Exploratory Regression, Generate Network Spatial Weights Tools
Exploratory Regression
How to use Exploratory Regression Tool in Arc Toolbox??
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| Exploratory Regression |
Path to access the tool
:
Exploratory
Regression Tool, Modeling Spatial Relationships Toolset, Spatial
Statistics Tools Toolbox
Exploratory Regression
The Exploratory
Regression tool evaluates all possible combinations of the input candidate
explanatory variables, looking for OLS models that best explain the dependent
variable within the context of user-specified criteria.
You can access the
results of this tool (including the optional report file) from the Results
window. If you disable background processing, results will also be written to
the Progress dialog box.
1. Input Features
The feature class or
feature layer containing the dependent and candidate explanatory variables to
analyze.
2. Dependent Variable
The numeric field
containing the observed values you want to model using OLS.
3. Candidate Explanatory Variables
A list of fields to try
as OLS model explanatory variables.
4. Weights Matrix File (optional)
A file containing
spatial weights that define the spatial relationships among your input
features. This file is used to assess spatial autocorrelation among regression
residuals. You can use the Generate Spatial Weights Matrix File tool to create
this. When you do not provide a spatial weights matrix file, residuals are
assessed for spatial autocorrelation based on each feature's 8 nearest
neighbors.
Note: The spatial
weights matrix file is only used to analyze spatial structure in model
residuals; it is not used to build or to calibrate any of the OLS models.
5. Output Report File (optional)
The report file contains
tool results, including details about any models found that passed all the
search criteria you entered. This output file also contains diagnostics to help
you fix common regression problems in the case that you don't find any passing
models.
6. Output Results Table (optional)
The optional output
table created containing the explanatory variables and diagnostics for all of
the models within the Coefficient p-value and VIF value cutoffs.
7. Maximum Number of Explanatory Variables (optional)
All models with
explanatory variables up to the value entered here will be assessed. If, for
example, the Minimum Number of Explanatory Variables is 2 and the Maximum
Number of Explanatory Variables is 3, the Exploratory Regression tool will try
all models with every combination of two explanatory variables, and all models
with every combination of three explanatory variables.
8. Minimum Number of Explanatory Variables (optional)
This value represents
the minimum number of explanatory variables for models evaluated. If, for
example, the Minimum Number of Explanatory Variables is 2 and the Maximum
Number of Explanatory Variables is 3, the Exploratory Regression tool will try
all models with every combination of two explanatory variables, and all models
with every combination of three explanatory variables.
9. Minimum Acceptable Adj R Squared (optional)
This is the lowest
Adjusted R-Squared value you consider a passing model. If a model passes all of
your other search criteria, but has an Adjusted R-Squared value smaller than
the value entered here, it will not show up as a Passing Model in the Output Report
File. Valid values for this parameter range from 0.0 to 1.0. The default value
is 0.05, indicating that passing models will explain at least 50 percent of the
variation in the dependent variable.
10. Maximum Coefficient p value Cutoff (optional)
For each model
evaluated, OLS computes explanatory variable coefficient p-values. The cutoff
p-value you enter here represents the confidence level you require for all
coefficients in the model in order to consider the model passing. Small
p-values reflect a stronger confidence level. Valid values for this parameter
range from 1.0 down to 0.0, but will most likely be 0.1, 0.05, 0.01, 0.001, and
so on. The default value is 0.05, indicating passing models will only contain
explanatory variables whose coefficients are statistically at the 95 percent
confidence level (p-values smaller than 0.05). To relax this default you would
enter a larger p-value cutoff, such as 0.1. If you are getting lots of passing
models, you will likely want to make this search criteria more stringent by
decreasing the default p-value cutoff from 0.05 to 0.01 or smaller.
11. Maximum VIF Value Cutoff (optional)
This value reflects how
much redundancy (multicollinearity) among model explanatory variables you will
tolerate. When the VIF (Variance Inflation Factor) value is higher than about
7.5, multicollinearity can make a model unstable; consequently, 7.5 is the
default value here. If you want your passing models to have less redundancy,
you would enter a smaller value, such as 5.0, for this parameter.
12. Minimum Acceptable Jarque Bera p value (optional)
The p-value returned by
the Jarque-Bera diagnostic test indicates whether the model residuals are
normally distributed. If the p-value is statistically significant (small), the
model residuals are not normal and the model is biased. Passing models should
have large Jarque-Bera p-values. The default minimum acceptable p-value is 0.1.
Only models returning p-values larger than this minimum will be considered
passing. If you are having trouble finding unbiased passing models, and decide
to relax this criterion, you might enter a smaller minimum p-value such as
0.05.
13. Minimum Acceptable Spatial Autocorrelation p value (optional)
For models that pass all
of the other search criteria, the Exploratory Regression tool will check model
residuals for spatial clustering using Global Moran's I. When the p-value for
this diagnostic test is statistically significant (small), it indicates the
model is very likely missing key explanatory variables (it isn't telling the
whole story). Unfortunately, if you have spatial autocorrelation in your
regression residuals, your model is misspecified, so you cannot trust your
results. Passing models should have large p-values for this diagnostic test.
The default minimum p-value is 0.1. Only models returning p-values larger than
this minimum will be considered passing. If you are having trouble finding
properly specified models because of this diagnostic test, and decide to relax
this search criteria, you might enter a smaller minimum such as 0.05.
Generate Network Spatial Weights
How to use Generate Network Spatial Weights Tool in Arc Toolbox??
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| Generate Network Spatial Weights |
Path to access the tool
:
Generate
Network Spatial Weights Tool, Modeling Spatial
Relationships Toolset, Spatial Statistics Tools Toolbox
Generate Network Spatial Weights
Constructs a spatial
weights matrix file (.swm) using a Network dataset, defining feature spatial
relationships in terms of the underlying network structure.
1. Input Feature Class
The point feature class
for which network spatial relationships among features will be assessed.
2. Unique ID Field
An integer field
containing a different value for every feature in the input feature class. If
you don't have a Unique ID field, you can create one by adding an integer field
to your feature class table and calculating the field values to equal the FID
or OBJECTID field.
3. Output Spatial Weights Matrix File
The output network
spatial weights matrix (.swm) file.
4. Input Network
The network dataset for
which spatial relationships among features in the input feature class will be
defined. Network datasets most often represent street networks but may
represent other kinds of transportation networks as well. The network dataset
needs at least one time-based and one distance-based cost attribute.
5. Travel Mode (optional)
The mode of
transportation for the analysis. Custom is always a choice. For other travel
modes to appear, they must be present in the network dataset specified in the
Network Dataset parameter.
A travel mode is defined
on a network dataset and provides override values for parameters that model
car, truck, pedestrian, or other modes of travel.
6. Impedance Attribute
The type of cost units
to use as impedance in the analysis.
7. U-turn Policy (optional)
Specifies optional U-turn restrictions.
- ALLOW_UTURNS—U-turns will be possible anywhere. This is the default
- NO_UTURNS—No U-turns will be allowed during navigation
- ALLOW_DEAD_ENDS_ONLY—U-turns will be possible only at dead ends (that is, single-valent junctions)
- ALLOW_DEAD_ENDS_AND_INTERSECTIONS_ONLY—U-turns will be possible only at dead ends and intersections
8. Restrictions (optional)
A list of restrictions.
Check the restrictions to be honored in spatial relationship computations.
9. Use Hierarchy in Analysis (optional)
Specifies whether or not to use a hierarchy in the analysis.
- Checked—Will use the network dataset's hierarchy attribute in a heuristic path algorithm to speed analysis.
- Unchecked—Will use an exact path algorithm instead. If there is no hierarchy attribute, this option does not affect analysis.
10. Impedance Cutoff (optional)
Specifies a cutoff value
for INVERSE and FIXED conceptualizations of spatial relationships. Enter this
value using the units specified by the Impedance Attribute parameter.
A value of zero
indicates that no threshold is applied. When this parameter is left blank, a
default threshold value is computed based on input feature class extent and the
number of features.
11. Maximum Number of Neighbors (optional)
An integer reflecting
the maximum number of neighbors to find for each feature.
12. Barriers (optional)
The name of a point
feature class with features representing blocked intersections, road closures,
accident sites, or other locations where travel is blocked along the network.
13. Search Tolerance (optional)
The search threshold
used to locate features in the Input Feature Class onto the network dataset.
This parameter includes a search value and the units for the tolerance.
14. Time of Day (optional)
Specifies whether travel
times should consider traffic conditions. Especially in urbanized areas,
traffic conditions can significantly impact the area covered within a specified
travel time. If no date or time is specified, the distance covered during a specified
travel time will not be impacted by traffic.
15. Conceptualization of Spatial Relationships (optional)
Specifies how the weighting associated with each spatial relationship is specified.
- INVERSE—Features farther away have a smaller weight than features nearby.
- FIXED—Features within the Impedance Cutoff are neighbors (weight of 1); features outside the Impedance Cutoff are not weighted (weight of 0).
16. Exponent (optional)
Parameter for the
INVERSE Conceptualization of Spatial Relationships calculation. Typical values
are 1 or 2. Weights drop off quicker with distance as this exponent value
increases.
17. Row Standardization (optional)
Row standardization is recommended whenever feature distribution is potentially biased due to sampling design or to an imposed aggregation scheme.
- Checked—Spatial weights are standardized by row. Each weight is divided by its row sum.
- Unchecked—No standardization of spatial weights is applied.
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