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推出階段說明。
註冊即可試用 Places Insights！
PLACES_COUNT 函式
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
PLACES_COUNT 函式會根據指定的搜尋區域和搜尋篩選器，傳回地點的單一計數值。您必須為 PLACES_COUNT 函式指定搜尋區域，並可選擇指定其他篩選參數，例如地點類型、營業狀態、價格等級等。
由於 PLACES_COUNT 函式會傳回單一值，請使用 SELECT 子句呼叫該函式。
範例：計算搜尋半徑內的商家數量
最簡單的 PLACES_COUNT 函式呼叫會傳回地理區域中所有地點的單一計數。在本例中，您會傳回帝國大廈方圓 1000 公尺內所有營業場所的數量。
這個範例使用 BigQuery ST_GEOGPOINT 函式，從點傳回 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;
回應包含單一計數：
更典型的呼叫會對搜尋區域套用篩選器。下一個範例使用篩選器，將搜尋範圍限制為只傳回以下項目的計數：
- 類型為
restaurant 且最低評分為 3 的地點
- 價格等級為「平價」或「中等」
- 目前可正常運作
- 可帶狗入內
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;
篩選後的回應：
請注意，地點資料集查詢會強制執行至少 5 個的計數門檻。地點計數函式的優點之一是可傳回任何計數，包括 0。舉例來說，下列呼叫會傳回計數 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：使用多邊形計算餐廳數量
您可以使用多邊形指定搜尋區域。使用多邊形時，多邊形的點必須定義封閉迴路，也就是多邊形的第一個點與最後一個點相同。
這個範例使用 BigQuery ST_GEOGFROMTEXT 函式，從多邊形傳回 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;
可視區域的回應：
示例：使用線條計算餐廳數量
在下一個範例中，您會使用一條由相連點組成的線定義搜尋區域，並以該線為中心，設定 100 公尺的搜尋半徑。這條線類似於 Routes API 計算出的路線。路線可能適用於車輛、自行車或行人：
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;
該行的回應：
範例：合併多個呼叫的結果
您可以合併多次呼叫 PLACES_COUNT 函式的結果。
舉例來說，您想在特定區域內，取得下列價格範圍的餐廳數量結果：
PRICE_LEVEL_INEXPENSIVEPRICE_LEVEL_MODERATEPRICE_LEVEL_EXPENSIVEPRICE_LEVEL_VERY_EXPENSIVE"
在這個範例中，您會建立迴圈，針對每個價格層級呼叫 PLACES_COUNT 函式，並將每次呼叫的結果插入臨時表格。接著查詢臨時資料表，即可顯示結果：
-- 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;
合併回覆：
另一種做法是使用 UNION ALL 指令，合併多個 SELECT 陳述式的結果。下列範例顯示的結果與上一個範例相同：
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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上次更新時間：2025-07-17 (世界標準時間)。
[null,null,["上次更新時間：2025-07-17 (世界標準時間)。"],[],[],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```"]]
