Best Paper of the Year Award for Volume 13

The past editors are pleased to present last year's best paper award for Volume 13 of the Journal of Map & Geography Libraries to Ms. Tamar Ganor for her article “An Integrated Spatial Search Engine for Maps and Aerial Photographs on a Google Maps API Platform.” The article was published in the second issue. This article presents a unique approach to collection discovery through a spatial search engine that accommodates the unique spatial metadata components found in maps and aerial photographs. Ganor outlines the problems associated with attempting a single search of both maps and aerial photos based on these variations, and then describes the concepts and technologies to support this alternate approach. This article is a clearly written overview with a timeline of processes. It also provides specific details about the HuFind platform and its outcomes. It is supported with helpful illustrations and meaningful background research and references. Ganor states, “[t]his article presents the Hebrew University of Jerusalem's (HUJI) map and aerial photograph collections and the spatial search engine that was developed in order to provide users with a tool to conduct preliminary independent research, enable access to material that is stored in closed stacks, and introduce complementary cartographic collections side-by-side. The rationale for displaying both material types together is discussed and the process of devising such a search engine is portrayed. This article also presents other spatial search engines that address these issues in different ways, as well as future prospects for the HUJI platform.” The additional unique aspect of this research, beyond that it successfully integrates spatial search outcomes between and across two cartographic formats, is that it encompasses the country of Israel. The Best Paper Award Committee believes that Ganor’s concept, technical approach, and presentation of research will offer valuable new insights and will enable and inspire others with geospatial data platforms to learn from the processes that she has described. In addition, Ganor’s work serves as a positive example of collaboration between different