View a shared map

Snapping Points to Roads
Road Segmentation

Space

Example of Space Aggregation (e.g. counting how many points in each polygon.)

Select a variable:
Select p-value threshold:
Select weights:
Randomization (#permutations):

Number of Cluster:

Select Weights:

Minimum Bound:

Select Variables:

Number of Cluster:

Select Weights:

Select Method:

Minimum Bound:

Select Variables:

Select Weights:

Select Method:

Tabu Length:

Cooling Rate:

Minimum Bound:

Minimum # per Region:

# Iterations:

Select Variables:

Select a variable:
Select p-value threshold:
Select weights:
	Calculates the Getis-Ord Gi* statistic?
Randomization (#permutations):

Select a variable:
Select p-value threshold:
Select weights:
Randomization (#permutations):

Select a variable:
Select p-value threshold:
Select weights:
Randomization (#permutations):

Select a variable:
Select weights:

Open in new window?

Select X variable:	Select Y variable:

Open in new window?

Select variables in Matrix plot:

Open in new window?

Select variables in Parallel Coordinates:

Open in new window?

Select a variable:

Open in new window?

Select variables in Box plot:

Open in new window?

Select a configuration:
Fill Color:
Stroke Width:
Stroke Color:
Transparency:		0.9

Input a map configuration name:

Select quantification method:
Select a variable:
Input category number:
Select a color scheme:

Please input a Weights name:

Contiguity
Distance
Adaptive Kernel

Contiguity Type:
Order of Contiguity:
Include Lower Orders:
Distance Threshold:

Select Distance Metric
k-Nearest Neighbors	# of neighbors
Threshold Distane
Power of Inverse Distance

Kernel Function:	Diagonal weights=1 Apply kernel to diagonal weights
Specify Bandwidth:
Adaptive bandwidth:	Numbe of Neighbors:
Use max knn distance as bandwidth

Current selected weights

Input an unique ID name:

Report

Parameters:

Transformation:

Select variables:

Parameters:

Number of Clusters:
Transformation:
Initialization Re-runs:
Maximal Iterations:
Methos:
Distance Function:

Output:

Save Cluster in Field:

Select variables:

Use Geometric Centroids:

Model Specifications

Tips: Drag and drop variables from the Variables Panel. Double click variable to remove it.

Y (Required)

Add your items here

YE

Add your items here

Instruments

Add your items here

R

Add your items here

X (Required)

Add your items here

Setup Weights (optional)

Kernel Weights (optional)

select a kernel weights for spatial regression

Distance
Adaptive Kernel

Select Distance Metric
k-Nearest Neighbors	# of neighbors
Binary Distane Band
Power of Inverse Distance

Select Kernel Function Type
Numbe of Neighbors

Model Weights (optional)

select a model weights for spatial regression

Variables

x1

Report
Predicted Values and Residuals

Std Dev
GMM
ML
Instruments
Output
Regimes
Other

Improved Efficiency
Maximum Iteration
Stopping Criterion (change in Lambda)
Spatial Error Model
Inference on Lambda
Heteroskedasticity
Computation of Inverse
Step 1c from Arraiz et al (2010)

Diagnostics
ML Diagnostics
Methods
ML Method
Tolerance Criterion

Order of Spatial Lags for Instruments
Include Lags of User-Specified Instruments

Show Variance-Covariance Matrix
Save Predicted Values and Residuals
Save Detailed Model Specification

Error by Regimes
Spatial Lag by Regimes

Diagnostics
OLS Diagnostics
White Test (OLS only)
Moran's I of the Residuals
Data
Replace Missing Values With

Input a model name:

Select a spreg model:

	N-K	N
OLS
2SLS
GM-Lag
All Other Models

Input name for visualization
Input description for visualization (This editor supports Markdown gramma)

Select a point layer
Select a road layer
Input the name of new snapping field

Select a road layer
Input a maximum length of segemented road (ft)
Input a file name for saving the segemented roads

1.Select the polygon layer:
2.Select the point layer:
3.Input the name of new aggregate field:

Model Specifications

Estimation

Setup Weights (optional)

Variables