Map Viewer in ArcGIS Online Gets New Spatial Analysis Tools

Spatial analysis employs location as a connective thread to help people understand where things are happening, how they are related, and where patterns exist. Putting various data types into geographic context helps people make decisions for today and predict the impacts of those decisions on tomorrow. For users of ArcGIS Online, it is now possible to leverage new feature and raster analysis tools in Map Viewer.

A dark gray map of Washington, DC, that shows the metro lines in various colors plus light purple and dark purple polygons surrounding them that reveal areas within a half-mile and mile walk, respectively, of metro stops
A walkshed analysis of Washington, DC, shows communities that are located within a half-mile (light purple) and mile (dark purple) walk of metro stops.

Map Viewer allows ArcGIS Online users to create, visualize, and analyze data. It is designed to be approachable and accessible, making it useful for a wide range of users—from novices to experts, and from those who work independently to people who are part of a large organization. With the right tools and techniques, GIS practitioners of any level and in any field can perform an array of spatial analysis operations, including taking simple measurements, completing complex geoprocessing tasks, and uncovering patterns and relationships in data.

The new spatial analysis tools in Map Viewer can benefit myriad ArcGIS Online users in their daily work. Read on to find out how.

The Differences Between Feature and Raster Analyses

The spatial analysis tools in Map Viewer can be split into two main categories: feature analysis tools and raster analysis tools. The latter is available to users who have the ArcGIS Image for ArcGIS Online user type extension.

Feature analysis focuses on vector data, which represents geographic features expressed as points, lines, and polygons. Doing feature analysis involves examining the spatial relationships among individual features, such as their location, size, shape, and attributes.

Raster analysis, on the other hand, centers on raster data, which represents geographic features as a grid of cells, and each cell has its own value. Performing raster analysis entails examining the spatial relationships between these cells, such as differences in their values and other patterns.

Choosing which kind of analysis to perform depends on the problem that needs to be solved and the type of data that’s available. Whether employing feature or raster data, Map Viewer enables users to analyze the data and visualize the results. Additionally, while each form of analysis is powerful on its own, integrating multiple tools in a single workflow in Map Viewer offers the greatest impact.

A Roundup of Key New Analysis Tools in Map Viewer

A broad array of spatial analysis tools is available in Map Viewer now, with more to come. Check out what a few of them do and get an idea of how to use them.

Generate Travel Areas

The Generate Travel Areas tool calculates the area that can be reached within a specified time or distance along a street network based on travel mode. Multiple travel modes are available, including walking, driving, and trucking. This tool employs street network data—such as one-way streets, speed limits, and other rules—to calculate the service, or reachable, area. Depending on the use case, the service area polygon can also include reachable streets, meaning the streets that can be traveled within the time or distance specified.

A map of Appalachia with certain areas colored in dark blue, light blue, red, and orange, plus dots of the same color scattered throughout the map and two inset maps showing zoomed-in details of two areas in Appalachia that largely lack sufficient internet access
Areas where households lack internet access are shown in red, while areas where households have internet access are shown in blue.

Enrich Layer

The Enrich Layer tool makes thousands of data attributes for more than 170 countries available in Map Viewer. In addition to common census data, this tool has lifestyle, consumer habits, and spending data. The data can be added to any point, line, or polygon geometry to supplement analysis and visualization. Through the process of apportionment, the data is aggregated at the requested area, allowing for usage outside traditional geographies such as states, counties, and census tracts.

Find Hot Spots

Find Hot Spots is a powerful tool that identifies statistically significant clustering in the spatial pattern of data. Every dataset can exhibit some degree of potentially random spatial clustering. Performing a hot spot analysis identifies areas on a map where high or low values are located next to similarly high or low values. Analyzing the data in context with neighboring features helps determine where statistically significant clustering is occurring. This kind of analysis is important to do on datasets that might show clusters due to high population, such as crime or internet access datasets. Doing a hot spot analysis would help determine if crimes in an area are occurring at a higher-than-average rate or if fewer than the average number of households has access to the internet. This kind of analysis can also be performed to find higher- or lower-than-average costs of medical care, numbers of traffic accidents, and more.

Summarize Within

The Summarize Within tool calculates statistics where an input layer overlaps a boundary layer. Not only does the tool tally the number of times the layers overlap, but it also can calculate the statistics for any numerical attributes of the points, lines, or areas within the input layer. This tool is useful in cases where data needs to be analyzed inside a specific geography, such as a county or state, or binned into squares or hexagons.

An aerial map of a residential neighborhood with pools encircled in red
Users with the ArcGIS Image for ArcGIS Online extension can use the Detect Object with Deep Learning tool, which can detect objects such as swimming pools in imagery.

Detect Object with Deep Learning

With ever more imagery available from a variety of sources, it is becoming increasingly important to automate and scale the creation of foundational GIS content, such as building footprints. For users who have the ArcGIS Image Online extension in Map Viewer, they have access to several raster analysis tools built around deep learning. One such tool is Detect Object with Deep Learning, which employs a deep learning model to locate and identify desired objects within an image.

Get Started with Spatial Analysis in Map Viewer

In addition to being able to leverage the new feature and raster analysis tools in Map Viewer, users can always take advantage of the ready-to-use layers and authoritative content available in ArcGIS Living Atlas of the World. Together, these and other features make ArcGIS Online and Map Viewer indispensable to anyone’s GIS arsenal.

To learn more about how to leverage Map Viewer in ArcGIS Online, explore in-depth use cases and tutorials. And find out more about the recent spatial analysis additions to Map Viewer.