Imagine having more spatial data than you know what to do with, and yet it holds the key to improving your customer’s experience and your company’s bottom line. That’s the situation that Uber found themselves in, so they created a Hexagonal Hierarchical Spatial Indexing library (called H3) that indexes the world in 16 different levels of resolution as the foundation for solving their challenge. Fortunately for all of us, Uber open sourced H3 and the upcoming FME 2021.0 release will provide no-code, format agnostic access to its functionality. Join us to learn theory behind H3 directly from Unfolded’s Isaac Brodsky, one of the creators of H3, and see firsthand how you might apply the upcoming FME H3 transformer to solve a variety of real-world, big-data spatial problems.

1. Hexagonify The World!
The Theory and Applications of Uber H3

2. Meet the Presenters
Dale Lutz Jovita Chan Debbi Leung
Isaac Brodsky Dmitri Bagh

3. Years of solving data challenges
25+
10,000+
Organizations trusting us worldwide
Partners supporting our network
150+
128
Countries with FME customers
Safe Software
Company Profile
www.safe.com

4. Our Mission
To help you maximize the value of data.

5. Hexagonify the World: Intro to H3
Isaac Brodsky
Tech Lead, H3
Co-founder, Unfolded

6. ● The H3 grid system
● What has H3 done so far?
● Where is H3 going next?
Agenda

17. Where is H3 from?

21. lat, lng, val = tiff_to_xyz(img)
id = geo_to_h3(lat, lng, 10)

24. What’s next for H3?

25. ● Unlikely to see any changes in the index structure
● Improvements on our roadmap for the C library
API: standardized error handling, naming, etc -
mostly low level changes
● More support for indexing vertexes and cell edges
- useful for some algorithms and optimization
approaches
● More upcoming H3 talks and conferences
H3 as a library is very stable

26. ● Data analysis and optimization
○ Marketplaces, logistics, POI, traffic
○ Environment, agriculture
● Engineering
○ Spatial indexing (most uses aren’t publicly visible)
○ Augmented reality games, gamification
● Data interchange
○ Placekey (https://www.placekey.io/)
○ Data privacy
Some promising use cases

27. ●
●

30. Halftime Break:
Q&A
Questions? Please type
them in the Q&A panel.

31. What is FME?

32. Connect Your Data Sources Transform Your Data Automate Your Workflows

33. FME®
Integration Platform
Connect. Transform. Automate.
FME Desktop FME Server FME Cloud
Build & Run Data Workflows Automate Data Workflows
(on-premises)
Automate Data Workflows
(hosted FME Server)
Mobile Apps
Access the Power of FME
Wherever You Are
Get a free trial of FME Desktop and FME Server at safe.com

34. Unrivaled Data Support

35. FME & H3

36. FME & H3 Integration: Goals
● Provide simple no-code way to apply
H3 power
● Provide tools for turning spatial
problems into relational problems
● Optimize for high data volumes
● Provide easy way to spatially shard
data
● Make H3 easily accessible for both:
○ Visualization outputs
○ Downstream analysis

37. Introducing...

41. Flash Demo:
City Population Heat Map!

42. A very simple heat map with just a single transformer
City Population Heat Map

43. Demo: Piracy Hotspots
(Inspired by Fast Point-in-Polygon Analysis with GeoPandas and
Uber’s H3 Spatial Index

44. Source Data: Geodatabase of Incidents
Points of
incidences of
hostility or
aggression
Data from NGA’s
Maritime Safety
Information

45. Geodatabase to Google KML
Bonus: check out this article on how to create a legend as an overlay.
Main transformers needed

46. Output Visual Preview

47. Output KML Extrusions
https://earth.google.com/web/

49. Apply H3 to Mobile Asset Tracking
Scenario
Discard data based on location in relation
to a geofence.
Input
Kafka messages with location data.
Output
Live map of data within geofence
boundary.

50. Workspace

51. FME Server Streams

52. Live Map of Mobile Assets

53. Demo: Snowy Owl Migration

54. Special thanks to Dr. Karen Wiebe, University of Saskatchewan
Apply H3 to Snowy Owl
Migration Patterns
Step 1:
Affix a GPS transmitter to
the Snowy Owl.

55. Apply H3 to Snowy Owl Migration Patterns
Step 2: Collect the data

56. Apply H3 to Snowy Owl Migration Patterns
Step 3: Use FME to index and overlay with satellite imagery, and
produce a video...

57. Step 4: Sit back and enjoy the video
Apply H3 to Snowy Owl Migration Patterns

59. Wrap Up
(Before Overtime...)

60. Resources
H3
● Unfolded.ai
● H3 Library (Github) (Intro)
● PostgreSQL bindings for H3
● Podcast: The Mapscaping Podcast:
H3 Geospatial Indexing System
● Video: Geospatial Analysis with H3
FME
● FME Free Trial
● Transformer:
H3HexagonalIndexer
● Blog: 5 Data Integration Trends
to Watch For in 2021
● FME Community

61. Pre-Overtime
Q&A
Questions? Please type
them in the Q&A panel.

62. Thank you!
Special thanks to Isaac Brodsky for joining us today.

63. Overtime: Bonus Demos

64. Advanced City Population Distribution

65. Tree Health Analysis

66. Tree Health Analysis Results
Tree distribution Tree health Dead and poor health
tree distribution
Percentage of dead
and poor health trees

67. 3D Globes

68. http://fme.ly/hexart
HexArt

69. Turbo Layers
An old technique for speeding the display of background vectors

70. Overtime: H3 Surprises

71. How Many Vertices Does A Hexagon Have?
● Naive answer: 6
● H3 answer: 5, 6, 7, 8, or 10!
○ H3 Indexes have “geoboundaries”
that are not necessarily hexagons
○ Non-hexagons are very rare:
■ Intentional pentagons
■ Additional distortion vertices
● See this stack exchange post and the
H3 Index Inspector for details

72. I Might Not be Contained in My Hexagon
● Cartesian containment is not the same
as spherical containment.
● H3 is a spherical framework
● See Nick Rabinowitz’s interactive post
for details
○ Point =
○ Red = Cartesian
○ Blue = Spherical

73. The Parent of My Parent Might Not be My
Grandparent
● Child hexagons are not strictly inside their
parent hexagons
● A point near the edge of a child hexagon
could be outside the parent hexagon
● Matters when comparing a point index to a
compacted fill set of indexes
○ Don’t recompute point indexes at
various levels
○ Use H3’s Parent call instead to get a
parent hexagon id for a query
● (Thanks Isaac for this explanation)

74. Compacted Filling Appears To Leave Slivers
● Artifact of child hexagons not strictly
inside their parent hexagons
● Point containment will still work if you
use same level for index as fill
○ Then use parent index calls to get
parents to compare

75. Filling Leaves Gaps Around The Edges
● Buffering can help
● H3 not a replacement for
PointOnAreaOverlayer or SpatialFilter
if exact accuracy needed

76. Final
Q&A
Questions? Please type
them in the Q&A panel.

77. Thank you!
(again)