Assets
Within the Home Page of the website, we have placed a photo essay portraying the nuances of food accessibility through a visual gallery that captures the intersectionality between food insecurity and individual identities. We chose to use a photo essay as we hope to create a story through a series of images depicting the narratives of those affected by food deserts and to implement a new way of understanding and thinking about food accessibility. We used CORGIS’s food access dataset from Food Access Research Atlas to visualize food accessibility indicators and factors in different neighborhoods to gain more insight on grocery market access--in relation to intersectional factors such as income levels, transportation accessibility, and age groups. We hope to observe food accessibility and food deserts through these intersectionalities and “From Crenshaw’s grounded analysis in the 1980s to now, intersectionality has come to signify the ways that oppression manifests through multiple facets of identity that confer” (Risam, 16). Our goal is to use this intersectional method and perspective to draw attention to the ways that racial, socioeconomic, and gender intersections affect food accessibility.
Services
For the creation of our online site, we used Google Sites to build and manage our digital humanities project. We chose Google Sites to create our website as it is accessible through multiple electronic devices and compatible through different browsers–as well as being easily navigable through a user perspective. Its interface also makes collaboration and updates simple, allowing our team to work effectively together. Additionally, we used the Pandas data cleaning package to manage the large amount of data involved in our project. Pandas is a crucial tool for managing and arranging the unprocessed data we gathered regarding Californian demographics and food access. Our interactive map can easily incorporate this data by converting it into a geojson file, which enables users to view complex information spatially. To code the interactive map visualization, we used SvelteKit, which provides a robust framework for creating dynamic web applications quickly. In our interactive map, users can explore and interact with data points in real-time; hence, its capacity to handle reactivity and deliver a seamless user experience is essential. Although data cleaning is an essential step to our process, it is important to remember that, as stated by Rawson and Muñoz, "The persistence of an element that is 'out of focus' in discussions of data-intensive research does not invalidate the findings of such research, nor is it meant to cast researchers using these methods under suspicion" (1). This indicates that, despite our best efforts to guarantee the purity and accuracy of our data, little errors are unavoidable and do not invalidate our conclusions as a whole. We may successfully manage and present our data while acknowledging the difficulties inherent in data-intensive research by utilizing tools such as Pandas.
Interface
To ensure an easily navigable site, we have designed our site through simple UI and divided the website content into distinct tabs which are further divided into smaller sections within each page. Users can easily find specific information or explore related topics without feeling overwhelmed thanks to this hierarchical arrangement. For our data visualizations and visual components of the site, we have included multiple interactive and accommodating features. With the ability to click, hover, and zoom, users may engage with charts and graphs to explore the data in greater detail. Users can find patterns and insights from the data that static photos cannot by using these interactive features, which make the data more interesting and understandable. In creating an interface that can best aid and accommodate those with visual impairments and colorblindness, we have chosen a color palette with distinct hues, variations, and saturation. We also prioritize a variety of visual cues beyond color, including: shape, depth, and texture of the data points to ensure multiple discernable traits. This method ensures that viewers can appropriately comprehend the visuals even if they are unable to discern color changes. We also include textual descriptions, inline tags, and alt texts for images and visualizations to provide more online accessibility. In the interactive map, users can hover their mouse over a geographical area and see a pop-up tab with textual descriptions related to the intersectionalities related to food insecurity. One of the features we will implement in the future is including multiple language options for people who would prefer accessing our website in another language.
In analyzing the CORGIS dataset on food access, we took a look at the data and all the variables that it included–which seemed to mostly consist of data that measures how groups of different ethnicities, ages, and income levels had access to a supermarket that was 1/2 mile, 1 mile, 10 miles and 20 miles away. We first created two bar charts that compared the averages of the number of people that have low access to a supermarket 1/2 mile away and 20 miles away. To better understand the data with respect to the different categories and the share each group holds within the population, we visualized the percentage of different racial groups in respect to each other. We also created visualizations that demonstrated how the population data of different age groups can also differ in food availability and access. One potential challenge and important thing to note is that many of our visualizations take the averages of the data so there might be some places with more or less food accessibility which may create obscured biases and skewed data.