PLACES_COUNT 函数
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根据您的偏好保存内容并对其进行分类。
PLACES_COUNT 函数会根据指定的搜索区域和搜索过滤条件，返回一个表示地点数量的数值。您必须为 PLACES_COUNT 函数指定搜索区域，还可以选择指定其他过滤参数，例如地点类型、营业状态、价格水平等。
由于 PLACES_COUNT 函数返回单个值，因此请使用 SELECT 子句调用该函数。
示例：计算搜索半径内的地点数量
最简单的 PLACES_COUNT 函数调用会返回地理区域中所有地点的单个计数。在此示例中，您返回了帝国大厦 1000 米范围内的所有营业场所的数量。
此示例使用 BigQuery ST_GEOGPOINT 函数从点返回 GEOGRAPHY 值。
SELECT `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(
JSON_OBJECT(
'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building
'geography_radius', 1000 -- Radius in meters
)
) as count;
响应包含一个计数：
更典型的调用会对搜索区域应用过滤条件。以下示例使用过滤条件来限制搜索范围，仅返回以下内容的数量：
- 类型为
restaurant 且最低评分为 3 的地点
- 价格水平为低价或中等价位
- 目前可正常运行
- 允许带狗
SELECT `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(
JSON_OBJECT(
'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building
'geography_radius', 1000, -- Radius in meters
'types', ["restaurant"],
'min_rating', 3,
'price_level', ['PRICE_LEVEL_INEXPENSIVE', 'PRICE_LEVEL_MODERATE'],
'business_status', ['OPERATIONAL'],
'allows_dogs', TRUE
)
) as count;
过滤后的响应：
请注意，地点数据集查询会强制执行 5 的最小计数阈值。位置计数函数的一项优势在于，它们可以返回任何数量，包括 0。例如，以下调用会返回 1：
SELECT `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(
JSON_OBJECT(
'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building
'geography_radius', 500, -- Radius in meters
'types', ["restaurant"],
'min_rating', 4.0,
'free_parking_lot', TRUE,
'good_for_watching_sports', TRUE
)
) as count;
示例：使用多边形计算餐厅数量
您可以使用多边形指定搜索区域。使用多边形时，多边形的点必须定义一个闭环，其中多边形的第一个点与最后一个点相同。
此示例使用 BigQuery ST_GEOGFROMTEXT 函数从多边形返回 GEOGRAPHY 值。
DECLARE geo GEOGRAPHY;
SET geo = ST_GEOGFROMTEXT('''POLYGON((-73.985708 40.75773,-73.993324 40.750298,
-73.9857 40.7484,-73.9785 40.7575,
-73.985708 40.75773))'''); -- NYC viewport
SELECT `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(
JSON_OBJECT(
'geography',geo, -- viewport
'types', ["restaurant"],
'min_rating', 1.0,
'max_rating', 4.5,
'min_user_rating_count', 1,
'max_user_rating_count', 10000,
'price_level', ['PRICE_LEVEL_INEXPENSIVE', 'PRICE_LEVEL_MODERATE'],
'business_status', ['OPERATIONAL'],
'allows_dogs', TRUE
)
) as count;
视口的响应：
示例：使用直线计算餐厅数量
在下一个示例中，您将使用一条由若干个相连的点组成的线来定义搜索区域，并在线周围设置 100 米的搜索半径。
此线条类似于 Routes API 计算出的出行路线。路线可能适用于车辆、自行车或步行者：
DECLARE geo GEOGRAPHY;
SET geo = ST_GEOGFROMTEXT('LINESTRING(-73.98903537033028 40.73655649223003,-73.93580216278471 40.80955538843361)'); -- NYC line
SELECT `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(
JSON_OBJECT(
'geography',geo, -- line
'geography_radius', 100, -- Radius around line
'types', ["restaurant"],
'min_rating', 1.0,
'max_rating', 4.5,
'min_user_rating_count', 1,
'max_user_rating_count', 10000,
'price_level', ['PRICE_LEVEL_INEXPENSIVE', 'PRICE_LEVEL_MODERATE'],
'business_status', ['OPERATIONAL'],
'allows_dogs', TRUE
)
) as count;
相应行的回答：
示例：合并多次调用的结果
您可以合并多次调用 PLACES_COUNT 函数的结果。
例如，您希望获得一个结果，其中显示特定区域内以下价位的餐厅数量：
PRICE_LEVEL_INEXPENSIVEPRICE_LEVEL_MODERATEPRICE_LEVEL_EXPENSIVEPRICE_LEVEL_VERY_EXPENSIVE"
在此示例中，您将创建一个循环，以便针对每个价格水平调用 PLACES_COUNT 函数，并将每次调用的结果插入到临时表中。然后，您查询临时表以显示结果：
-- Create a temp table to hold the results.
CREATE TEMP TABLE results (type STRING, count INT64);
-- Create a loop that calls PLACES_COUNT for each price level.
FOR types IN (SELECT type FROM UNNEST(["PRICE_LEVEL_INEXPENSIVE", "PRICE_LEVEL_MODERATE", "PRICE_LEVEL_EXPENSIVE", "PRICE_LEVEL_VERY_EXPENSIVE"]) as type)
DO
INSERT INTO results VALUES (types.type, `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(
JSON_OBJECT(
'types', ["restaurant"],
'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building
'geography_radius', 1000, -- Radius in meters
'business_status', ['OPERATIONAL'],
'price_level', [types.type]
)));
END FOR;
-- Query the table of results.
SELECT * FROM results;
合并后的回答：
另一种方法是使用 UNION ALL 命令来合并多个 SELECT 语句的结果。以下示例显示的结果与上一个示例相同：
SELECT "PRICE_LEVEL_INEXPENSIVE" as price_level, `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(
JSON_OBJECT(
'types', ["restaurant"],
'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building
'geography_radius', 1000, -- Radius in meters
'business_status', ['OPERATIONAL'],
'price_level', ['PRICE_LEVEL_INEXPENSIVE']
)
) as count
UNION ALL
SELECT "PRICE_LEVEL_MODERATE" as price_level, `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(
JSON_OBJECT(
'types', ["restaurant"],
'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building
'geography_radius', 1000, -- Radius in meters
'business_status', ['OPERATIONAL'],
'price_level', ['PRICE_LEVEL_MODERATE']
)
) as count
UNION ALL
SELECT "PRICE_LEVEL_EXPENSIVE" as price_level, `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(
JSON_OBJECT(
'types', ["restaurant"],
'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building
'geography_radius', 1000, -- Radius in meters
'business_status', ['OPERATIONAL'],
'price_level', ['PRICE_LEVEL_EXPENSIVE']
)
) as count
UNION ALL
SELECT "PRICE_LEVEL_VERY_EXPENSIVE" as price_level, `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(
JSON_OBJECT(
'types', ["restaurant"],
'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building
'geography_radius', 1000, -- Radius in meters
'business_status', ['OPERATIONAL'],
'price_level', ['PRICE_LEVEL_VERY_EXPENSIVE']
)
) as count
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最后更新时间 (UTC)：2025-07-17。
[null,null,["最后更新时间 (UTC)：2025-07-17。"],[],[],null,["The `PLACES_COUNT` function returns a single count value of places based on the\nspecified search area and search filters. You must specify the search area to\nthe `PLACES_COUNT` function and can optionally specify additional filter\nparameters, such as place type, operating status, price level, and more.\n\nBecause the `PLACES_COUNT` function returns a single value, call it using\na `SELECT` clause.\n\n- Input parameters:\n\n - **Required** : The `geography` [filter parameter](/maps/documentation/placesinsights/experimental/filter-params) that\n specifies the search area. The `geography` parameter takes a value defined\n by the BigQuery\n [`GEOGRAPHY`](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#geography_type)\n data type, which supports points, linestrings, and polygons.\n\n - **Optional** : Additional [filter](/maps/documentation/placesinsights/experimental/filter-params) parameters to refine your\n search.\n\n- Returns:\n\n - A single `count` value as an `INT64`.\n\nExample: Calculate the number of places in a search radius\n\nThe simplest `PLACES_COUNT` function call returns a single count of all places\nin a geographical area. In this example, you return the count of all operational\nplaces within 1000 meters of the Empire State building.\n\nThis example uses the BigQuery\n[`ST_GEOGPOINT`](https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions#st_geogpoint)\nfunction to return a `GEOGRAPHY` value from a point. \n\n```googlesql\nSELECT `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(\n JSON_OBJECT(\n 'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building\n 'geography_radius', 1000 -- Radius in meters\n )\n) as count;\n```\n\nThe response contains a single count:\n\nA more typical call applies filters to the search area. The next example uses\nfilters to limit the search to only return a count of:\n\n- Places of type `restaurant` with the minimum rating of 3\n- A price level of inexpensive or medium\n- Currently operational\n- Allows dogs\n\n```googlesql\nSELECT `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(\n JSON_OBJECT(\n 'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building\n 'geography_radius', 1000, -- Radius in meters\n 'types', [\"restaurant\"],\n 'min_rating', 3,\n 'price_level', ['PRICE_LEVEL_INEXPENSIVE', 'PRICE_LEVEL_MODERATE'],\n 'business_status', ['OPERATIONAL'],\n 'allows_dogs', TRUE\n )\n) as count;\n```\n\nThe filtered response:\n\nRemember that place dataset queries enforce a minimum count threshold of\n5. One of the advantages of the place count functions is\nthat they can return any counts, including 0. For example, the following call\nreturns a count of 1: \n\n```googlesql\nSELECT `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(\n JSON_OBJECT(\n 'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building\n 'geography_radius', 500, -- Radius in meters\n 'types', [\"restaurant\"],\n 'min_rating', 4.0,\n 'free_parking_lot', TRUE,\n 'good_for_watching_sports', TRUE\n )\n) as count;\n```\n\nExample: Calculate the number of restaurants using a polygon\n\nYou can use a polygon to specify the search area. When using a polygon,\nthe points of the polygon must define a closed loop where the first point in the\npolygon is the same as the last point.\n\nThis example uses the BigQuery\n[`ST_GEOGFROMTEXT`](https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions#st_geogfromtext)\nfunction to return a `GEOGRAPHY` value from a polygon. \n\n```googlesql\nDECLARE geo GEOGRAPHY;\nSET geo = ST_GEOGFROMTEXT('''POLYGON((-73.985708 40.75773,-73.993324 40.750298,\n -73.9857 40.7484,-73.9785 40.7575,\n -73.985708 40.75773))'''); -- NYC viewport\n\nSELECT `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(\n JSON_OBJECT(\n 'geography',geo, -- viewport \n 'types', [\"restaurant\"],\n 'min_rating', 1.0,\n 'max_rating', 4.5,\n 'min_user_rating_count', 1,\n 'max_user_rating_count', 10000,\n 'price_level', ['PRICE_LEVEL_INEXPENSIVE', 'PRICE_LEVEL_MODERATE'],\n 'business_status', ['OPERATIONAL'],\n 'allows_dogs', TRUE\n )\n) as count;\n```\n\nThe response for the viewport:\n\nExample: Calculate the number of restaurants using a line\n\nIn the next example, you define the search area using a line of connected\npoints with a search radius of 100 meters around the line.\nThe line is similar to a travel route calculated by the [Routes\nAPI](/maps/documentation/routes). The route might be for a vehicle, a bicycle,\nor for a pedestrian: \n\n```googlesql\nDECLARE geo GEOGRAPHY;\nSET geo = ST_GEOGFROMTEXT('LINESTRING(-73.98903537033028 40.73655649223003,-73.93580216278471 40.80955538843361)'); -- NYC line\n\nSELECT `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(\n JSON_OBJECT(\n 'geography',geo, -- line\n 'geography_radius', 100, -- Radius around line\n 'types', [\"restaurant\"],\n 'min_rating', 1.0,\n 'max_rating', 4.5,\n 'min_user_rating_count', 1,\n 'max_user_rating_count', 10000,\n 'price_level', ['PRICE_LEVEL_INEXPENSIVE', 'PRICE_LEVEL_MODERATE'],\n 'business_status', ['OPERATIONAL'],\n 'allows_dogs', TRUE\n )\n) as count;\n```\n\nThe response for the line:\n\nExample: Combine the results of multiple calls\n\nYou can combine the results of multiple calls to the `PLACES_COUNT` function.\nFor example, you want a single result showing the number of restaurants for\nthe following price levels within a specific area:\n\n- `PRICE_LEVEL_INEXPENSIVE`\n- `PRICE_LEVEL_MODERATE`\n- `PRICE_LEVEL_EXPENSIVE`\n- `PRICE_LEVEL_VERY_EXPENSIVE\"`\n\nIn this example, you create a loop to call the `PLACES_COUNT` function for each\nprice level, and insert the results of each call to a temporary table. You then\nquery the temporary table to display the results: \n\n```googlesql\n-- Create a temp table to hold the results.\nCREATE TEMP TABLE results (type STRING, count INT64);\n\n-- Create a loop that calls PLACES_COUNT for each price level.\nFOR types IN (SELECT type FROM UNNEST([\"PRICE_LEVEL_INEXPENSIVE\", \"PRICE_LEVEL_MODERATE\", \"PRICE_LEVEL_EXPENSIVE\", \"PRICE_LEVEL_VERY_EXPENSIVE\"]) as type)\nDO\n INSERT INTO results VALUES (types.type, `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(\n JSON_OBJECT(\n 'types', [\"restaurant\"],\n 'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building\n 'geography_radius', 1000, -- Radius in meters\n 'business_status', ['OPERATIONAL'],\n 'price_level', [types.type]\n )));\nEND FOR;\n\n-- Query the table of results.\nSELECT * FROM results;\n```\n\nThe combined response:\n\nAnother option is to use the `UNION ALL` command to combine the results of\nmultiple `SELECT` statements. The following example shows the same results as\nfrom the previous example: \n\n```googlesql\nSELECT \"PRICE_LEVEL_INEXPENSIVE\" as price_level, `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(\n JSON_OBJECT(\n 'types', [\"restaurant\"],\n 'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building\n 'geography_radius', 1000, -- Radius in meters\n 'business_status', ['OPERATIONAL'],\n 'price_level', ['PRICE_LEVEL_INEXPENSIVE']\n )\n) as count\n\nUNION ALL\n\nSELECT \"PRICE_LEVEL_MODERATE\" as price_level, `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(\n JSON_OBJECT(\n 'types', [\"restaurant\"],\n 'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building\n 'geography_radius', 1000, -- Radius in meters\n 'business_status', ['OPERATIONAL'],\n 'price_level', ['PRICE_LEVEL_MODERATE']\n )\n) as count\n\nUNION ALL\n\nSELECT \"PRICE_LEVEL_EXPENSIVE\" as price_level, `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(\n JSON_OBJECT(\n 'types', [\"restaurant\"],\n 'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building\n 'geography_radius', 1000, -- Radius in meters\n 'business_status', ['OPERATIONAL'],\n 'price_level', ['PRICE_LEVEL_EXPENSIVE']\n )\n) as count\n\nUNION ALL\n\nSELECT \"PRICE_LEVEL_VERY_EXPENSIVE\" as price_level, `maps-platform-analytics-hub.sample_places_insights_us.PLACES_COUNT`(\n JSON_OBJECT(\n 'types', [\"restaurant\"],\n 'geography', ST_GEOGPOINT(-73.9857, 40.7484), -- Empire State Building\n 'geography_radius', 1000, -- Radius in meters\n 'business_status', ['OPERATIONAL'],\n 'price_level', ['PRICE_LEVEL_VERY_EXPENSIVE']\n )\n) as count\n```"]]
