Geographically Weighted Regression
أداة الانحدار
الموزون جغرافياً
ArcMap
ArcGIS
How to use Geographically
Weighted Regression Tool in Arc Toolbox?? |
| Geographically Weighted Regression |
كيفية استخدام أداة الانحدار الموزون جغرافياً ؟؟
Path to access the toolمسار الوصول الى الأداة
:
Geographically Weighted Regression Tool, Modeling Spatial Relationships Toolset, Spatial
Statistics Tools Toolbox
Geographically Weighted Regression
Performs Geographically
Weighted Regression (GWR), a local form of linear regression used to model
spatially varying relationships.
An enhanced version of
this tool has been added to ArcGIS Pro 2.3. This is the tool documentation for
the deprecated tool. It is recommended that you upgrade and use the new
Geographically Weighted Regression tool in ArcGIS Pro or later.
يقوم بتنفيذ الانحدار الموزون جغرافيًا (GWR) ،
وهو شكل محلي من الانحدار الخطي يستخدم لنمذجة العلاقات المتغيرة مكانيًا.
تمت إضافة إصدار محسّن من هذه الأداة إلى ArcGIS Pro 2.3. هذه هي وثائق الأداة للأداة المهملة. يوصى بالترقية واستخدام
أداة الانحدار الموزون جغرافيًا الجديدة في ArcGIS Pro أو أحدث.
1.
Input features ادخل المعالم
The feature class
containing the dependent and independent variables.
فئة المعلم التي تحتوي على المتغيرات التابعة
والمستقلة.
Dependent variable
The numeric field containing the values that will be modeled.
Explanatory variable(s)
A list of fields representing independent explanatory variables in the
regression model.
Output feature class
The output feature class that will receive dependent variable estimates
and residuals.
Kernel type
Specifies whether the kernel is constructed as a fixed distance, or if it
is allowed to vary in extent as a function of feature density.
· FIXED—The spatial context (the Gaussian kernel) used to solve each local
regression analysis is a fixed distance.
· ADAPTIVE —The spatial context (the Gaussian kernel) is a function of a
specified number of neighbors. Where feature distribution is dense, the spatial
context is smaller; where feature distribution is sparse, the spatial context
is larger.
Bandwidth method
Specifies how the extent of the kernel will be determined. When AICc
(corrected Akaike Information Criterion) or CV (cross validation) is selected,
the tool will find the optimal distance or number of neighbors for you.
Typically, you will select either AICc or CV when you aren't sure what to use
for the distance or number_of_neighbors parameter. Once the tool determines the
optimal distance or number of neighbors, however, you'll use the
BANDWIDTH_PARAMETER option.
· AICc—The extent of the kernel is determined using the Akaike Information
Criterion.
· CV—The extent of the kernel is determined using cross validation.
· BANDWIDTH_PARAMETER—The extent of the kernel is determined by a fixed
distance or a fixed number of neighbors. You must specify a value for either
the distance or number_of_neighbors parameters.
Distance (optional)
The distance to use when the Kernel type parameter is set to FIXED and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Number of neighbors (optional)
The exact number of neighbors to include in the local bandwidth of the
Gaussian kernel when the Kernel type parameter is set to ADAPTIVE and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Weights (optional)
The numeric field containing a spatial weighting for individual features.
This weight field allows some features to be more important in the model
calibration process than others. This is useful when the number of samples
taken at different locations varies, values for the dependent and independent
variables are averaged, and places with more samples are more reliable (should
be weighted higher). If you have an average of 25 different samples for one
location but an average of only 2 samples for another location, for example,
you can use the number of samples as your weight field so that locations with
more samples have a larger influence on model calibration than locations with
few samples.
Coefficient raster workspace
(optional)
The full path to the workspace where the coefficient rasters will be
created. When this workspace is provided, rasters are created for the intercept
and every explanatory variable.
Output cell size (optional)
The cell size (a number) or reference to the cell size (a path to a raster
dataset) to use when creating the coefficient rasters.
The default cell size is the shortest of the width or height of the extent
specified in the geoprocessing environment output coordinate system, divided by
250.
Prediction locations (optional)
A feature class containing features representing locations where estimates
should be computed. Each feature in this dataset should contain values for all
of the explanatory variables specified; the dependent variable for these
features will be estimated using the model calibrated for the input feature
class data.
Prediction explanatory variable(s)
(optional)
A list of fields representing explanatory variables in the Prediction locations
feature class. These field names should be provided in the same order (a
one-to-one correspondence) as those listed for the input feature class
Explanatory variables parameter. If no prediction explanatory variables are
given, the output prediction feature class will only contain computed
coefficient values for each prediction location.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
2.
Dependent variable المتغير التابع
The numeric field
containing the values that will be modeled.
الحقل الرقمي الذي يحتوي على القيم التي سيتم
نمذجتها.
Explanatory variable(s)
A list of fields representing independent explanatory variables in the regression
model.
Output feature class
The output feature class that will receive dependent variable estimates
and residuals.
Kernel type
Specifies whether the kernel is constructed as a fixed distance, or if it
is allowed to vary in extent as a function of feature density.
· FIXED—The spatial context (the Gaussian kernel) used to solve each local
regression analysis is a fixed distance.
· ADAPTIVE —The spatial context (the Gaussian kernel) is a function of a
specified number of neighbors. Where feature distribution is dense, the spatial
context is smaller; where feature distribution is sparse, the spatial context
is larger.
Bandwidth method
Specifies how the extent of the kernel will be determined. When AICc
(corrected Akaike Information Criterion) or CV (cross validation) is selected,
the tool will find the optimal distance or number of neighbors for you.
Typically, you will select either AICc or CV when you aren't sure what to use
for the distance or number_of_neighbors parameter. Once the tool determines the
optimal distance or number of neighbors, however, you'll use the
BANDWIDTH_PARAMETER option.
· AICc—The extent of the kernel is determined using the Akaike Information
Criterion.
· CV—The extent of the kernel is determined using cross validation.
· BANDWIDTH_PARAMETER—The extent of the kernel is determined by a fixed
distance or a fixed number of neighbors. You must specify a value for either
the distance or number_of_neighbors parameters.
Distance (optional)
The distance to use when the Kernel type parameter is set to FIXED and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Number of neighbors (optional)
The exact number of neighbors to include in the local bandwidth of the
Gaussian kernel when the Kernel type parameter is set to ADAPTIVE and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Weights (optional)
The numeric field containing a spatial weighting for individual features.
This weight field allows some features to be more important in the model
calibration process than others. This is useful when the number of samples
taken at different locations varies, values for the dependent and independent
variables are averaged, and places with more samples are more reliable (should
be weighted higher). If you have an average of 25 different samples for one
location but an average of only 2 samples for another location, for example,
you can use the number of samples as your weight field so that locations with
more samples have a larger influence on model calibration than locations with
few samples.
Coefficient raster workspace
(optional)
The full path to the workspace where the coefficient rasters will be created.
When this workspace is provided, rasters are created for the intercept and
every explanatory variable.
Output cell size (optional)
The cell size (a number) or reference to the cell size (a path to a raster
dataset) to use when creating the coefficient rasters.
The default cell size is the shortest of the width or height of the extent
specified in the geoprocessing environment output coordinate system, divided by
250.
Prediction locations (optional)
A feature class containing features representing locations where estimates
should be computed. Each feature in this dataset should contain values for all
of the explanatory variables specified; the dependent variable for these
features will be estimated using the model calibrated for the input feature
class data.
Prediction explanatory variable(s)
(optional)
A list of fields representing explanatory variables in the Prediction
locations feature class. These field names should be provided in the same order
(a one-to-one correspondence) as those listed for the input feature class
Explanatory variables parameter. If no prediction explanatory variables are
given, the output prediction feature class will only contain computed
coefficient values for each prediction location.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
3.
Explanatory variable(s) المتغير
(المتغيرات) التفسيرية
A list of fields
representing independent explanatory variables in the regression model.
قائمة الحقول التي تمثل المتغيرات التوضيحية
المستقلة في نموذج الانحدار.
Output feature class
The output feature class that will receive dependent variable estimates
and residuals.
Kernel type
Specifies whether the kernel is constructed as a fixed distance, or if it
is allowed to vary in extent as a function of feature density.
· FIXED—The spatial context (the Gaussian kernel) used to solve each local
regression analysis is a fixed distance.
· ADAPTIVE —The spatial context (the Gaussian kernel) is a function of a
specified number of neighbors. Where feature distribution is dense, the spatial
context is smaller; where feature distribution is sparse, the spatial context
is larger.
Bandwidth method
Specifies how the extent of the kernel will be determined. When AICc
(corrected Akaike Information Criterion) or CV (cross validation) is selected,
the tool will find the optimal distance or number of neighbors for you.
Typically, you will select either AICc or CV when you aren't sure what to use
for the distance or number_of_neighbors parameter. Once the tool determines the
optimal distance or number of neighbors, however, you'll use the
BANDWIDTH_PARAMETER option.
· AICc—The extent of the kernel is determined using the Akaike Information
Criterion.
· CV—The extent of the kernel is determined using cross validation.
· BANDWIDTH_PARAMETER—The extent of the kernel is determined by a fixed
distance or a fixed number of neighbors. You must specify a value for either
the distance or number_of_neighbors parameters.
Distance (optional)
The distance to use when the Kernel type parameter is set to FIXED and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Number of neighbors (optional)
The exact number of neighbors to include in the local bandwidth of the
Gaussian kernel when the Kernel type parameter is set to ADAPTIVE and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Weights (optional)
The numeric field containing a spatial weighting for individual features.
This weight field allows some features to be more important in the model
calibration process than others. This is useful when the number of samples
taken at different locations varies, values for the dependent and independent
variables are averaged, and places with more samples are more reliable (should
be weighted higher). If you have an average of 25 different samples for one
location but an average of only 2 samples for another location, for example,
you can use the number of samples as your weight field so that locations with
more samples have a larger influence on model calibration than locations with
few samples.
Coefficient raster workspace
(optional)
The full path to the workspace where the coefficient rasters will be
created. When this workspace is provided, rasters are created for the intercept
and every explanatory variable.
Output cell size (optional)
The cell size (a number) or reference to the cell size (a path to a raster
dataset) to use when creating the coefficient rasters.
The default cell size is the shortest of the width or height of the extent
specified in the geoprocessing environment output coordinate system, divided by
250.
Prediction locations (optional)
A feature class containing features representing locations where estimates
should be computed. Each feature in this dataset should contain values for all
of the explanatory variables specified; the dependent variable for these
features will be estimated using the model calibrated for the input feature
class data.
Prediction explanatory variable(s)
(optional)
A list of fields representing explanatory variables in the Prediction
locations feature class. These field names should be provided in the same order
(a one-to-one correspondence) as those listed for the input feature class
Explanatory variables parameter. If no prediction explanatory variables are
given, the output prediction feature class will only contain computed
coefficient values for each prediction location.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
4.
Output feature class فئة المعلم
المخرجة
The output feature class
that will receive dependent variable estimates and residuals.
فئة ميزة الإخراج التي ستتلقى تقديرات متغيرة
تابعة وبقايا.
Kernel type
Specifies whether the kernel is constructed as a fixed distance, or if it
is allowed to vary in extent as a function of feature density.
· FIXED—The spatial context (the Gaussian kernel) used to solve each local
regression analysis is a fixed distance.
· ADAPTIVE —The spatial context (the Gaussian kernel) is a function of a
specified number of neighbors. Where feature distribution is dense, the spatial
context is smaller; where feature distribution is sparse, the spatial context
is larger.
Bandwidth method
Specifies how the extent of the kernel will be determined. When AICc
(corrected Akaike Information Criterion) or CV (cross validation) is selected,
the tool will find the optimal distance or number of neighbors for you.
Typically, you will select either AICc or CV when you aren't sure what to use
for the distance or number_of_neighbors parameter. Once the tool determines the
optimal distance or number of neighbors, however, you'll use the
BANDWIDTH_PARAMETER option.
· AICc—The extent of the kernel is determined using the Akaike Information
Criterion.
· CV—The extent of the kernel is determined using cross validation.
· BANDWIDTH_PARAMETER—The extent of the kernel is determined by a fixed
distance or a fixed number of neighbors. You must specify a value for either
the distance or number_of_neighbors parameters.
Distance (optional)
The distance to use when the Kernel type parameter is set to FIXED and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Number of neighbors (optional)
The exact number of neighbors to include in the local bandwidth of the
Gaussian kernel when the Kernel type parameter is set to ADAPTIVE and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Weights (optional)
The numeric field containing a spatial weighting for individual features.
This weight field allows some features to be more important in the model
calibration process than others. This is useful when the number of samples
taken at different locations varies, values for the dependent and independent
variables are averaged, and places with more samples are more reliable (should
be weighted higher). If you have an average of 25 different samples for one
location but an average of only 2 samples for another location, for example,
you can use the number of samples as your weight field so that locations with
more samples have a larger influence on model calibration than locations with
few samples.
Coefficient raster workspace
(optional)
The full path to the workspace where the coefficient rasters will be
created. When this workspace is provided, rasters are created for the intercept
and every explanatory variable.
Output cell size (optional)
The cell size (a number) or reference to the cell size (a path to a raster
dataset) to use when creating the coefficient rasters.
The default cell size is the shortest of the width or height of the extent
specified in the geoprocessing environment output coordinate system, divided by
250.
Prediction locations (optional)
A feature class containing features representing locations where estimates
should be computed. Each feature in this dataset should contain values for all
of the explanatory variables specified; the dependent variable for these
features will be estimated using the model calibrated for the input feature
class data.
Prediction explanatory variable(s)
(optional)
A list of fields representing explanatory variables in the Prediction
locations feature class. These field names should be provided in the same order
(a one-to-one correspondence) as those listed for the input feature class
Explanatory variables parameter. If no prediction explanatory variables are
given, the output prediction feature class will only contain computed
coefficient values for each prediction location.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
5.
Kernel type نوع النواة
Specifies whether the
kernel is constructed as a fixed distance, or if it is allowed to vary in
extent as a function of feature density.
·
FIXED—The spatial context (the Gaussian kernel) used to solve each
local regression analysis is a fixed distance.
·
ADAPTIVE —The spatial context (the Gaussian kernel) is a function
of a specified number of neighbors. Where feature distribution is dense, the
spatial context is smaller; where feature distribution is sparse, the spatial
context is larger.
يحدد ما إذا كانت النواة مبنية على أنها مسافة
ثابتة ، أو إذا كان مسموحًا لها بالتنوع في المدى كدالة لكثافة الميزة.
• ثابت - السياق المكاني
(النواة الغاوسية) المستخدم لحل كل تحليل انحدار محلي هو مسافة ثابتة.
• التكيف - السياق
المكاني (النواة الغاوسية) هو دالة لعدد محدد من الجيران. عندما يكون توزيع
الميزات كثيفًا ، يكون السياق المكاني أصغر ؛ عندما يكون توزيع الميزات متناثرًا ،
يكون السياق المكاني أكبر.
Bandwidth method
Specifies how the extent of the kernel will be determined. When AICc
(corrected Akaike Information Criterion) or CV (cross validation) is selected,
the tool will find the optimal distance or number of neighbors for you.
Typically, you will select either AICc or CV when you aren't sure what to use
for the distance or number_of_neighbors parameter. Once the tool determines the
optimal distance or number of neighbors, however, you'll use the
BANDWIDTH_PARAMETER option.
· AICc—The extent of the kernel is determined using the Akaike Information
Criterion.
· CV—The extent of the kernel is determined using cross validation.
· BANDWIDTH_PARAMETER—The extent of the kernel is determined by a fixed
distance or a fixed number of neighbors. You must specify a value for either
the distance or number_of_neighbors parameters.
Distance (optional)
The distance to use when the Kernel type parameter is set to FIXED and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Number of neighbors (optional)
The exact number of neighbors to include in the local bandwidth of the
Gaussian kernel when the Kernel type parameter is set to ADAPTIVE and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Weights (optional)
The numeric field containing a spatial weighting for individual features.
This weight field allows some features to be more important in the model
calibration process than others. This is useful when the number of samples
taken at different locations varies, values for the dependent and independent
variables are averaged, and places with more samples are more reliable (should
be weighted higher). If you have an average of 25 different samples for one
location but an average of only 2 samples for another location, for example,
you can use the number of samples as your weight field so that locations with
more samples have a larger influence on model calibration than locations with
few samples.
Coefficient raster workspace
(optional)
The full path to the workspace where the coefficient rasters will be
created. When this workspace is provided, rasters are created for the intercept
and every explanatory variable.
Output cell size (optional)
The cell size (a number) or reference to the cell size (a path to a raster
dataset) to use when creating the coefficient rasters.
The default cell size is the shortest of the width or height of the extent
specified in the geoprocessing environment output coordinate system, divided by
250.
Prediction locations (optional)
A feature class containing features representing locations where estimates
should be computed. Each feature in this dataset should contain values for all
of the explanatory variables specified; the dependent variable for these
features will be estimated using the model calibrated for the input feature
class data.
Prediction explanatory variable(s)
(optional)
A list of fields representing explanatory variables in the Prediction
locations feature class. These field names should be provided in the same order
(a one-to-one correspondence) as those listed for the input feature class
Explanatory variables parameter. If no prediction explanatory variables are
given, the output prediction feature class will only contain computed
coefficient values for each prediction location.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
6.
Bandwidth method طريقة عرض النطاق
الترددي
Specifies how the extent
of the kernel will be determined. When AICc (corrected Akaike Information
Criterion) or CV (cross validation) is selected, the tool will find the optimal
distance or number of neighbors for you. Typically, you will select either AICc
or CV when you aren't sure what to use for the distance or number_of_neighbors
parameter. Once the tool determines the optimal distance or number of
neighbors, however, you'll use the BANDWIDTH_PARAMETER option.
·
AICc—The extent of the kernel is determined using the Akaike
Information Criterion.
·
CV—The extent of the kernel is determined using cross validation.
·
BANDWIDTH_PARAMETER—The extent of the kernel is determined by a
fixed distance or a fixed number of neighbors. You must specify a value for
either the distance or number_of_neighbors parameters.
يحدد كيف سيتم تحديد مدى kernel. عند
تحديد AICc (معيار معلومات Akaike
المصحح) أو السيرة الذاتية (التحقق المتقاطع) ، ستجد الأداة المسافة المثالية أو
عدد الجيران المناسبين لك. عادةً ، ستحدد إما AICc أو CV عندما
لا تكون متأكدًا مما يجب استخدامه لمعلمة المسافة أو معامل number_of_neighbours. بمجرد أن تحدد الأداة المسافة المثلى أو عدد الجيران ، ستستخدم
خيار BANDWIDTH_PARAMETER.
• AICc - يتم
تحديد مدى النواة باستخدام معيار معلومات Akaike.
• السيرة الذاتية - يتم
تحديد مدى النواة باستخدام التحقق المتقاطع.
• BANDWIDTH_PARAMETER - يتم تحديد مدى النواة بمسافة ثابتة أو عدد ثابت من الأجهزة
المجاورة. يجب تحديد قيمة إما لمعلمات المسافة أو معلمات number_of_neighbours.
Distance (optional)
The distance to use when the Kernel type parameter is set to FIXED and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Number of neighbors (optional)
The exact number of neighbors to include in the local bandwidth of the
Gaussian kernel when the Kernel type parameter is set to ADAPTIVE and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Weights (optional)
The numeric field containing a spatial weighting for individual features.
This weight field allows some features to be more important in the model
calibration process than others. This is useful when the number of samples
taken at different locations varies, values for the dependent and independent
variables are averaged, and places with more samples are more reliable (should
be weighted higher). If you have an average of 25 different samples for one
location but an average of only 2 samples for another location, for example,
you can use the number of samples as your weight field so that locations with
more samples have a larger influence on model calibration than locations with
few samples.
Coefficient raster workspace
(optional)
The full path to the workspace where the coefficient rasters will be
created. When this workspace is provided, rasters are created for the intercept
and every explanatory variable.
Output cell size (optional)
The cell size (a number) or reference to the cell size (a path to a raster
dataset) to use when creating the coefficient rasters.
The default cell size is the shortest of the width or height of the extent
specified in the geoprocessing environment output coordinate system, divided by
250.
Prediction locations (optional)
A feature class containing features representing locations where estimates
should be computed. Each feature in this dataset should contain values for all
of the explanatory variables specified; the dependent variable for these
features will be estimated using the model calibrated for the input feature
class data.
Prediction explanatory variable(s)
(optional)
A list of fields representing explanatory variables in the Prediction
locations feature class. These field names should be provided in the same order
(a one-to-one correspondence) as those listed for the input feature class
Explanatory variables parameter. If no prediction explanatory variables are
given, the output prediction feature class will only contain computed
coefficient values for each prediction location.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
7.
Distance (optional) المسافة
(اختياري)
The distance to use when
the Kernel type parameter is set to FIXED and the Bandwidth method parameter is
set to BANDWIDTH_PARAMETER.
المسافة التي يجب استخدامها عند تعيين معلمة نوع Kernel على Fixed
وتعيين معلمة طريقة النطاق الترددي على BANDWIDTH_PARAMETER.
Number of neighbors (optional)
The exact number of neighbors to include in the local bandwidth of the
Gaussian kernel when the Kernel type parameter is set to ADAPTIVE and the
Bandwidth method parameter is set to BANDWIDTH_PARAMETER.
Weights (optional)
The numeric field containing a spatial weighting for individual features.
This weight field allows some features to be more important in the model
calibration process than others. This is useful when the number of samples
taken at different locations varies, values for the dependent and independent
variables are averaged, and places with more samples are more reliable (should
be weighted higher). If you have an average of 25 different samples for one
location but an average of only 2 samples for another location, for example,
you can use the number of samples as your weight field so that locations with
more samples have a larger influence on model calibration than locations with
few samples.
Coefficient raster workspace
(optional)
The full path to the workspace where the coefficient rasters will be
created. When this workspace is provided, rasters are created for the intercept
and every explanatory variable.
Output cell size (optional)
The cell size (a number) or reference to the cell size (a path to a raster
dataset) to use when creating the coefficient rasters.
The default cell size is the shortest of the width or height of the extent
specified in the geoprocessing environment output coordinate system, divided by
250.
Prediction locations (optional)
A feature class containing features representing locations where estimates
should be computed. Each feature in this dataset should contain values for all
of the explanatory variables specified; the dependent variable for these
features will be estimated using the model calibrated for the input feature
class data.
Prediction explanatory variable(s)
(optional)
A list of fields representing explanatory variables in the Prediction
locations feature class. These field names should be provided in the same order
(a one-to-one correspondence) as those listed for the input feature class
Explanatory variables parameter. If no prediction explanatory variables are
given, the output prediction feature class will only contain computed
coefficient values for each prediction location.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
8.
Number of neighbors (optional) عدد
الجيران (اختياري)
The exact number of
neighbors to include in the local bandwidth of the Gaussian kernel when the
Kernel type parameter is set to ADAPTIVE and the Bandwidth method parameter is
set to BANDWIDTH_PARAMETER.
العدد الدقيق للجيران المراد تضمينه في النطاق
الترددي المحلي لنواة Gaussian عندما يتم تعيين معلمة
نوع Kernel على ADAPTIVE ويتم تعيين معلمة طريقة النطاق الترددي على BANDWIDTH_PARAMETER.
Weights (optional)
The numeric field containing a spatial weighting for individual features.
This weight field allows some features to be more important in the model
calibration process than others. This is useful when the number of samples
taken at different locations varies, values for the dependent and independent
variables are averaged, and places with more samples are more reliable (should
be weighted higher). If you have an average of 25 different samples for one
location but an average of only 2 samples for another location, for example,
you can use the number of samples as your weight field so that locations with
more samples have a larger influence on model calibration than locations with
few samples.
Coefficient raster workspace
(optional)
The full path to the workspace where the coefficient rasters will be
created. When this workspace is provided, rasters are created for the intercept
and every explanatory variable.
Output cell size (optional)
The cell size (a number) or reference to the cell size (a path to a raster
dataset) to use when creating the coefficient rasters.
The default cell size is the shortest of the width or height of the extent
specified in the geoprocessing environment output coordinate system, divided by
250.
Prediction locations (optional)
A feature class containing features representing locations where estimates
should be computed. Each feature in this dataset should contain values for all
of the explanatory variables specified; the dependent variable for these
features will be estimated using the model calibrated for the input feature
class data.
Prediction explanatory variable(s)
(optional)
A list of fields representing explanatory variables in the Prediction
locations feature class. These field names should be provided in the same order
(a one-to-one correspondence) as those listed for the input feature class
Explanatory variables parameter. If no prediction explanatory variables are
given, the output prediction feature class will only contain computed
coefficient values for each prediction location.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
9.
Weights (optional) الأوزان
(اختياري)
The numeric field
containing a spatial weighting for individual features. This weight field
allows some features to be more important in the model calibration process than
others. This is useful when the number of samples taken at different locations
varies, values for the dependent and independent variables are averaged, and
places with more samples are more reliable (should be weighted higher). If you
have an average of 25 different samples for one location but an average of only
2 samples for another location, for example, you can use the number of samples
as your weight field so that locations with more samples have a larger
influence on model calibration than locations with few samples.
الحقل الرقمي الذي يحتوي على ترجيح مكاني للمعالم
الفردية. يسمح حقل الوزن هذا بأن تكون بعض الميزات أكثر أهمية في عملية معايرة
النموذج من غيرها. يكون هذا مفيدًا عندما يختلف عدد العينات المأخوذة في مواقع
مختلفة ، ويتم حساب متوسط قيم المتغيرات التابعة والمستقلة ، وتكون الأماكن التي
تحتوي على المزيد من العينات أكثر موثوقية (يجب ترجيحها أعلى). إذا كان لديك ما
متوسطه 25 عينة مختلفة لموقع واحد ولكن متوسط عينتين فقط لموقع آخر ، على سبيل
المثال ، يمكنك استخدام عدد العينات كحقل وزنك بحيث يكون للمواقع التي تحتوي على
المزيد من العينات تأثير أكبر على معايرة النموذج من المواقع التي تحتوي على عينات
قليلة.
Coefficient raster workspace
(optional)
The full path to the workspace where the coefficient rasters will be
created. When this workspace is provided, rasters are created for the intercept
and every explanatory variable.
Output cell size (optional)
The cell size (a number) or reference to the cell size (a path to a raster
dataset) to use when creating the coefficient rasters.
The default cell size is the shortest of the width or height of the extent
specified in the geoprocessing environment output coordinate system, divided by
250.
Prediction locations (optional)
A feature class containing features representing locations where estimates
should be computed. Each feature in this dataset should contain values for all
of the explanatory variables specified; the dependent variable for these
features will be estimated using the model calibrated for the input feature
class data.
Prediction explanatory variable(s)
(optional)
A list of fields representing explanatory variables in the Prediction
locations feature class. These field names should be provided in the same order
(a one-to-one correspondence) as those listed for the input feature class
Explanatory variables parameter. If no prediction explanatory variables are
given, the output prediction feature class will only contain computed
coefficient values for each prediction location.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
10.
Coefficient raster workspace (optional) مساحة عمل المعامل النقطي (اختياري)
The full path to the
workspace where the coefficient rasters will be created. When this workspace is
provided, rasters are created for the intercept and every explanatory variable.
المسار الكامل إلى مساحة العمل حيث سيتم إنشاء
المعامل النقطية. عندما يتم توفير مساحة العمل هذه ، يتم إنشاء نقطية للتقاطع وكل
متغير توضيحي.
Output cell size (optional)
The cell size (a number) or reference to the cell size (a path to a raster
dataset) to use when creating the coefficient rasters.
The default cell size is the shortest of the width or height of the extent
specified in the geoprocessing environment output coordinate system, divided by
250.
Prediction locations (optional)
A feature class containing features representing locations where estimates
should be computed. Each feature in this dataset should contain values for all
of the explanatory variables specified; the dependent variable for these
features will be estimated using the model calibrated for the input feature
class data.
Prediction explanatory variable(s)
(optional)
A list of fields representing explanatory variables in the Prediction
locations feature class. These field names should be provided in the same order
(a one-to-one correspondence) as those listed for the input feature class
Explanatory variables parameter. If no prediction explanatory variables are
given, the output prediction feature class will only contain computed
coefficient values for each prediction location.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
11.
Output cell size (optional) حجم
خلية الإخراج (اختياري)
The cell size (a number)
or reference to the cell size (a path to a raster dataset) to use when creating
the coefficient rasters.
The default cell size is
the shortest of the width or height of the extent specified in the
geoprocessing environment output coordinate system, divided by 250.
حجم الخلية (رقم) أو مرجع إلى حجم الخلية (مسار
إلى مجموعة بيانات نقطية) لاستخدامه عند إنشاء المعامل النقطي.
حجم الخلية الافتراضي هو أقصر عرض أو ارتفاع للمدى
المحدد في نظام إحداثيات إخراج بيئة المعالجة الجغرافية ، مقسومًا على 250.
Prediction locations (optional)
A feature class containing features representing locations where estimates
should be computed. Each feature in this dataset should contain values for all
of the explanatory variables specified; the dependent variable for these
features will be estimated using the model calibrated for the input feature
class data.
Prediction explanatory variable(s)
(optional)
A list of fields representing explanatory variables in the Prediction
locations feature class. These field names should be provided in the same order
(a one-to-one correspondence) as those listed for the input feature class
Explanatory variables parameter. If no prediction explanatory variables are
given, the output prediction feature class will only contain computed
coefficient values for each prediction location.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
12.
Prediction locations (optional) مواقع
التنبؤ (اختياري)
A feature class
containing features representing locations where estimates should be computed.
Each feature in this dataset should contain values for all of the explanatory
variables specified; the dependent variable for these features will be
estimated using the model calibrated for the input feature class data.
فئة معلم تحتوي على ميزات تمثل المواقع التي يجب
حساب التقديرات فيها. يجب أن تحتوي كل ميزة في مجموعة البيانات هذه على قيم لجميع
المتغيرات التوضيحية المحددة ؛ سيتم تقدير المتغير التابع لهذه الميزات باستخدام
النموذج الذي تمت معايرته لبيانات فئة ميزة الإدخال.
Prediction explanatory variable(s)
(optional)
A list of fields representing explanatory variables in the Prediction
locations feature class. These field names should be provided in the same order
(a one-to-one correspondence) as those listed for the input feature class
Explanatory variables parameter. If no prediction explanatory variables are
given, the output prediction feature class will only contain computed
coefficient values for each prediction location.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
13.
Prediction explanatory variable(s) (optional) المتغير (المتغيرات) التفسيرية للتنبؤ (اختياري)
A list of fields
representing explanatory variables in the Prediction locations feature class.
These field names should be provided in the same order (a one-to-one
correspondence) as those listed for the input feature class Explanatory
variables parameter. If no prediction explanatory variables are given, the
output prediction feature class will only contain computed coefficient values
for each prediction location.
قائمة الحقول التي تمثل المتغيرات التوضيحية في
فئة معالم مواقع التنبؤ. يجب تقديم أسماء الحقول هذه بنفس الترتيب (تطابق واحد
لواحد) مثل تلك المدرجة لمعلمة متغيرات توضيحية لفئة خاصية الإدخال. إذا لم يتم
تقديم متغيرات توضيحية للتنبؤ ، فإن فئة ميزة التنبؤ بالإخراج ستحتوي فقط على قيم
المعامل المحسوبة لكل موقع تنبؤ.
Output prediction feature class
(optional)
The output feature class to receive dependent variable estimates for each
feature in the Prediction locations feature class.
14.
Output prediction feature class (optional) فئة معلم توقع المخرجة (اختياري)
The output feature class
to receive dependent variable estimates for each feature in the Prediction
locations feature class.
فئة معلم المخرجة لتلقي تقديرات متغيرة تابعة لكل
معلم في فئة معلم مواقع التنبؤ.
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