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FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
FME Delicacies: Tasty Examples from the City of St. Albert
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FME Delicacies: Tasty Examples from the City of St. Albert

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  • 1. FME Delicacies:Tasty Examples from the City of St. Albert<br />Tammy Kobliuk<br />GIS Coordinator<br />
  • 2. On the Menu:<br />Appetizer: 2010 Municipal Census<br />Salad Course: Business Licensing<br />Main Course: Fire Services 9-1-1 Dispatch<br />Dessert: Questions? (you’re all on a diet)<br />
  • 3. Appetizer<br />Municipal Census<br />
  • 4. Appetizer: 2010 Municipal Census<br />The challenge for the kitchen:<br />City-conducted municipal census<br />All residences within City boundaries<br />First on-line census<br />No previous spatial census-base layer available<br />No spatial address point layer available<br />No single address database available<br />All residences require a secure PIN number<br />All residences will be mailed their PIN number<br />A complete residential address listing is required<br />
  • 5. Available Ingredients<br />Census Landbase<br />Postal Codes<br />Enumeration Areas<br />Enumeration<br />Districts<br />
  • 6. Desired Outputs<br />Primary and secondary PIN numbers for each address<br />A mail merge database including postal codes and PIN numbers<br />A list of addresses requiring in person PIN letter delivery<br />A list of addresses requiring institutional enumeration<br />A final GIS census polygon landbase including assigned PIN numbers<br />
  • 7. Finding the Right Recipe<br />The goals for the PIN numbers:<br />No less than 5 characters long<br />No more than 8 characters long<br />Primary and secondary PINs<br />No duplicate PINs<br />Repeatable method<br />No confusing characters (e.g. 0 vs O)<br />
  • 8. Trial 1: Using Existing Info<br />The City of Airdrie did it so why can’t we?<br />Available Info:<br />Street names<br />Legallot ID numbers<br />Centroid X/Y coordinates<br />Neighbourhood names<br />Strip them down, mash them together, in short make an unproductive mess…<br />
  • 9. Sampling Ingredients<br />1. Input Landbase<br />5. Generate random 9-digit number<br />2. Fetch CentroidCoodinates<br />6. Generate single random number<br />3. Trim off decimal places<br />7. Extract first letters<br /> of street names<br />4. Replace decimal<br />
  • 10. And It Tastes Like?<br />Ummm…best not to be sampled:<br />Generated duplicates<br />Complicated at best<br />Seemed like it would take more work than it was worth to get it to work<br />Like Plan B would be better: why use existing ingredients when you can just create some tasty new ones?<br />
  • 11. Plan B: Just Random<br />Use individually generated random numbers that match the ASCII character designations for capital letters A to Z.<br />Stick with just letters instead of mixing in numbers<br />Generate 7 individual letters<br />Combine in order for the initial 7-digit PIN<br />Recombine to create an 8-digit PIN<br />Little chance of generating duplicates as there are now 26 choices, instead of 10, for each character<br />
  • 12. Appetizer: The Model<br />1. Generate<br />Random<br />Letters<br />2. Create first PIN<br />3. Check for duplicates<br />4. Create second PIN<br />5. GIS Output<br />
  • 13. Appetizer: The Model (more yummies)<br />7. Address merge<br />file for Admin staff<br />(Access table)<br />6. Addresses<br />must be sorted<br />In ascending order of:<br /><ul><li>Enumeration Area
  • 14. Enumeration District
  • 15. Street
  • 16. House number
  • 17. Suite number</li></ul>8. Addresses for<br />hand delivery of<br />PIN letters<br />(Access table)<br />9. Addresses for<br />City staff enumeration<br />(Access table)<br />11. Blank database<br />for census contractor<br />(CSV file)<br />10. Get rid of <br />offending commas<br />
  • 18. Appetizer in Review<br />We went with simple instead of fancy for PIN number generation.<br />We chose to optimize our development time.<br />We had difficulties in joining postal codes to address points in FME so ended up doing that in ArcGIS as pre-processing.<br />Overall development time was approximately 1.5 days, including initial sandbox time.<br />Very happy with final result.<br />
  • 19. SALAD COURSE<br />Mapping Home-Based Businesses<br />
  • 20. Salad Course:Mapping Business Licenses<br />The challenge for the kitchen:<br />To map the home-based business licenses<br />The license database is not spatial<br />The license database is address-based<br />We don’t have a spatial point address layer<br />We do have a census landbase<br />Address format structure does not match between the two databases<br />We wish to have an automated process for producing updated business license datasets<br />
  • 21. Available Ingredients<br />Census Landbase<br />Business License<br />Database<br />NAICS Code<br />Lookup Table<br />
  • 22. Desired Outputs<br />GIS point layer of home-based businesses<br />Business trade name<br />Address (full and parsed)<br />License number<br />License category/subcategory<br />NAICS code<br />NAICS code full description<br />NAICS sector<br />NAICS class<br />
  • 23. Salad Course: The Model<br />Create address points;<br />Match to BL data<br />Create address<br />join string<br />Merge with NAICS<br />lookup table<br />
  • 24. Resulting Delicacy<br />
  • 25. Salad Course in Review<br />Overall development time was approximately 5 days, including initial sandbox time.<br />Very happy with final result. Have already updated the data with the latest monthly data dump.<br />Challenges:<br />Understanding the tools<br />Chaining multiple models together<br />Parameterizing the model for flexibility<br />
  • 26. Main Course<br />Fire Services 9-1-1 Dispatch<br />
  • 27. Main Course:Fire Services 9-1-1 Dispatch<br />The challenge for the kitchen:<br />9-1-1 calls come in from Telus<br />Telus feed has specifically formatted addresses<br />9-1-1 addresses must be structured to match Telus feed data as matching is automated<br />9-1-1 system requires address point dataset<br />All non-relevant addresses are to be stripped<br />Each address must have a unique ID, repeatable through each data load<br />Addresses cannot be duplicated<br />The City does not have a spatial address dataset<br />
  • 28. Available Ingredients<br />Parcel Polygons<br />Townhouse Polygons<br />Apartment/Condo<br />Suites<br />Additional Address<br />Points<br />
  • 29. Desired Outputs<br />GIS address file<br />Shapefile format<br />Point feature type<br />Containing only addresses that will be referenced by Telus<br />Containing only those fields required by CriSys<br />Formatted to match Telus feed<br />
  • 30. Challenge for the Kitchen<br />Must be repeatable<br />Must be automated as much as possible<br />Must be able to easily QC for duplicate IDs and Addresses<br />Must be fast<br />Must be flexible to deal with changing inputs<br />
  • 31. Main Course: The Model<br />
  • 32. Building the Crust<br />Convert polygons to points<br />Filter out non-relevant addresses<br />Filter out Null addresses/IDs – multiple methods required<br />
  • 33. Building the Crust<br />Convert UPPER CASE street names to Sentence Case<br />Fix McKenney Avenue (inner capital letter)<br />Adjust spelling of key road names to match CriSys/Telus<br />
  • 34. Building the Crust<br />Checking for duplicate IDs<br />Filtering out or dealing with duplicate IDs<br />Filter out specific addresses for replacement<br />
  • 35. Building the Crust<br />Converting LegallotID value to CriSys ID<br />Automated removal of duplicate addresses<br />Output QC datasets for parcel addresses and duplicates<br />
  • 36. Making the Filling<br />Convert townhouse polygons to points<br />Change UPPER CASE street names to Sentence Case<br />Fix McKenney Ave case<br />
  • 37. Making the Filling<br />Filter for addresses with/without suite numbers<br />Create unique ID numbers from LegallotID, suite and house numbers<br />Deal with two specific problem properties<br />Output QC dataset<br />
  • 38. Making the Topping<br />Converting multi-story suite polygons to points<br />Change UPPER CASE street names to Sentence Case<br />Fix McKenney Ave case<br />
  • 39. Making the Topping<br />Create final ID string from LegallotID, house number and suite number (when applicable)<br />Output QC dataset<br />
  • 40. Putting It All Together<br />Adding in the extra points<br />Streaming all the outputs into a single final address point file<br />
  • 41. Main Course: The Model<br />
  • 42. Resulting Delicacy<br />
  • 43. Main Course in Review<br />This was actually our first (and most complex) FME project<br />This is the second iteration of the model<br />Taking the course helped in refining the model (thanks!)<br />It’s a live model and is expected to have to constantly evolve to deal with data oddities<br />The model is currently pretty robust<br />Challenges were mostly related to discovering and dealing with data oddities<br />
  • 44. A Quick Desert<br />Recap and Thoughts<br />
  • 45. Recap and Thoughts<br />We LOVE FME!<br />The software is intuitive<br />It’s also powerful and complicated<br />It definitely meets our needs<br />We see it playing a key role in data integration<br />We use it from soup to nuts – everything from simple field rearranging to complex translation and integration models<br />We see it being useful for more than just spatial information<br />
  • 46. Thank You!<br />Questions?<br />For more information:<br />Tammy Kobliuk tkobliuk@st-albert.net<br />City of St Albert<br />Enter other resources<br />

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