In Geocoding in the LCSH Biodiversity Library, the authors detail how they have taken MARC record locations and ingested them through the Google Maps API to generate a geocoded version of the LCSH.

Most interesting to me was the section on limitations.

“Other limitations in the display relate to how Google Maps geocodes values and how it then displays those Placemarks. By definition a Placemark is a single point on a map, which works well with traditional uses of Google Maps, such as displaying points for street addresses. However, geocoding returns a somewhat different result for less granular place names like ‘Missouri.’ The point associated with ‘Missouri’ is the centroid, or center point, of the polygon defined by the boundaries of the state of Missouri.”

Given that early encoding of the IMH may use just these sorts of generic names in some places, a preponderance of markers at the center of a state should be evaluated critically. Even later encoding may reveal more state or country level encoding than expected–we should be aware of this possibility.

Another consideration is the lack of density representation available in the API:

“A further complication with the single Placemark paradigm relates to the inability to visually represent areas of density within Google Maps. Rather than viewing single Placemarks at each country’s centroid, a more compelling display would be to view a map with shading to represent countries associated with more digitized content, similar in display to a population density map. It is possible to cluster Placemarks such that multiple points are represented by a single Placemark when zoomed out on the map, allowing developers to streamline maps with many points in close proximity to one another. However, clustering still doesn’t allow for visual ranking or weighting of results.”

This is where the Maps of American Fiction project’s use of MONK yields much more nuanced maps. We are also investigating OpenLayers, but I should look at other projects to see if there are ways to build maps in Google that reveal the kind of detail we’d like.