate, Trump v Biden in the 2020 presidential debate. What a trip this one was. In reality, this debate was more of a who can talk louder and not let the other person answer argument. Surprisingly, most of the words that made the chart aren’t very relevant. Although, there are three that are important- deal, million and China. These three words may not seem too crazy but, they encapsulate most of the topics talked about throughout the debates. The Green New “Deal” was brought up often and attacked relentlessly by team Trump. The Green New Deal was a progressive proposal to effectively fight against climate change by reducing greenhouse gasses, create higher paying jobs, invest in new infrastructure. Trump’s criticism was basically that doing this would cost too much money and hurt the economy (as if letting climate change run rampid would be any better). The word “million” was used a number of times. Pleading to America that the coronavirus is tearing the United States apart, Biden said, “Over 7 million people who have contracted this disease. One in five businesses closed. We’re looking at frontline workers who have been treated like sacrificial workers. We are looking at over 30 million people who in the last several months had to file for unemployment.” biden’s approach was to level with the people by providing numbers and talking to the camera often. China was another hot topic all through the year. 2020 was a very unique year with Covid-19 locking us all away in our homes and has probably been the most talked about topic across the world since then. With the virus originating from Wuhan, China, Trump often put blame on them for the pandemic and used the term “China Virus” when talking about Covid-19. On top of that, China was also talked about often when it comes to trade. Biden said he would make China play by the international rules. He believed Trump was too soft with foreign “thug” leaders like Putin and Xi Jinping and made it clear he wanted to stand up to them. this debate wasn’t about facts a lot fo the time and was mostly a screaming match which means, there was a lot of misinformation about China being shared on both sides. Biden falsely stated the deficit had increased with China and trump had falsely claimed Hunter Biden was given millions of dollars from China.

Hi all, sorry for the late post. Being sick the whole week I decided to begin this project wasn’t ideal and definitely kept me from really diving in. I wouldn’t usually share that but, sense we are sharing our experiences with the project I feel like it fits. As usual with most mapping based projects, the hardest part is finding the data you want in a form where it can be mapped. I searched for plenty of different ideas and datasets but, usually let to a lot of frustration. Sooner or later the idea of mapping the California wildfires came to mind. At first I thought I would only be able find a dataset with coordinates like long/lat which would only give me something like an origin or where the fire started. I also figured I’d only find a dataset with this years data but, lucky, I was wrong! I ended up finding out there is data out there of all California wildfires since 1950 and the perimeters of those fires.

With that, I was able to take the data into ArcGIS and map it onto an interactive map of California. The data had a few different fields to play with. The ones that I kept ended up being:

• YEAR_: The decade the fire occurred
• STATE: The state the fire occurred in
• FIRE_NAME: The name of the fire
• ALARM_DATE: Date the fire was reported
• CONT_DATE: Date the fire was contained
• CAUSE: What caused the fire
• GIS_ACRES: How many acres the fire spread across

WIth that information we are able to see where, how and when a lot of these fires are occurring. To wrap the whole project together, I built a website where I could house the map and any other information I added to the project.