arcgis.features.analyze_patterns module¶

These functions help you identify, quantify, and visualize spatial patterns in your data.

calculate_density takes known quantities of some phenomenon and spreads these quantities across the map. find_hot_spots identifies statistically significant clustering in the spatial pattern of your data. interpolate_points predicts values at new locations based on measurements found in a collection of points.

calculate_density¶

analyze_patterns.calculate_density(input_layer, field=None, cell_size=None, cell_size_units='Meters', radius=None, radius_units=None, bounding_polygon_layer=None, area_units=None, classification_type='EqualInterval', num_classes=10, output_name=None, context=None, gis=None)¶

The calculate_density function creates a density map from point or line features by spreading known quantities of some phenomenon (represented as attributes of the points or lines) across the map. The result is a layer of areas classified from least dense to most dense.

input_layer: The point or line features from which to calculate density.
field: A numeric field name specifying the number of incidents at each location. If not specified, each location will be assumed to represent a single count.
cell_size: This value is used to create a mesh of points where density values are calculated. The default is approximately 1/1000th of the smaller of the width and height of the analysis extent as defined in the context parameter.
cell_size_units: The units of the cellSize value
radius: A distance specifying how far to search to find point or line features when calculating density values.
radius_units: The units of the radius parameter.
bounding_polygon_layer: A layer specifying the polygon(s) where you want densities to be calculated.
area_units: The units of the calculated density values.
classification_type: Determines how density values will be classified into polygons.
num_classes: This value is used to divide the range of predicted values into distinct classes. The range of values in each class is determined by the classificationType parameter.
output_name: Additional properties such as output feature service name.
context: Additional settings such as processing extent and output spatial reference.
gis :: Optional, the GIS on which this tool runs. If not specified, the active GIS is used.

result_layer : layer (FeatureCollection)

find_hot_spots¶

analyze_patterns.find_hot_spots(analysis_layer, analysis_field=None, divided_by_field=None, bounding_polygon_layer=None, aggregation_polygon_layer=None, output_name=None, context=None, gis=None)¶

The Find Hot Spots function finds statistically significant clusters of incident points, weighted points, or weighted polygons. For incident data, the analysis field (weight) is obtained by aggregation. Output is a hot spot map.

gis : The GIS used for running this analysis analysis_layer : Required layer (see Feature Input in documentation)

The point or polygon feature layer for which hot spots will be calculated.

analysis_field: The numeric field in the AnalysisLayer that will be analyzed.

divided_by_field : Optional string

bounding_polygon_layer: When the analysis layer is points and no AnalysisField is specified, you can provide polygons features that define where incidents could have occurred.
aggregation_polygon_layer: When the AnalysisLayer contains points and no AnalysisField is specified, you can provide polygon features into which the points will be aggregated and analyzed, such as administrative units.
output_name: Additional properties such as output feature service name.
context: Additional settings such as processing extent and output spatial reference.
gis :: Optional, the GIS on which this tool runs. If not specified, the active GIS is used.

dict with the following keys:: “hot_spots_result_layer” : layer (FeatureCollection) “process_info” : list of messages

interpolate_points¶

analyze_patterns.interpolate_points(input_layer, field, interpolate_option='5', output_prediction_error=False, classification_type='GeometricInterval', num_classes=10, class_breaks=[], bounding_polygon_layer=None, predict_at_point_layer=None, output_name=None, context=None, gis=None)¶

The Interpolate Points function allows you to predict values at new locations based on measurements from a collection of points. The function takes point data with values at each point and returns areas classified by predicted values.

input_layer: The point layer whose features will be interpolated.
field: Name of the numeric field containing the values you wish to interpolate.
interpolate_option: Integer value declaring your preference for speed versus accuracy, from 1 (fastest) to 9 (most accurate). More accurate predictions take longer to calculate.
output_prediction_error: If True, a polygon layer of standard errors for the interpolation predictions will be returned in the predictionError output parameter.
classification_type: Determines how predicted values will be classified into areas.
num_classes: This value is used to divide the range of interpolated values into distinct classes. The range of values in each class is determined by the classificationType parameter. Each class defines the boundaries of the result polygons.
class_breaks: If classificationType is Manual, supply desired class break values separated by spaces. These values define the upper limit of each class, so the number of classes will equal the number of entered values. Areas will not be created for any locations with predicted values above the largest entered break value. You must enter at least two values and no more than 32.
bounding_polygon_layer: A layer specifying the polygon(s) where you want values to be interpolated.
predict_at_point_layer: An optional layer specifying point locations to calculate prediction values. This allows you to make predictions at specific locations of interest.
output_name: Additional properties such as output feature service name.
context: Additional settings such as processing extent and output spatial reference.
gis :: Optional, the GIS on which this tool runs. If not specified, the active GIS is used.

dict with the following keys:: “result_layer” : layer (FeatureCollection) “prediction_error” : layer (FeatureCollection) “predicted_point_layer” : layer (FeatureCollection)