"What Makes a Place More Familiar?": Implications of Geospatial Information Format and Content

Geo-local systems can significantly increase users' familiarity with new places. However, for these systems to be useful, geospatial information needs to be presented in ways that those systems can minimize users' difficulties of learning about a new place. This raises a fundamental question about what kinds and representations of geospatial information are effective in making a place more familiar, so that people can adjust to the place more easily even before visiting the unfamiliar world. This study focuses on modeling representations of geospatial information, and their effects on people's familiarity of places. The results show that content and format of geospatial information matter in their familiarity about a place in terms of their perceptions and knowledge. Designers and researchers of social computing systems can benefit from this study so that geospatial information can be more effectively distributed through online systems.

This project was originally initiated as a class project from the INST 741 (Social Computing) class in 2013, and was redesigned as a research project and accepted to the CHI '15 Work-in-progress session. I organized a 4-member team and we collaborated on designing this study as a team. The video was created originally for Social Media Expo, iConfernece 2014, so the title is different from that of the CHI paper. This research project tried to identify the effect of geospatial information format (image or text) and content (place or space) on people's familiarity with new places.

ACM Conference on Human Factors in Computing Systems (CHI '15). April 18-23. Seoul, Korea.
Myeong Lee
Luis Santos
Wei Zhao
Preeti Lakhole
Brian Butler

Lee, M., Santos, L., Zhao, W., Lakhole, P., & Butler, B. (2015). What Makes a Place More Familiar?: Implications of Geospatial Information Format and Content. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '15). pp. 1079-1084. http://dx.doi.org/10.1145/2702613.2732911. New York, NY: ACM.