GeoAnalytics Tools in Run Python Script

The Run Python Script task allows you to programmatically execute most GeoAnalytics Tools with Python using an API that is available when you run the task. A geoanalytics object is instantiated automatically and gives you access to each tool using the syntax shown in the example and table below. Each tool accepts input layers as Spark DataFrames and will return results as a Spark DataFrame or collection of Spark DataFrames. To learn more, see Reading and writing layers in pyspark. DataFrames are held in memory and can be written to a data store at any time. This allows you to chain together multiple GeoAnalytics Tools without writing out intermediate results.

In the example below, the Detect Incidents task and Find Hot Spots task are used together and only the final DataFrame is written to a data store as a feature service layer. The input layer (represented in the example below by layers[0]) is a big data file share dataset of city bus locations recorded at 1-minute intervals for 15 days. To learn more about using layers, see Reading and writing layers in pyspark.

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import time

# Run Detect Incidents to find all bus locations where delay status has changed from False to True
exp = "var dly = $track.field[\"dly\"].history(-2); return dly[0]==\"False\" && dly[1]==\"True\""
delay_incidents = geoanalytics.detect_incidents(
                                input_layer = layers[0],
                                track_fields = ["vid"],
                                start_condition_expression = exp,
                                output_mode = "Incidents")

# Use the resulting DataFrame as input to the Find Hot Spots task
delay_hotspots = geoanalytics.find_hot_spots(
                                point_layer = delay_incidents,
                                bin_size = 0.1,
                                bin_size_unit = "Miles",
                                neighborhood_distance = 1,
                                neighborhood_distance_unit = "Miles",
                                time_step_interval = 1,
                                time_step_interval_unit = "Days")

# Write the Find Hot Spots result to the spatiotemporal big data store
delay_hotspots.write.format("webgis").save("Bus_Delay_HS_{0}".format(time.time()))

For more examples, see Examples: Scripting custom analysis with the Run Python Script task.

The table below describes the method signature for GeoAnalytics Tools in Run Python Script. All tools can be called except for Copy To Data Store and Append Data. The parameter syntax is the same as that of the REST API except where noted. See the documentation for each tool for descriptions of parameter syntax and tool outputs.

Tool	Syntax	Returns	Notes
Aggregate Points	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 12 13 aggregate_points( point_layer, bin_type = None, bin_size = None, bin_size_unit = None, polygon_layer = None, time_step_interval = None, time_step_interval_unit = None, time_step_repeat = None, time_step_repeat_unit = None, time_step_reference = None, summary_fields = None )`	DataFrame
Build Multi-Variable Grid	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 build_multi_variable_grid( bin_type = "Square", bin_size = None, bin_size_unit = None, input_layers = None, variable_calculations = None )`	DataFrame	`input_layers` should be list of DataFrames.
Calculate Density	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 calculate_density( input_layer, fields = None, weight = "Uniform", bin_type = "Square", bin_size = None, bin_size_unit = None, time_step_interval = None, time_step_interval_unit = None, time_step_repeat = None, time_step_repeat_unit = None, time_step_reference = None, radius = None, radius_unit = None, area_units = "SquareKilometers" )`	DataFrame
Calculate Field	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 calculate_field( input_layer, field_name, data_type, expression, track_aware = None, track_fields = None, time_boundary_split = None, time_boundary_split_unit = None, time_boundary_reference = None )`	DataFrame
Calculate Motion Statistics	Use dark colors for code blocksCopy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 calculate_motion_statistics( input_layer, track_fields, track_history_window = 3, motion_statistics = ["All"], idle_distance_tolerance = None, idle_distance_tolerance_unit = None, idle_time_tolerance = None, idle_time_tolerance_unit = None, time_boundary_split = None, time_boundary_split_unit = None, time_boundary_reference = None, distance_method = "Geodesic", distance_unit = "Meters", duration_unit = "Seconds", speed_unit = "MetersPerSecond", acceleration_unit = "MetersPerSecondSquared", elevation_unit = "Meters" )	DataFrame
Clip Layer	Use dark colors for code blocksCopy `1 2 3 4 clip_layer( input_layer, clip_layer )`	DataFrame
Create Buffers	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 create_buffers( input_layer, distance = None, distance_unit = None, field = None, method = "Planar", dissolve_option = None, dissolve_fields = None, summary_fields = None, multipart = False )`	DataFrame
Create Space Time Cube	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 create_space_time_cube( point_layer, bin_size, bin_size_unit, time_step_interval, time_step_interval_unit, time_step_alignment = None, time_step_reference = None, summary_fields = None, output_name = None )`	String	Returns the local path to the resulting space-time cube on a ArcGIS GeoAnalytics Server machine. The cube is written to a temp directory and will be deleted if not copied to a different location.
Describe Dataset	Use dark colors for code blocksCopy `1 2 3 4 5 describe_dataset( input_layer, sample_size = None, extent_output = False )`	Dictionary	Example result: Use dark colors for code blocksCopy `1 2 3 4 5 6 { "output":DataFrame, "outputJSON":string, "extentLayer":DataFrame, "sampleLayer":DataFrame }`
Detect Incidents	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 detect_incidents( input_layer, track_fields, start_condition_expression, end_condition_expression = None, output_mode = "AllFeatures", time_boundary_split = None, time_boundary_split_unit = None, time_boundary_reference = None )`	DataFrame
Dissolve Boundaries	Use dark colors for code blocksCopy `1 2 3 4 5 6 dissolve_boundaries( input_layer, dissolve_fields = None, summary_fields = None, multipart = False )`	DataFrame
Enrich From Multi-Variable Grid	Use dark colors for code blocksCopy `1 2 3 4 5 enrich_from_multi_variable_grid( input_features, grid_layer, enrich_attributes = None )`	DataFrame
Find Dwell Locations	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 12 13 14 find_dwell_locations( input_layer, track_fields, distance_method = "Planar", distance_tolerance, distance_tolerance_unit, time_tolerance, time_tolerance_unit, summary_fields = None, output_type = "DwellMeanCenters", time_boundary_split = None, time_boundary_split_unit = None, time_boundary_reference = None )`	DataFrame
Find Hot Spots	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 find_hot_spots( point_layer, bin_size, bin_size_unit, neighborhood_distance, neighborhood_distance_unit, time_step_interval = None, time_step_interval_unit = None, time_step_alignment = None, time_step_reference = None )`	DataFrame
Find Point Clusters	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 find_point_clusters( input_layer, cluster_method = "DBSCAN", time_method = None, search_duration = None, search_duration_unit = None, min_features_cluster = None, search_distance = None, search_distance_unit = None )`	DataFrame
Find Similar Locations	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 find_similar_locations( input_layer, search_layer, analysis_fields, most_or_least_similar = "MostSimilar", match_method = "AttributeValues", number_of_results = 10, append_fields = None )`	Dictionary	Example result: Use dark colors for code blocksCopy `1 {"output":DataFrame, "processInfo":string}`
Forest-based Classification And Regression	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 forest_based_classification_and_regression( prediction_type = "Train", in_features = None, features_to_predict = None, variable_predict = None, explanatory_variables = None, number_of_trees = 100, minimum_leaf_size = None, maximum_tree_depth = None, sample_size = 100, random_variables = None, percentage_for_validation = 10, create_variable_importance_table = False, explanatory_variable_matching = None )`	Dictionary	Example result: Use dark colors for code blocksCopy `1 2 3 4 5 6 { "outputTrained":DataFrame, "variableOfImportance":DataFrame, "outputPredicted":DataFrame, "processInfo":string }`
Generalized Linear Regression	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 generalized_linear_regression( input_layer, features_to_predict = None, dependent_variable = None, explanatory_variables = None, regression_family = "Continuous", generate_coefficient_table = False, explanatory_variable_matching = None, dependent_mapping = None )`	Dictionary	Example result: Use dark colors for code blocksCopy `1 2 3 4 5 6 { "output":DataFrame, "coefficientTable":DataFrame, "outputPredicted":DataFrame, "processInfo":string }`
Geocode Locations	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 geocode_locations( input_layer, geocode_service_url, geocode_parameters, source_country = None, category = None, include_attributes = None, locator_parameters = None )`	DataFrame
Geographically Weighted Regression	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 12 geographically_weighted_regression( input_layer, explanatory_variables, dependent_variable, model_type = "Continuous", neighborhood_type = "NumberOfNeighbors", neighborhood_selection_method = "UserDefined", distance_band = None, distance_band_unit = None, number_of_neighbors = None, local_weighting_scheme = "Bisquare" )`	DataFrame
Group By Proximity	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 group_by_proximity( input_layer, spatial_relationship, spatial_near_distance = None, spatial_near_distance_unit = None, temporal_relationship = None, temporal_near_distance = None, temporal_near_distance_unit = None )`	DataFrame
Join Features	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 join_features( target_layer, join_layer, join_operation = "JoinOneToOne", keep_all_target_features = False, join_fields = None, summary_fields = None, spatial_relationship = None, spatial_near_distance = None, spatial_near_distance_unit = None, temporal_relationship = None, temporal_near_distance = None, temporal_near_distance_unit = None, attribute_relationship = None, join_condition = None )`	DataFrame
Merge Layers	Use dark colors for code blocksCopy `1 2 3 4 5 merge_layers( input_layer, merge_layer, merging_attributes = None )`	DataFrame
Overlay Layers	Use dark colors for code blocksCopy `1 2 3 4 5 6 overlay_layers( input_layer, overlay_layer, overlay_type = "Intersect", include_overlaps = True )`	DataFrame
Reconstruct Tracks	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 12 13 14 reconstruct_tracks( input_layer, track_fields, method = "Planar", buffer_field = None, summary_fields = None, time_split = None, time_split_unit = None, distance_split = None, distance_split_unit = None, time_boundary_split = None, time_boundary_split_unit = None, time_boundary_reference = None )`	DataFrame
Summarize Attributes	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 summarize_attributes( input_layer, fields, summary_fields = None, time_step_interval = None, time_step_interval_unit = None, time_step_repeat = None, time_step_repeat_unit = None, time_step_reference = None )`	DataFrame
Summarize Center And Dispersion	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 summarize_center_and_dispersion( input_layer, summary_type, ellipse_size = None, weight_field = None, group_fields = None )`	Dictionary	Example result: Use dark colors for code blocksCopy `1 2 3 4 5 6 { "centralFeatureLayer":DataFrame, "meanCenterLayer":DataFrame, "medianCenterLayer":DataFrame, "ellipseLayer":DataFrame }`
Summarize Within	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 12 13 14 summarize_within( summary_polygons = None, bin_type = None, bin_size = None, bin_size_unit = None, summarized_layer = None, standard_summary_fields = None, weighted_summary_fields = None, sum_shape = True, shape_units = None, group_by_field = None, minority_majority = False, percent_shape = False )`	Dictionary	Example result: Use dark colors for code blocksCopy `1 2 3 4 { "output":DataFrame, "groupBySummary":DataFrame }`
Trace Proximity Events	Use dark colors for code blocksCopy `1 2 3 4 5 6 7 8 9 10 11 12 13 14 trace_proximity_events( input_points, entity_id_field, entities_of_interest_ids, entities_of_interest_layer, distance_method, spatial_search_distance, spatial_search_distance_unit, temporal_search_distance, temporal_search_distance_unit, include_tracks_layer = false, max_trace_depth = 2147483647, attribute_match_criteria = None )`	Dictionary	Example result: Use dark colors for code blocksCopy `1 2 3 4 { "output":DataFrame, "tracksLayer":DataFrame }`

In addition to the tools listed above, a project tool is provided with the geoanalytics package that allows you to project the geometry of a DataFrame into the specified spatial reference.

Tool Syntax Returns Notes

Tool	Syntax	Returns	Notes
Project	Use dark colors for code blocksCopy `1 project(input_features, output_coord_system)`	DataFrame	`input_features` is the DataFrame to project and `output_coord_system` is the WKT or WKID of the spatial reference to use. Example: Use dark colors for code blocksCopy `1 geoanalytics.project(df, 2796)`

Project

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project(input_features, output_coord_system)

DataFrame

input_features is the DataFrame to project and output_coord_system is the WKT or WKID of the spatial reference to use.

Example:

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geoanalytics.project(df, 2796)

ArcGIS REST APIs

GeoAnalytics Tools in Run Python Script