[null,null,["最終更新日 2025-07-17 UTC。"],[],[],null,["In the Preview release, Places Insights provides a sample dataset in a BigQuery\ndata clean room which includes the data for a top city for each of the supported\ncountries: Sydney (AU), Sao Paulo (BR), Toronto (CA), Zurich (CH), Berlin (DE),\nMadrid (ES), Paris (FR), London (UK), Jakarta (ID), Mumbai (IN), Rome (IT),\nTokyo (JP), Mexico City (MX), New York City (US).\n\nEach sample dataset is intended to allow you to try out Places Insights so that\nyou can assess the usability and value of the product.\n| **Note:** For the Preview release, the brands data is only available for the United States sample dataset.\n\nEach sample dataset has its own data clean room that you must subscribe to\nseparately. For more information on subscribing to a clean room, see [Set up\nPlaces Insights](/maps/documentation/placesinsights/cloud-setup).\n\nThe datasets are designed for you to derive aggregated insights about places\ndata based on a variety of attributes such as place types, ratings, store hours,\nwheelchair accessibility, and more.\n\nSample dataset region location\n\nIn BigQuery, [datasets](https://cloud.google.com/bigquery/docs/datasets-intro)\nare stored in a specific region or multi-region\n[location](https://cloud.google.com/bigquery/docs/locations). A *region* is a\ncollection of data centers within a geographical area, and a *multi-region* is a\nlarge geographic area that contains two or more geographic regions.\n\nFor the Preview release of Places Insights, the sample datasets are stored in\nthe **`US` multi-region**.\n| **Note:** Because the Places Insights dataset tables are stored in the `US` multi-region, you cannot write query results to a table in another region, and you cannot join Places Insights tables with tables in another region.\n\nTo perform a join, you can create a replica of your data in the `US`\nmulti-region. For more information on dataset replication, see [Cross-region\ndataset replication](https://cloud.google.com/bigquery/docs/data-replication).\n\nDataset schemas\n\nThe places dataset schema for each country is comprised of two parts:\n\n- The [core schema](/maps/documentation/placesinsights/reference/core-schema) that is common to the datasets for all countries.\n- A [country-specific schema](/maps/documentation/placesinsights/reference/country-schema) that defines schema components specific to that country.\n\nFor example, if you are working with the dataset for Spain (ES), reference both\nthe core schema and the ES-specific schema.\n\nThe schema for the brands dataset defines three fields:\n\n- `id`: The brand ID.\n- `name`: The name of the brand, such as \"Hertz\" or \"Chase\".\n- `category`: The high-level category of the brand, such as \"Gas Station\", \"Food and Drink\", or \"Lodging\".\n\nSample data for Preview\n\nFor the Preview release, the dataset for each country contains information for a\ntop city. Even though the data is for a single city, the dataset schema is the\nsame for the entire country.\n\nThe dataset only contains data for the city itself. It does not contain data for\nthe surrounding metropolitan area.\n\nAdditional notes\n\nThe `rating` and `user_rating_count` data are statistics calculated using user\nreviews. User reviews are not verified by Google, but Google checks for and\nremoves fake content when it's identified.\n\nThe dataset may not be accurate and is refreshed monthly.\n\nAttribution requirements\n\nWhen displaying Places Insights data, you must display the required\nattributions. For attribution requirements, see [Policies for\nPlaces Insights](/maps/documentation/placesinsights/policies)."]]