geo_polygon_to_s2cells()

项目
09/29/2024

适用于：✅Azure 数据资源管理器✅Azure Monitor✅Microsoft Sentinel

计算覆盖地球上的多边形或多多边形的 S2 单元标记。此函数是一种有用的地理空间加入工具。

语法

geo_polygon_to_s2cells(polygon [, level[, radius]])

参数

客户	类型	必需	说明
polygon	`dynamic`	✔️	采用 GeoJSON 格式的多边形或多多边形。
level	`int`		定义所请求的单元格级别。支持的值范围为 [0, 30]。如果未指定，则使用默认值 `11`。
radius	`real`		以米为单位的缓冲区半径。如果未指定，则使用默认值 `0`。

覆盖多边形或多多边形的 S2 单元标记字符串的数组。如果 radius 设置为正值，则除输入形状外，将会覆盖输入几何图形半径内的所有点。如果 polygon、level 和 radius 无效，或者单元格计数超出限制，则查询会生成 null 结果。

注意

在将坐标与可能包含这些坐标的多边形进行匹配以及将多边形与多边形匹配时，使用 S2 单元标记来覆盖多边形会很有用。
覆盖标记的多边形具有相同的 S2 单元级别。
每个多边形的最大标记计数为 65536。
用于在地球上进行测量的大地基准是一个球体。多边形的边是球体上的测地线。
如果输入多边形边缘是直笛卡尔线，请考虑使用 geo_polygon_densify() 以将平面边缘转换为测地线。

使用 S2 单元标记覆盖多边形的动机

如果没有此函数，则可以使用下面的方法将坐标归类为包含这些坐标的多边形。

let Polygons = 
    datatable(description:string, polygon:dynamic)
    [  
      "New York",  dynamic({"type":"Polygon","coordinates":[[[-73.85009765625,40.85744791303121],[-74.16046142578125,40.84290487729676],[-74.190673828125,40.59935608796518],[-73.83087158203125,40.61812224225511],[-73.85009765625,40.85744791303121]]]}),
      "Seattle",   dynamic({"type":"Polygon","coordinates":[[[-122.200927734375,47.68573021131587],[-122.4591064453125,47.68573021131587],[-122.4755859375,47.468949677672484],[-122.17620849609374,47.47266286861342],[-122.200927734375,47.68573021131587]]]}),
      "Las Vegas", dynamic({"type":"Polygon","coordinates":[[[-114.9,36.36],[-115.4498291015625,36.33282808737917],[-115.4498291015625,35.84453450421662],[-114.949951171875,35.902399875143615],[-114.9,36.36]]]}),
    ];
let Coordinates = 
    datatable(longitude:real, latitude:real)
    [
      real(-73.95),  real(40.75), // New York
      real(-122.3),  real(47.6),  // Seattle
      real(-115.18), real(36.16)  // Las Vegas
    ];
Polygons | extend dummy=1
| join kind=inner (Coordinates | extend dummy=1) on dummy
| where geo_point_in_polygon(longitude, latitude, polygon)
| project longitude, latitude, description

输出

longitude	latitude	description
-73.95	40.75	纽约市
-122.3	47.6	西雅图
-115.18	36.16	拉斯维加斯

虽然此方法在某些情况下有效，但效率低下。此方法执行交叉联接，这意味着它会尝试将每个多边形与每个点进行匹配。此过程会消耗大量的内存和计算资源。与之相反，我们希望将每个多边形匹配到包含成功概率很高的点，并筛选掉其他点。

可以通过以下过程实现此匹配：

将多边形转换为级别 k 的 S2 单元；
将点转换为级别 k 的同一个 S2 单元；
基于 S2 单元进行联接；
通过 geo_point_in_polygon() 进行筛选。如果可以存在一定数量的假正，则可以省略此阶段。最大误差将是超出多边形边界的 k 级 s2 单元的面积。

选择 S2 单元级别

理想情况下，我们希望通过一个或几个独特的单元覆盖每个多边形，以避免两个多边形共用同一单元。
如果多边形彼此靠近，请选择 S2 单元级别，使其单元边缘小于平均多边形的边缘（小 3、7、11 倍）。
如果多边形彼此远离，请选择 S2 单元级别，使其单元边缘类似于或大于平均多边形的边缘。
实际上，使用多于 10,000 个单元覆盖多边形可能不会有良好的性能。
示例用例：
S2 单元级别 5 可能证明适合覆盖国家/地区。
S2 单元级别 16 可以覆盖密集且相对较小的曼哈顿（纽约）社区。
S2 单元级别 11 可用于覆盖澳大利亚郊区。
查询运行时间和内存消耗可能因 S2 单元级别值的不同而产生很大差异。

警告

使用小面积单元覆盖大面积多边形可能会导致产生大量的覆盖单元。因此，查询可能会返回 null。

注意

性能改进建议：

如果可能，请在联接前减少坐标表大小，方法是使用地理空间聚类分析对彼此非常接近的坐标进行分组，或者根据数据或业务需求的性质筛选掉非结构化坐标。
如果可能，请根据数据或业务需求的性质减少多边形计数。在联接之前筛选掉不必要的多边形、将范围缩小到感兴趣的区域或统一多边形。
如果多边形非常大，请使用 geo_polygon_simplify() 缩减其大小。
更改 S2 单元级别可能会提高性能和内存消耗。
更改联接类型和提示可以提高性能和内存消耗。
如果半径设置为正值，则可以尝试通过使用 geo_polygon_buffer () 将缓冲形状恢复到半径 0 来提高性能。

示例

下面的示例将坐标归类为多边形。

运行查询

let Polygons = 
    datatable(description:string, polygon:dynamic)
    [
        'Greenwich Village', dynamic({"type":"Polygon","coordinates":[[[-73.991460000000131,40.731738000000206],[-73.992854491775518,40.730082566051351],[-73.996772,40.725432000000154],[-73.997634685522883,40.725786309886963],[-74.002855946639244,40.728346630056791],[-74.001413,40.731065000000207],[-73.996796995070824,40.73736378205173],[-73.991724524037934,40.735245208931886],[-73.990703782359589,40.734781896080477],[-73.991460000000131,40.731738000000206]]]}),
        'Upper West Side',   dynamic({"type":"Polygon","coordinates":[[[-73.958357552055688,40.800369095633819],[-73.98143901556422,40.768762584141953],[-73.981548752788598,40.7685590292784],[-73.981565335901905,40.768307084720796],[-73.981754418060945,40.768399727738668],[-73.982038573548124,40.768387823012056],[-73.982268248204349,40.768298621883247],[-73.982384797518051,40.768097213086911],[-73.982320919746599,40.767894461792181],[-73.982155532845766,40.767756204474757],[-73.98238873834039,40.767411004834273],[-73.993650353659021,40.772145571634361],[-73.99415893763998,40.772493009137818],[-73.993831082030937,40.772931787850908],[-73.993891252437052,40.772955194876722],[-73.993962585514595,40.772944653908901],[-73.99401262480508,40.772882846631894],[-73.994122058082397,40.77292405902601],[-73.994136652588594,40.772901870174394],[-73.994301342391154,40.772970028663913],[-73.994281535134448,40.77299380206933],[-73.994376552751078,40.77303955110149],[-73.994294029824005,40.773156243992048],[-73.995023275860802,40.773481196576356],[-73.99508939189289,40.773388475039134],[-73.995013963716758,40.773358035426909],[-73.995050284699261,40.773297153189958],[-73.996240651898916,40.773789791397689],[-73.996195837470992,40.773852356184044],[-73.996098807369748,40.773951805299085],[-73.996179459973888,40.773986954351571],[-73.996095245226442,40.774086186437756],[-73.995572265161172,40.773870731394297],[-73.994017424135961,40.77321375261053],[-73.993935876811335,40.773179512586211],[-73.993861942928888,40.773269531698837],[-73.993822393527211,40.773381758622882],[-73.993767019318497,40.773483981224835],[-73.993698463744295,40.773562141052594],[-73.993358326468751,40.773926888327956],[-73.992622663865575,40.774974056037109],[-73.992577842766124,40.774956016359418],[-73.992527743951555,40.775002110439829],[-73.992469745815342,40.775024159551755],[-73.992403837191887,40.775018140390664],[-73.99226708903538,40.775116033858794],[-73.99217809026365,40.775279293897171],[-73.992059084937338,40.775497598192516],[-73.992125372394938,40.775509075053385],[-73.992226867797001,40.775482211026116],[-73.992329346608813,40.775468900958522],[-73.992361756801131,40.775501899766638],[-73.992386042960277,40.775557180424634],[-73.992087684712729,40.775983970821372],[-73.990927174149746,40.777566878763238],[-73.99039616003671,40.777585065679204],[-73.989461267506471,40.778875124584417],[-73.989175778438053,40.779287524015778],[-73.988868617400072,40.779692922911607],[-73.988871874499793,40.779713738253008],[-73.989219022880576,40.779697895209402],[-73.98927785904425,40.779723439271038],[-73.989409054180143,40.779737706471963],[-73.989498614927044,40.779725044389757],[-73.989596493388234,40.779698146683387],[-73.989679812902509,40.779677568658038],[-73.989752702937935,40.779671244211556],[-73.989842247806507,40.779680752670664],[-73.990040102120489,40.779707677698219],[-73.990137977524839,40.779699769704784],[-73.99033584033225,40.779661794394983],[-73.990430598697046,40.779664973055503],[-73.990622199396725,40.779676064914298],[-73.990745069505479,40.779671328184051],[-73.990872114282197,40.779646007643876],[-73.990961672224358,40.779639683751753],[-73.991057472829539,40.779652352625774],[-73.991157429497036,40.779669775606465],[-73.991242817404469,40.779671367084504],[-73.991255318289745,40.779650782516491],[-73.991294887120119,40.779630209208889],[-73.991321967649895,40.779631796041372],[-73.991359455569423,40.779585883337383],[-73.991551059227476,40.779574821437407],[-73.99141982585985,40.779755280287233],[-73.988886144117032,40.779878898532999],[-73.988939656706265,40.779956178440393],[-73.988926103530844,40.780059292013632],[-73.988911680264692,40.780096037146606],[-73.988919261468567,40.780226094343945],[-73.988381050202634,40.780981074045783],[-73.988232413846987,40.781233144215555],[-73.988210420831663,40.781225482542055],[-73.988140000000143,40.781409000000224],[-73.988041288067166,40.781585961353777],[-73.98810029382463,40.781602878305286],[-73.988076449145055,40.781650935001608],[-73.988018059972219,40.781634188810422],[-73.987960792842145,40.781770987031535],[-73.985465811970457,40.785360700575431],[-73.986172704965611,40.786068452258647],[-73.986455862401996,40.785919219081421],[-73.987072345615601,40.785189638820121],[-73.98711901394276,40.785210319004058],[-73.986497781023601,40.785951202887254],[-73.986164628806279,40.786121882448327],[-73.986128422486075,40.786239001331111],[-73.986071135219746,40.786240706026611],[-73.986027274789123,40.786228964236727],[-73.986097637849426,40.78605822569795],[-73.985429321269592,40.785413942184597],[-73.985081137732209,40.785921935110366],[-73.985198833254501,40.785966552197777],[-73.985170502389906,40.78601333415817],[-73.985216218673656,40.786030501816427],[-73.98525509797993,40.785976205511588],[-73.98524273937646,40.785972572653328],[-73.98524962933017,40.785963139855845],[-73.985281779186749,40.785978620950075],[-73.985240032884533,40.786035858136792],[-73.985683885242182,40.786222123919686],[-73.985717529004575,40.786175994668795],[-73.985765660297687,40.786196274858618],[-73.985682871922691,40.786309786213067],[-73.985636270930442,40.786290150649279],[-73.985670722564691,40.786242911993817],[-73.98520511880038,40.786047669212785],[-73.985211035607492,40.786039554883686],[-73.985162639946992,40.786020999769754],[-73.985131636312062,40.786060297019972],[-73.985016964065125,40.78601423719563],[-73.984655078830457,40.786534741807841],[-73.985743787901043,40.786570082854738],[-73.98589227228328,40.786426529019593],[-73.985942854994988,40.786452847880334],[-73.985949561556794,40.78648711396653],[-73.985812373526713,40.786616865357047],[-73.985135209703174,40.78658761889551],[-73.984619428584324,40.786586016349787],[-73.981952458164173,40.790393724337193],[-73.972823037363767,40.803428052816756],[-73.971036786332192,40.805918478839672],[-73.966701,40.804169000000186],[-73.959647,40.801156000000113],[-73.958508540159471,40.800682279767472],[-73.95853274080838,40.800491362464697],[-73.958357552055688,40.800369095633819]]]}),
        'Upper East Side',   dynamic({"type":"Polygon","coordinates":[[[-73.943592454622546,40.782747908206574],[-73.943648235390199,40.782656161333449],[-73.943870759887162,40.781273026571704],[-73.94345932494096,40.780048275653243],[-73.943213862652243,40.779317588660199],[-73.943004239504688,40.779639495474292],[-73.942716005450905,40.779544169476175],[-73.942712374762181,40.779214856940001],[-73.942535563208608,40.779090956062532],[-73.942893408188027,40.778614093246276],[-73.942438481745029,40.777315235766039],[-73.942244919522594,40.777104088947254],[-73.942074188038887,40.776917846977142],[-73.942002667222781,40.776185317382648],[-73.942620205199006,40.775180871576474],[-73.94285645694552,40.774796600349191],[-73.94293043781397,40.774676268036011],[-73.945870899588215,40.771692257932997],[-73.946618690150586,40.77093339256956],[-73.948664164778933,40.768857624399587],[-73.950069793030679,40.767025088383498],[-73.954418260786071,40.762184104951245],[-73.95650786241211,40.760285256574043],[-73.958787773424007,40.758213471309809],[-73.973015157270069,40.764278692864671],[-73.955760332998182,40.787906554459667],[-73.944023,40.782960000000301],[-73.943592454622546,40.782747908206574]]]}),
    ];
let Coordinates = 
    datatable(longitude:real, latitude:real)
    [
        real(-73.9741), 40.7914, // Upper West Side
        real(-73.9950), 40.7340, // Greenwich Village
        real(-73.9584), 40.7688, // Upper East Side
    ];
let Level = 16;
Polygons
| extend covering = geo_polygon_to_s2cells(polygon, Level) // cover every polygon with s2 cell token array
| mv-expand covering to typeof(string)                     // expand cells array such that every row will have one cell mapped to its polygon
| join kind=inner hint.strategy=broadcast                  // assume that Polygons count is small (In some specific case)
(
    Coordinates
    | extend covering = geo_point_to_s2cell(longitude, latitude, Level) // cover point with cell
) on covering // join on the cell, this filters out rows of point and polygons where the point definitely does not belong to the polygon
| where geo_point_in_polygon(longitude, latitude, polygon) // final filtering for exact result
| project longitude, latitude, description

输出

longitude	latitude	description
-73.9741	40.7914	上西区
-73.995	40.734	格林尼治村
-73.9584	40.7688	上东区

下面对上述查询进行了更多改进。按美国州统计风暴事件数。下面的查询执行了非常高效的联接，因为它不通过联接携带多边形，而是使用查找运算符

运行查询

let Level = 6;
let polygons = materialize(
    US_States
    | project StateName = tostring(features.properties.NAME), polygon = features.geometry, id = new_guid());
let tmp = 
    polygons
    | project id, covering = geo_polygon_to_s2cells(polygon, Level) 
    | mv-expand covering to typeof(string)
    | join kind=inner hint.strategy=broadcast
            (
                StormEvents
                | project lng = BeginLon, lat = BeginLat
                | project lng, lat, covering = geo_point_to_s2cell(lng, lat, Level)
            ) on covering
    | project-away covering, covering1;
tmp | lookup polygons on id
| project-away id
| where geo_point_in_polygon(lng, lat, polygon)
| summarize StormEventsCountByState = count() by StateName

输出

StateName	StormEventsCountByState
Florida	960
格鲁吉亚	1085
...	...

下面的示例筛选掉与感兴趣的多边形区域不相交的多边形。最大误差是 s2cell 长度的对角线。本示例基于夜间光栅文件中的多边形地球。

let intersection_level_hint = 7;
let area_of_interest = dynamic({"type": "Polygon","coordinates": [[[-73.94966125488281,40.79698248639272],[-73.95841598510742,40.800426144169315],[-73.98124694824219,40.76806170936614],[-73.97283554077148,40.7645513650551],[-73.94966125488281,40.79698248639272]]]});
let area_of_interest_covering = geo_polygon_to_s2cells(area_of_interest, intersection_level_hint);
EarthAtNight
| project value = features.properties.DN, polygon = features.geometry
| extend covering = geo_polygon_to_s2cells(polygon, intersection_level_hint)
| mv-apply c = covering to typeof(string) on
(
    summarize is_intersects = take_anyif(1, array_index_of(area_of_interest_covering, c) != -1)
)
| where is_intersects == 1
| count

输出

计数
83

使用级别 5 的 S2 单元覆盖某个多边形时需要的单元数。

运行查询

let polygon = dynamic({"type":"Polygon","coordinates":[[[0,0],[0,50],[100,50],[0,0]]]});
print s2_cell_token_count = array_length(geo_polygon_to_s2cells(polygon, 5));

输出

s2_cell_token_count
286

使用小面积单元覆盖大面积多边形会返回 null。

运行查询

let polygon = dynamic({"type":"Polygon","coordinates":[[[0,0],[0,50],[100,50],[0,0]]]});
print geo_polygon_to_s2cells(polygon, 30);

输出

print_0

使用小面积单元覆盖大面积多边形会返回 null。

运行查询

let polygon = dynamic({"type":"Polygon","coordinates":[[[0,0],[0,50],[100,50],[0,0]]]});
print isnull(geo_polygon_to_s2cells(polygon, 30));

输出

print_0
1

通过

geo_polygon_to_s2cells()

语法

参数

返回

示例

其他资源