With more and more people abandoning traditional phone lines in favor of cell phones, municipalities are presented with a difficult challenge in making targeted emergency broadcasts (Reverse 911) to their citizens. Since cell phones are by definition mobile, it can be very difficult to adequately apply a specific location to a device. For one city, traditional methods of geocoding resulted in the unacceptable situation where almost 35% of the numbers were unusable for these notifications. Something needed to be done to increase the accuracy and reliability of the geocoding process in a way that was manageable, repeatable, and cost-effective. Learn how FME was able to programmatically normalize data from different vendors and geocode data using multiple sources to achieve a hit rate in excess of 99.5%.

1. CONNECT. TRANSFORM. AUTOMATE.
Maximizing Data for Reverse 911
Amanda Graf
Senior Project Manager
California CAD Solutions, Inc.

2. Situation
! Local municipality needed to leverage their
existing GIS system to enable reverse 911
notification of emergency events to mobile phones
! GIS must interface with existing TENS (Telephone
Emergency Notification System)
! County Emergency Dispatch system had a match
rate of less than 65% for mobile numbers
! County system could not interface with City GIS
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3. Complicating Factors
! Non-repeatable County process meant the
updates were excruciatingly tedious and prone to
error
! No documentation of the County geocoding
process led to no confidence in the data results
! Erratic updates to base data used for geocoding
! Jurisdictional battles between City and County
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4. Major Factors Impeding Success
! Multiple Data Vendors with radically different data
structures and update methodologies
! AT&T – Monthly updates with a complete listing of
all phone records
! Verizon – Weekly updates with incremental
changes from the prior update delivery
! Inability to get AT&T and Verizon to make
changes to data anomalies (errors)
! Multiple sources of Address information
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5. Plan
! Document address data sources and determine
hierarchy of processing
! Normalize address notations among all the data
sources used
! Process and normalize AT&T data
! Process and normalize Verizon data
! Deliver Geocoded dataset themed by source
! Deliver List of unmatched addresses
! Deliver documentation of entire process
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6. Process / Approach
! Granular approach to the problem was the most
effective
! Multiple FME routines
! 1 - Process AT&T Data
! 2 – Process Verizon Data
! 3 – Combine datasets into single datastore
! 4 – Geocode the data
! Scripted batch files to automate processing
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7. 1 – Process AT&T Data
! AT&T Data
! Straight forward CSV file
! “Street Name” included both street name and
street type in a single field
! Liberal use of SubstringExtractors,
AttributeTrimmers, and Testers used to break the
information out into separate fields
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8. 2 – Process Verizon Data
! Verizon Data
! Fixed Length format requiring use of
SubstringExtractors
! Critically important to process the data sequentially
since a single number can be entered more than
once in any particular update file
! Determine if Insert, Update, or Delete is the
appropriate action for each record
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9. Insert, Update or Delete?
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10. 3 - Merge Datasets
! Massive Data normalization process
! AT&T, Verizon, County Assessor, City Public Works
! Each organization has their own way of designating
(and spelling) addresses
! 1st or First?
! AV or AVE or Ave.?
! Mc Clay or McClay? (Use the MC Hammer)
! Green Oak PL should be Green Oak DR
! Misspellings Agencies won’t fix
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11. Normalize Data
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12. FME Advantages
! Update FME routine with known exceptions and
the work only needs to be done once
! Quick and easy to incorporate new exceptions as
they are found
! Original source data is unaltered thereby enabling
a viable audit trail of information
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13. 4 - Geocoding
! 7 data sources used in geocoding process
(sources noted in order of priority)
! County Assessor Data
! City Situs Address Data
! County Assessor Mobile Home Data
! City Situs Mobile Home Address Data
! Street Centerline (Address Range Matching)
! Lat/Lon Lookup Table
! Known Invalid Addresses
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14. Verification Process
! USPS.com
! Matched with LatLon Lookup table.
The lookup table was created by
looking up the addresses on
www.batchgeocode.com/lookup.
All addresses are verified as
valid addresses against USPS.com.
! Loop Back through FME Routine 3 & 4 with edits
and additional exceptions
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15. Unmatched Examples
! 201 FOREIGN EXCHANGE
! 0 AFB
! 1 VOIP CALLER
! T-MOBILE@HOME SERVICE
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16. Batch Processing
! FME routines can be run from a batch file
! By using published parameters the FME routines
stay the same even as the source dataset names
change each quarter
! Use a template to create a .bat file for processing
the data for the current quarter
! Input names of source files (published
parameters)
! Run
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17. Display Themed Data in Map
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18. Pull Reports and Notify Residents
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19. Results
FME Saved the Day!!
! RESULTS!!! 99.6% of all records were matched
(100% of all records that had valid addresses
were matched)
! Fast, easy integration with the existing City GIS
site
! Documented, traceable results of worked
performed
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20. Thank You!
! Questions?
! For more information:
! Amanda Graf – amanda.graf@calcad.com
! California CAD Solutions, Inc.
www.calcad.com
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