SpatialBinnedPointData
SpatialBinnedPointData
SpatialBinnedPointData[{reg1val1,reg2val2,…}]
表示与不相连区域 regi 相关联的值 vali.
SpatialBinnedPointData[{reg1,reg2,…}{val1,val2,…}]
给出同样的结果.
SpatialBinnedPointData[…,reg]
给出整个观察区域 reg.
更多信息和选项
- SpatialBinnedPointData 使处理聚合空间点数据成为可能，如人口普查信息、传染病数据和犯罪数据中经常报告的那样.
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- 通过用 InhomogeneousPoissonPointProcess 进行模拟，可将 SpatialBinnedPointData 用作单个点数据，以进行多种计算.
- 区域 regi 和 reg 可以是几何区域，也可以是地理区域，但都应属于同一类型.
- 区域 regi 和 reg 不应含有任何参数，应是全维的和有界的，SpatialObservationRegionQ 的结果应为真.
- 假定区域 regi 互不相连，并被包含于观察区域 reg 中.
- 值 vali 可以是标量或任意维度的向量，但维度应相同.
- 应将整数值 vali 理解为对应于每个区域的点数，使得 SpatialBinnedPointData 可被视为具有分段恒定强度函数的 InhomogeneousPoissonPointProcess.
- 基本属性包括：
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"Bins" 分组区域列表 "BinCount" 组数 "BinMeasures" 分组区域的测度 "Dimension" 分组区域的维度 "Values" 每个分组区域的值 "ConfigurationCount" 同样的分组情况下已分组观察区域的数量 "ObservationRegion" 全局观察区域 - SpatialBinnedPointData 可用在如 PointProcessFitTest 和 SpatialRandomnessTest 这样的函数中.
范例
打开所有单元 关闭所有单元基本范例 (2)
用地理实体指定分组，创建 SpatialBinnedPointData：
(# -> CountryData[#, "Population"])& /@ CountryData["SouthAmerica"]SpatialBinnedPointData[%]%["DensityVisualization"]用地理实体指定分组，创建 SpatialBinnedPointData：
countries = Entity["GeographicRegion", "Europe"][EntityProperty["GeographicRegion", "Countries"]];airports = CountryData[#, "Airports"]& /@ countries;data = SpatialBinnedPointData[countries -> airports]data["DensityVisualization"]范围 (4)
根据象限观察区域创建 SpatialBinnedPointData：
bins = Flatten@Table[Rectangle[{i, j}, {i + 1, j + 1}], {i, 0, 10}, {j, 0, 10}];Graphics[{FaceForm[ColorData[97, 1]], EdgeForm[Black], bins}]vals = RandomInteger[10, Length[bins]];用分组和值创建 SpatialBinnedPointData：
data = SpatialBinnedPointData[bins -> vals]int = HistogramPointDensity[data]int["DensityVisualization"]SpatialRandomnessTest[data, "TestConclusion"]SeedRandom[2];
regions = Disk[#, 2]& /@ RandomInteger[{-10, 10}, {5, 2}];sp = SpatialPointData[RandomReal[{-10, 10}, {100, 2}], Rectangle[{-10, -10}, {10, 10}]];sb = SpatialBinnedPointData[sp, regions]%["Values"]regplot = Region[RegionUnion@regions, Axes -> True]proc = InhomogeneousPoissonPointProcess[HistogramPointDensity[sb], 2]sample = RandomPointConfiguration[proc, RegionUnion@regions];Show[regplot, ListPlot[sample, PlotStyle -> Black]]SeedRandom[2];
regions = Disk[#, 2]& /@ RandomReal[{-10, 10}, {5, 2}];ns = RandomInteger[10, 5];SpatialBinnedPointData[ns, regions]sb = SpatialBinnedPointData[{{5, 11, 10, 5, 4, 11, 7, 7, 5, 10, 11, 9, 5}},
SpatialAnalysis`RegionPartition["AggregatedMeshRegion",
MeshRegion[{{-0.024328249475753738, 0.11959962427071952}, {-0.016453346499402777,
0.5405894326667253}, {1.0041 ... {0.7272568409884758, 0.6785813396468685}, {0.908403190951901, 0.13905862821805207},
{0.920862437803657, 0.5360317046598139}, {0.8319208691069746, 0.8619734273549864}}], {13, 2}],
Association["ConfigurationCount" -> 1, "GeoQ" -> False]];SpatialRandomnessTest[sb]属性和关系 (1)
data = SpatialBinnedPointData[{{9, 7, 7, 4, 3, 7, 6, 6, 15, 9, 8, 9, 10}},
SpatialAnalysis`RegionPartition[{"AggregatedMeshRegion", "Geo"},
MeshRegion[{{-0.008726700812770683, -0.008682821301233276},
{0.008726700812770686, -0.008682821301233276} ... 398}}],
"Projection" -> {"LambertAzimuthal", "Centering" ->
GeoPosition[{0.4975047854908847, 0.4999999999999999}]}, "Datum" -> "ITRF00",
"MeasureUnit" -> "Meters"^2], {13, 2}], Association["ConfigurationCount" -> 1, "GeoQ" -> True]];data["Properties"]
Wolfram Research (2020)，SpatialBinnedPointData，Wolfram 语言函数，https://reference.wolfram.com/language/ref/SpatialBinnedPointData.html.
文本
Wolfram Research (2020)，SpatialBinnedPointData，Wolfram 语言函数，https://reference.wolfram.com/language/ref/SpatialBinnedPointData.html.
CMS
Wolfram 语言. 2020. "SpatialBinnedPointData." Wolfram 语言与系统参考资料中心. Wolfram Research. https://reference.wolfram.com/language/ref/SpatialBinnedPointData.html.
APA
Wolfram 语言. (2020). SpatialBinnedPointData. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/SpatialBinnedPointData.html 年