$geometry 運算子會指定地理空間查詢的 GeoJSON 幾何物件。 它用於其他地理空間運算子內,以定義空間計算的圖形和點。
語法
{
$geometry: {
type: <GeoJSON type>,
coordinates: <coordinates>
}
}
參數
| 參數 | Description |
|---|---|
type |
GeoJSON 物件類型 (Point、Polygon、MultiPolygon 等) |
coordinates |
定義 GeoJSON 物件作為陣列的座標 |
範例
我們來了解 stores 資料集範例 json 的使用方式。
{
"_id": "0fcc0bf0-ed18-4ab8-b558-9848e18058f4",
"name": "First Up Consultants | Beverage Shop - Satterfieldmouth",
"location": {
"lat": -89.2384,
"lon": -46.4012
},
"staff": {
"totalStaff": {
"fullTime": 8,
"partTime": 20
}
},
"sales": {
"totalSales": 75670,
"salesByCategory": [
{
"categoryName": "Wine Accessories",
"totalSales": 34440
},
{
"categoryName": "Bitters",
"totalSales": 39496
},
{
"categoryName": "Rum",
"totalSales": 1734
}
]
},
"promotionEvents": [
{
"eventName": "Unbeatable Bargain Bash",
"promotionalDates": {
"startDate": {
"Year": 2024,
"Month": 6,
"Day": 23
},
"endDate": {
"Year": 2024,
"Month": 7,
"Day": 2
}
},
"discounts": [
{
"categoryName": "Whiskey",
"discountPercentage": 7
},
{
"categoryName": "Bitters",
"discountPercentage": 15
},
{
"categoryName": "Brandy",
"discountPercentage": 8
},
{
"categoryName": "Sports Drinks",
"discountPercentage": 22
},
{
"categoryName": "Vodka",
"discountPercentage": 19
}
]
},
{
"eventName": "Steal of a Deal Days",
"promotionalDates": {
"startDate": {
"Year": 2024,
"Month": 9,
"Day": 21
},
"endDate": {
"Year": 2024,
"Month": 9,
"Day": 29
}
},
"discounts": [
{
"categoryName": "Organic Wine",
"discountPercentage": 19
},
{
"categoryName": "White Wine",
"discountPercentage": 20
},
{
"categoryName": "Sparkling Wine",
"discountPercentage": 19
},
{
"categoryName": "Whiskey",
"discountPercentage": 17
},
{
"categoryName": "Vodka",
"discountPercentage": 23
}
]
}
]
}
範例 1:尋找距離點幾何圖形最近的商店
若要獲得更好的效能,請從建立必要的 2dsphere 索引開始。
db.stores.createIndex({ location: "2dsphere" })
查詢最多會擷取兩個最接近座標點 [46.2917, -62.6354] 的存放區,依鄰近程度排序。 它會使用 $near 運算符,依距離特定點來排序結果,協助尋找最接近指定位置的存放區。
db.stores.find({
location: {
$near: {
$geometry: {
type: "Point",
coordinates: [46.2917, -62.6354]
}
}
}
}, {
name: 1,
location: 1
}).limit(2)
此查詢傳回的前兩個結果是:
[
{
"_id": "59c355e9-586c-44f8-bbaf-a87989142119",
"name": "Relecloud | Outdoor Furniture Shop - Chetside",
"location": { "lat": 46.188, "lon": -62.2789 }
},
{
"_id": "d3a9cc23-e6ae-4806-93ac-1ade2f624742",
"name": "VanArsdel, Ltd. | Furniture Place - North Dustinside",
"location": { "lat": 47.3426, "lon": -62.4031 }
}
]
範例 2:尋找距離多邊形幾何圖形最近的商店
此查詢會尋找最多兩個存放區,其位置與所定義的矩形多邊形交集,其座標範圍從 [-80.0, -75.0] 到 [-55.0, -70.0]。
$geoIntersects 運算子會尋找與多邊形界限重疊或接觸的商店 - 非常適合用來識別哪些位置與特定地理區域有交集,無論是完全在內部,還是僅僅跨越邊緣。
db.stores.find({
location: {
$geoIntersects: {
$geometry: {
type: "Polygon",
coordinates: [[
[-80.0, -75.0], // Bottom-left
[-80.0, -70.0], // Top-left
[-55.0, -70.0], // Top-right
[-55.0, -75.0], // Bottom-right
[-80.0, -75.0] // Close polygon
]]
}
}
}
}, {
name: 1,
location: 1,
city: 1
}).limit(2)
此查詢傳回的前兩個結果。
[
{
"_id": "6bba7117-d180-4584-b50c-a2f843e9c9ab",
"name": "Wide World Importers | Craft Supply Mart - Heaneybury",
"location": { "lat": -64.4843, "lon": -107.7003 },
"city": "Heaneybury"
},
{
"_id": "2fd37663-e0ff-41d0-9c5a-3aec86285daa",
"name": "Relecloud | Cleaning Supply Closet - Patiencehaven",
"location": { "lat": -70.6077, "lon": -105.9901 },
"city": "Patiencehaven"
}
]
範例 3:尋找距離多邊形幾何圖形最近的商店
此範例會擷取最多兩個商店,其位置落在定義的兩個矩形區域之一(MultiPolygon)中:一個區域靠近坐標 [120.0, -13.0] 至 [125.0, -10.0],另一個區域靠近坐標 [44.0, -64.0] 至 [48.0, -61.0]。
它會使用具有 MultiPolygon 幾何的 $geoWithin 運算符來搜尋任何指定多邊形所包圍的商店,使其可用於同時查詢多個非相鄰地理區域。
db.stores.find({
location: {
$geoWithin: {
$geometry: {
type: "MultiPolygon",
coordinates: [
[[
[120.0, -13.0],
[120.0, -10.0],
[125.0, -10.0],
[125.0, -13.0],
[120.0, -13.0]
]],
[[
[44.0, -64.0],
[44.0, -61.0],
[48.0, -61.0],
[48.0, -64.0],
[44.0, -64.0]
]]
]
}
}
}
}, {
name: 1,
location: 1
}).limit(2)
此查詢傳回的前兩個結果是:
[
{
"_id": "6d70de9c-7b83-426d-81aa-f2173f97b64d",
"name": "Fabrikam, Inc. | Footwear Haven - Port Erling",
"location": { "lat": 45.641, "lon": -118.4963 }
},
{
"_id": "96d48224-ce10-4a61-999a-8536d442f81a",
"name": "Wide World Importers | Eyewear Bazaar - West Oletachester",
"location": { "lat": 47.3461, "lon": -61.6605 }
}
]