Sources
Process
Presentation
Technical levels to a Digital Humanities project.
Sources
Our team utilized the “Norms and Decision-Making” data available on the World Bank Gender Data Portal to conduct a thorough investigation on women’s decision-making capacities in 43 Sub-Saharan African countries regarding marriage and household dynamics from 1970 to 2022. Additionally, our secondary sources outside of our dataset primarily consisted of scholarly journals and articles from a plethora of credible news sources. After the careful parsing of fifteen articles, we were able to reveal a nuanced landscape of marriage, women’s empowerment, and related issues in Sub-Saharan Africa. A notable trend highlights a shift towards delayed marriage, influenced by socioeconomic factors and urbanization. However, challenges continue to arise in implementing legal reforms, with customary practices often clash with gender equality goals. Legal frameworks, especially minimum marriage age laws, can effectively reduce harmful practices like child marriage, but implementation hurdles persist. Early marriage continues to adversely impact education, highlighting the need for targeted interventions. The articles also reveal that intimate partner violence remains a concern, with rural-urban residence and education influencing its prevalence (Nabaggala et al. 2021). The complex relationship between women’s empowerment and fertility preferences varies regionally, emphasizing the necessity for context-specific interventions.
Ultimately, a comprehensive approach, considering cultural, legal, and socioeconomic factors, is vital for promoting gender equality and women’s rights in the contexts of Sub-Saharan Africa. Overall, our secondary sources worked well together to provide a strong background for our research.
Process
The “Norms and Decision-Making” dataset consisted of a number of survey questions–called indicators–that were answered by people from all over the world addressing the role gender plays in determining societal norms and every-day decision-making processes. We focused our research on the data collected from countries located in sub-Saharan Africa, and narrowed it even further to just the data that addressed the role gender plays in marriage, family, and household dynamics.
While the information from The World Bank was initially clean–very few typos, erroneous data points, et cetera–we had to perform a few manipulations to narrow the set down to what we needed. The data was presented in three files: Contextual Indicators–consisting of information about a country’s demographics to give insight about their lifestyle, Metadata–providing further detail about the indicators themselves, and Norms and Decision-making–containing the results of the survey questions. The data that we analyzed and used for our visualizations came almost entirely from the Norms and Decision-making file, which initially had over 190,000 lines of data. Using OpenRefine, we altered this file to exclude data from any country not located in sub-Saharan Africa and any indicators that did not address marriage, family, or household dynamics. Also, we added a column to specify which region (Sahel, Guinea, Congo Basin, Eastern Africa, or Southern Africa) a data point belonged to, and another column to distinguish between indicators that addressed our research questions from a legal rights, demographic, or belief-based perspective. This modified data set was saved into one CSV file and accessed by all of us to perform our research and analysis.
The visualizations were created using Tableau. Each visualization focuses on a perspective–like legal rights, demographics, or beliefs–and compares the results of the indicators in that perspective across the regions or countries within a specific reason. The data these visualizations were based off of was very expansive, to the point where it became difficult to convey all of the information in a digestible visualization. Tableau only allows for one dimension of color change, making it difficult to represent multiple indicators across multiple regions in one graph. Consequently, some of our visualizations, such as Data Visualizations #2-6, address the same topic, but were made using multiple books in order to make these layers of information comprehensible.
Presentation
After careful research, we compiled our team’s work to present our findings on this Humspace hosted WordPress, provided by UCLA’s Digital Humanities department. With a combination of outside research, our visualizations, and our cleaned data, we were able to create a user-friendly website that allows for users to easily navigate the information. Originally, we chose to forego the usage of a template, but saw it more useful to utilize one for both UI/UX reasons and professionalism. As a theme, we chose to use three colors for our palette: our main color (yellow), a secondary color (blue), in addition to an accent color(purple). Applying the 60/30/10 rule to our website design for optimization, 60% of our website used our primary color, 30% the secondary, and 10% the accent color. Dealing with multiple layers of data, the visualizations all utilize the same color palette that can be found on the home page for consistency and readability. Each visualization was boldly labeled and separated in dark blue text. The project also includes basic CSS and HTML code embedded to further enhance the website’s overall appearance and performance.
For digital accessibility, we ensured that every image and visualization had supporting alt text. The final website has high-contrast colors, a color-blind palette, and underwent multiple accessibility checking such as tab checking and tab testing.
