Summarize Data

The Summarize Data toolset contains tools that calculate total counts, lengths, areas, and basic descriptive statistics of features and their attributes within areas or near other features.

• Aggregate Points calculates statistics about points that fall within specified areas or bins.
• Build Multi-Variable Grid generates a grid of square or hexagonal bins and calculates values for each bin based on the proximity of each input layer.
• Describe Dataset calculates a summary of your input, and can optionally generate a sample of your input layer.
• Join Features calculates statistics about features that share a spatial, temporal, or attribute relationship with other features.
• Reconstruct Tracks calculates statistics about points or polygons that belong to the same track and reconstructs inputs into tracks.
• Summarize Attributes calculates statistics about feature or tabular data that shares attributes.
• Summarize Within calculates statistics for area features and attributes that overlap each other.

Aggregate Points

Using a layer of point features and either a layer of area features or bins defined by a specified distance, this tool determines which points fall within each area or bin and calculates statistics about all the points within each area or bin. You can optionally apply time slicing with this tool.

The following are examples:

• 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-kilometer 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 examples:

• 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 U.S.?
• Given a layer of household income, where in the U.S. is the variation of income in the surrounding 50 miles the greatest?

Describe Dataset

This tool summarizes input features with statistics, sample layers, and visualization. You can choose to output a sample layer or extent layer.

The following are examples:

• Reference the calculated summary statistics to understand what values your dataset contains.
• Instead of drawing the entire dataset, create a subset layer to visualize features and their attributes when added to a map.
• Save time and resources and run analysis on a sample layer before running analysis on the entire dataset.

Join Features

Using either feature layers or tabular data, you can join features and records based on specific relationships between the input layers or tables. Joins will be determined by spatial, temporal, and attribute relationships, and summary statistics can be optionally calculated.

The following are examples:

• Given point locations of crime incidents within a time, join the crime data to itself specifying a spatial relationship of crimes within 1 kilometer of each other and that occurred within 1 hour of each other to determine if there is a sequence of crimes close to each other in space and time.
• Given a table of ZIP Codes with demographic information and area features representing residential buildings, join the demographic information to the residences so each residence now has the information.

Reconstruct Tracks

Using a time-enabled layer of point or polygon features that represent an instant in time, this tool determines which input features belong in a track and will order the inputs sequentially in time. Statistics are optionally calculated for the input features within each track.

The following are examples:

• Given point locations and time of hurricane measurements, calculate the mean wind speed and max wind pressure of the hurricane.
• Use GPS measurements of ships to reconstruct ship paths as lines.

Summarize Attributes

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

The following are examples:

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

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 examples:

• Detect hurricane measurements that are Category 4 hurricanes based on a wind speed attribute.
• Using GPS measurements of ship movement with a field representing the ship speed, find measurements that are greater than 1.5 times the mean of the previous five measurements.

Find Dwell Locations

This tool works with track datasets; it is composed of a time-enabled layer of points to find where objects dwell within a specified distance and duration.

The following are examples:

• Improve conservation efforts by analyzing animal movement patterns. Use this tool to uncover popular animal stay points as potential protected areas to preserve biodiversity.
• Using GPS locations for vehicles, find and address where slowdowns occur to improve traffic safety.

Analyze Patterns

These tools 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.
• Find Point Clusters finds clusters of point features in surrounding noise based on their spatial distribution.
• Forest-based Classification and Regression creates models and generates predictions using an adaptation of Leo Breiman's random forest algorithm, a supervised machine learning method.
• Generalized Linear Regression generates predictions or models a dependent variable in terms of its relationship to a set of explanatory variables.
• Geographically Weighted Regression creates models using a local form of linear regression that is used to model spatially varying relationships.

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 examples:

• 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 determine 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 to plan where new roads should be constructed.

Find Hot Spots

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

• Are your points (crime incidents, trees, traffic accidents) really clustered? How can you be sure?
• Have you truly 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?

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 examples:

• Given data on occurrence of seagrass, as well as a number of environmental explanatory variables represented as both attributes that have been enriched using a multivariable 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.
• 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.

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 examples:

• What demographic characteristics contribute to high rates of public transportation usage?
• Is there a positive relationship between vandalism and burglary?
• What 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 performs Geographically Weighted Regression (GWR), which is a local form of linear regression used to model spatially varying relationships.

The following are examples:

• 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 districts in which children are achieving high test scores? What characteristics seem to be associated? Where is each characteristic most important?

Trace Proximity Events

This tool 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 value of 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, the company 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.

Manage Data

These tools are used for the day-to-day management of geographic and tabular data.

• Append Data appends features to an existing hosted feature layer.
• Calculate Field creates a new field or modifies an existing field and creates a new layer in your contents.
• Clip Layer copies features that intersect a defined area of interest.
• Copy to Data Store copies data to your ArcGIS Data Store and creates a layer in your contents or copies data to a big data file share.
• Dissolve Boundaries merges areas that share a common boundary or attribute value.
• Merge Layers copies all features from two layers into a single result layer.
• Overlay Layers combines two or more layers into a single layer.

Merge Layers

This tool combines two feature layers to create a single result layer. All fields from the merge layer will be included by default, or you can specify custom merge rules to define the resulting schema.

The following are examples:

• Merge three layers containing demographic information from England, Wales, and Scotland to make a single layer of Great Britain.
• Combine two layers containing parcel information for contiguous townships, keeping only the fields that have the same name and type in the two layers.

Calculate Motion Statistics

This tool calculates groups of motion statistics and measurements for time-enabled points that represent tracks.

The following are examples:

• A city is monitoring snowplow operations and wants to better understand vehicle movement. The city wants to enrich its dataset with slope and speed statistics to determine where it can improve snowplow coverage and maintain safety for higher-grade roads.
• A shipping company wants to understand where it is losing the most time while delivering, so it enriches its dataset with idle and duration statistics.

Enrich From Multi-Variable Grid

This tool efficiently 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:

• 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.