2013Q1.0 Detailed Address Coverage by Country

What do the different fields mean?

There is a lot of information contained within the tables below. Having a better understanding of how these figures are derived helps to better make use of the data.

Counts: This field contains the number of records within the reference dataset for a particular country. Its important to note that this does not correspond to the number of individual addresses covered, since premises are often compressed into premise ranges. This means that, for instance, premises 100-199 on a particular street will only increase the ‘Counts’ value by 1 even though that may refer to 99 individual premises. The ‘Counts’ value is useful for understanding the volume of data associated with a particular country’s reference dataset.

Verification/Geocoding Level: Please view the simplified list for information on how to understand these fields.

Administrative Areas, Localities, Thoroughfares, etc: These fields contain the number of distinct values within a particular country dataset for select address data fields. The full list of address fields we support can be found here. This information is useful in determining the level of coverage within a country. For instance, comparing Bangladesh with Argentina, we see that the Localities count is higher on Bangladesh, but the Thoroughfare count is much lower. This gives an indication that the Thoroughfare coverage for Bangladesh is probably not as complete as the Thoroughfare coverage for Argentina, meaning that there are likely to be more cases where a Locality verification is the best that can be achieved. We would love to be able to provide percentage coverage information per country, but unfortunately this is not something that we are able to obtain from all our data providers.

Premise Number Ranges: As described in the ‘Counts’ field description above, premise numbers are generally compressed into ranges. This field contains the number of distinct ranges within a dataset. As with the values for other address data fields, this information can to provide further insight into the likely level of coverage across a country.