- Operations:Aggregate Points, Append Data, Build Multi-Variable Grid, Calculate Density, Calculate Field, Calculate Motion Statistics, Clip Layer, Copy to Data Store, Create Buffers, Create Space Time Cube, Describe Dataset, Detect Incidents, Dissolve Boundaries, Enrich from Multi-Variable Grid, Find Dwell Locations, Find Hot Spots, Find Point Clusters, Find Similar Locations, Forest-based Classification and Regression, Generalized Linear Regression, Geocode Locations, Geographically Weighted Regression, Join Features, Merge Layers, Overlay Layers, Reconstruct Tracks, Run Python Script, Summarize Attributes, Summarize Center and Dispersion, Summarize Within, Trace Proximity Events
The GeoAnalytics Tools service contains a number of tasks that you can access and use in your apps. GeoAnalytics Tools are available in your ArcGIS Enterprise portal's Map Viewer, ArcGIS Pro, the ArcGIS REST API, and the ArcGIS API for Python. The categories are logical groupings and do not affect how you access or use the tasks in any way.
Tasks that summarize data
The tasks that summarize data are described in the following table:
This tool works with a layer of point features and a layer of areas features. Input area features can be from a polygon layer or they can be square or hexagonal bins calculated when the tool is run. The tool first determines which points fall within each specified area. After determining this point-in-area spatial relationship, statistics about all points in the area are calculated and assigned to the area. The most basic statistic is the count of the number of points within the area, but you can get other statistics as well.
This tool generates a grid of square or hexagonal bins and calculates variables for each bin based on the proximity of one or more input layers.
The Describe Dataset task provides an overview of your big data. By default, the tool outputs a table layer containing calculated field statistics and a JSON string outlining geometry and time settings for the input layer.
The Join Features task works with two layers. Join Features joins attributes from one feature to another based on spatial, temporal, and attribute relationships or some combination of the three. The tool determines all input features that meet the specified join conditions and joins the second input layer to the first. You can optionally join all features to the matching features or summarize the matching features.
This tool works with a time-enabled layer of either point or area features that represent an instant in time. It first determines which features belong to a track using an identifier. Using the time at each location, the tracks are ordered sequentially and transformed into a line or area representing the path of movement over time. Optionally, the input can be buffered by a field, which will create an area at each location. These buffered points, or input areas, are then joined sequentially to create a track as an area where the width is representative of the attribute of interest. Resulting tracks have a start and end time, which represent temporally the first and last feature in a given track. When the tracks are created, statistics about the input features are calculated and assigned to the output track. The most basic statistic is the count of points within the area, but other statistics can be calculated as well.
Summarize Attributes takes an input layer and summarizes and calculates statistics on like values. The most basic statistic is the count of the number of features with a specified value, but you can get other statistics as well.
The SummarizeCenterAndDispersion task finds central features and directional distributions. It can be used to answer questions such as the following:
The Summarize Within task finds features (and portions of features) that are within the boundaries of areas in the first input layer. The following are examples:
Tasks that find locations
The Detect Incidents task works with a time-enabled layer of points, lines, areas, or tables that represents an instant in time. Using sequentially ordered features, called tracks, this tool determines which features are incidents of interest. Incidents are determined by conditions that you specify. First, the tool determines which features belong to a track using one or more fields. Using the time at each feature, the tracks are ordered sequentially and the incident condition is applied. Features that meet the starting incident condition are marked as an incident. You can optionally apply an ending incident condition; when the end condition is true, the feature is no longer an incident. The results will be returned with the original features with new columns representing the incident name and indicate which feature meets the incident condition. You can return all original features, only the features that are incidents, or all of the features within tracks where at least one incident occurred.
The Geocode Locations task geocodes a table from a big data file share. The task uses a geocode utility service configured with your portal. If you do not have a geocode utility service configured, talk to your administrator. Learn more about configuring a locator service.
The Find Dwell Locations task works with time-enabled points of type instant to find where points dwell within a specific distance and duration.
The Find Similar Locations task measures the similarity of candidate locations to one or more reference locations.
Tasks that analyze patterns
The Calculate Density task creates a density map from point features by spreading known quantities of some phenomenon (represented as attributes of the points) across the map. The result is a layer of areas classified from least dense to most dense.
Create Space Time Cube works with a layer of point features that are time enabled. It aggregates the data into a three-dimensional cube of space-time bins. When determining the point in a space-time bin relationship, statistics about all points in the space-time bins are calculated and assigned to the bins. The most basic statistic is the number of points within the bins, but you can calculate other statistics as well.
Create Space Time Cube is not available in the ArcGIS Enterprise portal's Map Viewer. Create Space Time Cube is available through ArcGIS Pro and the ArcGIS Server REST API.
The Find Hot Spots task analyzes point data (such as crime incidents, traffic accidents, trees, and so on) or field values associated with points. It finds statistically significant spatial clusters of high incidents (hot spots) and low incidents (cold spots). Hot spots are locations with lots of points and cold spots are locations with very few points.
The Find Point Clusters tool finds clusters of point features within surrounding noise based on their spatial or spatiotemporal distribution.
The Forest-based Classification and Regression task 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.
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 and OLS), binary (logistic), and count (Poisson) models.
This tool performs GeographicallyWeightedRegression (GWR), which is a local form of linear regression used to model spatially varying relationships.
Tasks that use proximity
The tasks that use proximity are described in the following table:
Buffers are typically used to create areas that can be further analyzed using other tools. For example, if the question is what buildings are within 1 mile of the school, the answer can be found by creating a 1-mile buffer around the school and overlaying the buffer with the layer containing building footprints. The end result is a layer of those buildings within 1 mile of the school.
The Trace Proximity Events task analyzes time-enabled point features representing moving entities. The task will follow entities of interest in space (location) and time to see which other entities the entities of interest have interacted with. The trace will continue from entity to entity to a configurable maximum degrees of separation from the original entity of interest.
Tasks that enrich data
The Calculate Motion Statistics task calculates motion statistics and descriptors for time-enabled points that represent one or more moving entities. Points are grouped together into tracks representing each entity using a unique identifier. Motion statistics are calculated at each point using one or more points in the track history. Calculations include summaries of distance traveled, duration, elevation, speed, acceleration, bearing, and idle status. The result is a new point layer enriched with the requested statistics.
The EnrichFromMultiVariableGrid task joins attributes from a multivariable grid to a point layer. The multivariable grid must be created using the BuildMultiVariableGrid task. Metadata from the multivariable grid is used to efficiently enrich the input point features, making it faster than the Join Features task. Attributes in the multivariable grid are joined to the input point features when the features intersect the grid.
Tasks that manage data
This tool appends data to an existing hosted feature layer. Append Data modifies the original input layer and does not generate a new output layer. You can match fields based on the field name and field type, or you can apply more advanced matching methods.
The Calculate Field task works with a layer to create and populate a new field or edit and existing field. The output is a new feature service that is the same as the input features, with the newly calculated values.
The Clip Layer task extracts input point, line, or polygon features that overlay the clip areas. The output is a subset of your input data based on the areas of interest.
The Copy To Data Store task takes an input layer and copies it to a data store. Data is copied to ArcGIS Data Store, configured as either a relational or spatiotemporal big data store.
The Dissolve Boundaries task finds polygons that intersect or have the same field values and merges them to form a single polygon.
The Merge Layers task combines two feature layers to create a single output layer.
Overlay Layers combines two or more layers into one layer. You can think of overlay as peering through a stack of maps and creating a single map containing all the information found in the stack. Overlay is more than a merging of line work; all the attributes of the features in the overlay are included in the final product. Overlay is used to answer one of the most basic questions of geography: What is on top of what?