Map and weather data licences

1. Filtering.

A place is kept only when the country code of its first address is the country being loaded, the corner of its bounding box lies inside that country's extraction box, its operating status is "open" or not given, its confidence is 0.5 or higher, and it has a non-empty primary name. Every other place is removed.

2. Category mapping.

Overture's basic_category and taxonomy fields are replaced by one of eight Lila categories. A place that matches none of them is removed.

cafe: basic_category cafe or coffee_shop. restaurant: restaurant or fast_food_restaurant. park: park or dog_park. gym: gym or fitness_studio. pharmacy: pharmacy_and_drug_store. grocery: the taxonomy hierarchy contains grocery_store, or the primary taxonomy category is grocery_store, supermarket or convenience_store, or basic_category is supermarket or convenience_store. library: library. museum: museum.

3. Truncation and rounding.

The primary name is trimmed and cut to at most 120 characters. The display address is the first address's free-form line and locality, joined and cut to at most 200 characters. The position is the centre of the place's bounding box, rounded to 6 decimal places. The confidence is rounded to 3 decimal places.

4. Dropped fields.

Lila keeps only the Overture id, the primary name, the Lila category, the position, the display address, the country code, the names of the datasets that contributed the record, the confidence and the release id. Every other field is dropped, including websites, phone numbers, email addresses, social media links, brand, alternate names, opening hours and the sources of individual properties.

5. Duplicate folding.

When Lila answers a nearby search, places with the same name (ignoring case and spacing) whose positions are less than 150 metres apart are shown once. At most 10 places are shown, and each distance is rounded to 0.1 km.

1. Provision of the Data

1.1. A Data Recipient may use, modify, and share the Data made available by Data Provider(s) under this agreement if that Data Recipient follows the terms of this agreement.

1.2. This agreement does not impose any restriction on a Data Recipient’s use, modification, or sharing of any portions of the Data that are in the public domain or that may be used, modified, or shared under any other legal exception or limitation.

2. Conditions for Sharing Data

2.1. A Data Recipient may share Data, with or without modifications, so long as the Data Recipient makes available the text of this agreement with the shared Data.

3. No Restrictions on Results

3.1. This agreement does not impose any restriction or obligations with respect to the use, modification, or sharing of Results.

4. No Warranty; Limitation of Liability

4.1. All Data Recipients receive the Data subject to the following terms:

THE DATA IS PROVIDED ON AN "AS IS" BASIS, WITHOUT REPRESENTATIONS, WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OR CONDITIONS OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.

NO DATA PROVIDER SHALL HAVE ANY LIABILITY FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING WITHOUT LIMITATION LOST PROFITS), HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE DATA OR RESULTS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

5. Definitions

5.1. "Data" means the material received by a Data Recipient under this agreement.

5.2. "Data Provider" means any person who is the source of Data provided under this agreement and in reliance on a Data Recipient’s agreement to its terms.

5.3. "Data Recipient" means any person who receives Data directly or indirectly from a Data Provider and agrees to the terms of this agreement.

5.4. "Results" means any outcome obtained by computational analysis of Data, including for example machine learning models and models’ insights.

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We also encourage you to join our Placemaker community where you can contribute and provide suggestions to improve the accuracy of the Data for future releases for yourself and others.