Our group utilized critical race theory and intersectionality theory to frame our research. Critical race theory emphasizes the examination of systems that perpetuate racism instead of racism at the individual level. Therefore, to understand the disparities we saw in our data, we looked for academic sources that examined healthcare inequalities at the system level. Additionally, intersectionality theory states that multiple facets of identity contribute to an individual’s privilege or oppression. Being familiar with intersectionality theory encouraged us to explore the compounding effects of multiple identities on cancer rates. 

 

Furthermore, we looked to reader response theory for guidance on how to design the interface of our project. encourages users to interpret visualized data based on their own context and experience, making user interaction an integral part of narrative and analysis. This way of communicating with users has provided us with great inspiration for the complete perspective of the work, and we also hope to supplement the information of our digital humanities website more comprehensively by creating areas of interaction with readers. It ensures that our content is not only informative but also evocative and able to elicit individual reflection and response, which is essential for educational and advocacy purposes.

 

In addition, the various detailed visualization techniques in the Manovich article from class can help us to present our data. This includes creating interactive maps, bar charts, and heat maps to visually illustrate complex data sets. The interactive map presents different data sets through different geographical locations, and the color differentiation can more intuitively show the regional differences in cancer data. Visualizations such as bar charts show the differences in data in a clear and intuitive way. This approach not only makes the information easy to understand but also interactive, allowing users to engage with the data in a meaningful way. Using these techniques is critical to showing geographic and demographic patterns of cancer incidence, making abstract data concrete and interpretation available to a wider audience.