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Maximizing Data for Reverse 911
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Maximizing Data for Reverse 911


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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 …

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%.

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  • 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 CONNECT. TRANSFORM. AUTOMATE.
  • 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 CONNECT. TRANSFORM. AUTOMATE.
  • 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 CONNECT. TRANSFORM. AUTOMATE.
  • 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 CONNECT. TRANSFORM. AUTOMATE.
  • 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 CONNECT. TRANSFORM. AUTOMATE.
  • 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 CONNECT. TRANSFORM. AUTOMATE.
  • 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 CONNECT. TRANSFORM. AUTOMATE.
  • 9. Insert, Update or Delete? CONNECT. TRANSFORM. AUTOMATE.
  • 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 CONNECT. TRANSFORM. AUTOMATE.
  • 11. Normalize Data CONNECT. TRANSFORM. AUTOMATE.
  • 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 CONNECT. TRANSFORM. AUTOMATE.
  • 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 CONNECT. TRANSFORM. AUTOMATE.
  • 14. Verification Process ! !  Matched with LatLon Lookup table. The lookup table was created by looking up the addresses on All addresses are verified as valid addresses against !  Loop Back through FME Routine 3 & 4 with edits and additional exceptions CONNECT. TRANSFORM. AUTOMATE.
  • 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 CONNECT. TRANSFORM. AUTOMATE.
  • 17. Display Themed Data in Map CONNECT. TRANSFORM. AUTOMATE.
  • 18. Pull Reports and Notify Residents CONNECT. TRANSFORM. AUTOMATE.
  • 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 CONNECT. TRANSFORM. AUTOMATE.
  • 20. Thank You! !  Questions? !  For more information: !  Amanda Graf – !  California CAD Solutions, Inc. CONNECT. TRANSFORM. AUTOMATE.