Incremental Spatial Autocorrelation, Multi-Distance Spatial Cluster Analysis and Spatial Autocorrelation Tools
Incremental Spatial Autocorrelation
How to use Incremental Spatial Autocorrelation Tool in Arc Toolbox??
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| Incremental Spatial Autocorrelation |
Path to access the tool
:
Incremental
Spatial Autocorrelation Tool, Analyzing Patterns Toolset, Spatial
Statistics Tools Toolbox
Incremental Spatial Autocorrelation
Measures spatial
autocorrelation for a series of distances and optionally creates a line graph
of those distances and their corresponding z-scores. Z-scores reflect the
intensity of spatial clustering, and statistically significant peak z-scores
indicate distances where spatial processes promoting clustering are most
pronounced. These peak distances are often appropriate values to use for tools
with a Distance Band or Distance Radius parameter.
1. Input Features
The feature class for
which spatial autocorrelation will be measured over a series of distances.
2. Input Field
The numeric field used
in assessing spatial autocorrelation.
3. Number of Distance Bands
The number of times to
increment the neighborhood size and analyze the dataset for spatial
autocorrelation. The starting point and size of the increment are specified in
the Beginning Distance and Distance Increment parameters, respectively.
4. Beginning Distance (optional)
The distance at which to
start the analysis of spatial autocorrelation and the distance from which to
increment. The value entered for this parameter should be in the units of the
Output Coordinate System environment setting.
5. Distance Increment (optional)
The distance to increase
after each iteration. The distance used in the analysis starts at the Beginning
Distance and increases by the amount specified in the Distance Increment.
The
value entered for this parameter should be in the units of the Output Coordinate
System environment setting.
6. Distance Method (optional)
Specifies how distances are calculated from each feature to neighboring features.
- EUCLIDEAN—The straight-line distance between two points (as the crow flies)
- MANHATTAN—The distance between two points measured along axes at right angles (city block); calculated by summing the (absolute) difference between the x- and y-coordinates
7. Row Standardization (optional)
Row standardization is recommended whenever the distribution of your features is potentially biased due to sampling design or an imposed aggregation scheme.
- Checked—Spatial weights will be standardized; each weight is divided by its row sum (the sum of the weights of all neighboring features).
- Unchecked—No standardization of spatial weights is applied.
8. Output Table (optional)
The table to be created with each distance band and associated
z-score result.
9. Output Report File (optional)
The PDF file to be
created containing a line graph summarizing results.
Multi-Distance Spatial Cluster Analysis (Ripleys K Function)
How to use Multi-Distance Spatial Cluster Analysis (Ripleys K Function) Tool in Arc Toolbox??
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| Multi-Distance Spatial Cluster Analysis (Ripleys K Function) |
Path to access the tool
:
Multi-Distance
Spatial Cluster Analysis (Ripleys K Function) Tool, Analyzing
Patterns Toolset, Spatial Statistics Tools Toolbox
Multi-Distance Spatial Cluster
Analysis (Ripleys K Function)
Determines whether
features, or the values associated with features, exhibit statistically
significant clustering or dispersion over a range of distances.
1. Input Feature Class
The feature class upon
which the analysis will be performed.
2. Output Table
The table to which the
results of the analysis will be written.
3. Number of Distance Bands
The number of times to
increment the neighborhood size and analyze the dataset for clustering. The
starting point and size of the increment are specified in the Beginning
Distance and Distance Increment parameters, respectively.
4. Compute Confidence Envelope (optional)
The confidence envelope is calculated by randomly placing feature points (or feature values) in the study area. The number of points/values randomly placed is equal to the number of points in the feature class. Each set of random placements is called a permutation and the confidence envelope is created from these permutations. This parameter allows you to select how many permutations you want to use to create the confidence envelope.
- 0_PERMUTATIONS_-_NO_CONFIDENCE_ENVELOPE—Confidence envelopes are not created.
- 9_PERMUTATIONS—Nine sets of points/values are randomly placed.
- 99_PERMUTATIONS—99 sets of points/values are randomly placed.
- 999_PERMUTATIONS—999 sets of points/values are randomly placed.
5. Display Results Graphically (optional)
Specifies whether the tool will create a graph layer summarizing results.
- Checked—A line graph will be created summarizing results.
- Unchecked—No graphical summary will be created.
6. Weight Field (optional)
A numeric field with weights
representing the number of features/events at each location.
7. Beginning Distance (optional)
The distance at which to
start the cluster analysis and the distance from which to increment. The value
entered for this parameter should be in the units of the Output Coordinate
System.
8. Distance Increment (optional)
The distance to
increment during each iteration. The distance used in the analysis starts at
the Beginning Distance and increments by the amount specified in the Distance
Increment. The value entered for this parameter should be in the units of the
Output Coordinate System environment setting.
9. Boundary Correction Method (optional)
Method to use to correct for underestimates in the number of neighbors for features near the edges of the study area.
- NONE—No edge correction is applied. However, if the input feature class already has points that fall outside the study area boundaries, these will be used in neighborhood counts for features near boundaries.
- SIMULATE_OUTER_BOUNDARY_VALUES—This method simulates points outside the study area so that the number of neighbors near edges is not underestimated. The simulated points are the "mirrors" of points near edges within the study area boundary.
- REDUCE_ANALYSIS_AREA—This method shrinks the study area such that some points are found outside of the study area boundary. Points found outside the study area are used to calculate neighbor counts but are not used in the cluster analysis itself.
- RIPLEY_EDGE_CORRECTION_FORMULA—For all the points (j) in the neighborhood of point i, this method checks to see if the edge of the study area is closer to i, or if j is closer to i. If j is closer, extra weight is given to the point j. This edge correction method is only appropriate for square or rectangular shaped study areas.
10. Study Area Method (optional)
Specifies the region to use for the study area. The K Function is sensitive to changes in study area size so careful selection of this value is important.
- MINIMUM_ENCLOSING_RECTANGLE—Indicates that the smallest possible rectangle enclosing all of the points will be used.
- USER_PROVIDED_STUDY_AREA_FEATURE_CLASS—Indicates that a feature class defining the study area will be provided in the Study Area Feature Class parameter.
11. Study Area Feature Class (optional)
Feature class that delineates the area over which the input
feature class should be analyzed. Only to be specified if
USER_PROVIDED_STUDY_AREA_FEATURE_CLASS is selected for the Study Area Method
parameter.
Spatial Autocorrelation (Morans I)
How to use Spatial Autocorrelation (Morans I) Tool in Arc Toolbox??
.webp "How to use Spatial Autocorrelation (Morans I) Tool in Arc Toolbox??") |
| Spatial Autocorrelation (Morans I) |
Path to access the tool
:
Spatial
Autocorrelation (Morans I) Tool, Analyzing
Patterns Toolset, Spatial Statistics Tools Toolbox
Spatial Autocorrelation (Morans I)
Measures spatial
autocorrelation based on feature locations and attribute values using the
Global Moran's I statistic.
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 Feature Class
The feature class for
which spatial autocorrelation will be calculated
2. Input Field
The numeric field used
in assessing spatial autocorrelation.
3. Generate Report (optional)
Specifies whether the tool will create a graphical summary of results.
- Checked—A graphical summary will be created as an HTML file.
- Unchecked—No graphical summary will be created. This is the default.
4. Conceptualization of Spatial Relationships
Specifies how spatial relationships among features are defined.
- INVERSE_DISTANCE—Nearby neighboring features have a larger influence on the computations for a target feature than features that are far away.
- INVERSE_DISTANCE_SQUARED—Same asINVERSE_DISTANCE except that the slope is sharper, so influence drops off more quickly, and only a target feature's closest neighbors will exert substantial influence on computations for that feature.
- FIXED_DISTANCE_BAND—Each feature is analyzed within the context of neighboring features. Neighboring features inside the specified critical distance (Distance Band or Threshold Distance) receive a weight of one and exert influence on computations for the target feature. Neighboring features outside the critical distance receive a weight of zero and have no influence on a target feature's computations.
- ZONE_OF_INDIFFERENCE—Features within the specified critical distance (Distance Band or Threshold Distance) of a target feature receive a weight of one and influence computations for that feature. Once the critical distance is exceeded, weights (and the influence a neighboring feature has on target feature computations) diminish with distance.
- CONTIGUITY_EDGES_ONLY—Only neighboring polygon features that share a boundary or overlap will influence computations for the target polygon feature.
- CONTIGUITY_EDGES_CORNERS—Polygon features that share a boundary, share a node, or overlap will influence computations for the target polygon feature.
- GET_SPATIAL_WEIGHTS_FROM_FILE—Spatial relationships are defined by a specified spatial weights file. The path to the spatial weights file is specified by the Weights Matrix File parameter.
5. Distance Method
Specifies how distances are calculated from each feature to neighboring features.
- EUCLIDEAN_DISTANCE—The straight-line distance between two points (as the crow flies)
- MANHATTAN_DISTANCE—The distance between two points measured along axes at right angles (city block); calculated by summing the (absolute) difference between the x- and y-coordinates
6. Standardization
Row standardization is recommended whenever the distribution of your features is potentially biased due to sampling design or an imposed aggregation scheme.
- NONE—No standardization of spatial weights is applied.
- ROW—Spatial weights are standardized; each weight is divided by its row sum (the sum of the weights of all neighboring features).
7. Distance Band or Threshold Distance (optional)
Specifies a cutoff
distance for Inverse Distance and Fixed Distance options. Features outside the
specified cutoff for a target feature are ignored in analyses for that feature.
However, for Zone of Indifference, the influence of features outside the given
distance is reduced with distance, while those inside the distance threshold
are equally considered. The distance value entered should match that of the
output coordinate system.
For the inverse distance
conceptualizations of spatial relationships, a value of 0 indicates that no
threshold distance is applied; when this parameter is left blank, a default
threshold value is computed and applied. This default value is the Euclidean
distance that ensures every feature has at least one neighbor.
This parameter has no
effect when polygon contiguity (CONTIGUITY_EDGES_ONLY or
CONTIGUITY_EDGES_CORNERS) or GET_SPATIAL_WEIGHTS_FROM_FILE spatial
conceptualizations are selected.
8. Weights Matrix File (optional)
The path to a file containing weights that define spatial, and potentially temporal, relationships among features.
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