recommend further that development of standards for geographical data security and advanced geographical data protection is now critical. In addition, the work of Zandbergen (2008), which characterizes the capabilities of reverse geocoding (i.e., deriving an address from a position, rather than vice versa) using a range of different network analysis methods, offers a promising example of how research on this topic could make advances over the next 10 years.

The urgent need for work on privacy protections for locational data becomes clear when one considers that, despite efforts to ensure the privacy of personal information (e.g., protection of social security, credit card, and driver’s license numbers), no explicit regulation currently protects locational privacy in the United States. It is important to note that data availability and concerns about privacy vary by culture. For example, in the United States, Google has responded to privacy concerns by testing and gradually implementing a face-blurring algorithm for its Street View service (Figure 11.3), but Canada has enacted an identity protection law, requiring Google to blur not only faces, but also license plates. Bitouk et al. (2008) have developed software that goes beyond the simple blurring of a face in a photograph to “swapping” the features in a face with random features from a library of faces (such as a Flickr library). The result is a composite photograph that changes the identity of the person in order to further protect his or her privacy.

Over the next 10 years, geographical scientists should continue research on responsible locational data release formats, while working to develop codes of practice for LBS use. The work of Onsrud (2003) and Solove and Rothenberg (2003) shows that there is great potential in collaborations with legal scholars to identify principles governing the dissemination of personal geographical information in various contexts. This will allow researchers to estimate the social benefits and costs of information dissemination, and to identify potential conflicts.

FIGURE 11.3 Implementation of the face-blurring algorithm in Google Street View. SOURCE: maps.google.com/help/maps/street-view (accessed January 20, 2010).

FIGURE 11.3 Implementation of the face-blurring algorithm in Google Street View. SOURCE: maps.google.com/help/maps/street-view (accessed January 20, 2010).



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