Author: roze

The website for Digital Harlem is headed with Digital Harlem: Everyday life 1915-1930. Its homepage outlines the sources from which content is drawn to create mapped data. These sources include the District Attorney’s Closed Case Files, Probation Department Case Files, investigation reports, W.P.A. research on institutions and life in Harlem, compiled in the late 1930s, and two weekly newspapers from Harlem in the 20s, the New York Age, associated with Booker T. Washington and promotional of middle-class respectability, and the New York Amsterdam News, which published more sensational stories. The maps can be filtered through categories such as “race”, “gender”, “occupation”, “types of event” or “charge/conviction”, each referring to a categorical ontology.

That these maps are based on data that is largely drawn from penal system records asks crucial questions about the assumptions and biases that all maps reflect. Given that the penal system has historically perpetuated violence against marginalized groups, a system to which a historically Black neighborhood like Harlem would be especially vulnerable, any record of its making will only reflect a version of history which benefits those in power and therefore exclude those who are not. Such records cannot adequately tell the story of “everyday life”, rather, they often do work to erase it. To move in the direction of the project to which the title of the website refers, the creators might have sought out to centralize, along with the W.P.A. files and African American newspapers, family records from Harlem during that time and used that kind of content to create mapping categories.

The EconData.XLS dataset includes the unemployment and inflation rate, as well as presidential approval (sourced from Gallup Poll) and consumer confidence (sourced from University of Michigan) ratings in post WWII election years (1948-2016).  This dataset follows a sequence of time, or a “time series”, so that once it was imported into Google Fusion Tables, the data was easily read, as Nathan Yau would predict, into the different “continuous variable “charts with the x-axis labeled as “year”, or post WWII years.

Time series data and analysis is typically used to extract hopefully meaningful connections or correlations between variables in the ways that they pool across time. In the continuous variable chart above, which plots presidential approval and consumer confidence ratings from 1948 to 2016, we can infer a few things fairly quickly that we may have struggled to read in the data alone:  one, that there is consistent positive correlation (points move in same direction rather than opposite) between the two polls, and two, we can much more easily identify those years when polls suggested that presidential approval and consumer confidence were at their lowest, namely, in 1952, and in 2008.

From these visualizations, we can locate those datapoints that may offer a window into a deeper understanding of the data. In the case of the exceptionally low rates in confidence and approval of the incumbent president in years 1952 and 2008, we may want to dig for how during each of these years, the data supports or contradicts election voter polls, upholds or opposes ideological norms of the time, and potentially what similarities or interesting contrasts there may be between the two election periods.

Another useful way to look at the data could be through bar or categorical charts. In the chart above, I’ve filtered the categories down to unemployment and inflation, leaving the  x-axis as “year”. In this kind of chart, we get fairly clear comparative visualizations, both for when the relationship of unemployment rates to inflation rate changed dramatically, and when and for how long the change was gradual. Such visualizations might shift our focus towards such dramatic increase or decrease as when 1980 hits and inflation drops heavily from higher to lower than unemployment, and employment rises from lower to higher than inflation.

The Gender Breakdown of City Workers by Department dataset consists of information represented in integer and character values for an “analysis of 2015 full-time employee earnings by gender across the various Departments of the City of Los Angeles”. Recorded in this dataset are column values such as the year, department title, employee, total payroll #female, #male, %female, %male, female total salary, male total salary, female average salary, male average salary, etc. According to Wallack and Srinivasan in “Local-Global: Recovering Mismatched Ontologies in Development Information Systems”, a dataset ontology represents reality, “but this representation of information may in turn become the basis for actions that shape reality”. The authors note the heightened problematics this dynamic introduces when state’s typically have more power to affect communities than the other way around through action based on state meta ontologies that are either inadequate or incomplete.

In the case of the Gender Breakdown Dataset, those who are likely using this data the most as an empirical representation of reality are social scientists, and policy makers and employers. While this dataset makes a good argument for the gender pay gap where “women” or “females” are paid substantially less than “men” or “males” across vocations in the year 2015, it is predicated upon the gender binary and does not necessarily account for individuals who have made gender transitions, potentially excluding an ontology that would include gender non-conforming or transgender individuals. When we talk about gender pay gaps, we should be talking about how differently the gender pay gap and the sexism at its root affects women from different races, classes, and citizenship status among other social categories in the workplace. Another absence might be that the dataset only includes statistics from 2015 limiting the ways we can read the current state of the gender pay gap in relationship to past years.

It should be noted, there is a tab in the dataset titled “discuss” which makes space for comments and discussion around the dataset, but which has not been used at all. In“Local-Global”, one way to mitigate the disparities between official portrayals and statistics about the gender pay gap and workers’ understanding of that context (including that of everyday sexism in the workplace) would be to add a feature on the Control Panel dataset for Gender Breakdown that would explicitly request comments and updates from the people the data affects about how the data is portrayed and what might be missing. But making the raw data directly available to those it affects would be one way to go further.

 

The finding aid for the Virgina Espino and Renee Tajima-Pena Collection of Sterilization Records articulates the events surrounding Madrigal v. Quilligan, a 1970s federal class action lawsuit brought to court by ten Latina women suing E.J. Quilligan, M.D. and his colleagues for the coerced sterilization of Latina women at the LA County University of Southern California Medical Center. The case was lost, but in the momentum of the civil rights movements, it increased public awareness and activism against the forced sterilization to minority women.

The project is divided into two main record types: court documents from 1975-1979, and oral history recordings from 1994-2001 in the form of 10 cassette tape interviews of those involved in the Latina rights movement, those who supported their case, and a resident at the hospital where and when the women were admitted and coerced into sterilization. The court documents are each described with a Box number, Folder number, date, title and content note, the associated content of these descriptors likely only able to parse together the skeleton of what it meant to be the plaintiffs in this court case. That is, while these documents do work to fill out much of the court case narrative and how it may have given voice to many of the issues at the center of the Latina women’s movement, it cannot capture the psychological effects that the case or the events it addresses had on its subjects then and over time. That the judge ruled against the Latina women would alone suggest that content in the court case records is going to be filtered through a biased, racist lens.

The interviews had in the tape cassettes, and in documentaries like No Más Bebés, which, according to the finding aid, sourced many of the records in this collection can help begin to fill in the missing perspectives and counteract the skewed narratives in this story.

A collaboration between the Digital Humanities department, faculty, and students at UCLA and community researchers and leaders, Mapping Indigenous Los Angeles is a website that makes visible and interactive through maps, images, relevant documents, videos and other visualizations the many Indigenous histories of Southern California including those of the Gabrielino Tongva, the Latin American diaspora, and Pacific Islander communities— people that have been displaced by processes such as neo-liberal economic policies, and intersecting colonial histories— that are often obscured yet deeply rooted in Los Angeles history, culture and geography.

Sources: Because this project relies on story-telling through interactive digital maps, and community-based research collaboration, it draws from multiple sources across the digital and physical landscape. It lists teaching and reading materials such as texts ranging from federal documents to oral history projects as well as interviews with community leaders. Videos, links within the text, scanned artworks, and other sources are used consistently throughout the available and interactive maps for Los Angeles waterways, American Indian Health and Education Resources and Latin American Indigenous diaspora.

Processes: The primary processing tool used is the ESRI Story Maps program through Arcgis, which is a geographic information mapping system available online for public use.

Presentations: Each storymap includes accompanying text embedded with links to documents and terms referenced, further expanding the field of information made immediately web-accessible. One of the websites featured links available on the dashboard is “Create your own storymap” where the hugely collaborative element of this project is encouraged all the more.