According to Hutchinson, the purpose of his research has been to “change the paradigm of geocode processing, creating software that learns from its interactions with the user.”
A geocoding process takes a reference to a physical street address and determines its’ location on the earth’s surface, and Hutchinson says the rationale behind this change in focus is that there are property and street addresses for which current geocoding software cannot resolve their locations.
“After a period of great progress, mapping programs are coming close to reaching their full potential using current geocoding techniques,” Hutchinson says, adding that it is currently estimated that between 5-10 per cent of addresses will require new techniques to accurately geocode.
Hutchinson said that as recently as 2003, five per cent of addresses in rural areas were inaccurate by about three kilometres or more, and five per cent of all suburban addresses could not be geocoded within 421 meters.
“Although this has been improved significantly, many areas are still not accurate enough for purposes such as responding to emergency calls. Problems can be caused when there are two streets with the same name in the region, or a street number on the wrong side of the road.
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“Other complications, such as abbreviated addresses or addresses in private estates can also arise, when the person describing the address uses the estate instead of the suburb. One way around this is to use semantics.”
“Semantics involves the differences in meaning that occur when we use natural, everyday language for modelling the real world in digital databases. Natural language can be subjective and people can also use different vocabularies and descriptions for address locations.”
Hutchinson also says that further development in learning and semantics in geocoding would allow intelligent geocoders to infer new knowledge based on the information it already possesses.
“This effectively means that the software will be able to learn.The result would mean that if someone inputted the word ‘park’, the geocoder could also try the words ‘reserve’ and ‘oval’ to see if they should be used instead.”
According to Hutchinson, these forms of improvements mean that geocoding software will become more personalised and contextualised to specific types of users and geographic locations, and he says that it is envisioned that with further work, geocoders will increasingly use intelligent information about the user or about the location to obtain an accurate geocode result.
Hutchinson said the framework used in his prototype would be usable in online mapping products and GPS devices, making them more user-friendly.
“The prototype has shown the ability to use intelligence to identify locations of problem addresses. Given the increasing importance of geographic information, the use of intelligence in geocoding will really enhance applications that rely on location.”