This document discusses using Oracle ML, analytics, and APEX to find optimal locations for electric vehicle charging stations. It begins by introducing the presenter and providing background on electric vehicles and charger terminology. It then outlines the challenge of ensuring adequate charging infrastructure, especially in rural areas. The document demonstrates how to use APEX's new native map region to visualize existing chargers and traffic hotspots. It shows how spatial ML and analytics can resolve duplicate geopoints and score potential new charger locations. The results are overlaid on interactive maps. Future plans include expanding the optimal placement model and developing funding models.

1. Charge Me Up!
Using Oracle ML, Analytics,
and APEX For Finding
Optimal Charge Points
Jim Czuprynski
@JimTheWhyGuy
Zero Defect Computing, Inc.

2. Who Am I, and What Am I Doing Here?
➢E-mail me at jim@jimthewhyguy.com
➢Follow me on Twitter (@JimTheWhyGuy)
➢Connect with me on LinkedIn (Jim Czuprynski)
Traveler & public speaker Summers:
Wisconsin
Winters:
Illinois
Cyclist
XC skier
Avid
amateur
bird
watcher
Oldest dude in
martial arts class

3. Jim Czuprynski
Liron Amitzi
https://www.beyondtechskills.com
The podcast that talks about everything tech – except tech.TM

4. EVs: Coming to a Garage Near You Soon
Ford F-150 Lightning has received
over 200,000 orders so far
GM and Honda are aggressively
expanding into EV market
New solid-state battery
technology coming shortly
Electricity demand will likely at
least double as EVs are adopted
What will happen to
gas stations?
How will governments fund
road upkeep without
revenue from gas taxes?

5. EV Chargers: Terminology and Capacity
Term /
Acronym
Charging
Voltage
Description
Level 1
Charger
120V
Delivers slow “trickle” charge (3–5 miles of range per hour); OK for
most PHEVs, but not BEVs
Level 2
Charger
208V – 240V
Delivers moderate charging (12–80 miles of range per hour);
preferred by most BEV owners for overnight home charging
Level 3
Charger
400V – 900V
Delivers extremely fast charging (3 – 20 miles of range per minute);
also known as DC Fast Chargers (DCFCs) or SuperChargers
Source: https://www.forbes.com/wheels/advice/ev-charging-levels/

6. The Conundrum of Electric Vehicle (EV) Adoption
Even in 2022, several larger
towns already have rapid
charging stations in case my
EV’s battery is running low
A normal drive to our Wisconsin
cabin is leisurely & passes through
larger towns and smaller villages …

7. The Conundrum of Electric Vehicle (EV) Adoption
Even in 2022, several larger
towns already have rapid
charging stations in case my
EV’s battery is running low
A normal drive to our Wisconsin
cabin is leisurely & passes through
larger towns and smaller villages …
… but what happens if I
suddenly need to drive to my
older sister’s home in Chetek,
WI in an emergency?
I might be forced to re-route
significantly just to have an
option in case I didn’t have
time to charge my EV’s battery
Rural areas of northern and
western Wisconsin currently offer
very few EV charging options

8. You Start Coding. Meanwhile, I’ll Go Ask Our Users For Requirements
Identify where charging stations are already located
in Wisconsin, including public vs. private access
Create a simple-to-use mobile-capable app that lets EV drivers
quickly find the nearest charging station to their current or target
location
Figure out which potential locations for new charging
stations should be placed along key travel routes, as
well as within metropolitan & rural areas

9. Native Map Regions: New In APEX 21.1
APEX 21.1 offers a new Native Map region that lets you build maps to visualize location data
on a map, without any plug-in required!
• Five different layer types: Point, Line, Polygon, Heat Map, and Extruded (3D) Polygon
• All background maps are sourced directly from the Oracle Maps Cloud
• Fully interactive map features, including Drag, Zoom, and ToolTips
• Plot map features from:
• Simple numeric columns (Longitude and Latitude)
• SDO_GEOMETRY datatype
• GeoJSON objects
• Leverages Oracle Spatial features, including Spatial Indexes and Coordinate Transformations (if they
are available in your data model)
• Visualize data from SQL queries against local database, REST-Enabled SQL, or from REST Data Sources
• No API Key required!

10. Using Native Map Regions (1)
Select the new
Map Region …

11. Using Native Map Regions (1)
Select the new
Map Region …
… name the
new Page …

12. Using Native Map Regions (1)
Select the new
Map Region …
… name the
new Page …
… and set up the
new page’s
Navigation entry

13. Using Native Map Regions (3)
Choose a target table …

14. Using Native Map Regions (3)
Choose a target table …
… pick either a column
with an SDO_GEOMETRY
datatype, or select a pair of
Longitude / Latitude
columns for map
positioning
Note the option to add
a Faceted Search region
for the new page

15. Using Native Map Regions (3)
Just about any SQL query can
be the basis of a Map Region
Note we can overlay multiple
layers for the same Map
Region

16. Using Native Map Regions (3)
Just about any SQL query can
be the basis of a Map Region
Note we can overlay multiple
layers for the same Map
Region
The Native Map region offers
excellent control over the appearance,
navigation, and initial focus of the
map that’s displayed

17. Results: A (Not-So-) Simple Map
We can also see details about
each point’s location …
… and use these mapping tools
to drill down further into the
map, reposition its center, and
even measure distances
between points

18. Using Heat Maps to Display Traffic Intensity (1)
Create a new page for
Traffic Heat Mapping

19. Using Heat Maps to Display Traffic Intensity (1)
Create a new page for
Traffic Heat Mapping
Supply a query
with appropriate
columns and GIS
features

20. Using Heat Maps to Display Traffic Intensity (1)
Create a new page for
Traffic Heat Mapping
Supply a query
with appropriate
columns and GIS
features
Specify
where the
traffic hot
spots are
located, as
well as their
intensity

21. Using Heat Maps to Display Traffic Intensity (2)
Here’s the results: A map of
typical weekend traffic during
a Wisconsin summer …
… or, as dey say in Cheeseland:
Oh, ya, hey – it’s all dem FIBs
comin’ up from Illinois that’s
causin’ all deese traffic jams!

22. Using Heat Maps to Display Traffic Intensity (3)
Drilling into the heat map
shows greater levels of
detail, including the
“hottest” hot spots based
on specific traffic
monitoring points

23. Using Heat Maps to Display Traffic Intensity (3)
Drilling into the heat map
shows greater levels of
detail, including the
“hottest” hot spots based
on specific traffic
monitoring points
We can choose from myriad
arrays of color schemes to
highlight “heat” as desired

24. Which Potential Charging Stations Satisfy Underserved Locations?
For each location that’s
part of a selected subset
of key traffic hotspots:
Locate all
potential
charging
locations …
… that are within a
specified distance
(1 KM) …
… but that currently
have no charging
station within 1 KM

25. Leveraging Spatial ML & Analytics To Resolve Duplicate GeoPoints (1)
CREATE TABLE wtfc.deduplicated_locations (
tid NUMBER
,rid1 VARCHAR2(24)
,rid2 VARCHAR2(24)
) TABLESPACE data;
A table with these mandatory columns …
BEGIN
SDO_SAM.COLOCATED_REFERENCE_FEATURES(
theme_tablename => 'CHARGER_STATIONS'
,theme_colname => 'CS_GEOLOCATION'
,theme_predicate => NULL
,tablename => 'POTENTIAL_LOCATIONS'
,colname => 'PL_GEOLOCATION'
,ref_predicate => NULL
,dst_spec => 'distance=100 unit=M'
,result_tablename => 'DEDUPLICATED_LOCATIONS'
,commit_interval => 100
);
END;
/
… will capture any potentially
duplicate locations within a 100-
meter threshold of each other via
this bit of Spatial ML magic

26. Leveraging Spatial ML & Analytics To Resolve Duplicate GeoPoints (2)
SELECT
cs_name || ': ' || cs_address || ', ' || cs_city || ', ‘ ||
cs_state_abbr || ' ' || cs_zip_code AS actual_location
,pl_name || ': ' || pl_address || ', ' || pl_city || ', ‘ ||
pl_state_abbr || ' ' || pl_zip_code AS potential_location
FROM wtfc.deduplicated_locations XR
,wtfc.charger_stations CS
,wtfc.potential_locations PL
WHERE CS.rowid = XR.RID2
AND PL.rowid = XR.RID1;
This query lets us evaluate
the resulting geographic
deduplication

27. Leveraging Spatial ML & Analytics To Resolve Duplicate GeoPoints (2)
SELECT
cs_name || ': ' || cs_address || ', ' || cs_city || ', ‘ ||
cs_state_abbr || ' ' || cs_zip_code AS actual_location
,pl_name || ': ' || pl_address || ', ' || pl_city || ', ‘ ||
pl_state_abbr || ' ' || pl_zip_code AS potential_location
FROM wtfc.deduplicated_locations XR
,wtfc.charger_stations CS
,wtfc.potential_locations PL
WHERE CS.rowid = XR.RID2
AND PL.rowid = XR.RID1;
This query lets us evaluate
the resulting geographic
deduplication
Note that deduplication
happens based on the
locations’ geometry, not
their reported addresses

28. Finding Under-Served Traffic Hot Spots: Infrastructure
CREATE TABLE wtfc.charger_location_scoring (
tid NUMBER
,rid1 VARCHAR2(24)
,rid2 VARCHAR2(24)
) TABLESPACE data;
CREATE TABLE wtfc.charger_universe (
id NUMBER(6,0) NOT NULL
,type VARCHAR2(15) NOT NULL
,geolocation SDO_GEOMETRY
) TABLESPACE data;
Create two tables: One to store
nearby location attribute
ROWIDs, and one to hold the
universe of existing and
potential charger sites

29. Finding Under-Served Traffic Hot Spots: Infrastructure
CREATE TABLE wtfc.charger_location_scoring (
tid NUMBER
,rid1 VARCHAR2(24)
,rid2 VARCHAR2(24)
) TABLESPACE data;
CREATE TABLE wtfc.charger_universe (
id NUMBER(6,0) NOT NULL
,type VARCHAR2(15) NOT NULL
,geolocation SDO_GEOMETRY
) TABLESPACE data;
Create two tables: One to store
nearby location attribute
ROWIDs, and one to hold the
universe of existing and
potential charger sites
INSERT INTO user_sdo_geom_metadata
VALUES (
'CHARGER_UNIVERSE'
,'GEOLOCATION'
,SDO_DIM_ARRAY(
SDO_DIM_ELEMENT('Longitude', -180, 180, 0.5)
,SDO_DIM_ELEMENT('Latitude', -90, 90, 0.5)
)
,8307);
COMMIT;
CREATE INDEX wtfc.charger_universe_spidx
ON wtfc.charger_universe(geolocation)
INDEXTYPE IS MDSYS.SPATIAL_INDEX_V2;
Set up spatial index for
all entries in the
CHARGER_UNIVERSE
table INSERT INTO wtfc.charger_universe
SELECT cs_id, 'A' as cs_geolocation
FROM wtfc.charger_stations;
INSERT INTO wtfc.charger_universe
SELECT pl_id, 'P', pl_geolocation
FROM wtfc.potential_locations
WHERE pl_type <> 'D';
COMMIT;
Finally, add all existing
chargers and all non-
duplicate candidate
locations into
CHARGER_UNIVERSE

30. Finding Under-Served Traffic Hot Spots: Using Map Layers
Each map layer contains a
different piece of the puzzle:
existing chargers, high-volume
traffic spots, and the resulting
best potential spots for new
chargers
Based on average daily
traffic counts captured at
specific traffic monitoring
points, we can apply
spatial algorithms for
optimal placement of new
chargers

31. Finding Under-Served Traffic Hot Spots: Automatic Reoptimization
Increasing the
minimum traffic
volume threshold …
… automatically triggers
recalculation of the universe
of potential EV chargers based
on that new minimum

32. Plans for Future Development
Build ML model handling cost factors for different charger
types (L2 vs. L3) for optimal locations near traffic hotspots
Expand optimal charger placement model to include
support for long-haul EV trucking
Build funding models to account for USA Build Back Better
(BBB) Justice40 initiatives for equitable placement of EV
charging infrastructure

33. Public Data Sources and Additional Reference Material
As Biden plans EV charger rollout, location questions take the fore:
https://www.smartcitiesdive.com/news/as-biden-plans-ev-charger-rollout-location-questions-take-the-fore/619466/
National Electric Vehicle Infrastructure (NEVI) Formula Program:
https://www.transportation.gov/briefing-room/president-biden-usdot-and-usdoe-announce-5-billion-over-five-years-national-ev
State of Wisconsin County Mapping Resources:
https://data-wi-dnr.opendata.arcgis.com/
USA Tax Information by State:
https://www.irs.gov/statistics/soi-tax-stats-individual-income-tax-statistics-2019-zip-code-data-soi
Ford may have just changed our electric-vehicle future:
https://www.washingtonpost.com/opinions/2022/05/17/ford-f150-electric-pickup-transforms-american-car-culture/
PJM releases road map for creating ‘grid of the future’ to handle coming renewables, storage wave:
https://www.utilitydive.com/news/pjm-grid-future-report-energy-transition/623630/

34. Useful Resources and Documentation
APEX 21.1 Native Map Regions:
https://docs.oracle.com/en/database/oracle/application-express/21.1/htmdb/creating-maps.html
Carsten Czarski Article on APEX Native Map Region:
http://www.oraworld.org/fileadmin/documents/26-ORAWORLD.pdf
Oracle Spatial Analysis and Mining Concepts:
https://docs.oracle.com/en/database/oracle/oracle-database/19/spatl/spatial-analysis-mining.html