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Mapping Important Rules

The document outlines rules for mapping relevance, pin accuracy, name accuracy, and address accuracy in query results. It distinguishes between explicit and implicit location intents, provides guidelines for rating navigational results, and specifies how to handle parking intents. Additionally, it details criteria for assessing the accuracy of pins, names, and addresses associated with points of interest.

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0% found this document useful (0 votes)
5 views3 pages

Mapping Important Rules

The document outlines rules for mapping relevance, pin accuracy, name accuracy, and address accuracy in query results. It distinguishes between explicit and implicit location intents, provides guidelines for rating navigational results, and specifies how to handle parking intents. Additionally, it details criteria for assessing the accuracy of pins, names, and addresses associated with points of interest.

Uploaded by

tlhologelomashi
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Mapping Important Rules

RELEVANCE

You might see some queries that have no Maps intent. You can rate them all as Bad.

Example: “temperature”, “time in X city”, “Facebook”.

Is there a navigational result for this query?

If the result is unique, meaning there is only one of it in the world, then it is a navigational
result. Examples: Monuments like Eiffel Tower or Sydney Opera House or a complete address.

Location Intent

There are two possible location intents: Explicit or Implicit.

An Explicit Location Intent is when the user states exactly where they want to find the result.
An example of an Explicit Location Intent is a query looking for Boston Museums, the user
expects the result to show only Museums in the city of Boston.

An Implicit Location Intent is where a user has not stated where they want the result to be
located. An example is when the query is simply ‘Chinese’

● When the query has an Explicit location intent you can ignore user and viewport
locations.

The only exception is when the user asks queries such as “X near me/my location”. Then you
can only ignore the viewport but take the user location into account.

Example: “restaurants near me”, “nearby Starbucks”, “my location”

● Consider the viewport fresh when the viewport age is missing.


● If no results can be found in or near the viewport, use the user location as a
secondary location intent.
● If the user is missing, use the stale viewport as location intent.

Parking Intent
● Free and paid parking are equally relevant.
● If you can find evidence that a result is for private parking that cannot be used by the
general public, give it a rating of Bad.

If the query has the business name and the address and the result returns the address without
the business name rate the relevance as Bad.

If the query is a street name and the result is one of the businesses on the street rate the
relevance as Bad.

PIN ACCURACY

● If a pin for a result does not appear on a map, rate the pin Wrong.
● If a pin lands in the parking lot of the campus, rate it as Approximate.
● A feature cannot be Next Door to another feature within the same property
boundaries. This means that two buildings in the same shared parking lot or parcel can
never be rated as Next Door to one another.

For pins that are on single rooftops refer to this table:

For pins that are on multiple rooftops that are not campuses (factories, gas stations,
apartment complexes, etc) refer to this table:

For pins that are on multiple rooftops that are campuses (universities, hospitals, airports,
resorts, zoos etc) refer to this table:

NAME ACCURACY

● Name not Applicable (n/a): The n/a rating should be used for all address type results,
including residential addresses, streets, localities, and so on.
● If you see that the result name has incorrect or missing punctuation or special characters
and you can still clearly understand which business it’s referring to, you should demote
the name accuracy to Partially Correct.
○ Examples: Macys (Macy’s), HM (H&M), Uhaul (U-Haul), att (AT&T)
● However, if you see severe misspellings that prevent the user from identifying the
business because of either a change in the meaning or the name becoming
unrecognizable the rating turns into Incorrect.

If a result name is incorrect, the final Name Accuracy rating will always be Incorrect, even if
the classification is correct.
When the classification is wrong, the final Name Accuracy rating is always Incorrect.

If either name or classification is incorrect, the final rating will always be incorrect.

ADDRESS ACCURACY

● If a business or POI has more than one official address for the same location, accept any
of them as Correct.
● Natural features are specific landforms or ecosystems like rivers, mountains, jungles,
and other geological features and they do not have a street address. If a street address
is present, it is considered Incorrect – Other Issue, even when pointing to a building
that is associated with the feature (like a ranger station or visitor center).
● If the full address belongs to another building not associated with the POI (even if they
are on the same street) it should be rated Incorrect.

For many more examples on how to rate results please refer to Section 10 of the guidelines.

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