The T-RACES and Hypermedia Berlin projects described below exemplify the efforts needed for data acquisition and aggregation as well as the intense interdisciplinary collaboration that are crucial to successful HASS CI. Together, they have the potential to contribute to many HASS disciplines, including but not limited to history, urban studies, ethnic studies, anthropology, human geography, literature, linguistics, art and architectural history, musicology, philosophy, history of science, political science, and sociology.

These projects (as well as Patricia Seed’s African map project described elsewhere in this issue of CTWatch) have very different objectives, data, and interfaces, but they hold in common the need for similar technological solutions. Both utilize GIS, historic maps, and historical and cultural data spatially associated with the maps. Both also are taking digital HASS forward in that they not only provide increased access to data but have the potential to create new knowledge that would not be possible without these digital resources. New research methodologies are coming into being.

T-RACES: Testbed for the Redlining Archives of California’s Exclusionary Spaces, a collaborative endeavor of the San Diego Supercomputer Center (SDSC) and the University of California Humanities Research Institute (UCHRI), will preserve, analyze, and make publicly accessible online digital versions of historical documents relating to the practice of “redlining” neighborhoods in the 1930s and 1940s in eight California cities. The research will make use of the UCHRI HASS grid, a CI initiative to bring the benefits of advanced information technologies to the humanities, arts, and social sciences at all 10 University of California campuses. The project is supported by a National Leadership Grant for Building Digital Resources from the Institute of Museum and Library Services (IMLS).

Data for the T-RACES project comprise neighborhood maps, interviews, financial and banking documents, and detailed city surveys. Thus far, 11 large historical color maps and 5,000 pages of text are included. These documents are part of the “Confidential Residential Security Maps,” a national collection established by the federal Home Owners’ Loan Corporation (HOLC) for all major US cities. Signed into law by Franklin Delano Roosevelt in 1933 as one of several New Deal measures, HOLC initiated the practice of redlining. Frequently conceived as housing loan discrimination resulting from individual bias in the banking industry, redlining’s historic, federal origins and institutionally deliberate dimensions often go unrecognized. The confidential maps and associated secret City Survey Files, compiled by thousands of HOLC agents, reflect neighborhood desirability and loan-granting conditions in over 200 US urban centers. Four classifications were used. First, Second, Third and Fourth grades were coded as A, B, C and D, and Green, Blue, Yellow and Red respectively. Redlined areas are typically characterized by “detrimental influences, undesirable population or infiltration of it.” In southeast San Diego, for example, which was redlined by design in the 1930s, residents of two categories of neighborhoods (A and B) enjoyed preferential treatment of their loan applications and significantly lower lending rates, while the opportunities of others were severely restricted. La Jolla was almost entirely “green” (A) or “blue” (B). Its one “red” (D) section was known as the district’s “servants’ quarters” and was “set aside by common consent for the colored population.”⁴

As is often the case with technological HASS projects, obtaining the data has turned out to be a considerable challenge. The bulk of the collection of once-confidential (now de-classified) redlining files currently sits on the shelves of the National Archives in Washington, D.C., and can only be browsed in the research room there. It is also the first time project co-PI Richard Marciano, a computer scientist and director of SDSC’s Sustainable Archives and Library Technologies (SALT) lab, has dealt with paper data; in other projects, content was born digital. The human and financial resources needed to bring the data into the digital realm were grossly underestimated and necessitated some creative problem-solving for image scanning.

The digitized content will form a unique collection spanning eight California cities. This will include color maps and textual documents. The maps will be vectorized (image to lines and polygons) and the text will be OCR-ed (image to searchable text) using ABBYY FineReader 8.0. This will allow for searchable PDFs and databases to be created from the text with linkages to the maps. The data will be spatially and temporally geolinked, providing area comparison of past to present with simultaneous viewing of historic and contemporary maps.