Gaelic Language Mapping

In 2019, I had the pleasure of studying abroad in Edinburgh, Scotland. While there, I studied Scots Gaelic Language and Culture.  

Scots Gaelic (Gadhlig) is considered to be critically endangered. While once the founding language of Scotland, Gaelic has undergone a significant decline in the last couple centuries. This is in part due to purposeful political efforts to push it into extinction. For example, the 1872 Education Act shifted the official language of formal education to English. Gaelic became associated with rural, poor, and less educated communities in areas such as the Highlands. This caused Gaelic to become a marginalized language associated with discriminated groups. 

However, in recent years, efforts have been made to increase appreciation, preservation, and learning of the Gaelic language. For example, Gaelic language returned to schools in 1985, the BBC now has a Gaelic language channel, and Gaelic is offered via Duolingo worldwide. Furthermore, the Bord na Gadhlig, “a public body responsible for promoting Gaelic development” (https://www.gaidhlig.scot/en/our-work/) was created in the early 2000s. 

For this mapping assignment, I aimed to visualize the effects of these valiant efforts to preserve and develop the Gaelic language. Through Ildefonso’s “Finding the Right Tools for Mapping”, I knew that Tableau would be the best choice for my skill level and mapping goals. Through the Scotland Census website (https://www.scotlandscensus.gov.uk/), I was able to download the JSON file of Scotland’s Local Authority (LA) region boundaries as well as the csv files for Gaelic language data in both 2011 and 2019. First, I had to synthesize the Gaelic language data. Since Gaelic is a traditionally oral language, I wanted to sum the count of all Gaelic speakers (regardless of reading or writing capabilities) for each LA region. Additionally, I had to ensure that each LA region name matched those in the JSON file. Then, I was able to relate the two data files in Tableau and visualize, by color, the number of speakers in each region. What I found was a significant increase in total number of speakers in every region with numbers doubling in city centers such as Glasgow and Edinburgh.  

If I were to move forward with this project, I would love to include location pins with pop out windows of individual stories of native speakers. This would allow the viewer to understand the acquisition of the language and its connection to the real lives of Scottish people. Additionally, I would love to make more maps that show the diaspora of Scots and the hubs of Gaelic language around the world, specifically in Canada.  

Throughout this process, I thought often of Dr. Nelson’s presentation on “Critical Cartography”. I aimed to be accessible to those with colorblindness by making the color analysis a gradient scale. I also recognized that as the creator of the map, I was holding power to tell a narrative that is not mine. While this map can be interpreted to show a success in the attempt to bring back the Gaelic language, it only tells a small portion of the story. By omitting data from before 2011, the viewer may not understand the history of hostility and cultural oppression that led to the necessity of these attempts.  

Irish National Archaeological Monuments Mapping

This summer, I spent time in Ireland doing fieldwork at the country’s midlands monastic sites. For my praxis mapping project, I created a map of Ireland’s National Monuments, color-coded by county. This is a work in progress, as I’m trying to layer in visitor data from 2021-2023 to look for trends about how visitor numbers to site has changed over the past three years.

I used Tableau Public to create this map, mainly because I am now learning the program in the intro Data Viz and Design course. I am looking forward to trying the other mapping programs, especially as I am interested in remote sensing use to detect and monitor archaeological sites; ArchGIS is one that I have often seen cited.

I pulled the listing of national monuments dataset here: https://data.gov.ie/dataset/national-monuments-service-monuments-to-visit. This dataset shows Irish national monuments that are accessible to the public where visitors services are maintained. After creating an initial map with these, I used Tableau to color-code the monuments by county. I then tried the Tool Tip feature to add/eliminate data about each site (eliminating the longitude and latitudes in favor of descriptions of each monument).

I then located visitor data at the Office of Public Works at gov.ie: https://www.gov.ie/pdf/?file=https://assets.gov.ie/288264/b8df6873-57e8-4f91-9324-962100e3a395.pdf#page=null This was downloadable as a PDF, so I am in the process of working out exactly how to extract the data from the Excel file that I used Tableau to create from the PDF. As someone brand-new to mapping and to Tableau, this process is taking a while, but I am excited to continue and see which trends I can notice, and then think about additional questions raised by this visualization.

Making Lemonade from Lemons Map

For this assignment I had planned on created a global map of the history of how the term algorithm has come to be and be used as a concept. The inspiration was from a book I’m reading Filterworld: How Algorithms Flattened Culture by Kyle Chayka. I researched a few mapping tools such as ARCGIS, Tableau, and Leaflet, but determined the learning curve to understand how to approach them was far greater than the time I had to complete this assignment. I wound up using EasyMapMaker instead (https://www.easymapmaker.com/) which graciously supplied some sample excel sheets that I could easily modify with my information.

I modified my initial excel sheet to the longitude and latitude of various ancient cities/countries reference in the early history of algorithms (ie: Baghdad, Greece) and including a link to further information, an accompanying image, and a brief description. Sadly, I kept receiving an error message that the locations were not all found. This seems to be due to geographical limitations of EasyMapMaker. Upon further investigation, it seems only a few handfuls of countries can be used with this platform. Are these limitations of a free platform without the support to staff a larger swath of countries, or? Below is a list of most of the countries available- which is not completely Euro-centric, but limited nonetheless.

Facing this limitation and time crunch I switched gears and thought of the Panama Diaspora in the United States (FYI the Panamanian Parade is October 12th near the Brooklyn Museum). I quickly modified my algorithm spreadsheet with information for the Brooklyn parade, San Francisco, and a Diablo Rojo (iconic bus) in Washington DC. I stopped here due to time constraints, but can think of a dance group in Orlando and a “Reina” competition in Austin, Texas off the top of my head. There would of course be more data upon further research.

The map itself is ok considering the ease of use and total lack of previous experience:

https://www.easymapmaker.com/map/8d403dec03bbebb540b99e9164ef664b

6/10, would recommend for ease of use and if concerned with one particular country within their list, rather than a global map. Did not test more granular addresses, but seem to have this capability particularly in the United States.

The Politics Behind Design, Space, and Maps

Short Response #1 | Madison Watkins

In exploring both Dr. Erica Nelson’s “Critical Cartography: The Subjectivity, Politics, and Power of Spatial Data” and Bonilla and Hantel’s “Visualizing Sovereignty,” I found myself deeply fascinated by the political nature of maps and spatial data. Dr. Nelson’s discussion on the politics of mapping, in particular, stood out to me. She describes maps as powerful tools that “delineate and reinforce power,” emphasizing how they’ve historically been used to marginalize certain groups and uphold the interests of those in control. This resonated with me because, to this day, we can see how the design of cities, borders, and infrastructure continues to reflect inequalities of power. Take the Mercator Map, for instance: it’s a stark reminder of how even the ways we visually represent the world can uphold power imbalances, distorting land masses and reinforcing colonial ideologies. 

To me, it’s almost infuriating how such maps are still so widely accepted and used. But, I mean, here in the West, it’s what we’ve been taught—so how is the average person supposed to know the truth? How are we supposed to uncover and then unlearn the lies we’ve been told since the beginning? Power, racism, and division run so deep that it’s scary to think about how embedded these forces are in each of our everyday lives. What excites me, though, is the potential to redelegate that power. I’m excited to see and contribute to more efforts that return the narrative, the representation, and the control of space to those who have historically had it taken from them. Throughout history, communities have been displaced, misrepresented, and marginalized through the manipulation of space, and maps have played a significant role in that process. Using mapping as a tool to empower these communities feels like a step toward justice.

These ideas, actually, connect directly to what we discussed in my Environmental Social Science class this week. We were to read Ingold’s “Culture, Perception, and Cognition“, he discusses how individuals’ sensory experiences shape their perception of the world. Ingold emphasizes that perception is not just shaped by cultural factors but is also deeply personal, rooted in how we physically engage with our surroundings. This made me reflect on how different people experience and interact with spaces based on their unique lived experiences. Access to resources, mobility, and the physical design of spaces all impact how individuals relate to their environment, especially in urban settings where marginalized communities often face greater barriers.

Edward T. Hall’s “Psychology and Architecture” from The Hidden Dimension (1969) builds on this by examining how physical spaces affect psychological well-being. He argues that the built environment shapes our behavior and interactions, but even more, it affects us on a subconscious level—how we feel and move through spaces is often determined by design choices we might not even be aware of. This made me think of how urban infrastructure like public transit systems and green spaces can either promote inclusion and accessibility or perpetuate exclusion. The way Hall connects spatial design with psychological responses emphasizes the importance of considering the lived experiences of individuals when designing equitable environments, much like Dr. Nelson’s call to critically examine how we map and design spaces through a political lens.

Jane Jacobs’ “The Uses of Sidewalks: Safety” and Kevin Lynch’s “The City Image and Its Elements” also speak to the power of thoughtful, human-centered design in creating more inclusive and socially cohesive spaces. Jacobs highlights how well-designed public spaces, like sidewalks, can foster community, while Lynch offers a framework for understanding how we mentally map and make sense of urban environments. Both authors underscore the importance of designing spaces that prioritize the human experience—spaces that empower rather than exclude.

These readings, across both my Environmental Social Science and Digital Humanities courses, have strengthened my understanding of how perception, behavior, space, and politics are all deeply intertwined. They’ve pushed me to think critically about how maps and urban design reflect power structures and, most importantly, how we can work to challenge those structures by creating more inclusive and equitable environments.

Blog Post Mapping Praxis Assignment

Carol Harris

Tableau is a user-friendly digital tool that allows you analyze and visualize data in multiple ways, using techniques such as graphs, charts, scatterplots and geospatial maps. This was my first time interacting with tableau as a creator. At my place of work there are a plethora of dashboards created using Tableau. As such I frequently interact with Tableau as an end user. Furthermore, being able to create interactive dashboards is one of the biggest advantages of Tableau, particularly as it relates to business intelligence.  

One of my favorite things to do on Saturdays is to visit the farmers market at Grand Army Plaza. Farmers market is a way to get fresh produce while interacting with and supporting local farmers. I grew up on a small island in the Caribbean. My dad was a fisherman, and my mom grew vegetables and flowers in our garden. On Saturday’s folks on the island would come together at a designated spot, the nexus of town. Folks would sell and buy the goods. It was also an opportunity for interaction with friends and family. When my family and I go to different parts of New York state we search for farmers market and if one was conveniently located, we would visit it. This got me thinking about where the farmers markets are located across New York. I was able to pull data on the farmers market in New York state from the Data.gov website. https://catalog.data.gov/dataset/farmers-markets-in-new-york-state . It’s an uncomplicated data file that list the address of farmers markets by counties, cities and zip codes for New York State. The file also includes their website address and whether they accept SNAP as payment. It turns out there are approximately 646 farmers markets across New York. Importing the data into Tableau was straightforward. This data was in a CSV file and Tableau can import a variety of different file formats. Once the data comes into Tableau it automatically decides what variable type to assign. Interestingly, Tableau is able to assign geospatial data as such from a string variable. So, you don’t need to have longitude and latitude if you are using state, city or zip code data. You do need to be careful though because if you just include the county without the state Tableau may give you some unknown or null field for that county. Once you have ascertained that the variables have been correctly labeled the next step is to create a worksheet where you can essentially drag and drop fields. Tableau will give you an instant preview. It’s interactive so you can play around until you are satisfied with the outcome.

Using a map to represent this data made it easier to see where the markets are located geographically. I could have made a table of counts to show which country had the most farmers’ markets. But that wouldn’t situate the location of the markets across the state like a map does.  That was the biggest advantage to me. Even using a bar chart wouldn’t have give the entire picture.

Praxis Mapping Project – Visualizing Eurovision Winners (2009 – 2024) with Leaflet

Map: https://ahutnick.github.io/eurovisionmap/ Repository: https://ahutnick.github.io/eurovisionmap/

As I mentioned in class last week, I have relatively recently become a huge fan of the Eurovision Song Contest. Though I do not always agree with the politics of the competition (or the winners selected), I find the kinship, experimentation, and cultural pride to be inspiring.

When I was trying to think of what to map for this project, I figured that it would be neat to see who won each year in Eurovision compared to who would win strictly according to the jury vote and the televote respectively. To put it simply, Eurovision winners are determined by points allocated by the televote (the viewers) and the jury (representatives from each nation participating in the competition. As such, a nation can fail to win the most votes from either or both the jury and televote systems and still win. I was curious to see how often since the joint system was put in place in 2009 all three winners were the same. So, I loaded up Eurovision World (a fan archive of Eurovision contest history) and pulled the data into a spreadsheet.

After considering free ArcGISOnline, I decided to use Leaflet since I have a basic understanding of JavaScript and felt more familiar with writing the code myself. I figured I’d program the map using simple JavaScript and HTML and host the site on GitHub Pages. Also, as someone with Ukrainian heritage, I admittedly felt biased toward the Kyiv-originated open-source site. I pulled up as many tutorials as I thought would be relevant, loaded in a basic tile map, and got to work.

My idea for the map was that the user would see markers, one for each year of the competition from 2009 – 2024, noting the host cities. When the user clicked on one, they would see a popup with the year and the host city name, and then the winners from that year would be colored in. I decided that yellow would represent the official winner, dark blue would represent the jury vote winner, and a sort of fuschia would represent the televote winner. If the official winner also won the televote and / or the jury vote, then the country would remain yellow. I chose these colors for their distinctiveness and also because they roughly matched Eurovision’s 2022 color palette, which was based around those 3 colors.

Adding the markers and popups were easy, and thanks to a Leaflet tutorial, I was able to separate the tilemap layer from the label layer so the markers and color fills wouldn’t obscure the nation labels.

My first big hurdle arrived with adding the country data so I could add in the colorfill since I didn’t want the map to be crowded with markers. In order to fill in each specific country, I would need to pull in geoJSON geometry data, consisting in a collection of points to have the computer draw the borders. After some searching, I found rapomon’s geojson-places module, which contained a function to pull geojson information for countries by two letter country code. Unfortunately, since I wanted to keep the site setup simple, I wound up copying and pasting geojson geometry coordinates for each country into my own geojson file from the downloaded module. This was somewhat time consuming, but ultimately worked. Figuring out how to reliably change the country colors on marker click was a little more challenging considering that the geojson information isn’t inherently accessible after adding it to the map. After much Googling and scouring Stack Exchange, I figured out how to create a Layer Group and change the fill colors per year there.

For finishing touches, I needed to decide where to focus the map. This was especially tricky considering that I not only had to account for Israel being involved in the competition, but also Australia. While these inclusions spark a vigorous debate as to “What is Europe, really?”, I had to decide whether I wanted to show every country on load and have the markers blend into each other more, or focus on most of the countries and have the markers be a little clearer (thanks again for hosting twice in the same location in the past 15 years, Sweden). In the end, I decided on the latter. I also decided to add specifically which country and song won which vote into the Host City popup to aid in map navigation and also include more context behind the win.

Along the way, I found myself adding more details to my to do list – update the markers, add in the other participating countries per year, have each country have a pop up including their song per year – but in the interest of time, I decided to ship the map once my initial plan was done. GitHub Pages also takes a considerable amount of time to deploy the first time, so I definitely wanted to give extra time for troubleshooting in case it failed.

For the current minimum viable product, when you open the map, the popup for the 2009 Moscow competition is displayed, and Norway, winner of all three categories, glows yellow. The idea is that the user will then follow the gold path to the next marker in chronological order, since the winning country hosts the next year’s competition. The only exception is reflected in 2023, where runner up and jury vote winner UK hosted since the winner, Ukraine, could not host due to the war with Russia. The user would have already passed by the one Ukrainian marker at that point, and would ideally be drawn toward the only UK marker in the dark blue filled country.

Some improvements I would like to make:

  • Add in the participating countries for each year to flesh out who was in the Europe that decided the winners, and lead the audience to ask why.
  • Change the marker appearance and size. As previously mentioned, Sweden hosted two competitions in Malmö in the past 15 years. Add the Copenhagen competition and you have three markers practically overlapping unless you’re zoomed in on Sweden and Denmark. I was originally also going to change the marker appearance to include the logos from each competition, but refrained due to copyright concerns. Replacing the markers with empty hearts – a nod to Eurovision’s logo – may be a better move.
  • Perhaps add a line between each of the competitions chronologically to aid the user
  • Add a legend for the color scheme
  • Automate the popup population for the host cities
  • Add in accessibility improvements (i.e. fine tuning the color palette, ensuring I hav ealt text, etc)
  • Figure out how to remove Crimea from Russia’s geometry
  • Update the README

Issues inherent to this map:

  • Pulling someone else’s geoJSON geometry means that I am forced to use their defined boundaries. For example, as mentioned above, Crimea is included in Russia’s geoJSON geometry. I need to learn how to either pull this data myself or edit this data to be more cognizant of contentious borders.
  • Similarly, hard coding the geoJSON geometry means that this map cannot reflect any changing borders without me going in and updating the coordinates manually.
  • My premise of “highest jury / televote score” may be slightly misleading – just because a song “won” one of these categories does not mean that they won by a large percent, or even that they placed higher than every other song except for the actual winner. Furthermore, some years the jury vote and televote are at opposite ends of the spectrum, with most of each votes going to their respective songs, and sometimes the highest jury vote or televote counts aren’t that far apart. It would be easy for someone with, say, an anti-jury bias to take a look at the map for the 2023 contest and argue that the jury “rigged” the competition in favor of Sweden over the televote winner Finland when in reality, the televote totals between Sweden and Finland were not far off. My current plan to improve this point is to add in difference in points for that particular category from the other winners.

Blog Post#1: Short Response to Weekly Reading – Mapping

The readings this week were illuminating and completely changed my perception of maps as a reliable representation of the world.

Dr. Nelson’s discussion of the role of mapmakers in creating and sustaining systems of power was painfully insightful in its analysis of the ways that the representations of spatiality are and continue to be Eurocentric, patriarchal and racist. The consideration of implicit/explicit biases and the decisions made during map-making have a crucial effect on the outcome – size was shown to have a direct relation to the importance relegated to Western countries, categorization have consequences on programmatic decisions and allocation of resources/funding and design choices affect accessibility. The 4 minutes spent on the social impacts of map projections in the West Wing episode recommended by Dr. Nelson is commendable for this brief effort in an otherwise commercial venture.

The takeaway of the readings are that to use maps to promote social change, both the cartographer and the audience need to educate themselves and use that knowledge to challenge systems of oppression. These conversations are relatable to the data/capta conundrum we explored last class and Data Feminism’s assertion that data is power and more importantly, that data feminism is for everyone. It helps us understand what critical cartography requires from us. We need to question the context of what is being represented, examine the agenda of the makers and the purpose the representations are meant to serve. Monmonier’s How to Lie with Maps was an apt precursor to comprehend the implications of concrete examples presented in the webinar of misrepresentation, underrepresentation, the political motivations driving polarizing mapping practices of disputed lands and gerrymandering all in the service of systems of power.

Visualizing Sovereignty provided some fascinating examples on possible ways to address some of the issues by taking out size considerations from the representation and introducing a significantly different narrative style to better accommodate the volatile conditions of Caribbean history. All the sites explored showcased examples of activism in action, worthy projects highlighting the inequality and blatant racism in mapping practices as well as ongoing efforts to decolonize representations. 

However, as someone who only now recognizes the immensely problematic practices employed in creating maps, the material did not provide enough direction to create a meaningful map on my own. My attempts involved using Carto and Tableau to create a map for the setting of Robin Cook’s medical fictions to show how he never ventured far from home in his fictional journey, but the result was not satisfactory enough to submit as an assignment. Since I could not find mapping data, I created a csv source file with the latitude and longitude for Cook’s 46 story locations and his place of birth, education and residence. Most novels are set in New York and without a specific location which would have required a review of each book, I avoided duplication by mapping the novels to current hospitals in NY. Another attempt with a dataset on world grain production was not very successful. Although, I was grateful to get a result because it came after several trial and error sessions and that helped me gain some understanding of the tools and the complexities of mapping.

Below is a screenshot of the Cook map.

Mapping of Robin Cook's novel settings

What is Digital Humanities? Definitions and Perspectives.

Before diving into the different definitions of Digital Humanities (DH), it’s worth exploring the etymology of the words themselves. Understanding the roots of “digital” and “humanities” should give us insight into the field’s core purpose.

The word digital originates from the Latin word “digitus”, meaning “finger or toe”. Historically, humans used fingers for counting and basic calculations, making them the earliest “digital” tools. Over time, “digital” evolved to describe anything manipulated by numbers or “digits,” which led to its use in technology. Towards the mid-twentieth century in the 30s, “digital” came to signify a system of electronic signals based on binary code (a series of 0s and 1s). In this sense, “digital” refers to the way we now manipulate information electronically, a modern parallel to how fingers once helped us count. 

www.vocabulary.com

Now, combine this with humanities—the study of human culture, history, language, and expression. When put together, Digital Humanities reflects a merging of traditional humanistic study with the power of digital technology to study, analyze, and interpret vast amounts of data, offering new methods of exploring human culture with respect to time.

 Defining Digital Humanities

There isn’t one fixed definition of Digital Humanities; rather, it covers a broad range of approaches that vary based on perspective. At its core, DH uses computational tools to advance the study of humanities subjects such as literature, history, philosophy, and art. This could involve analyzing texts using algorithms, visualizing historical data, or creating digital archives. For example, researchers might apply text-mining techniques to large collections of literature to uncover patterns in language or themes that would be impossible to detect manually.

Another major aspect of DH involves digitization and the creation of digital archives. In this sense, digital tools are used to preserve rare texts, manuscripts, or cultural artifacts that can be accessed and studied by a global audience. For instance, projects like the Caribbean Digital Archive make historically significant, pre-20th-century Caribbean texts available online, bringing marginalized voices into the spotlight. This aspect of DH emphasizes inclusivity and open access, helping to democratize knowledge.

Furthermore, collaboration is a key feature of Digital Humanities. Researchers from different fields—historians, coders, data scientists—work together to build interactive platforms like digital exhibitions or visualizations that engage not only academic circles but also the public. This interdisciplinary approach makes DH dynamic and far-reaching.

Lastly, some view DH as having a social justice focus. – Digital tools can be used to expose inequalities or recover stories that have been erased by traditional archives. Projects that map historical immigration patterns or highlight the role of enslaved peoples in shaping culture are examples of how DH plays a crucial role in activism.

DH is an evolving field that blends the “fingerlike” manipulation of data with deep, humanistic questioning. Whether through computational analysis, the creation of digital archives, interdisciplinary collaboration, or social justice projects, Digital Humanities is transforming how we explore and interpret human culture. Just as early humans used their digits—fingers & toes—for counting, we now use digital tools to process and engage with vast amounts of data, offering new ways of seeing the world and its histories. This fusion of the ancient and modern makes Digital Humanities both a continuation of and a revolution in the study of humanity.

A digital humanities world.

Kelechi Iwuagwu (PRagmatic)

Data Analytics & Viz, The Graduate Center, CUNY

Using QGIS To Map Panama

Data Sources: STRI GIS Portal

Last week, I introduced the fact that my mum is British-Guyanese. On top of that, my father is Panamanian. He comes from Bocas del Toro, an area where Chiquita bananas (owned by the United Fruit Company) were produced. In January, we visited his hometown together for the first time; he was surprised at the changes. Entering the port via water taxi, we passed massive ships carrying cargo. Cranes could be seen on the land lifting containers up and down. My father could barely recognize where he was; he remarked that Chiquita must have paved the roads to make transportation easier for themselves.

I thought of Panama not just because of my experience there earlier in the year, but because of the climate issues that it is facing. Gardi Sugdub, an island off the coast of Panama, is sinking (due to rising sea levels). Primarily a fishing town, it’s home to an indigenous community. This past summer, Panamanian officials relocated locals.

Initially, I wanted to create maps tracking the changes in climate and sea level over Gardi Sugdub and surrounding islands. Unable to find the specific files that I wanted to use, however, I decided to begin by making basic maps of the general country within QGIS.

QGIS is a free, open-source software used for mapping and geographical information systems (GIS). Without some sort of tutorial or how-to, it can be confusing. Because I’ve used QGIS before to map NYC census data, I treated my map-making as a “refresher”. However, even with the previous experience, it is not as “common sense” as it seems. My first time using it, I didn’t understand the meaning of a “Vector Layer”. Additionally, I had to google “how to make a map QGIS” to get from the “project” mode to the “layout” mode. After using the software a couple of times for basic map-making, the process becomes almost automatic.

I used data from the Smithsonian Tropical Research Institute: GIS Data Portal. Because the chosen data is so organized, I didn’t have to do any JSON edits or cleanup. I made four distinctive maps: Provinces of Panama (2016), Areas in Panama With Electricity (2010), Areas in Panama Without Electricity (2010 – Modified Legend), and Areas in Panama Without Electricity (2010 – Original Legend).

Provinces of Panama (2016) showcased the general administrative areas set by the government of the provinces within the country. I have previously never seen Panama’s indigenous provinces or areas mentioned on a map or in general conversation, and so when turning on the “labels” of the different areas, I was happily surprised. However, with the labels, I found it difficult to position them exactly the way I wanted; it was either they hovered over the areas and obstructed outlines, or they overlapped with one another – making it confusing to read.

With population information from the 2010 census, Areas in Panama With Electricity (2010) displayed the general areas in Panama where there was electricity. STRI did not specify whether it was people, households, or entire neighborhoods that had electricity; the distinction is important as the count would drastically change. Moreover, it was on this map that I was not entirely sure whether it was my skills, the information data, or the spread of the population that was leading the map to look the way it did. The two areas where electricity seems at its highest were near the capital, Panama City, and Chiriquí, where many power plants are located. To modify the legend, I changed the styling to my “population” layer from a single symbol to graduated, with an equal interval for my VPOCLUEL (with electricity) value.

I treated Areas in Panama Without Electricity (2010 – Modified Legend) and Areas in Panama Without Electricity (2010 – Original Legend) as experiments, due to the different highlighted areas. After changing the legend values, there were no areas on the map displaying any sort of color values. This would suggest that all areas seemingly had electricity. However, in the original legend, the values for those without electricity were low, with the last spread ranging from 317 – 397. Having this sort of legend spread didn’t make sense considering the last legend contained values in the thousands.

Ultimately, I want to continue exploring QGIS features, such as the plugins, the Python and SQL servers, etc. Ujaval Gandhi makes great step-by-step tutorials ranging from basic to advanced and with time, I’ll be following those.

Google Slides: https://docs.google.com/presentation/d/1oJx6LofrL0rzZkqGGs_qOxHh42Nr073Mqq2xcvScrAM/edit?usp=sharing

Why Data Needs Feminism & Graphical Display

The readings on Why Data Needs Feminism and Humanities Approaches to Graphical Display have many overlapping themes. Graphical Display argues the limits and skewing of data to present an outcome or serve a purpose. Data should be seen as “capta”, in simple terms, subjective. One way to do that, and to push forward the argument in Graphical Display is to view Data under a feminist lens. In Why Data Needs Feminism, authors briefly touch upon the inherent erasure of human experience in data, how data can remove complexities, as mentioned in Graphical Display. However, Data Needs Feminism argues “Data are part of the problem, to be sure. But they are also part of the solution.”

Reading Graphical Design was a great segway to Data Needs Feminism, Graphical Design highlights the consensus of the general public of data taken as fact, but argues “Data are capta, taken not given, constructed as an interpretation of the phenomenal world, not inherent in it.” So what interpretations of the world can we make using Data? How can we use those interpretations to advocate for ourselves and others? Does Data take new forms, with a humanist lens? Data Needs Feminism is one of many examples on using Data to not just be in interpretation of racism and sexism, but to display the intersectionality of discrimination based on race and sex. Data Needs Feminism does not shy away of the critiques of data, by referring to Data as a tool to consolidate power over lives. A quote that embodies the consolidation of power: “The process of converting life experience into data always necessarily entails a reduction of that experience—along with the historical and conceptual burdens of the term.”

Data Needs Feminism not only highlights the drawbacks of Data, similar to Graphical Design, but also lifts up the triumphs. In the case of Christine Mann Darden, the complex relationship between Data and lived experience comes into play. The way data not only validating Darden experience of workplace discrimination but gave her and colleagues the ability to advocate on a larger scale. In a sense, propelling an individual truth to a larger scale, a scale those with power could no longer ignore. Does this story, like many others, insinuate data is needed to validate lived experience? How can data shift and morph, from points on graph to pictures in a digital gallery, with feminism or more broadly a humanist approach?