The San Francisco public art map application

Abstract

The City of San Francisco is endowed with a rich variety of art in public spaces. The San Francisco Arts Commission (SFAC) alone curates over 800 pieces of public art. In addition to the SFAC holdings, there are colorful murals, landscaped parks, art monuments, street art of mysterious origin, and pieces curated by other organizations. People taking public transportation, playing in parks, reading in libraries, or performing daily tasks that take them into San Francisco's built environment see art.There is no one place to learn about all the different art freely available. Existing mobile and web applications that map art locations silo information by covering only certain categories of art such as publicly funded pieces, murals, or street art. No application uses social media as a source of art data. In contrast, the San Francisco Public Art Map presented in this paper (SFPAM at www.sfpublicart.com) is a web application that aggregates location-based publicly accessible art data in San Francisco. SFPAM addresses the gap identified in other applications.The application has three levels of art data curation: organizational, administrative, and volunteered. The project compiles art location information from disparate institutional sources that fund and curate public art. Administrative scraping of web sites and local knowledge create additional layers. Volunteered Geographic Information (VGI) creates a dynamic layer of pictures and comments on public art through social media streams such as Flickr, Panoramio, Instagram, and YouTube from contributors who unknowingly take on the role of curator through posting and tagging images. Combining data from multiple curators creates a richer set of art data points for visitors and residents in San Francisco interested in public art.Assessment of SFAC's art location data reveals quality issues with positional accuracy. Some points are located miles from their actual location and multiple points are collocated. Comparison of sample points from the SFAC dataset and peer reviewed VGI suggests that VGI can improve the quality of the application dataset.