Exploring Global Music Trends with Tableau: 2nd Praxis Blogpost.

As a graduate student taking a digital humanities course, I explored the dataset titled Spotify – Top Songs by Country Charts from Kaggle, containing the top 50 songs for each country on Spotify as of May 2020 with columns such as Country, Continent, Rank, Title, Artists, Album, Explicit, and Duration, this data offers a snapshot of global music preferences, highlighting how tastes vary across regions and continents. This dataset, spanning 29 European countries, 11 Asian countries, 1 African country, 10 North American countries, 9 South American countries, and 1 country in Australia, provided a rich ground for examining global music trends through Tableau Public. My analysis focused on three key visualizations: Top Songs by Country and Rank, Top Artist by Country, and Top Artist/Song by Map.

Top Songs by Country and Rank

In the first worksheet, I created a heat map to visualize the distribution of song ranks by country. Each cell represented a rank (1–50) for a specific country, with the color intensity corresponding to the duration of the song. This visualization revealed intriguing regional preferences: European countries, for instance, displayed a broader range of top-ranked songs compared to the Americas, where songs like Safaera by Bad Bunny, Jowell & Randy, and Nengo Flow dominated other North & South American charts in May. At the same time, Rockstar by Dababy & Roddy Ricch topped the chart in the US and Canada. The heat map provided an intuitive way to compare how different countries favored certain ranks and song durations.

Top Artist by Country

The second worksheet highlighted the most prominent artists in each country using a Crosstab (text table). By filtering the dataset by Rank and Country, I discovered that local artists heavily influenced charts in Europe and South America, while North America and Asia leaned toward global superstars. This visualization emphasized the intersection of cultural identity and music consumption, illustrating how countries’ diversity shapes global trends.

Top Artist/Song by Map

The third worksheet utilized a Mapbox to represent the average rank of songs by country. This visualization underscored the geographic spread of Spotify’s influence, with darker colors indicating higher-ranked songs. Filtering by Continent, Country, and Rank provided further insights—for example, South Africa showed a strong love for TheWeeknd in artist popularity. While Europe’s charts reflected diverse tastes across its countries. I tried adding explicit content as a filter revealing how regions like South America and Europe were more likely to feature explicit songs in their top charts.

Reflections

Having used Tableau before but not consistently with the tool, I can say it was both rewarding and instructive. Its interactive features allowed me to craft dynamic visualizations that conveyed the global reach and regional nuances of Spotify’s top charts. However, challenges like cleaning data (e.g., converting song durations into total seconds) and balancing interactivity with clarity highlighted the importance of thoughtful design in this digital humanities project.

This project aligns with our readings on the storytelling power of data visualization, showing how tools like Tableau can illuminate cultural and social patterns. By mapping music’s global influence, I gained a deeper appreciation for the intersection of technology, data, and human expression. This praxis not only improved my technical skills but also underscored the potential of digital tools to explore and share complex cultural narratives.

Kelechi Iwuagwu (Data Analytics & Viz, CUNY Grad Center)

11/20 Mapping with QGIS Workshop

I attended the Mapping in QGIS via zoom this week. I mentioned earlier in the semester that taking an online course via dhrift.org, can be an alternative to attending in person workshops. I think I was biased on how well that workshop went, the in person experience really allowed me to glide through the online instructions. This workshop there was a bit of a lag on following the steps on github. Offering Mapping in QGIS in person would be great, it would allow participants to ask quick questions about correctly downloading, and jumping back on track after a wrong click. With 2 hours on zoom, I do believe 2 hours in person we could have gotten into more tricks on the tool. But overall, the workshop was very beginner friendly, from downloading the tool to opening and importing the data, it was very click and drop like other tools we were exposed to during this semester.

I would recommend the workshop, the 2 hours went by so quick because each step of the way you’re learning how to use the tool, but given the time to go at your own pace. What I liked most was the question posed to get us thinking about what data we would need to get us started. It allowed the group to think about a question/a project first, and the group offered insight on the best places to find the information you’re looking for. I would urge for a in person session, but via zoom I learned a bunch I didn’t know before.

Instructions to download QGIS:

1. Visit the QGIS download page: [QGIS Download]

2. Choose the installer for your system:  

   – PC users: Download the 3.4 version compatible with your Windows system.  

   – Mac users:Download the 3.4 macOS version compatible with your operating system.  

Resources for Data:

https://gss.norc.org/us/en/gss/get-the-data.html

https://www.kaggle.com

https://www.pewresearch.org

Github, step by step instructions: mapping/sections/1basic.md at master · GC-DRI/mapping · GitHub

I’ll update and share the slides once received.

#workshops #mapping #tools #qgis

Intro to Mapping with QGIS Reflection

On November 20th, I attended the Intro to Mapping with QGIS workshop led by Parisa Sateyesh through CUNY digital fellows. The workshop started with short introductions that included each participant’s interest in mapping with QGIS. I was excited for this workshop as it related to my final project goal of mapping language change in Scotland.  

I enjoyed the introduction to the practical part of the workshop. Ms. Sateyesh discussed how maps are not neutral representations of a universal reality, rather they are social constructs. Maps are tools of communication and are often used to represent relationships of power. Ms. Sateyesh shared her screen to show a fascinating map that questions our assumptions about the United States. It was a map of indigenous territories. Rather than the stark lines and colors separating the colonized states, this map had many overlapping structures and almost translucent colors to allow for the overlapping areas.  

I also enjoyed how Ms. Sateyesh went through chronologically the questions one must ask themselves when one wants to build a map. For example, one must ask “what story do I wish to tell?”, “What is my budget”, “What is my timeline?”, and “Do I want a static or interactive map?” Finally, one must find the proper data files to create a map. At the very least, one ust have a spatial data file that contains the geometry points to populate in QGIS. After that, one must gather the data files relevant to the research question. 

For the practical part of the workshop, we aimed to create a map of NYC that plots 10 popular sites to compare neighborhood data on median household income. To begin, we cleaned our data files and imported the spatial data into QGIS. I quickly found that my computer could not handle running zoom and QGIS at the same time. To continue with the workshop, I closed QGIS and followed along while taking notes on each step. Once it came time to import the secondary location data file, most of my peers had technical difficulties. The facilitators helped by explaining which settings to toggle, but it was unclear why those settings were that way. Overall, the technical difficulties took away some time and we did not end with the map we set out to create. During my mapping praxis project, I played around with QGIS before settling on Tableau for my final product. I found the ‘playing around’ and trial and error to be very effective in helping my understanding of the software. I did not find the workshop to help my understanding as much. 

Overall, I highly enjoyed the conceptual discussion on mapping with QGIS and the organization suggestions provided by the facilitators. 

AI: Humanity’s Frankenstein’s Monster?

In Mary Shelley’s novel Frankenstein; Or, the Modern Prometheus, the titular Victor Frankenstein devotes his college career to manufacturing life. Once he figures out how to accomplish this, he immediately runs out of the house in fear of his own creation. This creation, functionally a newborn, learns from Victor’s neglect and selfishness as well as the fear and disgust of those around him to become a villain to Victor and his family. The creature is not made evil. He instead learns of pain and the desire to lash out as a direct result from the world around him, especially from his creator.

When we think of AI, we think of a brilliant, apathetic, arcane assemblage of math, logic, and technology that can easily outsmart its creator. We imagine HAL 9000, Auto, or Ultron, potentially conscious beings that can turn against us all in the name of following their programming. AI is, in this train of thought, a villain in its own right that we were foolish enough to flip on.

Yet, much like Frankenstein’s Creature, AI isn’t a naturally malevolent force. It also isn’t some objective force inherently better than its creator. It is only as good as the influences that it can learn from. And it learns from us. It needs to be fed data to learn from before it can make any judgments or create any responses, and, as previously discussed in this class, our data is far from objective.

Like the Creature, AI is often seen as this Boogeyman in its own right – solely responsible for stealing jobs and leading to increased oppression. To clarify, AI is absolutely used to do these things, but it is not a sentient force that can be held responsible. As Dr. Lauren Klein points out, corporations and government agencies using AI want it to be perceived as this objective black box to obscure their own goals. Corporations who are looking to use AI to replace jobs would cut down work forces and look for cheaper labor without AI existing. Law enforcement racially profiles citizens with or without facial recognition software. With or without an algorithm, humanity grapples with who owns what information. Furthermore, doctors may not be so inclined to use biased AI note taking software during appointments if they weren’t so bogged down with work from the corporate healthcare model so prevalent in American society. The evils of AI are nothing more than children of the ignorance and greed of those in power.

Conscientious use of AI could result in allowing us to see situations from a different angle (i.e. in the case of Deep Blue or Alpha Go), or to lessen the emotional or physical toll of processing data. On its surface, AI is first and foremost a tool. Unlike the Creature, it doesn’t have an emotional state nor a desire to lash out at the world. It simply does what we tell it to do. If it floods the internet with indiscernible gray text, then, despite the horror stories going into print, that will be the result of people breaking the tap on the faucet for any number of reasons including additional ad space, pure malice, or the innately human desire to simply see what would happen.

The irony in writing this is that I, personally, am most inclined to, like Frankenstein, run out of the house on AI when I can recognize it. I refuse to use AI to generate text, code, or (especially) imagery. For the former, I would like to keep my own skills sharp and make sure that I understand what I am engaging with. For the latter, I refuse to benefit in any way off of the stolen artwork of artists who neither provided consent nor were in any way adequately compensated. With all that being said, even as someone who does her best to avoid AI (and almost certainly fails in some regard), I do believe that we need to analyze the problems with AI at the human source.

Grant Writing Workshop

In late October, I attended a grant writing workshop hosted by the Interactive Technology and Pedagogy program. The workshop by Mieasia Edwards provided an overview of the grant writing processes and promoted group work and collaboration. Participants went into breakout rooms to discuss our proposals and gave each other advice and feedback. At the end of the workshop, we were also given time to create a white paper that could later be refined for grant applications.  

The workshop discussed the grant lifecycle, which includes seven steps: generating an idea, finding funding, developing a proposal, submitting it, negotiating and setting up an award, managing the award, and closing the project. There was also a lot of emphasis on defining the “why” of the research. I found this section particularly helpful when writing the abstract for the DH project. The workshop also provided a list of funding sources.

The section I found most helpful was the one that showed the grant lifecycle through the eyes of both a grantee and a grantor. It helped me understand what a grantor would look for, how to frame my proposal, and the importance of communicating my project’s significance.

Here is the link to the slides from the workshop: https://docs.google.com/presentation/d/1eUIruNjSa0ioWQZwhKBHWEJuoBT8HAHG/edit#slide=id.g2d4e7775594_0_31

I hope to use what I learned in last week’s class and the ITP workshop to help me with my project proposal!

#Blogpost; Understanding Data Feminism for AI: A Talk by Dr. Lauren Klein – Mapping Feminicides, African Languages in AI, Equity and Liberation.

Data Feminism (2020), by Dr. Catherine D’Ignazio’s co-authored with Lauren F. Klein describes how feminist principles can reshape our relationship with data and technology. In a world increasingly dependent on AI and algorithmic systems, their work challenges us to critically examine who holds power, who is excluded, and how to address these imbalances.

A core argument in Dr. Klein’s lecture is the brittleness of AI systems. These systems, optimized for specific groups, often fail when applied to diverse populations. This brittleness extends beyond AI—social, governmental, and technical systems also favor certain groups while marginalizing others. The roots of this inequity often lie in biased training and missing data, which consciously or unconsciously perpetuates harmful exclusions.

Feminism, as outlined by Klein, is not just a belief in equal rights for all genders but a political action and intellectual heritage that seeks to dismantle unequal power structures. Intersectional feminism goes further by addressing the broader dynamics of privilege and oppression. It’s not solely about gender but about understanding and challenging power imbalances across all facets of society.

Data Feminism offers seven principles to guide data work: examining and challenging power, rethinking binaries and hierarchies, elevating emotions and embodiment, embracing pluralism, considering context, and making labor visible. Mimi Onuoha’s Library of Missing Data vividly demonstrates how missing or biased datasets harm marginalized communities. Examples include the lack of data on police violence, hate crimes against trans people, or systemic barriers for older adults with disabilities.

Dr. Marivate Masakhane of South Africa NLP project exemplifies efforts to preserve African languages in AI. These languages, often classified as “low resource,” are overpowered by dominant languages in mainstream AI models. This imbalance underscores how technology perpetuates hierarchies, sidelining voices that do not conform to the default settings established by powerful tech entities. The work of Masakhane challenges this narrative, striving to bake cultural and linguistic context into machine learning models.

One critical reference is the Data Against Feminicide project, which supports activists like Maria Salguero who uses data to map and document feminicides. Through AI co-design workshops, the project asked pivotal questions: Should tools be built at all? If so, how can they reflect the activists’ unique contexts and priorities? Notably, the activists resisted delegating their work to AI, recognizing that optimization often prioritizes efficiency over equity not factoring the nuances that makes us human. Instead, they emphasized building tools that empower their work rather than replacing it. Also noting that the risks of data activism are real.

Most importantly, the takeaway is designing AI and data systems must prioritize liberation over exploitation, amplifying marginalized voices while dismantling oppressive structures.

Finally, Dr. Klein asserts, that the fight for equitable data is essential for challenging systemic inequities and reclaiming power for the marginalized.

Link to Dr. Klein’s Talk

– Kelechi Iwuagwu (CUNY Graduate Candidate, Data Analysis & Viz)

Class Group Discussion Reflection

Going back to an earlier session on Epistemologies of DH, I wanted to reflect on a class discussion I found particularly interesting. One of the questions asked for the reading, “Why Data Science Needs Feminism” by Klein/D’Ignazio was “In what ways has data/technology been a tool of oppression but also a site of resistance?” My group discussed examples from readings outside the course, real life, and from the discussion text.

From the perspective of data being a tool of oppression, we discussed an example provided by Ruha Benjamin’s Race After Technology. She shares in her book how an algorithm created to track the recidivism rate of prisoners in Florida inaccurately predicted that black prisoners would re-offend at higher rates. And that’s just one example of the antiblack climate being perpetuated in justice systems. There’s also predictive policing software that leads to the over-policing of black neighborhoods because the data being fed into the software reflects surveillance priorities. I remember Benjamin stating that crime prediction algorithms were more like crime production algorithms.

We also shared ideas about future tools of oppression. For example, the OMNY system because of the type of data being collected and tracked. Why would OMNY need to collect our mailing address? Are we being profiled based on where we live? Another potential system discussed was the policing of sex trafficking with big data. The article brought up questions about a possible distinction between voluntary sex work and human trafficking. According to the article, we must be wary of labeling certain categories of sex workers as ‘trafficking victims’ when drawing only on open-source data.

As for how data could be a site of resistance, we drew an example from the discussion text. Darden, a black female data analyst at the NASA Langley Research Center realized her scientific accomplishments weren’t as acknowledged as the work done by her male counterparts. After consulting with Gloria Champine – a white woman working in Langley’s Equal Opportunity Office who had been compiling statistics about gender and rank- Darden’s realization was confirmed. The data revealed that the promotion rates of men and women with similar academic credentials were very disproportionate. She then takes the numerical data and creates a bar chart which she displays to her supervisor. He was “shocked at the disparity” and the chart proved to be the perfect solution to communicate a systemic problem. I think presenting the data in an easily digestible format worked in Darden’s favor. A bar chart is easy to read, and the simple design communicates the point without it being overwhelming.

Overall, I found our discussion very informative, and it was nice how we tied in readings and experiences from outside the course to answer the questions asked.

Article references:

https://www.crimejusticejournal.com/article/view/2139
https://omny.info/privacy-policy
https://data-feminism.mitpress.mit.edu/pub/frfa9szd/release/6

Reading Response: Public & Digital are not interchangeable

Although a short reading, “Public, First” is impacting the way I am thinking about my project. Digital and public aren’t interchangeable in humanities, you may have a digital project that can potentially “other” the public or community your project is intended for and/or about. This made me look at my project, how I want to center the project as a tool and resource for children & families that need guidance to navigate the Administration of Child Services. Although, I wanted to provide an immediate remedy during high stress situations (like run-ins with law enforcement), after reading “Public, First” the service-driven approach spoke to me, my values, and what I want to contribute to this community. I would like to include the shared authority approach, to invite to others to contribute to the project especially audiences outside of academia, as highlighted in the reading. What started as a mapping project, one method to demonstrate space, may turn into a project that creates a space. A space to have the community in conversation with each other, share the resources they have used, and share their experiences that can in hand help others in similar situation. The reading has allowed me to reposition my project with, what I argue, a more community-centered approach.

Reading Response

This week’s readings is a great addition to last’ weeks discussion regarding our digital lives and younger generations take on ‘the digital’. Looking at how students aren’t inherently drawn to DH, may find to be more knowledgeable of the digital than teachers, and are accustomed to digital tools more so than older generations of students and teachers brought a piece to the conversation that we began to discuss last week. In discussing what’s in and out of DH and what DH actually is, it was refreshing to read Ryan Cordell’s take on how not to teach DH. Although, theories and definitions are important, in any discipline, speaking of the not so fond memory of undergrad studies, it is hard to draw connections between lengthy and complex definitions and meaningful present research and projects. A huge point in the reading was engagement, which essentially draws to the essence of DH in collaborative work. Teaching should emphasize hands-on projects and critical engagement with DH methods that naturally integrate digital tools, allowing students to see DH’s relevance without extensive theoretical framing. This importance of teaching is highlighted in the piece “What We Teach When We Teach DH” (Brian Croxall and Diane K. Jakacki), how teachers of DH should encourage students to see themselves as active participants in knowledge production. To Cordell’s point to be active and engaged, not to see how well they know DH history and definitions. Both pieces touch on a flexible approach to teaching, Cordell with scaffolding and integrating, and Croxall and Jakacki with a collaborative approach. A flexible approach, Cordell argues helps demystify digital tools and encourages students to apply DH methods within their field of study organically, bringing them back to the foundational principles, definitions, and theories.

#teaching #pedagogy #collaborative #handson #engagement #students