# arcgis.geoanalytics.analyze_patterns module¶

These tools 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.

## calculate_density¶

analyze_patterns.calculate_density(fields: str = None, weight: str = 'Uniform', bin_type: str = 'Square', bin_size: float = None, bin_size_unit: str = None, time_step_interval: int = None, time_step_interval_unit: str = None, time_step_repeat_interval: int = None, time_step_repeat_interval_unit: str = None, time_step_reference: datetime.datetime = None, radius: float = None, radius_unit: str = None, area_units: str = 'SquareKilometers', output_name: str = None, gis=None)

Parameters:

input_layer: Input Points (Feature layer). Required parameter.

fields: Population Field (str). Optional parameter.

weight: Weight (str). Required parameter.

Choice list:[‘Uniform’, ‘Kernel’]

bin_type: Output Bin Type (str). Required parameter.

Choice list:[‘Square’, ‘Hexagon’]

bin_size: Output Bin Size (float). Required parameter.

bin_size_unit: Output Bin Size Unit (str). Required parameter.

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

time_step_interval: Time Step Interval (int). Optional parameter.

time_step_interval_unit: Time Step Interval Unit (str). Optional parameter.

Choice list:[‘Years’, ‘Months’, ‘Weeks’, ‘Days’, ‘Hours’, ‘Minutes’, ‘Seconds’, ‘Milliseconds’]

time_step_repeat_interval: Time Step Repeat Interval (int). Optional parameter.

time_step_repeat_interval_unit: Time Step Repeat Interval Unit (str). Optional parameter.

Choice list:[‘Years’, ‘Months’, ‘Weeks’, ‘Days’, ‘Hours’, ‘Minutes’, ‘Seconds’, ‘Milliseconds’]

time_step_reference: Time Step Reference (_datetime). Optional parameter.

radius: Radius (float). Required parameter.

radius_unit: Radius Unit (str). Required parameter.

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

area_units: Area Unit Scale Factor (str). Optional parameter.

Choice list:[‘SquareMeters’, ‘SquareKilometers’, ‘Hectares’, ‘SquareFeet’, ‘SquareYards’, ‘SquareMiles’, ‘Acres’]

output_name: Output Features Name (str). Required parameter.

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

Returns:

output - Output Features as a feature layer collection item

## create_space_time_cube¶

analyze_patterns.create_space_time_cube(bin_size: float, bin_size_unit: str, time_step_interval: int, time_step_interval_unit: str, time_step_alignment: str = None, time_step_reference: datetime.datetime = None, summary_fields: str = None, output_name: str = None, context: str = None, gis=None) → arcgis.geoprocessing._types.DataFile

Summarizes a set of points into a netCDF data structure by aggregating them into space-time bins. Within each bin, the points are counted and specified attributes are aggregated. For all bin locations, the trend for counts and summary field values are evaluated.

Parameters:

point_layer: Input Features (FeatureSet). Required parameter.

bin_size: Distance Interval (float). Required parameter.

bin_size_unit: Distance Interval Unit (str). Required parameter.

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

time_step_interval: Time Step Interval (int). Required parameter.

time_step_interval_unit: Time Step Interval Unit (str). Required parameter.

Choice list:[‘Years’, ‘Months’, ‘Weeks’, ‘Days’, ‘Hours’, ‘Minutes’, ‘Seconds’, ‘Milliseconds’]

time_step_alignment: Time Step Alignment (str). Optional parameter.

Choice list:[‘EndTime’, ‘StartTime’, ‘ReferenceTime’]

time_step_reference: Time Step Reference (datetime). Optional parameter.

summary_fields: Summary Fields (str). Optional parameter.

output_name: Output Name (str). Required parameter.

context: Context (str). Optional parameter.

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

Returns:

output_cube - Output Space Time Cube as a DataFile

## find_hot_spots¶

analyze_patterns.find_hot_spots(bin_size: float = 5, bin_size_unit: str = 'Miles', neighborhood_distance: float = 5, neighborhood_distance_unit: str = 'Miles', time_step_interval: int = None, time_step_interval_unit: str = None, time_step_alignment: str = None, time_step_reference: datetime.datetime = None, output_name: str = None, gis=None)

Parameters:

point_layer: Input Points (FeatureSet). Required parameter.

bin_size: Bin Size (float). Optional parameter.

bin_size_unit: Bin Size Unit (str). Optional parameter.

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

neighborhood_distance: Neighborhood Distance (float). Optional parameter.

neighborhood_distance_unit: Neighborhood Distance Unit (str). Optional parameter.

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

time_step_interval: Time Step Interval (int). Optional parameter.

time_step_interval_unit: Time Step Interval Unit (str). Optional parameter.

Choice list:[‘Years’, ‘Months’, ‘Weeks’, ‘Days’, ‘Hours’, ‘Minutes’, ‘Seconds’, ‘Milliseconds’]

time_step_alignment: Time Step Alignment (str). Optional parameter.

Choice list:[‘EndTime’, ‘StartTime’, ‘ReferenceTime’]

time_step_reference: Time Step Reference (_datetime). Optional parameter.

output_name: Output Features Name (str). Optional parameter.

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

Returns:

output - Output Features as a feature layer collection item

## forest¶

analyze_patterns.forest(var_prediction, var_explanatory, trees, max_tree_depth=None, random_vars=None, sample_size=100, min_leaf_size=None, prediction_type='train', features_to_predict=None, validation=10, importance_tbl=False, exp_var_matching=None, output_name=None, gis=None)

The ‘forest’ method is a forest-based classification and regression task that creates models and generates predictions using an adaptation of Leo Breiman’s random forest algorithm, which is a supervised machine learning method. Predictions can be performed for both categorical variables (classification) and continuous variables (regression). Explanatory variables can take the form of fields in the attribute table of the training features. In addition to validation of model performance based on the training data, predictions can be made to another feature dataset.

The following are examples:

• Given data on occurrence of seagrass, as well as a number of environmental explanatory variables represented as both attributes which has been enriched using a multi-variable grid to calculate distances to factories upstream and major ports, future seagrass occurrence can be predicted based on future projections for those same environmental explanatory variables.

• Suppose you have crop yield data at hundreds of farms across the country along with other attributes at each of those farms (number of employees, acreage, and so on). Using these pieces of data, you can provide a set of features representing farms where you don’t have crop yield (but you do have all of the other variables), and make a prediction about crop yield.

• Housing values can be predicted based on the prices of houses that have been sold in the current year. The sale price of homes sold along with information about the number of bedrooms, distance to schools, proximity to major highways, average income, and crime counts can be used to predict sale prices of similar homes.

Forest Based Classification and Regression is available at ArcGIS Enterprise 10.7.

 Argument Description input_layer required FeatureSet, The table, point, line or polygon features containing potential incidents. var_prediction Required dict. The variable from the input_layer parameter containing the values to be used to train the model, and a boolean denoting if it’s categorical. This field contains known (training) values of the variable that will be used to predict at unknown locations. var_explanatory Required List. A list of fields representing the explanatory variables and a Boolean value denoting whether the fields are categorical. The explanatory variables help predict the value or category of the var_prediction parameter. Use the categorical parameter for any variables that represent classes or categories (such as land cover or presence or absence). Specify the variable as true for any that represent classes or categories such as land cover or presence or absence and false if the variable is continuous. trees Required int. The number of trees to create in the forest model. More trees will generally result in more accurate model prediction, but the model will take longer to calculate. max_tree_depth Optional int. The maximum number of splits that will be made down a tree. Using a large maximum depth, more splits will be created, which may increase the chances of overfitting the model. The default is data driven and depends on the number of trees created and the number of variables included. random_vars Optional Int. Specifies the number of explanatory variables used to create each decision tree.Each of the decision trees in the forest is created using a random subset of the explanatory variables specified. Increasing the number of variables used in each decision tree will increase the chances of overfitting your model particularly if there is one or a couple dominant variables. A common practice is to use the square root of the total number of explanatory variables (fields, distances, and rasters combined) if your variablePredict is numeric or divide the total number of explanatory variables (fields, distances, and rasters combined) by 3 if var_prediction is categorical. sample_size Optional int. Specifies the percentage of the input_layer used for each decision tree. The default is 100 percent of the data. Samples for each tree are taken randomly from two-thirds of the data specified. min_leaf_size Optional int. The minimum number of observations required to keep a leaf (that is the terminal node on a tree without further splits). The default minimum for regression is 5 and the default for classification is 1. For very large data, increasing these numbers will decrease the run time of the tool. prediction_type Specifies the operation mode of the tool. The tool can be run to train a model to only assess performance, or train a model and predict features. Prediction types are as follows: Train - This is the default. A model will be trained, but no predictions will be generated. Use this option to assess the accuracy of your model before generating predictions. This option will output model diagnostics in the messages window and a chart of variable importance. TrainAndPredict - Predictions or classifications will be generated for features. Explanatory variables must be provided for both the training features and the features to be predicted. The output of this option will be a feature service, model diagnostics, and an optional table of variable importance. features_to_predict Optional dict. The variable from the input_layer parameter containing the values to be used to train the model, and a boolean denoting if it’s categorical. This field contains known (training) values of the variable that will be used to predict at unknown locations. validation Optional Int. Specifies the percentage (between 10 percent and 50 percent) of inFeatures to reserve as the test dataset for validation. The model will be trained without this random subset of data, and the observed values for those features will be compared to the predicted value. The default is 10 percent. importance_tbl Optional Boolean. Specifies whether an output table will be generated that contains information describing the importance of each explanatory variable used in the model created. exp_var_matching A list of fields representing the explanatory variables and a boolean values denoting if the fields are categorical. The explanatory variables help predict the value or category of the variable_predict. Use the categorical parameter for any variables that represent classes or categories (such as landcover or presence or absence). Specify the variable as true for any that represent classes or categories such as landcover or presence or absence and false if the variable is continuous. Syntax: [{“fieldName”:””, “categorical”:true}, fieldname is the name of the field in the inFeatures used to predict the variable_predict. categorical is one of: true or false. A string field should always be true, and a continue value should always be set as false. output_name optional String, The task will create a feature service of the results. You define the name of the service. gis optional GIS, the GIS on which this tool runs. If not specified, the active GIS is used.
Returns

Output feature layer item

## glr¶

analyze_patterns.glr(var_dependent, var_explanatory, regression_family='Continuous', features_to_predict=None, gen_coeff_table=False, exp_var_matching=None, dep_mapping=None, output_name=None, gis=None)

This tool performs Generalized Linear Regression (glr) to generate predictions or to model a dependent variable’s relationship to a set of explanatory variables. This tool can be used to fit continuous (Gaussian/OLS), binary (logistic), and count (Poisson) models.

The following are examples of the tool’s utility:

• What demographic characteristics contribute to high rates of public transportation usage?

• Is there a positive relationship between vandalism and burglary?

• Which variables effectively predict 911 call volume? Given future projections, what is the expected demand for emergency response resources?

• What variables affect low birth rates?

 Argument Description input_layer Required FeatureSet. The layer containing the dependent and independent variables. var_dependent Required String. The numeric field containing the observed values you want to model. var_explanatory Required String. One or more fields representing independent explanatory variables in your regression model. regression_family Required String. This field specifies the type of data you are modeling. regression_family is one of the following: Continuous - The dependent_variable is continuous. Themodel used is Gaussian, and the tool performs ordinary least squares regression. Binary - The dependent_variable represents presence orabsence. Values must be 0 (absence) or 1 (presence) values, or mapped to 0 and 1 values using the parameter. Count - The dependent_variable is discrete and representsevents, such as crime counts, disease incidents, or traffic accidents. The model used is Poisson regression. features_to_predict Required FeatureSet. A layer containing features representing locations where estimates should be computed. Each feature in this dataset should contain values for all the explanatory variables specified. The dependent variable for these features will be estimated using the model calibrated for the input layer data. Syntax: As described in Feature input, this parameter can be one of the following: A URL to a feature service layer with an optional filter to select specific features A URL to a big data catalog service layer with an optional filter to select specific features A feature collection gen_coeff_table Optional Boolean. Determines if a table with coefficient values will be returned. By default, the coefficient table is not returned. exp_var_matching Optional List. A list of the explanatoryVariables specified from the input_layer and their corresponding fields from the features_to_predict. By default, if an var_explanatoryiables is not mapped, it will match to a field with the same name in the features_to_predict. This parameter is only used if there is a features_to_predict input. You do not need to use it if the names and types of the fields match between your two input datasets. Syntax: [{“predictionLayerField”:””, “trainingLayerField”: “”},…] predictionLayerField is the name of a field specified in the var_explanatoryiables parameter. trainingLayerField is the field that will match to the field in the var_explanatoryiables parameter. REST scripting example: dep_mapping Optional List. A list representing the values used to map to 0 (absence) and 1 (presence) for binary regression. Syntax: [{“value0”:””},{“value1”:””}] value0 is the string that will be used to represent 0 (absence values). value1 is the string that will be used to represent 1 (presence values). output_name Optional String. The task will create a feature service of the results. You define the name of the service. gis Optional GIS, the GIS on which this tool runs. If not specified, the active GIS is used.
Returns

Output feature layer item