Hexagonal binning helps aggregate large point datasets like oil wells into hexagon polygons to reduce overlap and improve map visualization at smaller scales. The document describes using hexagonal binning to summarize over 50,000 oil wells in the Gulf of Mexico across different scales, calculating statistics like mean depth and earliest drilling date for each hexagon. It provides tips for storing interim results, exporting joined tables, and using multiple hexagon sizes to balance data density and map performance for ArcGIS Online.

1. Hexagon binning to better view 50K
Gulf of Mexico Wells in ArcGIS Online
Andrew Zolnai
from Ken Field, Esri
1 Sep 2015
zolnai.ca
https://commons.wikimedia.org/wiki/File%3ADevelopment-driller-2.jpg
By U.S. Coast Guard photo by Petty Officer 3rd Class Barry Bena [Public domain], via Wikimedia Commons

2. Hexagon binning
Set up bins for initial scale

3. BOEM well spots & infrastructure crowding
arcg.is/1SLEqb6

4. Hexagonal binning helps up-scale 50K wells
• Ken Field on Esri blog:
Presenting point-based data on a web map is challenging because of
the problem of overlapping symbology, particularly as you zoom out of
the map to view data at smaller scales. So-called push-pin web maps
are very easy to make with ArcGIS Online but making the map make
visual sense at the smaller scales requires a little more work. In this
blog entry we illustrate how data binning can be used to aggregate
large point-based datasets into hexagonal polygons to overcome the
problem and improve the web map across all scales.

5. Adapted beehive model
Workflow tip: store interim steps in default DB, final results in repository

6. Federal protractions, 10km hexagons & wells
1:2,500,000

7. Identity overlay between hexagons & wells 1

8. Identity overlay between hexagons & wells 2
1:5,000,000

9. Summary statistics

10. Join Summary statistics to hexagons

11. Hexagon 10km by mean TVD
1:5,000,000
2,527 depth hexagons out of
9,970 total (all visible in frame)

12. Hexagon 10km by mean water depth
1:5,000,000
2,527 depth hexagons out of
9,970 total (all visible in frame)

13. Hexagon 10km by first spud date 1
• Summary Statistics 1st Spud date still date/time format
• Joined table may lose added and calculated fields, so:
• export it join as a table
• add a FIRST_SPUD_YEAR field as short integer
• and in Field Calculator do:
FIRST_SPUD_YEAR = DatePart ( “yyyy”, [FIRST_SPUD_DATE] )
Workflow tip: export to repository, not default DB as this is a final, not an interim step
Caution: depending on repository DB, date argument may change (MS SQL Server here)

14. Hexagon 10km by first spud date 2
Note: SPUD_DATE and _YEAR are aliases

15. Hexagon 10km by first spud date 3
1:5,000,000
2,527 depth hexagons out of
9,970 total (all visible in frame)

16. Multiple scales
Scale dependency for ArcGIS Online

17. Hexagon 5km by mean TVD
1:2,500,000
5,340 depth hexagons (4,775
visible in frame) out of 35,028 total
(12,850 visible in frame)

18. Hexagon 5km by mean water depth
1:2,500,000
5,340 depth hexagons (4,775
visible in frame) out of 35,028 total
(12,850 visible in frame)

19. Hexagon 5km by first spud date
1:2,500,000
5,340 date hexagons (4,775 visible
in frame) out of 35,028 total
(12,850 visible in frame)

20. Reduce data density & overlap to better post online
• Explore hive sizes as compared to data density & extent
• Adjusted scales reflect hexagon size & speed up zooming
• Suggest scripts be run using default names, then rename by scale
• renaming script output can mess up Model Builder in background
• Models can be corrupted, so if results are odd &/or tables misread
• either restart session
• or restore original model
• or convert to Python scripts