Kernel Interpolation With Barriers

A moving window predictor that uses the shortest distance between points so that points on either side of the line barriers are connected

Interpolation

true

Contributor(s)

Initial contribute: 2019-05-09

Authorship

:  
View
Is authorship not correct? Feed back

Classification(s)

Method-focused categoriesData-perspectiveGeoinformation analysis

Detailed Description

English {{currentDetailLanguage}} English

Summary

A moving window predictor that uses the shortest distance between points so that points on either side of the line barriers are connected.

Usage

  • The absolute feature barrier employs a non-Euclidean distance approach rather than a line-of-sight approach. The line-of-sight approach requires that a straight line between the measured location and the location where the prediction is required do not intersect the barrier feature. If the distance around the barrier is within the searching neighborhood specifications, then it will be considered in this non-Euclidean distance approach.

  • The processing time is dependent on the complexity of the barrier feature classes geometry. Tools in the Generalization toolset can be used to create a new feature class by smoothing or deleting some of these features.

  • For EXPONENTIAL, GAUSSIAN, and CONSTANT kernel functions, a smoothing factor is applied so that the kernels have a finite radius that is equal to the specified bandwidth.

Syntax

KernelInterpolationWithBarriers_ga (in_features, z_field, {out_ga_layer}, {out_raster}, {cell_size}, {in_barrier_features}, {kernel_function}, {bandwidth}, {power}, {ridge}, {output_type})
Parameter Explanation Data Type
in_features

The input point features containing the z-values to be interpolated.

Feature Layer
z_field

Field that holds a height or magnitude value for each point. This can be a numeric field or the Shape field if the input features contain z-values or m-values.

Field
out_ga_layer
(Optional)

The geostatistical layer produced. This layer is required output only if no output raster is requested.

Geostatistical Layer
out_raster
(Optional)

The output raster. This raster is required output only if no output geostatistical layer is requested.

Raster Dataset
cell_size
(Optional)

The cell size at which the output raster will be created.

This value can be explicitly set under Raster Analysis from the Environment Settings. If not set, it is the shorter of the width or the height of the extent of the input point features, in the input spatial reference, divided by 250.

Analysis Cell Size
in_barrier_features
(Optional)

Absolute barrier features using non-Euclidean distances rather than line-of-sight distances.

Feature Layer
kernel_function
(Optional)

The kernel function used in the simulation.

  • EXPONENTIAL — The function grows or decays proportionally.
  • GAUSSIAN — Bell-shaped function that falls off quickly toward plus/minus infinity.
  • QUARTIC — Fourth-order polynomial function.
  • EPANECHNIKOV — A discontinuous parabolic function.
  • POLYNOMIAL5 — Fifth-order polynomial function.
  • CONSTANT —An indicator function.
String
bandwidth
(Optional)

Used to specify the maximum distance at which data points are used for prediction. With increasing bandwidth, prediction bias increases and prediction variance decreases.

Double
power
(Optional)

Sets the order of the polynomial.

Long
ridge
(Optional)

Used for the numerical stabilization of the solution of the system of linear equations. It does not influence predictions in the case of regularly distributed data without barriers. Predictions for areas in which the data is located near the feature barrier or isolated by the barriers can be unstable and tend to require relatively large ridge parameter values.

Double
output_type
(Optional)

Surface type to store the interpolation results.

  • PREDICTION —Prediction surfaces are produced from the interpolated values.
  • PREDICTION_STANDARD_ERROR — Standard Error surfaces are produced from the standard errors of the interpolated values.
String

Code Sample

KernelInterpolationWithBarriers (Python window)

Interpolate point features onto a rectangular raster using a barrier feature class.

import arcpy
arcpy.env.workspace = "C:/gapysamples/data"
arcpy.KernelInterpolationWithBarriers_ga("ca_ozone_pts", "OZONE", "outKIWB",
                                         "C:/gapyexamples/output/kiwbout", "2000",
                                         "ca_outline", "QUARTIC", "", "", "50", "PREDICTION")
KernelInterpolationWithBarriers (stand-alone script)

Interpolate point features onto a rectangular raster using a barrier feature class.

# Name: KernelInterpolationWithBarriers_Example_02.py
# Description: Kernel Interpolation with Barriers is a moving window predictor
#   that uses non-Euclidean distances.
# Requirements: Geostatistical Analyst Extension

# Import system modules
import arcpy

# Set environment settings
arcpy.env.workspace = "C:/gapyexamples/data"

# Set local variables
inPointFeatures = "ca_ozone_pts.shp"
zField = "ozone"
outLayer = "outKIWB"
outRaster = "C:/gapyexamples/output/kiwbout"
cellSize = 2000.0
inBarrier = "ca_outline.shp"
kernelFunction = "QUARTIC"
bandwidth = ""
power = ""
ridgeParam = "50"
outputType = "PREDICTION"

# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")

# Execute KernelInterpolationWithBarriers
arcpy.KernelInterpolationWithBarriers_ga(inPointFeatures, zField, outLayer, outRaster,
                                         cellSize, inBarrier, kernelFunction, bandwidth,
                                         power, ridgeParam, outputType)

模型元数据

{{htmlJSON.HowtoCite}}

ESRI (2019). Kernel Interpolation With Barriers, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/eb7473e2-153a-4a37-9eaa-82506059b29e
{{htmlJSON.Copy}}

Contributor(s)

Initial contribute : 2019-05-09

{{htmlJSON.CoContributor}}

Authorship

:  
View
Is authorship not correct? Feed back

QR Code

×

{{curRelation.overview}}
{{curRelation.author.join('; ')}}
{{curRelation.journal}}









{{htmlJSON.RelatedItems}}

{{htmlJSON.LinkResourceFromRepositoryOrCreate}}{{htmlJSON.create}}.

Drop the file here, orclick to upload.
Select From My Space
+ add

{{htmlJSON.authorshipSubmitted}}

Cancel Submit
{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
{{htmlJSON.Localizations}} + {{htmlJSON.Add}}
{{ item.label }} {{ item.value }}
{{htmlJSON.ModelName}}:
{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
名称 别名 {{tag}} +
系列名 版本号 目的 修改内容 创建/修改日期 作者
摘要 详细描述
{{tag}} + 添加关键字
* 时间参考系
* 空间参考系类型 * 空间参考系名称

起始日期 终止日期 进展 开发者
* 是否开源 * 访问方式 * 使用方式 开源协议 * 传输方式 * 获取地址 * 发布日期 * 发布者



编号 目的 修改内容 创建/修改日期 作者





时间分辨率 时间尺度 时间步长 时间范围 空间维度 格网类型 空间分辨率 空间尺度 空间范围
{{tag}} +
* 类型
图例


* 名称 * 描述
示例描述 * 名称 * 类型 * 值/链接 上传


{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
Title Author Date Journal Volume(Issue) Pages Links Doi Operation
{{htmlJSON.Cancel}} {{htmlJSON.Submit}}
{{htmlJSON.Add}} {{htmlJSON.Cancel}}

{{articleUploading.title}}

Authors:  {{articleUploading.authors[0]}}, {{articleUploading.authors[1]}}, {{articleUploading.authors[2]}}, et al.

Journal:   {{articleUploading.journal}}

Date:   {{articleUploading.date}}

Page range:   {{articleUploading.pageRange}}

Link:   {{articleUploading.link}}

DOI:   {{articleUploading.doi}}

Yes, this is it Cancel

The article {{articleUploading.title}} has been uploaded yet.

OK
{{htmlJSON.Cancel}} {{htmlJSON.Confirm}}