Case Study: Implementing Spatial-Hierarchy Metadata Methods in Photographic Collections.

Executive Summary: This field reflection details the initial phase of my digital curation internship at the Chula Vista Public Library Civic Center Branch, focusing on the high-volume digitization and cataloging of the John Rojas Photo Collection. It outlines the development and deployment of a custom, delimiter-separated Spatial-Hierarchy metadata methodology designed to translate complex visual assets into structured, highly searchable database records while preserving structural taxonomy.

Antonio Torres-Moreno

5/24/2026

Chula Vista Public Library Civic Center Branch

60-Hour Internship Progress Reflection

My primary responsibility is to assist digitizing and organizing the John Rojas Photo Collection, a donation to the Chula Vista Public Library that holds thousands of photographs documenting the city's historic houses, businesses, events, and residents. The collection is organized across at least 76 boxes, with each containing an estimated 80 to 100 photographs. Specifically, I utilize a specialized CZUR scanner to digitize these physical artifacts, subsequently cataloging and describing them within a centralized database for future public and research use (an excel sheet).

The technical workflow begins with carefully positioning each photograph to ensure a high-quality archival scan. After experimenting with various angles and room brightness levels to capture the optimal image, I transition to the analysis and data-entry phase. Working on a shared Excel spreadsheet, I update existing records by adding new identifiers to previously unlabeled photos to improve database searchability. My process involves transcribing any handwritten notes from the back of the photograph word-for-word, documenting any physical markers (such as numbers or symbols) omitted from older inventories, and noting whether the image is in black-and-white or color. Finally, I execute the most detailed aspect of the project: creating descriptive metadata. Using a Spatial-Hierarchy method, I construct structured metadata strings separated by delimiters. This system ensures that the primary subject—typically a historic house—takes precedence, followed systematically by the surrounding structures, immediate environment, and background elements.

To work effectively, I have developed a diverse set of technical and analytical skills. I became proficient with the CZUR scanning software, mastered spatial-hierarchical reasoning, learned to identify early 20th-century architectural terms and house styles, and engineered precise AI prompts to maintain strict consistency in my metadata formatting. My primary challenge initially was lack of an established framework for translating visual images into searchable text. Overcoming this required independent research into abstract data extraction, which trained me to analyze images through a lens perspective by distinguishing the subject from the foreground and background. Furthermore, learning to navigate the nuances of architectural styles—and recognizing that few historic structures reflect a single, "pure" style—demanded significant cognitive effort and study. My major accomplishment during this 60-hour block has been successfully refining and standardizing this database. The structural consistency of the updated spreadsheet has significantly improved its long-term utility, earning positive feedback and validation from my supervisor, the local history librarian.