Address Cleanse

What is Address Cleansing

Address Cleansing is the process of verifying, correcting, formatting and completing address data. If an address is missing values, performing address cleansing means filling in absent details with accurate results. If an address is not in the correct format, cleansing organises the address components to adhere to the local authoritative postal service address guidelines.

Ideal Postcodes' Address Data Cleansing solution attempts to return the closest matching address for any given address inputs. We also return a number of Match Level indicators that describe the degree to which the suggested address matches the input address. The more impaired the input address, the harder it is to cleanse.

Use the Confidence Score and Match Indicators to decide which addresses can be reliably cleansed.

Ways to Cleanse

• API. POST /cleanse/addresses takes one address and returns the nearest match with its indicators. See the API reference
• Command line. idpc cleanse takes a single address, a file or stdin and writes JSON or CSV. Install with npm install -g @ideal-postcodes/cli. See the CLI documentation
• Browser. ClickCleanse uploads a CSV, maps its columns and downloads the cleansed file. No code required
• Zapier. Cleanse addresses inside an existing Zap
• Managed. For large or one-off datasets, send us the file and we run the cleanse for you

All routes call the same matching engine and return the same indicators.

Add UPRNs to Your Address Data

As part of our address cleansing service, we can link UPRNs (Unique Property Reference Numbers) and rooftop accurate geocoordinates to the addresses in your database. This allows you to enhance your address records with a standardised property identifier, making it easier to reference locations accurately and support further integrations.

Input

The API takes the complete address as a single query string, with optional postcode, post_town and county fields when you hold them separately. The CLI takes one address per line. ClickCleanse needs a header row and two mapped columns, Street Address and Postcode. Combine address lines one to three into the Street Address column and keep the postcode in its own column.

Output

Every route returns the original input, the nearest matching address and a set of Match Indicators.

Suggested Address

match holds the closest address in our standard format: line_1, line_2, line_3, post_town and postcode, plus UPRN, coordinates and the other fields of a full address record.

Match Indicators

• count. The number of addresses we matched to the input. We return the closest match by default.
• organisation_match
• premise_match
• postcode_match
• thoroughfare_match
• locality_match
• post_town_match
• fit. A score between 1 and 0. Fit compares the address elements present in your query against the matching address elements. It does not incorporate elements you have not presented in the score. A partial address (e.g. 12 Pye Green Road) will have a fit of 1 even though it is missing post town and postcode. Its confidence score will be less than 1 however because it is missing some crucial elements.
• confidence. A confidence score between 1 and 0. 1 indicates a full match. 0 indicates no complete matching elements.

CSV output from the CLI and ClickCleanse carries the same values as columns.

Match Levels

For any given address attribute (e.g. premise, thoroughfare, locality), the match indicator states how far the input conforms to the cleansed address:

• FULL. A character for character match has been detected.
• PARTIAL. A close match has been detected.
• INCORRECT. The suggested address attribute and input address attribute do not match.
• MISSING. The suggested address has this attribute while the input address does not.
• NA. The suggested address does not use this particular attribute and so it is considered not applicable.

An input with no plausible match returns a no-match result rather than a low-confidence address.

Confidence Score

The confidence score is a number ranging between 0 and 1. Where 1 implies a full match and 0 implies no major elements completely match. Each incorrect, missing or misspelled element will subtract from the overall confidence score.

Deciding on an Acceptable Confidence Score Threshold

Different address cleanse projects can have radically different inputs. However, within each project, the inputs tend to repeat the same errors. For instance, some input datasets may be exclusively inputted manually and be prone to typos. Others may have a persistently missing datapoint such as organisation name or postcode. For this reason, it is important to understand that there is no absolute Confidence Score threshold. Instead, the acceptable confidence score must be determined on a project by project basis based on systematic errors present in the data and business goals.

When determining an acceptable Confidence Score threshold you should load a subset of the dataset into a spreadsheet application like Excel and sort on the score. Scrolling from top-to-bottom you will be able to observe matches from best to worst. As you start to hit the lower quality searches, you will be able to roughly determine:

• Which confidence scores indicate ambiguous matches (i.e. up to building level only)
• Which confidence scores indicate a poor or no match (i.e. the nearest matching address is too far from the input address)

Depending on your business goals, you can also use the Match Levels to determine an acceptable match. For instance, do you need to match up to the thoroughfare or building name only? Are accurate organisation names an important feature?

Ready to clean up your address data?

Connect with our team today and ensure your database is accurate and up-to-date.