Peter Johnson

A global street address interpolation service.

The Openstreetmap and Openaddresses projects provide a huge cache of street address information; between them around 500 million address points are freely available to download.

Some countries like Germany and the USA have dense address coverage while other have only sparse data available.

This project aims to 'fill in the gaps' in the data by intelligently estimating where the missing house numbers would lie on the… Mehr anzeigen

A global street address interpolation service.

The Openstreetmap and Openaddresses projects provide a huge cache of street address information; between them around 500 million address points are freely available to download.

Some countries like Germany and the USA have dense address coverage while other have only sparse data available.

This project aims to 'fill in the gaps' in the data by intelligently estimating where the missing house numbers would lie on the road.

Using this service we can greatly improve the coverage of open street address data, in countries like the United States we are able to claim near 100% coverage due to including census data from the TIGER project. Weniger anzeigen

This engine takes unstructured input text, such as 'Neutral Bay North Sydney New South Wales' and attempts to deduce the geographic area the user is referring to.

Human beings (familiar with Australian geography) are able to quickly scan the text and establish that there 3 distinct token groups: 'Neutral Bay', 'North Sydney' & 'New South Wales'.

The engine uses a similar technique to our brains, scanning across the text, cycling through a dictionary of learned terms and then… Mehr anzeigen

This engine takes unstructured input text, such as 'Neutral Bay North Sydney New South Wales' and attempts to deduce the geographic area the user is referring to.

Human beings (familiar with Australian geography) are able to quickly scan the text and establish that there 3 distinct token groups: 'Neutral Bay', 'North Sydney' & 'New South Wales'.

The engine uses a similar technique to our brains, scanning across the text, cycling through a dictionary of learned terms and then trying to establish logical token groups.

Once token groups have been established, a reductive algorithm is used to ensure that the token groups are logical in a geographic context. We don't want to return New York City for a term such as 'nyc france', so we need to only return things called 'nyc' inside places called 'france'.

The engine starts from the rightmost group, and works to the left, ensuring token groups represent geographic entities contained within those which came before. This process is repeated until it either runs out of groups, or would return 0 results.

The best estimation is then returned, either as a set of integers representing the ids of those regions, or as a JSON structure which also contains additional information such as population counts etc.

The data is sourced from the whosonfirst project, this project also includes different language translations of place names.

Placeholder supports searching on and retrieving tokens in different languages and also offers support for synonyms and abbreviations. Weniger anzeigen