This presentation focuses on the multiple methods you can use to effectively analyze your temporal data using ArcGIS and how to communicate the temporal nature of your data through maps designed to be shared in print, on-screen, and online. A variety of examples are used to demonstrate how ArcGIS can help you to provide greater understanding of your data through appropriate statistical analyses and share that understanding with aesthetically pleasing and effectively communicative visualizations.

1. Methods for analyzing and mapping
temporal data with ArcGIS
Aileen Buckley
Esri - Redlands

2. What is temporal data and why is it important?
Change /
growth
Change or growth
over an area
• Demographics
• Fire perimeter
Moving
features
Feature that move
over space
• Planes
• Vehicles
• Animals
• Satellites
• Storms
Discrete
events
Events that
happens at various locations
• Crimes
• Lightning
• Accidents
Stationary
recorders
Features stay in one place
and record changes
• Weather stations
• Traffic sensors

3. The nature of temporal data
• Conceptualizations of time vary
- Linear (unique, directional time periods)
- Cyclic (repeating after a specific range in time)
- Others
• Time is relative to something
- Clock-driven time – synchronized to a specific clock
- Event-driven time – synchronized to an event (e.g., BC, AD)
- State-driven time – synchronized to a change in state
• Time data can be:
- Point data – specific to point in time
- Range data – accumulated over an interval of time
http://www.businessinsider.com/how-different-cultures-understand-time-2014-5
T1 Sx, T2 Sx, T3 Sx, …
T1 – T0, T2 – T0, T3 – T0, …
S1, S2, S3, …
where T = Time and S = State

4. GIS integrates temporal data
• Regular or “irregular” time
T1
Analysis, simulation,
& modeling
Fixed time
temporal data
Temporal maps
Stationary
Mobile
Real-time
sensor network
x
y
T
Multidimensional
(x,y,z,t)
Time stamps /
time extents
Data Sources

5. Time is built into ArcGIS
• Unified experience for time
- Part of Desktop, Pro, Runtime, and Portal products
• Geoprocessing (GP) tools
- Managing time aware data
- Analyzing through time, or space-and-time
• Ability to share temporal data/maps
- Web layers and image services
- Videos, images, map series, packages

6. This presentation…
• Data types
• Managing
• Analyzing
• Mapping

7. Demo
Mosaic dataset data

8. Data Types
for temporal data

9. Supported data types
• Feature layers
• Mosaic datasets
• NetCDF layers
• Tables
• Raster catalogs
• Tracking layers / Streaming layers
• Network dataset layers with traffic data
• Plus service layers with historical content and updating data feeds

10. Feature layers – Separate features
• Enable time on the Time tab of the feature layer’s Layer Properties dialog box
• With feature layers, features can be visualized over time in two ways:
1. The shape and location of each feature changes over time
- Store separate features

11. Feature layers – Features joined to a table
• Enable time on the Time tab of the feature layer's Layer Properties dialog box
• With feature layers, features can be visualized over time in two ways:
1. The shape and location of each feature changes over time
- Store separate features
2. The shape and location of each feature is constant but attribute values change over time
- You can represent the changing attributes in a separate (one-to-many) joined table

12. Mosaic datasets
• Enable time on the Time tab of the mosaic
dataset’s Layer Properties dialog box
• Mosaic datasets store rasters that represent
change over time
• The time field is in the Footprint attribute
table of the mosaic dataset

13. NetCDF layers
• NetCDF is a file format for storing
spatiotemporal data
- Multiple dimensions (x, y, z, t)
- Multiple variables (temperature, pressure,
salinity, wind speed)
• Time values are stored as one dimension of the netCDF layer
• Enable time on the Time tab of the Layer Properties dialog box
• For netCDF feature layers, specify the layer time using a time dimension or the
attribute fields
• For netCDF raster layers, specify layer time using the time dimension
X
Y

14. Demo
Mosaic dataset data

15. Managing
temporal data

16. Temporal data stored in multiple columns
• Store temporal data in a row format
- Each feature in a row
• Transpose Fields GP tool
- Shifts data entered in columns into rows

 Best practices for managing temporal data

17. Number and Text field types
• Only “sortable” formats are supported
- YYYYMMDD 20160701 > 20150701 = TRUE
- MMDDYYYY 07012016 > 08012015 = FALSE
• Named month is not supported
- AUG-01-2016 would come before JUL-01-2016
• Index the field for faster display and query performance

18. Date field type
• Store time values in a date field
- A field type that stores dates, times, or dates and times
- Most efficient format for query and display performance
- Supports more sophisticated database queries
- Easiest to configure on the layer

A wide range of
standard formats

19. Converting to a Date field type
• Use Data Management GP tools to convert to a date field type
• Convert Time Field GP tool
- Converts custom Text/Number formats into a new Date field
- “July 09, 2016”  07/09/2016  MM/DD/YYYY
Date Date_Converted

The Date field can also
have a custom format
MM dd, yyyy HH:mm:ss

20. Setting duration
• Calculate End Time GP tool
- Populates an end time field with the next record’s start time
- The last record will not have a duration – the end time is calculated to be the same as the
start time of the feature

21. Analyzing
temporal data

22. Analyzing temporal data
• Geoprocessing tools to manage temporal data – we have already seen these
• All GP tools honor time
• GP tools that specifically analyze space and time
• ArcPy site-package

23. All GP tools honor time
• GP tools honor the temporal settings for time-enabled layers
• Tool process only those features within the time extent set in the Time Slider
• Similar to a selection or definition query

24. GP tools for space-time data: ArcGIS for Desktop and Pro
• ArcToolbox > Spatial Statistics > Mapping Clusters
• These use a spatial weights matrix that has a temporal constraint
- Hot Spot Analysis GP tool
- Creates a map of statistically significant hot and cold spots
- Cluster and Outlier Analysis GP tool
- Identifies statistically significant hot spots, cold spots, and spatial outliers
- Grouping Analysis GP tool
- Groups features based on feature attributes and
optional spatial/temporal constraints

25. GP tools for space-time data: Pro
• Space Time Pattern Mining toolbox
- Create Space Time Cube GP tool
- Summarizes a set of points into a netCDF data structure by aggregating them into space-time bins
- Emerging Hot Spot Analysis GP tool
- Identifies trends in the clustering of point counts or attributes in a netCDF space-time cube
- Local Outlier Analysis GP tool
- Identifies statistically significant clusters of high or
low values as well as outliers

26. Demo
Analyzing Temporal Data

27. Mapping
temporal data

28. A variety of ways to share temporal visualizations
• As a time-enabled web map
- TIP: Publish time-aware web maps from Pro (instead of per-layer in 10.x)
- Open Pro, import an .mxd, and publish the web map directly to AGOL
- Known issues with current AGOL means two edits to an imported .mxd before publishing:
1. Replace the basemap (to avoid group layers)
2. Do not use a definition query against a time field
• As time-enabled image services (Portal only)
• As an animation / video
• As a series of exported images
• As a temporal map book
• As “small multiples” on a single layout
• As map or layer packages

29. Create web map services
• Map services preserve the time information from time-enabled layers
- Used to query and display content (with the time slider)
• Animated GIFs can be used for icons (e.g., in the Living Atlas)
- LINK - ArcGIS Online web map search for 'time'
• Example temporal web maps:
- Atlantic Storms (1993-95)
- Imported an MXD, updated the basemap, publish
- One year of ice pack imagery (North Pole)
- Time-aware aerial imagery

30. The purpose of mapping temporal data
• To allow for estimation of the degree of change or spatial pattern of
cross-correlation between time periods
• The effectiveness of any particular mapping method is related to:
- Clear and accurate representation of the data and
- Comprehension by the reader
• Mapping temporal data usually results in increased complexity of the display
• This complexity can lead to:
- Misunderstanding or misinterpretation, or even…
- …missing the change altogether (change blindness)
• Maps of temporal data benefit from clear, explanatory indicators of the current time
step within the full range
- Time slider control
- Timeline, time text, etc…

31. Methods to map temporal data
• Static displays
- Superimposed features
- Isochron maps
- Small multiples
- Complementary graphics
- Change maps
- Change analysis maps
- Space-time cubes
• Dynamic displays
- Use movement or variation to show or draw
attention to change

32. Superimposed features
• Superimpose features using distinguishable graphic marks or symbols
• Example
- Zebra Mussel Sightings

33. Zebra Mussel Sightings, 1986-2011

34. Zebra Mussel Sightings, 1986-2011
Directional Trend and Mean Center of Distribution

35. Zebra Mussel Sightings, 1986-2011
Directional Trend and Mean Center of Distribution

36. Icochron maps
• Map the change over time as isochrons (lines connecting points relating to
the same time or equal time differences)
• Areas between isochrones can be colored
• Color selection should reflect the nature of the data (usually sequential)
• Example
- Age of the Ocean Floor
- Station Fire, 2009

42. https://www.greatamericaneclipse.com/maps-and-posters/oregon-state-map

43. Small multiples
• A series of displays with the same graphic design structure depicting changes from
multiple to multiple (i.e., map to map)
• Should be comparable, multivariate, “shrunken, high-density graphics” that are
based on a large data matrix and used to show shifts in relationships among
variables
• The consistency of design assures that attention is directed toward changes in the
data
• Example
- Dam Construction in the US
- Plate Tectonics

44. 2000
1910 1920 1930
1940 1950 1960
1970 1980

48. Complementary graphics
• Combining a map of a single time period with other graphics to display the temporal
data
• Graphics used to display the temporal data include:
- Timelines
- Time series graphs
- Spiral displays
- Circular graphs
• Examples
- Birds vs Aircraft by Month
- Water Balance App

49. http://www.esri.com/products/maps-we-love/birds-vs-aircraft
Birds vs Aircraft by Month

50. https://vannizhang.github.io/water-balance-app/

52. Change maps
• Depict the change over time at point locations, within homogeneous areas,
or along linear features
• Calculate the change and map it accordingly
• Often used to show change between only two time periods as the rate of
change
• Examples
- Percent Change in Total Population

55. Population Change, 1900 - 2010

56. Change analysis maps
• The change in a variable between time periods or over a range of time periods is
analyzed statistically and summarized in a single numerical index displayed on a
single map
- Hot spot analysis
- Cluster and outlier analysis
• The amount of change is sometimes simplified to a binary index (e.g.,
increase/decrease)
• Example
- Percent Change in Poverty Rates of School-Aged Children

60. Space-time cubes
• Superimpose features using distinguishable graphic marks or symbols
• Examples
- Space-Time Cube Explorer
- Napolean’s March on Moscow

61. www.esriurl.comSpaceTimeCubeExplorer

63. This presentation…
• Data types
• Managing
• Analyzing
• Mapping

64. abuckley@esri.com
@cartatesri
@mappingcenter
Questions???