Aggregate Points

Using a layer of point features and either a layer of area features or a distance used to calculate bins, this tool determines which points fall within each area and calculates statistics for all the points within each area. You can optionally apply time slicing to this tool.

The following are example scenarios for using this tool:

• Given point locations of crime incidents, count the number of crimes per county or other administrative district.
• Find the highest and lowest monthly revenues for franchise locations using 100 km bins.

Build Multi-Variable Grid

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 following are example scenarios for using this tool:

• Given multiple layers of public transportation infrastructure, what part of the city is least accessible by public transportation?
• Given layers of waterways, such as lakes and rivers, what is the name of the water body closest to each location in the United States?
• Given a layer of household income, where in the United States is the variation of income in the surrounding 50 miles the greatest?

Describe Dataset

This tool outputs feature sample and extent layers, calculates summary statistics, and outlines input layer properties.

The following are example scenarios for using this tool:

• Given a dataset of 2 billion features, create a sample layer of 1000 features to efficiently visualize and inspect features in a map. Inspect summary statistics for the full dataset by viewing the output summary statistics table.
• Given a big data file share dataset composed of 40 individual CSV files, output an extent layer to represent the spatial dispersion of input features without drawing them all on a map. View the output JSON overview to see the spatial reference, geometry type, and record count.

Join Features

Use a layer of point, line, or area features or a table and another layer of point, line, or area features or a table to join feature that exhibit a specified relationship. Spatial, temporal, and attribute relationships can be used to join features, and optionally calculate summary statistics.

The following are example scenarios for using this tool:

• Given point locations of crime incidents with a time, join the crime data to itself, specifying a spatial relationship of crimes within one square kilometer that occurred within one hour of each other, to determine if there is a sequence of crimes that are close to each other in space and time.
• Given a table of ZIP Codes with demographic information and area features representing residential building, join the demographic information to the residences so that each residence now has the information.

Reconstruct Tracks

Using either a layer of point features or polygon features that are time-enabled, this tool determines which input features belong in a track, ordering the inputs sequentially in time. It then calculates statistics about all the input features within each track.

The following is an example scenario for using this tool:

• Given point locations and time of hurricane measurements, calculate the mean wind speed and maximum wind pressure of the hurricane.

Summarize Attributes

Using either feature or tabular data, this tool summarizes statistics for fields.

The following are example scenarios for using this tool:

• Given locations of grocery stores with a field called COMPANY_NAME, summarize the stores by the company name to determine statistics for each company.
• Given a table of grocery stores with fields called COMPANY_NAME and COUNTY, summarize the stores by the company name and county to determine statistics for each company in each county.

Summarize Center and Dispersion

This tool finds central features and directional distributions.

The following are examples:

• Where is the geographic center of a point dataset?
• Which feature is the most accessible from all other features?
• How dispersed, compact, or integrated are the features?
• Are there directional trends?

Summarize Within

This tool finds areas (and portions of areas) that overlap between two layers and calculates statistics for the overlap.

The following are example scenarios for using this tool:

• Given a layer of watershed areas and a layer of land-use areas by land-use type, calculate total acreage of land-use type for each watershed.
• Given a layer of parcels in a county and a layer of city boundaries, summarize the average value of vacant parcels within each city.

Detect Incidents

This tool 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.

The following are example scenarios for using this tool:

• You're given a layer of GPS measurements of hurricanes every 10 minutes. Each GPS measurement records the hurricane's name, location, time of recording, and wind speed. Using these fields, create an incident where any measurement with a wind speed greater than 208 km/h is an incident titled Catastrophic
• Given a layer of sensor measurements, create an incident whenever values exceed the mean of the three previous values.

Find Dwell Locations

Using a layer of point features that are time-enabled, this tool identifies locations where track features are staying within a certain distance over a period of time.

The following are example scenarios for using this tool:

• Examine animal movement patterns to identify potential protected areas based on locations the animals are staying in.
• Improve traffic control in areas where vehicles spend time idling.

Find Similar Locations

Based on criteria you specify, the Find Similar Locations tool measures the similarity of locations in your candidate search layer to one or more reference locations.

The following are example scenarios for using this tool:

• Find the 10 most similar stores by examining the number of employees and the annual sales.
• Find the 100 most similar cities by examining the relationship between population, annual growth, and tax revenue.

Geocode Locations from Table

This tool converts addresses into coordinates. Use this tool on big data file share tables.

The following are example scenarios for using this tool:

• Geocode multiple CSVs representing addresses of crime location to explore hotspots of crime.
• Geocode a text file representing delivery locations for an online retailer to determine where marketing efforts have been most effective.

Calculate Motion Statistics

This tool calculates groups of motion statistics and measurements for time-enabled features of time type instant. Statistics and measurements are based on tracks that are determined by a unique identifier. Calculation groups include distance, speed, acceleration, duration, elevation, slope, idle, and bearing based on the provided time values and geometry of point-based events.

The following is an example scenario for using this tool;

• A city is monitoring snowplow operations and wants to better understand vehicle movement. You can use this tool to determine idle locations and time spent idling, average and maximum speeds over time, total distance covered, and other statistics.

Enrich From Multi-Variable Grid

This tool joins attributes from a multivariable grid to a point layer, allowing you to quickly add a large and diverse collection of information to point data for use in further spatial analysis.

The following is an example scenario for using this tool:

• Given a layer containing millions of power outage incidents, enrich the incident features with information about typical usage, environmental risks, and infrastructure conditions to study the relationship between these factors and power outage frequency.

Calculate Density

This tool 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 representing the density.

The following are example scenarios for using this tool:

• Calculate densities of hospitals within a county. The result layer will show areas with high and low accessibility to hospitals, and this information can be used to decide where new hospitals should be built.
• Identify areas that are at high risk of forest fires based on historical locations of forest fires.
• Locate communities that are far from major highways in order to plan where new roads should be constructed.

Find Hot Spots

This tool determines if there is any statistically significant clustering in the spatial pattern of your data.

The following are example questions this tool can help you answer:

• Are your points (crime incidents, trees, traffic accidents) clustered? How can you be sure?
• Have you discovered a statistically significant hot spot (for spending, infant mortality, consistently high test scores). or would your map tell a different story if you changed the way it was symbolized?

Find Point Clusters

This tool finds clusters of point features within surrounding noise based on their spatial distribution.

The following are example scenarios for using this tool:

• Find clusters of pest infested households to help target eradication efforts.
• Inform and act on rescue and evacuation needs based on the size and location of the clusters using geo-located tweets following natural hazards or terror attacks .

Forest-based Classification and Regression

This tool models and generates predictions using an adaptation of Leo Breiman's random forest algorithm, which is a supervised machine learning method

The following are example scenarios for using this tool:

• Given data on the occurrence of seagrass, as well as a number of environmental explanatory variables that have been enriched using a multivariable grid to calculate distances to factories upstream and in major ports, use this tool to predict future seagrass occurrence based on projections for those environmental explanatory variables.
• Crop yield data has been collected from hundreds of farms across the country along with other attributes at each of those farms (number of employees, acreage, and so on). Using this data, provide a set of features representing farms that don't have crop yield (but do have all 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.

Generalized Linear Regression

This tool generates predictions or models a dependent variable in terms of its relationship to a set of explanatory variables. This tool can be used to fit continuous (OLS), binary (logistic), and count (Poisson) models.

The following are example questions this tool can help you answer:

• 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?

Geographically Weighted Regression

This tool applies a local form of linear regression that is used to model spatially varying relationships.

The following are example questions this tool can help you answer:

• Is the relationship between educational attainment and income consistent across the study area?
• What are the key variables that explain high forest fire frequency?
• Where are the school districts where children are achieving high test scores? What characteristics seem to be associated? Where is each characteristic most important?

Create Space Time Cube

This tool summarizes a set of time-enabled points into a netCDF structure by aggregating them into space time bins.

The following are example scenarios for using this tool:

• Aggregate all crimes in a city into 1-km bins by month.
• Aggregate all 911 calls that occurred in a county over the last 50 years into 100-km bins with annual temporal bins.

Create Buffers

A buffer is an area that covers a given distance from a point, line, or polygon feature.

The following are example scenarios for using this tool:

• Using linear river features, buffer each river by 50 times the width of the river to determine a proposed riparian boundary.
• Given areas representing countries, buffer each country by 200 nautical miles to determine the maritime boundary.

Group by Proximity

This tool groups features that are within spatial or spatiotemporal proximity to each other.

The following are example scenarios for using this tool:

• Determine which roads are connected based on spatial proximity.
• Uncover groups of crime occurrences that happened within a close proximity in space and time.
• Identify polygons that overlap to improve accuracy of boundaries.

Snap Tracks

This tool matches time-enabled points to line features.

The following are example scenarios for using this tool:

• Match delivery service GPS tracks to roads to more accurately analyze delivery routes.
• Match snowplow movement data to road lines to identify gaps in service.

Trace Proximity Events

This tool analyzes time-enabled point features representing moving entities. The task follows entities of interest in space (location) and time to see which other entities the entities of interest have interacted with. The trace continues from entity to entity to a configurable maximum degrees of separation from the original entity of interest.

The following are examples:

• Suppose an organization monitors company-issued devices carried by workers. The company is interested in determining which employees were near an individual known to have COVID-19. Using the point layer representing device locations and time, they can identify devices that have been within 6 meters and 5 minutes of the contagious person and other possibly contagious employees.
• An NGO is monitoring salmon populations using GPS and is interested in tracking the spread of salmon lice between escaped farmed salmon and wild populations. Some GPS-tagged farmed salmon were tracked to see if they come in close proximity with tagged wild populations, and how those wild populations may further spread the disease.

Append Data

This tool appends point, line, area, or tabular datasets to an existing hosted feature layer of the same geometry type.

The following are example scenarios for using this tool:

• Given multiple datasets that have been generated monthly, append the datasets to a hosted feature layer to combine the data into an annual report.
• Given 10 datasets containing climate measurements from various sources, append the datasets to create a single layer of climate measurements. Correct for the schema differences for each source using field mapping.

Calculate Field

This tool calculates values for a new or existing field and creates a layer in your contents on ArcGIS Enterprise.

The following are example scenarios for using this tool:

• Modify an existing field named total to be the sum of revenue from the total_2016, total_2017, and total_2018 fields.
• Create a field to categorize hazard levels based on field values such as windspeed and pollutant.

Clip Layer

This tool extracts a subset of input layer features from a specified area to create a layer containing the subset.

The following are example scenarios for using this tool:

• Given a feature layer containing national earthquake occurrence data, use a California state boundary layer to extract only the earthquakes that overlay California.
• Given a buffer layer that extends 50 feet from the highway, clip the forest features that would be affected by highway expansion.

Copy to Data Store

This tool copies an input feature layer or table to an ArcGIS Data Store and creates a layer in your Web GIS.

The following are example scenarios for using this tool:

• Copy a collection of CSV files in a big data file share to the spatiotemporal data store for visualization.
• Copy the features in the current map extent that are stored in the spatiotemporal data store to the relational data store.

Dissolve Boundaries

This tool finds and merges area features that intersect spatially or share the same field value.

The following are example scenarios for using this tool:

• Given a feature layer of study areas, combine all features that have the same soil type value to create a layer representing areas by soil type.
• Given restricted areas and buffer zones, dissolve all features together to summarize which locations are closed to development.

Merge Layers

This tool combines two datasets to create a single output layer. Use merge attributes to determine the resulting schema.

The following are example scenarios for using this tool:

• Given feature layers for England, Wales, and Scotland, merge the layers to create a single feature layer of Great Britain.
• Two feature layers represent contiguous townships, each with different field names. Combine the layers using attribute rules to match fields, and output a single layer with the desired schema.

Overlay Layers

This tool combines two or more layers into one single layer.

The following are example questions this tool can help you answer:

• What parcels are within the 100-year floodplain? (Within is another way of saying on top of.)
• What land use is on top of what soil type?
• What wells are within abandoned military bases?