Technical Description
The assets in this project are files of graph visualizations showcasing the relationship among state, race, gender, age, and cancer type in regards to cancer mortality in the United States of America. Additionally, the site contains infographics from medical institutions and research organizations in order to present readable and digestible information about cancer at a quick glance. The dataset is provided by Dennis Kafura from the CORGIS dataset project, uploaded to the public website on 06/27/2019. We exported the dataset from CORGIS as a CSV into Google Sheets, which allows for collaboration for the data cleaning step. We utilized find functions on Google Sheets to look for missing data in the forms of zeroes, which we avoided removing or replacing these values through using averages on other data points to avoid making assumptions. We kept in mind the idea of data diversity and “to find the communities within which our data matters. With those communities in mind and even in dialogue with them, we must ask these questions: What are the concepts that structure this data? And how can this data, structured in this way, point to other people’s data?” (Rawson, K., T. Muñoz. “Against Cleaning”). There are columns that provide cancer death rates by race which is critical for applying the ideas of critical race theory when doing analysis as we are “studying and transforming the relationship among race, racism, and power” (Delgado, Stefancic “Critical Race Theory: An Introduction”) through revealing disparities and finding ways to address such disparities. Additionally, we also seek to apply the ideas of critical disability studies as “understanding critical disability studies as a methodology also means exploring issues of illness, health, and disease which often have important intersections with issues of race and class” as we attempt to “understand how race and (dis)ability continue to be mutually constitutive in our contemporary moment” (Schalk, “Critical Disability Studies as Methodology”). In addition, we came to notice that columns are structured with an implicit intersectional message by providing data on demographics using a combination of identities which includes sex, age, and race. Thus, we decided not to take out any columns as all columns are relevant for intersectional analysis in applying the ideas of critical race theory in conjunction as “no person has a single, easily stated, unitary identity” (Delgado, Stefancic “Critical Race Theory: An Introduction”) in investigating disparities and potential compounding inequalities that result from it in analyzing the ways “oppression manifests through multiple facets of identity that confer or withhold privilege” (Risam, “Beyond the Margins: Intersectionality and Digital Humanities”).
For visualizations and data analysis, we used Tableau to create visualizations for data analysis to discover potential disparities across different groups. We also decided to use palettes friendly to those that are colorblind. We believe that Tableau was a good choice for visualizations due to its interactivity and allowing users to click on different parts of the data to view numerical data, removing the need to view different colors as a measure for data which increases accessibility for all users in applying the ideas of universal design in “ensuring that our final product serves the needs of those with disabilities as well as those without” (Williams, “Disability, Universal Design, and the Digital Humanities”).
When users visit the site, they are greeted with an assortment of visualizations and subsequent information assessing the data, and its relationship to the inquiry of cancer mortality in the U.S. population. As users scroll down the main webpage, they will encounter the narrative of the digital humanities researchers and engage with their findings. The site contains multiple tabs, which organize the various pages that users will have access to, allowing the viewer to navigate across the project based on their preferences. The map and chart visualizations were created by contributors to create interactive demonstrations of the data on display, and come to their own conclusions with universal design in mind. Additional infographics sourced from medical institutions and cancer research organizations are displayed to give users quick and easy views of the information, with links navigating to the graphics origins if a user desires further inquiry. Next, users can engage with an interactive timeline demonstration of notable moments in the public health engagement with cancer from 1990-2013.
Acknowledgements
We acknowledge that this project would not have been possible without utilizing research done by others. We give thanks to Dennis Kafura from the CORGIS dataset project for curating the dataset that made this project possible. We give thanks to the Centers for Disease Control and Prevention and the National Center for Health Statistics for their countrywide data on cancer. We give thanks to the authors of research that made this project possible: Derya Cinar, Dilaver Tas, Kim H, Lim H, Moon A, Lawrence W.R, McGee-Avila JK, Vo J.B, Lyon J. L, Michelle Tong, Latoya Hill, Samantha Artiga, and others who have contributed to the research we utilized. (See Annotated Bibliography for more information). Additionally, we give thanks to the authors of the various writings on digital humanities theory that guided the creation of this project. We give thanks to Rawson, K., T. Muñoz, Delgado, Stefancic, Schalk, Risam, and Williams.