The Pulley Ridge Data Curation Experience Presenter: Timothy Norris, University of Miami Copresenters: Christopher Mader—University of Miami Center for Computational Science; Sreeharsha S Venkatapuram—University of Miami Center for Computational Science; Julio Perez—University of Miami Center for Computational Science; Chance Scott—University of Miami Center for Computational Science In 2011 the University of Miami Center for Computational Science (CCS) was invited to collaborate as data curators on a multi-year trans-disciplinary marine science project in the Gulf of Mexico. The CCS was tasked to build an online decision support resource with a data repository, a map-based data exploration tool, and a map- and data-based story telling tool. Additionally, the entire suite of tools is designed to be linked to the National Center for Environmental Information (NCEI). This presentation reports on the geographic data curation process and the cartographic approaches implemented in the construction of the online decision support resource.
Linking Historical Population Census Data to Individual House Locations Presenter: T. Wangyal Shawa, Princeton University The United States decennial population censuses of individuals are released to the public after 72 years. These releases give researchers rich historical records about people living in particular places at specific times; if the data is spatially tied to individual houses, it will become much richer. This presentation is based on my recent project to spatially link the 1900 Census data of Princeton, New Jersey to individual houses located in the borough (urban) and township (rural) of Princeton. The project explores methodology and workflows anddescribes the challenges and opportunities of developing historical location data.
Eastern Bloc Borderlands: Digitizing Russian Military Topographic Maps of Eastern Europe, 1883-1947 Presenter: Theresa Quill, Indiana University Bloomington Copresenter: Michelle Dalmau, Indiana University The Russian Military Topographic Map Collection at Indiana University (IU) contains just over 4,000 maps of Eastern Europe at various scales. In the years surrounding World War II, these maps were captured in the field by opposing forces, including German and American troops; a history told by stamps on the maps themselves. While Soviet military maps from the Cold War era are abundant, these maps provide a view of the pre-war landscape. Digitization of this collection includes georeferencing and creating a custom metadata scheme to trace changes in place names and provenance, as the maps were captured and recaptured.
Collaborative Geographic Indexing of Map Series: Geodex 2.0 Presenter: Stephen Appel, American Geographical Society Library, University of Wisconsin-Milwaukee Libraries In 1988, the American Geographical Society Library launched a geographic indexing software called Geodex. The software allowed for rapid input and searching of records for large paper map series and encouraged partner collections to reconcile index data to create a collaborative database. In recent years, the system has been redesigned using geodatabase architecture and Web GIS. With modern hosted GIS services, the potential of a shared geographic paper map index is within reach. This presentation will describe the platform, argue the benefits of Geodex for map collections and users, and share progress on its development.
Big Historical Geodata -- the next frontier Presenter: Nathan Piekielek, The Pennsylvania State University Libraries, museums and archives were the original big geospatial data repositories that to this day house thousands to millions of resources that contain research-quality geographic information. The problem is that these resources are not easily incorporated into the contemporary research process. Fortunately, big data tools and methods are equally as applicable to digitizations of geographic information that originated in physical form as they are to born-digital data. This presentation will provide an overview of several completed and on-going projects to turn print geographic information into big geospatial data by leveraging the power of computer-vision and machine-learning techniques.
