“Can I show you something?” is a question that often leads me to magnificent discoveries. So when I heard that question recently from Mitchell Yangson, a colleague working in the International Center at the Main Library here in San Francisco, I was eager to sit with him and see what was on his mind — and on the screen of his computer.
What Mitchell shared were the results of a mashup he developed — an interesting and potentially significant experiment in the use of big data for libraries. Using data from the San Francisco Public Library integrated library system (ILS) as well from the U.S. Census Bureau, he produced visualized data reports that drew from geographic information service (GIS) software (ArcGIS) that he obtained through City/County of San Francisco colleagues, Excel spreadsheets, and word processing programs.
The big benefits of incorporating big data into our work became viscerally clear at a glance. As he showed me a graphic representation of service areas for all of the public library facilities according to languages spoken in each area, I noticed a small corner of San Francisco that didn’t seem to have any activity — the area that served as home to very active shipyards decades ago.
“Want to play a game with that information?” I asked.
“Sure,” he answered.
“If we were going to make a bet, I’d bet that the next branch of the San Francisco Public Library would be there in the shipyards area,” I said.
And that’s when the usefulness of big data became clear to me — its ability to lead us to meaningful discoveries we were not even seeking. There was nothing in the graphic representation itself that overtly suggested the need for another library — it was an illustration of languages spoken, not population patterns. Mitchell’s data visualization, combined with my own knowledge of current business and residential development patterns in our city, inspired the conclusion I reached less than five seconds after seeing the visualization: that the long-ignored area around the shipyards will soon have thousands of new homes, plenty of new businesses, and (if all goes well) a magnificent new bayshore recreation area (the Blue Greenway), which means there will also be plenty of people in need of a neighborhood library that doesn’t currently exist.
There is plenty to learn from Mitchell’s ongoing explorations, and much of what he told me becomes more meaningful through the lens of Viktor Mayer-Schönberger and Kenneth Cukier’s book Big Data: A Revolution That Will Transform How We Live, Work, and Think. The authors remind us that using big data allows us “to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments, and more” (p. 6). They confirm what I learned from my conversation with Mitchell — that big data “is all about seeing and understanding the relations within and among pieces of information that, until very recently, we struggled to fully grasp” (p. 19). Moreover, they suggest that big data may be changing the way we view our world: “Big data may offer a fresh look and new insights precisely because it is unencumbered by the conventional thinking and inherent biases implicit in the theories of a specific field” (p. 71).
This brings us full circle back to Mitchell: “After I attended the intense four-hour [ArcGIS] training, I was hooked. Eventually, I explored ways that libraries could use GIS to make accurate community assessments not only for language communities, but for library users. If a library has a GIS system, they can actually do user assessments, which is different from community assessments.”
He sees numerous other possibilities through the library’s use of big data. If Mitchell were to obtain information from the City/County Planning Department, for example, he would be able to visualize where the next residential buildings would be constructed; this, in turn, would help identify the type of development and rental prices, which would help describe the type of community that will develop—“whether it be young, well-off singles, middle-class families, and so on,” he explained. “If we knew how to use population pyramids, we could visualize that data and anticipate if the population will experience a growth in children, so we could staff the library with the appropriate materials and programs.”
Other applications of GIS for libraries might include determining which areas within a library system’s service area have low numbers of people holding library cards, which neighborhood libraries would better serve their populations by increasing their collections of materials in languages other than English, and which areas might benefit from bookmobile service, he added.
Using big data, he suggested, is an area where “libraries are really behind.” He believes the work he is doing “is in its transition phase. If libraries are still with us in the future, the ideal [integrated library system] the library uses will have the capacity to combine user- and user-location-based data into visual formats and predictive analytics. This is something ILS developers should think about.”
N.B.: To reach Mitchell for more information about his big data exploration, please send a note to Mitchell.firstname.lastname@example.org.
--Paul Signorelli is a writer-trainer-instructional designer-social media strategist who has served on Horizon Project advisory boards since 2010; he can be reached at email@example.com.
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