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Sep 02, 2026

Visualizing time in Map Viewer

By Warren Davison

Time adds a new dimension to maps, helping you reveal patterns, communicate trends, and tell clearer stories about how your data changes over time.

Even if your data isn’t configured as time-enabled, recent Map Viewer enhancements that add support for time series and time-based layer visibility make it easier than ever to create these visualizations.

This post demonstrates how to create time-based visualizations in Map Viewer and highlights several practical use cases. Each example below includes a link to the web map where you can explore how the content was configured.

Mapping migratory species

Migratory species move with the ebb and flow of the seasons. Driven by temperature and instincts, these creatures make annual journeys between their seasonal ranges. Migratory birds are especially emblematic of this behavior, often traveling vast distances across countries and continents.

Mapping the movements of these species can help conservationists identify the habitats, migration corridors, and stopover areas they depend on throughout the year. These insights support more targeted conservation actions, from protecting key habitat and feeding areas to prioritizing restoration work and coordinating management across the countries and regions that migratory species move through.

In this example, GPS tracking data from two tundra swan studies was aggregated into global hex bins and mapped by the number of observed points in each bin.

Animated map of North America displayed over NASA Blue Marble: Next Generation imagery showing seasonal land cover and snow extent. Semi-transparent blue hexagons represent aggregated Tundra Swan GPS observations, with larger and brighter hexagons indicating higher observation counts. The greatest concentration of activity is visible along the Pacific coast of California, with smaller clusters extending along the Pacific Northwest, Alaska's Aleutian Islands, and portions of the eastern United States. Extensive snow and ice cover is visible across Canada and Alaska.
Tundra swan migration patterns visualized as weekly GPS observations aggregated into 100 km hexagons and animated through time alongside seasonally changing NASA Blue Marble imagery.

Visualizing GPS activity

To prepare the data for visualization, the GPS observations were summarized by the week of the year when they were recorded. These points were then grouped by this week attribute and spatially aggregated to the global hex bin where they occurred.

The resulting data structure contains a row for each hex bin and stores the counts of GPS point activity as a value across a series of columns for each week, creating a simplified measure of seasonal presence for birds within the studies: higher values indicate areas where there was more GPS activity observed in each week, while empty or low-value bins show places with little or no recorded activity.

Screenshot of an ArcGIS attribute table showing the column-based temporal structure used to animate Tundra Swan migration patterns. Each record represents a single 100 km global hexagon, identified by a global_id field. Weekly observation counts are stored in separate fields named week_00, week_01, week_02, week_03, week_04, and subsequent weeks of the year. Values within these fields represent the number of GPS tracking observations recorded within each hexagon during the corresponding week.
The column-based data structure stores time as attributes rather than individual records, enabling Map Viewer's time series capability to animate weekly changes in Tundra Swan activity by switching between weekly columns over time.

A time series in Map Viewer is built for this kind of column-based time structure: it reads change over time from multiple fields on the same feature, where each field represents the same attribute captured at regular intervals, such as hourly, weekly, or monthly observations. Meanwhile, the feature geometry remains constant while the values change, making it useful for sensor readings, monitoring stations, reporting areas, or other datasets where one feature stores a sequence of time-based values.

By keeping the geometry in a single row and storing repeated observations across columns, this approach can improve performance and reduce the need to duplicate the same feature geometry for each time step.

Configuring time series

To create this time series, open the layer’s Properties pane in Map Viewer and scroll down to the Time section. Click Time series with field values and Add a time series.

From there, name your time series, add the fields containing your values, and configure the time period and intervals.

 

Screenshot of the Configure time series with field values dialog in ArcGIS Map Viewer. The configuration is being applied to a dataset named "GPS Activity." Controls at the top allow the user to automatically assign dates to fields using a start date, time interval, and time unit. In this example, fields are configured as weekly observations beginning on January 1, 2004, with a one-week interval between values. A preview table lists fields named week_00, week_01, week_02, week_03, and additional weekly fields, each paired with an assigned date. This interface demonstrates how Map Viewer's time series capability maps attribute fields containing sequential observations to dates, enabling column-based temporal data to be animated through time.
Map Viewer's time series with field values maps weekly attribute fields to dates, allowing column-based observations to be animated over time.

Once complete, you can apply this time series to the map’s time slider.

Seasonal imagery

The movement of migratory species is driven by the seasons, so it seems only natural to support this map with NASA’s Blue Marble: Next Generation imagery. Built from 500-meter-resolution imagery from the MODIS satellite, these monthly images reveal seasonal changes in the land surface and provide a perfect backdrop for the tundra swans, which breed in the Arctic and overwinter in North America, Europe, and Asia.

The choreography of these imagery layers uses time-based layer visibility in Map Viewer. By configuring this layer visibility, each layer will automatically be toggled visible or invisible based on the position of the time slider.

Configure time-based layer visibility

To configure this setting, open the Properties pane for any layer in Map Viewer and scroll down to the Time section.  Toggle on time-based layer visibility and set a start and end date for when you would like your layer to be visible on the map.

Screenshot of the Properties pane for a layer in ArcGIS Map Viewer showing the Time-based layer visibility settings. The layer, named "Blue Marble - January," includes controls for transparency, visible scale range, and temporal visibility. The Time-based layer visibility option is enabled, revealing start and end date settings that determine when the layer appears in relation to the map's time slider. In this example, the layer is configured to be visible from January 1, 2004 through January 31, 2004. These settings allow layers to automatically appear and disappear as the time slider advances.
Time-based layer visibility allows layers to appear only during specified periods on the time slider. In this example, the 'January Blue Marble' imagery layer is configured to display only during January, enabling seasonal imagery and other temporal content to be synchronized with map animations.

Together these layers tell a richer story of tundra swan migration. The hex bin layer shows where swans were observed throughout the year, revealing broad patterns of movement, concentration, and seasonal presence. The monthly imagery adds environmental context, helping viewers connect those movements to the changing landscapes the birds move through.

As a time-based visualization, these changes are easier to see and understand, but migration is only one example. The same approach can also help communicate other dynamic phenomena.

Wildfires and air quality

Canada’s 2026 wildfire season has been fueled by a combination of lightning strikes, human activity, and unusually hot and dry conditions across several provinces. The smoke from these fires has spread across Canada and the U.S., triggering health advisories.

Using the archival data behind the OpenAQ Recent Conditions in Air Quality and National Weather Service Smoke Forecast in the ArcGIS Living Atlas of the World, it’s possible to create a time-based visualization to replay a specific time span and analyze the effects that wildfires and smoke had on air quality.

 

Animated map of Canada and the United States showing wildfire smoke concentrations and air quality conditions during the week of July 13, 2026. Semi-transparent yellow to orange areas represent atmospheric black carbon concentrations from NASA's GEOS-5 FP BCSMASS dataset, serving as a proxy for wildfire smoke. Colored point symbols represent OpenAQ air quality monitoring stations reporting PM2.5 concentrations, with larger and darker symbols indicating poorer air quality. Over the course of the animation, smoke plumes move eastward from active wildfire regions in central Canada toward the Great Lakes and northeastern North America. As the smoke spreads, monitoring stations within affected areas show increasing PM2.5 levels, illustrating the relationship between transported wildfire smoke and degraded air quality.
Wildfire smoke concentrations from NASA's GEOS-5 FP BCSMASS dataset are animated alongside three-hour PM2.5 averages from OpenAQ monitoring stations, revealing how smoke from the July 2026 Canadian wildfires corresponded with declining air quality across Canada and the United States.

In this example, each air quality sensor was represented by a single row in the data, with recurring measurements stored as multiple time attributes across columns for the temporal window. This column-based structure was then configured as a time series in Map Viewer, allowing the time slider to animate changing sensor readings at each station.

The underlying smoke polygons were already time-enabled based on a timestamp encoded in the data.

When played back, the time configuration aligns the layers together and the effect of the wildfire smoke on air quality becomes abundantly clear.

Hurricanes and stream gauges

Like wildfires, time-based visualizations can also be used to create novel maps of weather phenomena, such as hurricanes, by showing how heavy precipitation moves through a region and how stream gauges respond as rainfall turns into rising water levels and potential flooding.

Again, retrieving archival data from Live Stream Gauges in the ArcGIS Living Atlas of the World and the Historical Hurricane Tracks this example reconstructs the effects of hurricane Helene in 2024 on the Southeastern United States.

Animated map of the southeastern United States showing the progression of Hurricane Helene and changing stream gauge measurements during the storm. A hurricane track extends northward from the Gulf of Mexico while stream gauge locations are displayed as blue circles across the region. As the animation advances, circles grow and shrink to represent changing stream conditions at individual monitoring stations. The largest responses occur across Florida, Georgia, the Carolinas, Tennessee, and surrounding states as Hurricane Helene moves inland.
Stream gauge observations animated alongside Hurricane Helene's track reveal how waterways across the Southeastern United States responded as the storm moved inland.

For this example, the stream gauges were configured as a time series with their hourly readings stored as a series of columns in the data and the tracks were segmented with a combination filters and feature-specific effects in order to highlight the most recent section of the storm track.

The resulting time-based visualization reveals the rising stream levels brought about by the increased precipitation of the storm as it progresses inland.

Tides

One last example also focuses on changing water levels, but this time along the coastline. Tides create a predictable rise and fall in coastal water, yet the timing and height of those changes vary by time and place. By animating water level station readings over time, a map can show how water levels progress along a shoreline or into a bay, making it easier to compare local patterns, identify periods of high and low water, and understand the rhythm of coastal change.

This example uses the Water Level Prediction Stations in the ArcGIS Living Atlas of the World and pulls related water level measurements from NOAA’s Center for Operational Oceanographic Products and Services (CO-OPS) to reconstruct water levels in and around the San Francisco Bay.

Animated map of the San Francisco Bay region showing predicted tidal water levels at NOAA CO-OPS monitoring stations. Water level stations are represented by blue symbols positioned throughout the bay, from the Pacific coast and Golden Gate through San Francisco Bay and into the Sacramento-San Joaquin Delta. As the animation progresses through a 24-hour period, station symbols change in size and shade to represent rising and falling tidal heights. Larger, darker blue symbols indicate higher water levels, while smaller, lighter symbols indicate lower water levels. The pattern of changing symbols reveals how tidal fluctuations move from the Pacific Ocean through the Golden Gate and propagate across the bay and inland waterways.
Tidal water level predictions from NOAA CO-OPS monitoring stations animated through a 24-hour period. Symbol size and color represent changing water levels, revealing how tides propagate from the Pacific Ocean through the Golden Gate and across San Francisco Bay.

The water level stations were again configured as a time series with hourly readings stored in a series of columns. Over the course of the visualization, the station locations ‘pulse’ with high and low water levels as the tides flow in and out of the bay.

Visualizing time in maps

Time series and time-based layer visibility in Map Viewer give you flexible ways to build maps that do more than show where something is—they show how it changes over time.
After being configured in Map Viewer, these maps are ready to tell a larger story. Their time settings persist across the ArcGIS platform, allowing authors to maintain the same dynamic narrative in an Instant App for an interactive experience or integrated into ArcGIS StoryMaps with additional maps and explanatory text to enhance audience exploration of patterns.

Four screenshots arranged in a 2×2 grid showing time-enabled web maps running in ArcGIS Instant Apps. The upper-left map visualizes Tundra Swan migration using animated hexagons over NASA Blue Marble Next Generation imagery. The upper-right map shows wildfire smoke concentrations and air quality monitoring stations across North America. The lower-left map displays NOAA water level prediction stations throughout San Francisco Bay with a time slider for animating tidal changes. The lower-right map combines Hurricane Helene's storm track with animated stream gauge observations across the southeastern United States.
Four ArcGIS Instant Apps showcasing time-enabled visualizations created with Map Viewer's time series capability and time-based layer visibility.

These examples serve as a starting point. The same methods could also uncover other seasonal or event-related patterns, such as:

  • Wildlife telemetry paired with temperature, precipitation, or other environmental variables, where animal movement data can be animated alongside changing habitat conditions to show how species respond to vegetation, weather, and seasonal climate patterns.
  • Demographics, such as population growth, age distribution, household change, or migration patterns, where the same census geographies can be animated across multiple years to reveal how communities evolve over time.
  • Political voting patterns, where repeated election results, turnout, vote share, or margins of victory can be stored across consistent boundaries to show how electoral geography changes from one election to the next.

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Warren Davison

I specialize in creating engaging maps and graphics that tell immersive, place-based stories. Based in Kitchener, Canada, I work with Esri’s ArcGIS Storytelling team, where I enjoy sharing the creative process behind each project. When I'm not exploring vectors and pixels, you’ll likely find me cycling around town with my kids along for the ride.

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