地点数据分析数据简介
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
在预览版中,Places Insights 在 BigQuery 中提供了一个示例数据集,其中包含每个受支持国家/地区的热门城市的数据:悉尼 (AU)、圣保罗 (BR)、多伦多 (CA)、苏黎世 (CH)、柏林 (DE)、马德里 (ES)、巴黎 (FR)、伦敦 (UK)、雅加达 (ID)、孟买 (IN)、罗马 (IT)、东京 (JP)、墨西哥城 (MX)、纽约市 (US)。
每个示例数据集都旨在让您试用 Places Insights,以便您评估该产品的实用性和价值。
每个示例数据集都有自己的商品详情,您必须单独订阅。如需详细了解如何订阅商家信息,请参阅设置地点数据洞见。
这些数据集旨在帮助您根据各种属性(例如地点类型、评分、营业时间、轮椅通道等)得出有关地点数据的汇总分析洞见。
数据集架构
每个国家/地区的地点数据集架构包含两部分:
例如,如果您使用的是西班牙 (ES) 的数据集,请同时引用核心架构和 ES 特有的架构。
品牌数据集的架构定义了三个字段:
id
:品牌 ID。
name
:品牌名称,例如“Hertz”或“Chase”。
category
:品牌的高级类别,例如“加油站”“食品和饮料”或“住宿”。
预览版示例数据
对于预览版,每个国家/地区的数据集都包含一个热门城市的信息。虽然数据是针对单个城市,但数据集架构对于整个国家/地区都是相同的。
相应数据集仅包含该城市本身的数据。不包含周边都市区的数据。
其他说明
rating
和 user_rating_count
数据是使用用户评价计算得出的统计信息。用户评价未经 Google 核实,但 Google 会检查有无虚假内容,一旦发现会将其移除。
此数据集可能不准确,并且每月刷新一次。
署名要求
显示地点概览数据时,您必须显示所需的提供方信息。如需了解提供方信息要求,请参阅本地商家概览政策。
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC):2025-09-06。
[null,null,["最后更新时间 (UTC):2025-09-06。"],[],[],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)."]]