Data-Driven Public Safety: Reliable Data When Every Second Counts

When every second counts, you need access to data you can trust. In this webinar, we’ll explore how FME empowers public safety services to streamline their operations and safeguard communities. This session will showcase workflow examples that public safety teams leverage every day.

We’ll cover real-world use cases and demo workflows, including:

Automating Police Traffic Stop Compliance: Learn how the City of Fremont meets traffic stop data standards by automating QA/QC processes, generating error reports – saving over 2,800 hours annually on manual tasks.

Anonymizing Crime Data: Discover how cities protect citizen privacy while enabling transparent and trustworthy open data sharing.

Next Gen 9-1-1 Integration: Explore how Santa Clara County supports the transition to digital emergency response systems for faster, more accurate dispatching, including automated schema mapping for address standardization.

Extreme Heat Alerts: See how FME supports disaster risk management by automating the delivery of extreme heat alerts for proactive emergency response.

Our goal is to provide practical workflows and actionable steps you can implement right away. Plus, we’ll provide quick steps to find more information about our public safety subscription for Police, Fire Departments, EMS, HAZMAT teams, and more.

Whether you’re in a call center, on the ground, or managing operations, this webinar is crafted to help you leverage data to make informed, timely decisions that matter most.

1. Data-Driven
Public Safety:
Reliable Data
When Every
Second Counts

2. Tom
Seymour

Sales Manager -
Government,
Transportation & Education

Chris
Berger

Team Lead - Customer
Solutions

Dean
Hintz

Team Lead - Strategic
Solutions
PM Open Standards

Meet the Team
Nampreet
Singh
Technical Support
Team Lead, FME Form

3. Welcome to Livestorm.
A few ways to engage with us during the webinar:


Audio issues? Click this for 4 simple
troubleshooting steps.

4. ●May 6-8, 2025
●Be among the first to hear ground breaking
updates and launches!
●*New* - Get FME certified for free at the event!
○2 exams offered on Wednesday, May 7.
Special Interest Groups will be
hosted! Stay tuned for more details.

5. Agenda
1Introduction
2Police Traffic Stop Data - City of Fremont Story & Demo
3Crime Data Anonymization - Open Data Portals Demo
4Extreme Heat Alerts
5 Next Gen 911 - Santa Clara County Story & Demo
6 Public Safety FME Subscription
7Conclusion
8Resources & Next Steps
9Q&A

Agenda

6. 1.
Introduction

7. Benefits of Today’s Webinar:
What You’ll Leave With
●Real world examples: speed up dispatch, improve location accuracy, share
data publicly while protecting privacy, respond faster
●Ready-to-use workflows to improve public safety operations
●Clear idea of how FME’s Public Safety Subscription can help you manage
your data more efficiently
●Resources to dig deeper into using FME in your work

8. Common Challenges: Public
Safety Data Integration
●Siloed Data: Difficulty connecting information
across departments & systems when time is of
the essence.
●Manual Processes & Limited Resources:
Hours lost on tasks that could be automated.
●Privacy & Accuracy: Balancing compliance
with trustworthy, secure data.
●Legislative & Technological
Advancements: Upgrading to Next Gen 911
can be complex

9. Tackle these challenges with FME!
Simplify data integration, automate
workflows, and ensure information is secure
& accurate so your public safety team can
focus on what matters most: protecting
communities.

10. FME empowers public safety
services to access reliable data,
streamline operations, and
protect communities when every
second counts.

11. One platform, two technologies
FME Form FME Flow
Data Movement and transformations
(“ETL”) workflows are built here.
Brings life to FME Form workflows
FME Flow Hosted
Safe Software managed FME Flow
fme.safe.com/platform
FME Enterprise Integration Platform
Safe & FME

12. With 500+ supported data types in FME.
Unrivalled Data Support





GIS
CAD
Database
XML
Raster
3D
BIM
Web
Point
Cloud
Cloud
Big Data
IOT
Gaming
BI
Indoor
Mapping
AR/VR
Generative
AI
Cloud
Native
Tabular
All Data, Any AI.

13. 2.
Police Traffic
Stop Use Case

14. Police Traffic Stop Data
●California RIPA Law: Requires all municipalities to report police traffic stop data to the
CA Dept. of Justice.
●Collected Data: Includes occupants, age, sex, ethnicity, English fluency, stop reason
(e.g., weapons/drugs recovered).
●Challenges: Manual data entry via Survey123 affects accuracy.
●Processing Needs: QA/QC, CSV conversion, schema compliance, reporting, public
release.
●Expansion: Similar law proposed in AZ & CO.

15. Customer Story
City of Fremont
Project
Police traffic stop data submission was required to comply
with the State of California RIPA law. Errors occurred in officers
forms, causing submission rejections.

Solution
FME QA workflows perform 12 types of daily error checks and
summarize the results in a report.

Results
●3 hours of daily manual work is transformed into a
half-hour automation.
●780 hours are saved on RIPA processes alone yearly.
●Over 2800 hours each year are saved with automation
of all Fremont tasks.
“FME is a lifesaver, and from a productivity, efficiency, and cost standpoint,
it’s well worth its weight in gold.”
- John Leon, GIS Manager, City of Fremont

16. Slide Title
Submit data
collected to the
US Department
of Justice (DOJ)

Goal Block Key
Demo: Meeting DOJ Submission Requirements
Result
Output dynamic
schema based
on the number of
persons stopped

FME flexibility &
SchemaScanner
Transformer

Automtate data
validation &
output for DOJ
submission

17. FME Form
Combine Data
Generate Destination Schema

CSV
DOJ Submission ArcGIS Online
Persons Stopped
ArcGIS Online
Traffic Stops
Demo: Meeting DOJ Submission Requirements
Police Traffic Stop Use Case

18. Stop IDOfficerStop DateStop Type Address
00011 478820250102 290282 Riverside Point
00012 418520250102 1301 Dayton Alley
00013 455420250103 333114 Longview Drive
Person NumberStop IDGenderEthnicityStop Reason
100011 1 2 2
200011 2 6 2
300011 2 6 2
100012 1 1 10
100013 1 2 4
200013 3 3 4
Stop
ID OfficerStop Date
Stop
Type
Person
Number1 Gender1
Ethnicity
1
Stop
Reason1
Person
Number2 Gender2
Ethnicity
2
Stop
Reason2
Person
Number3 Gender3
Ethnicity
3
Stop
Reason3
00011 4788 20250102 2 1 1 2 2 2 2 6 2 3 2 6 2
00012 4185 20250102 1 1 1 1 10
00013 4554 20250103 3 1 1 2 4 2 3 3 4
ArcGIS Online
Traffic Stops
ArcGIS Online
Persons Stopped
CSV
DOJ Submission
Person 1 Attributes Person 2 Attributes Person 3 Attributes
Stop Attributes
Demo: Meeting DOJ Submission Requirements
Police Traffic Stop Use Case

19. Demo

20. ●Schema Scanner
●List Attributes
●Dynamic Schema Writing


FME’s Versatility
Demo: Meeting DOJ Submission Requirements
Police Traffic Stop Use Case
Resources
●Getting Started with List Attributes
●Dynamic Workflows with the SchemaScanner

21. 3.
Anonymizing Crime
Data for Secure
Sharing and
Analysis

22. Handling Sensitive Data
●Remove sensitive details to share
‘crime type’-focused data via public
portals:
○Location, Address, Crime Type,
etc.
●Retain sensitive crime data while
standardizing schemas for internal
use by your Public Safety Team.
Automating Crime Data Sharing with FME
●Develop near-real time mapping
updates for greater situational
awareness, respond faster, deploy
resources where they’re needed
most
●FME is data portal agnostic publish
to any technology ArcGIS Hub,
CKAN, Socrata, and more…

23. Streamline and Share
●Automate data transformations to
ensure accurate, anonymized, and
up-to-date Crime data is consistently
shared.
●Publish seamlessly to any platform,
including ArcGIS Hub, CKAN,
Socrata, and more, with FME’s
flexibility.
Automating Crime Data Sharing with FME
●

24. Slide Title
Create Crime
datasets suitable
for internal
analysis and
external sharing.

Goal Block Key
Anonymizing Crime Data for Secure
Sharing and Analysis
Result
Varying schema
and value
requirements
across use
cases.

Apply data
transformation to
anonymize and
redact sensitive
details.

Automated
updates to Crime
data for secure
internal and
public portals.

25. Demo

26. ●Trigger Automations: Process
crime data on a schedule or file
arrival.
●Redact Sensitive Data: Ensure
compliance and privacy.
●Publish Anywhere: Share
anonymous crime data with any
open data portal.
Automated Data Distribution
Automating Crime Data Sharing with FME

27. 4.
Extreme Heat
Alerts

28. Climate & Disaster Resilience Pilot 2024
●The Climate and Disaster Resilience
pilot: Jan-Sep 2024
●18 participating organizations

●Main motivation: Need for new
methods, tools, and systems to better
understand, predict, and address
(natural) phenomena, including
○intensification and changing
patterns of typhoons
○landslides
○flooding
○extreme heat events

29. Urban Heat Island Effect - Challenge
https://www.climatecentral.org/climate-matters/urban-heat-islands-2023
●Heat: deadliest natural hazard
●Urban areas experience greater heat
extremes due to the heat absorbent
artificial landscapes, lack of cooling
vegetation and water
●Cities experience temperature
increases > 7C
●80% of US population lives in cities
●No current real time warnings exist
that incorporate localized UHI effect
information

“The urban heat island effect is a measurable increase in ambient urban
air temperatures resulting primarily from the replacement of vegetation
with buildings, roads, and other heat-absorbing infrastructure.”
U.S. Environmental Protection Agency

30. Objectives:
●Survey available Urban Heat Island data and models
●Evaluate and prioritize primary urban heat island factors
●Develop experimental urban heat models for 2D and 3D using high resolution
localized data to produce more targeted estimates (DRI)
●Provide urban heat effect temperature deltas for warnings and for health hazard
assessments (ARD)

CDRP24: NYC Urban Heat Island Alerts
https://community.wmo.int/en/activity-areas/urban/urban-heat-island

31. Estimate of Temperature in Northern
Manhattan During Extreme Heat Wave
•Highest Temperature Reading In Central Park 100 degrees F. (NWS)
•Average Heat Island Effect + 8 F. (Climate Central)
•Measured Temperature on Residential Streets 108 degrees F.
•Estimated Humidity at Hottest Time of Day 40% +22 degrees (NWS)
•Heat Index “Felt” Temperature 130 degrees F.
•Interior Temp: Top Floor/South Exposure (TBD + 1 to +10 degrees F.
Max Interior Temperatures > 130 degrees F.

As temperatures decline during late afternoon through the night and early morning, humidity
levels rise, maintaining high felt temperatures

32. Urban Heat Grid Estimate
Input Data: NYC Open Data Surface Type & Impervious surfaces
Urban Heat Island Delta Grid FME Workflow:
1.Generate albedo grid
2.Invert to absorption grid
3.Multiply by cooling grid
4.Normalize grid
5.Scale by observed UHI temp range

33. Urban Heat Grid Estimate
Input Data: NYC Open Data Surface Type

34. Urban Heat Grid Estimate
Raster Algebra Workflow to generate normalized UHI Temperature Delta Grid from
Absorption and Cooling Grids
Albedo Grid
Absorption Grid Blue / Green Cooling Grid
UHI Temperature Delta Grid
X
1 - X

35. Urban Heat Grid Estimate
UHI Grid ARD Scaled by Observed Heat Island Temperature Range (11 F)
Estimated UHI delta: +8.31 F, at 191 West 116th

36. Weather Service Real Time
Data Feeds: FME Approach
●Goal: Extract weather event
metrics and combine them with
urban heat island estimates, to
provide localized weather
information to users via
weather alerts.

37. Slide Title
Need more
accurate
extreme heat
alerts for urban
areas

Goal Block Key
Urban Heat Island Estimates and Alerts
Result
Currently no
alerts that take
into account
urban heat island
effects

Use FME to estimate
urban heat effects
based on surface type
data and combine this
with NWS forecasts

Prototype urban
heat alerts taking
into account
localized
environmental
conditions

38. Estimated UHI delta at 191 West 116th: 4:00 pm: 92.8 F
Heat Grid Depiction for Central Harlem
UHI Grid ARD Scaled by Observed Heat Island Temperature Range (11 F)

39. Estimated UHI delta: 12 hour daytime animation by Navteca using UHI ARD from FME
Urban Heat Grid Estimate
UHI Grid ARD Scaled by Observed Heat Island Temperature Range (11 F)

40. National Weather Service API: api.weather.gov

41. DRI Example: Urban Temperature HTML Report

Estimated UHI: 82F Estimated UHI: 91F

42. ●Most weather warnings do not yet include
urban heat island effects
●Urban heat island models can support
more accurate and localized heat hazard
warnings based on local conditions & feed
impact estimates
●FME: can automate integration of weather
data with high-res local models for
real-time insights
●Need more local observations to better
track urban heat effects over time in order
to calibrate models


Key Takeaways

43. 5.
Next Gen 911

44. Slide Title
Transition 911
systems from
analog to digital
IP-based,
geo-enabled

Goal Block Key
Next Gen 911
Result
Consolidate
required data;
conform to
NENA NG911
standards

FME data
transformation,
validation & update
automation for
NG911

Geo enabled digital
NG911 will improve
emergency response
& save lives

45. Customer Story
Shelby County 9-1-1
Project
Faced with new and evolving standards, Shelby County 9-1-1
sought to automate their GIS data processes to help achieve
NG9-1-1 compliance.

Solution
Using FME, Shelby County 9-1-1 has reduced manual processes
and now creates error-free, tailored datasets to help comply with
NG9-1-1 standards. Dealing with data volumes across different
sources, FME has become a valuable (QA/QC) tool for NG911.
Results
●Enabled the integration and sharing of multimedia
information, including text, photos, and videos.
●Enhanced situational awareness and improved emergency
response capabilities for nearly 1 million residents.
●Shelby County 9-1-1 is positioned to scale and support
evolving NG9-1-1 standards.

“The time savings from FME have been immensely beneficial for preparing
for NG9-1-1. Automating this process has freed up time to focus on other
projects and improved overall productivity.”
- Bruno Blanco, GIS Engineer, Shelby County 9-1-1

46. Customer Story
Santa Clara County
Project
Improve 911 dispatch system with a map of city-sourced
address points, each with different source schemas.

Solution
Integrated 17 city datasets, covering the entire county,
performed QA, and generated multiple output formats.

Results
●Regional address maps that cities contribute
addresses to on a quarterly basis.
●50% increase in the number of known addresses.
●Improved emergency response time and location
accuracy.
“FME allowed me to make fast iterative changes to workflows as each city’s
data turned out to be a discovery process where something unexpected
always occurred. This flexibility better prepares me for upcoming changes
that may be necessary for Next Generation 911.”
- Steven Hong, Santa Clara County

47. Next Generation 911 - What?
●CRTC: “update networks from analog to
digital – ready to provide NG9-1-1 voice and
text messaging services”
●IP based system: support digital
communications from public to first responders
through the 911 network
●Support for a variety of digital
communications: real-time text messaging,
voice, photos, video (phase 2)
●Geodetic call routing - improve response
accuracy and speed using GPS and GIS data
https://crtc.gc.ca/eng/phone/911/gen.htm

48. Next Generation 911 - Why & When
Why
●Goal: safer, faster and more informed
emergency responses
●Improved accuracy
●Existing analog system is up to 55 years old
●Needs of the public have changed since
analog system was designed: landlines >
mobile

When - Canada
●Decommission analog system: Mar 2025 est
●Geodetic call routing estimated Mar 2027
●USA: varies by state
https://crtc.gc.ca/eng/phone/911/gen.htm

49. ●Aggressive regulatory timelines
●Rigorous standards requirements
●Foundational change: from analog to digital,
custom to common data models
●Comprehensive: affects every administrative
region in US and Canada
●Builds on work already done for E911, but
vastly improves capability
●Major geospatial data requirements
○Accuracy and data integrity (98%+)
○Continuous updates (< 72 hour)
Next Generation 911 - Challenges

50. Agencies
●NENA (National Emergency Number
Association)
●CRTC - ESWG (Emergency Services
Working Group)
●TIF92 - NG911 mapping and addressing TIF
(Task Id Form) working group

NENA Standards
●CLDFX-CA (STA-029) XML
●i3 (STA-010)
●NENA GIS Data model (exportable to)
Next Generation 911 - Standards

51. Next Generation 911 - NENA GIS Data Model
●Site Structure Address
Points
●Street Centerlines
●Service Boundaries -
PSAPs
●Service Boundaries -
Emergency Services
(Police, Ambulance, Fire)
●Provisioning Boundaries
●Water ways, bodies





github.com/NENA911/NG911GISDataModel/tree/main/esri_geodatabase

52. Next Generation 911 - NENA GIS Database Loader
City of Winnipeg Open Data Portal Parcels –> NG911 Database

53. Next Generation 911 - NENA GIS Database Loader
City of Winnipeg Open Data Portal Parcels –> NG911 Database
Input: GeoJSON Parcels
Output Geodatabase:
SiteStructureAddressPoints

54. Requirements:
●98% synchronization across related GIS
datasets (roads, addresses)
●Discrepancies need to be resolved - naming,
addressing, ids etc

Next Generation 911 - Validation
Checks (some examples):
●Incomplete, incorrect data
●Duplicate ids
●Road centrelines
○Topology gaps /
overlaps
○Break at boundaries
○Segments flow with
increasing address
range
●Addresses
○Overlaps in ranges, odd /
even consistent
○Duplicate points
●Provisioning boundaries -
within, gaps, overlaps

Uses extraction of schema business rules from spec as csv files

55. Next Generation 911 - Validation: Schema and Geometry

56. Demo

57. Customer Story
ICI Society
Long time FME customer - uses FME extensively to support BC parcel
and address data management workflows.

Project
Support validation of BC municipal data prior to loading into NENA
NG911 geodatabase data model

Challenge
●Source datasets of varying type, structure and quality
●Numerous complex validation rules
●Significant manual effort to diagnose and correct data problems

Approach
●Use FME to automate data extraction, transformation and
loading into NENA data model
●Develop FME workflows to automate data validation and
reporting to reduce manual effort involved with generating NENA
compliant datasets - validate early & often
icisociety.ca

58. Summary
●NextGen 911: Transitions emergency
call capabilities from analog to digital
●Challenges: Fast regulatory timelines,
geospatial standards compliance
●FME: transform, validate & automate for
NENA standards
●Safe is an active participant in NENA
CLDFX & CRTC NG911 working groups.
●Device location + GIS data aggregated
from local authorities > 911 dispatchers
execute geodetic call routing (Phase 1)
●Text, phone, video & photo, med alerts
enabled from devices > accurately
dispatch emergency crews (Phase 2)

https://www.peacearchnews.com/news/white-rock-pier-damaged-by-storm/

59. 6.
Public Safety
FME Subscription

60. Public Safety Subscription
●Flexible pricing based on
population served
●Designed for public safety
organizations, including Police, Fire,
EMS, SAR, Hazmat, Emergency
Dispatch Call Centres
●Unlimited use of the FME platform for
the entire organization for a single
annual price
●Immediately scalable deploy licenses
anytime, anywhere
A small paragraph
describing something
of note within imagery,
or providing a source.fme.safe.com/pricing

61. 29+
27K+
128
190
20K+
years of solving data
challenges
FME Community
members
countries with
FME customers
organizations worldwide
global partners with
FME services
30+
37K+
128
170+
25K+
years of solving data
challenges
FME Community
members
countries with
FME customers
organizations worldwide
global partners with
FME services
200K+
users worldwide
Safe & FME

62. 7.
Conclusion

63. Conclusion
●Automate Compliance: Save time on
data QA/QC.
●Protect Privacy: Anonymize data while
enabling open data sharing.
●Next Gen 9-1-1: Improve emergency
response with automated address
standardization and validation.
●Disaster Alerts: Automate localized extreme
heat alerts for proactive response.
●Get Started: Our Public Safety Subscription
unlocks the unlimited potential of FME when
every second counts
●Next Gen 911 deadline approaching, key
priority for all public safety & municipal
organizations. We can help!

64. 8.
Resources

65. Get our Ebook
Spatial Data for the
Enterprise

fme.ly/gzc


Guided learning
experiences at your
fingertips
academy.safe.com


FME Academy
Resources
Check out how-to’s &
demos in the knowledge
base
support.safe.com
Knowledge Base Webinars
Upcoming &
on-demand webinars

safe.com/webinars

66. ●The City of Fremont - RIPA data standards
●Anonymizing Crime Data with FME
●OGC Disaster & Climate Resilience Pilots
●CRTC NG911 Project
●NENA NG911 Project
●Wikipedia: NG911
●ESRI NG911 Resources
●NENA NG911 Github
●Shelby County NG911 with FME
●Santa Clara County NG911 with FME
●NG911 Loader and Validator Tutorial
Resources: References
https://www.peacearchnews.com/news/white-rock-pier-damaged-by-storm/

67. 9.
Next Steps

68. Get Started with an FME
Subscription for Public
Safety

Visit: fme.safe.com/public-safety

✔ Learn more about solutions tailored for
your needs.
✔ Explore more real-world use cases and
solutions tailored for public safety.
✔ Check out FAQs on our Public Safety
Subscription.

69. Experience the
FME Accelerator
A world where data is not
just a commodity but a
catalyst for real change.
fme.safe.com/accelerator



Next Steps

70. Claim Your Community Badge &
Dive into the new Community!
●Get community badges for watching
webinars
●community.safe.com
●Today’s code: C2CGGK


Join the Community today!
Next Steps

71. ●May 6-8, 2025
●Be among the first to hear ground breaking
updates and launches!
●*New* - Get FME certified for free at the event!
○2 exams offered on Wednesday, May 7.
Special Interest Groups will be
hosted! Stay tuned for more details.

72. 10.
Q&A

73. Thank You
Recap of Next Steps

1Follow us on LinkedIn!
2Contact us
3Experience the FME Accelerator

Please fill out our
webinar survey