arcgis.features.analyze_patterns module

input_layer

Required layer. The input table or features containing the variables that will be combined into the index.

Syntax: As described in detail in the Feature input topic, this parameter can be one of the following:

• A URL to a feature service layer with an optional filter to select specific features

• A FeatureCollection

#Example #1: Feature Layer with selection
>>> output = calculate_composite_index(
                input_layer= {
                    "url": <feature service layer url>,
                    "filter": <where clause>},
                ...
            )

#Example #2: Feature Collection
>>> output = calculate_composite_index(
                input_layer= {"
                    "layerDefinition": {},
                    "featureSet": {},
                    "filter": <where clause>},
                ...,
            )

index_method

Optional string. The methods that will be used to scale the inputVariables and combine the scaled variables to create the index.

Scaling is a type of preprocessing that ensures the variables are on a compatible scale before they are combined. These scaled variables are then combined to create a single index value. The following options are available:

• meanScaled the index by scaling the input variables between 0 and 1 (minimum-maximum scaling) and calculating the mean of the scaled values. This method is useful for creating an index that is easy to interpret. The shape of the distribution and outliers in the input variables will impact the index.

• meanPercentile creates the index by scaling the ranks of the input variables between 0 and 1 (scaling by percentile) and calculating the mean of the scaled ranks. This option is useful when the rankings of the variable values are more important than the differences between values. The shape of the distribution and outliers in the input variables will not impact the index.

• meanRaw creates the index by calculating the mean of the raw input variables. This option is useful when variables are already on a compatible scale.

• geomeanScaled creates the index by scaling the input variables between 0 and 1 (minimum-maximum scaling) and calculating the geometric mean of the scaled values. High values will not cancel low values, so this option is useful for creating an index in which higher index values will occur only when there are high values in multiple variables.

• geomeanPercentile creates the index by scaling the ranks of the input variables between 0 and 1 (scaling by percentile) and calculating the geometric mean of the scaled ranks. This option is useful when the rankings of the variable values are more important than the differences between values and when high variable values should not cancel out low variable values.

• geomeanRaw creates the index by calculating the geometric mean of the raw input variables. This option is useful when variables are already on a compatible scale and when high variable values should not cancel out low variable values.

• sumFlagsPercentile creates the index by counting the number of input variables with values greater than or equal to the 90th percentile. This method is useful for identifying locations that may be considered the most extreme or the most in need.

Values:

• meanScaled

• meanPercentile

• meanRaw

• geomeanScaled

• geomeanPercentile

• geomeanRaw

• sumFlagsPercentile

Default: meanScaled

output_index_min_max

Optional list of one dictionary. The minimum and maximum of the output index values. Specifying a minimum and maximum value will apply minimum-maximum scaling to the combined variables.

# Example:
>>> output = calculate_composite_index(
                ...,
                ouput_index_min_max= [
                        {'min': 0, 'max': 100}
                    ],
                ...,
            )

context

Optional dict. The Context parameter contains the following additional settings that affect task operation:

• Extent (extent)—A bounding box that defines the analysis area. Only input features that intersect the bounding box will be analyzed.

• Output spatial reference (outSR)—The output features will be projected into the output spatial reference.

# Example:
>>> output = calculate_composite_index(
                    ...,
                    context= {
                        "extent" : {extent},
                        "outSR" : {spatial reference}
                    },
                    ...,
            )

future

Optional boolean. If True, the task will be performed asynchronously.

input_layer

Required layer. The point or line features from which to calculate density. See Feature Input.

cell_size

Optional float. 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. The smaller the value, the smoother the polygon boundaries will be. Conversely, with larger values, the polygon boundaries will be more coarse and jagged.

radius

Optional float. A distance specifying how far to search to find point or line features when calculating density values.

bounding_polygon_layer

Optional layer. A layer specifying the polygon(s) where you want densities to be calculated. For example, if you are interpolating densities of fish within a lake, you can use the boundary of the lake in this parameter and the output will only draw within the boundary of the lake. See Feature Input.

classification_type

Optional string. Determines how density values will be classified into polygons. Choice list: [‘EqualInterval’, ‘GeometricInterval’, ‘NaturalBreaks’, ‘EqualArea’, ‘StandardDeviation’]

• EqualInterval - Polygons are created such that the range of density values is equal for each area.

• GeometricInterval - Polygons are based on class intervals that have a geometric series. This method ensures that each class range has approximately the same number of values within each class and that the change between intervals is consistent.

• NaturalBreaks - Class intervals for polygons are based on natural groupings of the data. Class break values are identified that best group similar values and that maximize the differences between classes.

• EqualArea - Polygons are created such that the size of each area is equal. For example, if the result has more high density values than low density values, more polygons will be created for high densities.

• StandardDeviation - Polygons are created based upon the standard deviation of the predicted density values.

output_name

Optional string or FeatureLayer. Existing feature layer will cause the new layer to be appended to the Feature Service. If overwrite is True in context, new layer will overwrite existing layer. If output_name not indicated then new FeatureCollection created.

gis

Optional, the GIS on which this tool runs. If not specified, the active GIS is used.

future

Optional, If True, a future object will be returned and the process will not wait for the task to complete. The default is False, which means wait for results.

analysis_layer

Required layer. The point or polygon feature layer for which hot spots will be calculated. See Feature Input.

divided_by_field

Optional string. The numeric field in the analysis_layer that will be used to normalize your data. For example, if your points represent crimes, dividing by total population would result in an analysis of crimes per capita rather than raw crime counts.

You can use esriPopulation to geoenrich each area feature with the most recent population values, which will then be used as the attribute to divide by. This option will use credits.

aggregation_polygon_layer

Optional layer. When the analysis_layer contains points and no analysis_field is specified, you can provide polygon features into which the points will be aggregated and analyzed, such as administrative units. The number of points that fall within each polygon are counted, and the point count in each polygon is analyzed. See Feature Input.

context

Optional dict. Additional settings such as processing extent and output spatial reference. For find_hot_spots, there are three settings.

• extent - a bounding box that defines the analysis area. Only those features in the input_layer that intersect the bounding box will be analyzed.

• outSR - the output features will be projected into the output spatial reference referred to by the wkid.

• overwrite - if True, then the feature layer in output_name will be overwritten with new feature layer. Available for ArcGIS Online and ArcGIS Enterprise 11.1+.

  # Example Usage
  context = {"extent": {"xmin": 3164569.408035,
                      "ymin": -9187921.892449,
                      "xmax": 3174104.927313,
                      "ymax": -9175500.875353,
                      "spatialReference":{"wkid":102100,"latestWkid":3857}},
              "outSR": {"wkid": 3857},
              "overwrite": True}
  

estimate

Optional Boolean. Is true, the number of credits needed to run the operation will be returned as a float.

cell_size

Optional float. The size of the grid cells used to aggregate your features. When aggregating into a hexagon grid, this distance is used as the height to construct the hexagon polygons.

distance_band

Optional float. The spatial extent of the analysis neighborhood. This value determines which features are analyzed together in order to assess local clustering.

future

Optional, If True, a future object will be returned and the process will not wait for the task to complete. The default is False, which means wait for results.

analysis_layer

Required feature layer. The point or polygon feature layer for which outliers will be calculated. See Feature Input.

divided_by_field

Optional string. The numeric field in the analysis_layer that will be used to normalize your data. For example, if your points represent crimes, dividing by total population would result in an analysis of crimes per capita rather than raw crime counts.

You can use esriPopulation to geoenrich each area feature with the most recent population values, which will then be used as the attribute to divide by. This option will use credits.

aggregation_polygon_layer

Optional layer. When the analysis_layer contains points and no analysis_field is specified, you can provide polygon features into which the points will be aggregated and analyzed, such as administrative units. The number of points that fall within each polygon are counted, and the point count in each polygon is analyzed. See Feature Input.

shape_type

Optional string. The shape of the polygon mesh the input features will be aggregated into.

• Fishnet - The input features will be aggregated into a grid of square (fishnet) cells.

• Hexagon - The input features will be aggregated into a grid of hexagonal cells.

cell_units

Optional string. The units of the cell_size value. You must provide a value if cell_size has been set.

Choice list: [‘Meters’, ‘Miles’, ‘Feet’, ‘Kilometers’]

band_units

Optional string. The units of the distance_band value. You must provide a value if distance_band has been set.

Choice list: [‘Meters’, ‘Miles’, ‘Feet’, ‘Kilometers’]

context

Optional dict. Additional settings such as processing extent and output spatial reference. For find_outliers, there are three settings.

• extent - a bounding box that defines the analysis area. Only those features in the input_layer that intersect the bounding box will be analyzed.

• outSR - the output features will be projected into the output spatial reference referred to by the wkid.

• overwrite - if True, then the feature layer in output_name will be overwritten with new feature layer. Available for ArcGIS Online and ArcGIS Enterprise 11.1+.

• randomGenerator - A string representing the integer and seed type that will initiate a random number generator. The seed type is always MERSENNE_TWISTER, for example, 13 MERSENNE_TWISTER. This parameter is available in ArcGIS Enterprise 11.2 or later.

  # Example Usage
  context = {"extent": {"xmin": 3164569.408035,
                      "ymin": -9187921.892449,
                      "xmax": 3174104.927313,
                      "ymax": -9175500.875353,
                      "spatialReference":{"wkid":102100,"latestWkid":3857}},
              "outSR": {"wkid": 3857},
              "overwrite": True,
              "randomGenerator": "13 MERSENNE_TWISTER"}
  

future

Optional, If True, a future object will be returned and the process will not wait for the task to complete. The default is False, which means wait for results.

analysis_layer

Required layer. The point feature layer for which density-based clustering will be calculated. See Feature Input.

search_distance

Optional float. The maximum distance to consider. The Minimum Features per Cluster specified must be found within this distance for cluster membership. Individual clusters will be separated by at least this distance. If a feature is located further than this distance from the next closest feature in the cluster, it will not be included in the cluster.

output_name

Optional string or FeatureLayer. Existing feature layer will cause the new layer to be appended to the Feature Service. If overwrite is True in context, new layer will overwrite existing layer. If output_name not indicated then new FeatureCollection created.

gis

Optional, the GIS on which this tool runs. If not specified, the active GIS is used.

future

Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.

sensitivity

Optional float. A double value between 0 and 100 that determines the compactness of the clusters.

This parameter is available in ArcGIS Enterprise 11.2 or higher.

search_time_interval

Optional float. A value that will be used to determine whether features form a space-time cluster. The search time interval spans before and after the time of each feature. This parameter is only available in ArcGIS Online.

Example: search_time_interval = 4

input_layer

Required layer. The point layer whose features will be interpolated. See Feature Input.

interpolate_option

Optional integer. Integer value declaring your preference for speed versus accuracy, from 1 (fastest) to 9 (most accurate). More accurate predictions take longer to calculate.

Choice list: [1, 5, 9].

The default is 5.

classification_type

Optional string. Determines how predicted values will be classified into areas.

• EqualArea - Polygons are created such that the number of data values in each area is equal.

For example, if the data has more large values than small values, more areas will be created for large values.

• EqualInterval - Polygons are created such that the range of predicted values is equal for each area.

• GeometricInterval - Polygons are based on class intervals that have a geometrical series. This method ensures that each class range has approximately the same number of values within each class and that the change between intervals is consistent.

• Manual - You to define your own range of values for areas. These values will be entered in the class_breaks parameter below.

Choice list: [‘EqualArea’, ‘EqualInterval’, ‘GeometricInterval’, ‘Manual’]

The default is ‘GeometricInterval’.

class_breaks

Optional list of floats. If classification_type 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 mst enter at least two values and no more than 32.

predict_at_point_layer

Optional layer. An optional layer specifying point locations to calculate prediction values. This allows you to make predictions at specific locations of interest. For example, if the input_layer represents measurements of pollution levels, you can use this parameter to predict the pollution levels of locations with large at-risk populations, such as schools or hospitals. You can then use this information to give recommendations to health officials in those locations.

If supplied, the output predicted_point_layer will contain predictions at the specified locations. See Feature Input.

context

Optional dict. Additional settings such as processing extent and output spatial reference. For interpolate_points, there are three settings.

• extent - a bounding box that defines the analysis area. Only those features in the input_layer that intersect the bounding box will be analyzed.

• outSR - the output features will be projected into the output spatial reference referred to by the wkid.

• overwrite - if True, then the feature layer in output_name will be overwritten with new feature layer. Available for ArcGIS Online or Enterprise 10.9.1+

# Example Usage

>>> context = {"extent": {"xmin": 3164569.408035,
                          "ymin": -9187921.892449,
                          "xmax": 3174104.927313,
                          "ymax": -9175500.875353,
                          "spatialReference":{"wkid":102100,
                                              "latestWkid":3857}},
               "outSR": {"wkid": 3857},
               "overwrite": True}

estimate

Optional boolean. If True, the number of credits to run the operation will be returned.