1. Generating PECAS Base Year Built Form for Clayton County in Atlanta TRB Innovations in Travel Modeling 2014 Geraldine J. Fuenmayor HBA Specto Incorporated University of Calgary gjfuemay@ucalgary.ca; gfm@hbaspecto.com John E. Abraham HBA Specto Incorporated jea@hbaspecto.com John Douglas Hunt HBA Specto Incorporated University of Calgary jdhunt@ucalgary.ca Wei Wang Atlanta Regional Commission wwang@atlantaregional.com

2. Context PECAS Spatial Economic and Land Use Model for Atlanta Constructed and calibrated – being used for policy analysis and forecasting (incl RTP) “Agile and Incremental Project Management” – Production-ready model – and ongoing improvements One type of ongoing improvement is replacing information on base-year built-form – And previous Clayton County data was quite bad

3. PECAS AA - Economic Interactions Module SD - Space Development Module Economy Size Rents Time t Time t + 1 Locations/ Interactions Space Inventory Travel Conditions AA - Economic Interactions Module Economy Size Economy size forecast (REMI) Transport demand model Economy size forecast (REMI) Transport demand model Locations/ Interactions Economic Conditions

4. Issues with land use data Spatial consumptions rates heterogeneous and elastic – Even within the most detailed industrial classifications Measurement errors in both employment and building data – Across the word, and even in the USA Categorical mismatch in built form descriptions

5. Employment Population Locations Employment Population Locations Input Output Economic Relationships Transport Costs (willingness to travel to interact) Floorspace consumption rates Activity Allocation Module elasticities/ substitutions Measured Quantity of Space by TAZ Modeled Quantity of Space by LUZ Observed Space Rents Modeled Space Rents Employment and floorspace calibration

6. Options for SD Base Year Parcel Database Observed Parcel GIS data Improvements measured by tax assessors Parcels database for SD model ? Consistent Floorspace Identified and addressed inconsistencies Option 1: SD uses observed parcel data, even thought it has obvious mistakes and is not compatible with AA’s view of the world. Difference stored in “FloorspaceDelta” file. Option NAO: Spend the rest of your life trying to “fix the parcel data” Option 2: Develop a Synthetic Parcel Database that respects the measured data as much as possible, but is consistent with simplified model and the tradeoffs made in calibration.

7. Clayton County

8. FS - Floorspace Synthesizer Output shape file (Initial runs) Calibration Strategies and adjustments Output shape file (Calibrated Targets) Scoring System: Level 1: assign a score from match column Level 2: score – penalty function (FAR) Level 3: final penalty (based on space) Parcel ID Observed Pecas type Oberved Pecas type description Obsserved FAR Assigned space Assigned FARBuilt 0001HMultifamily2.30722.221 0002LSingle family0.80760.771 0003OOffice1.80791.91 0004RRetail2.20832.161 0005DIndustrial0.60820.451 0006SInstitutional1.20821.151 0007AAgriculture0.06650.051 0008VVacant0.000 0 changing columns during FS assignment TAZ727679 1045600112570 105172132650 106998205632 107009987 PECAS SPACE TYPES: 72= Multifamily68= Industry 76= SingleFamily83= Institutional 79= Office65= Agriculture 82= Retail0 = Vacant MCT - Match Coefficient Table FI - Floorspace inventory PG - Parcel Geodatabase

9. PG - Parcel Geodatabase Parce l ID Observed Pecas type Observed Pecas type description Observed FAR Assig ned space Assigned FAR Built 0001HMultifamily2.30722.221 0002LSingle family0.80760.771 0003OOffice1.80791.91 0004RRetail2.20832.161 0005DIndustrial0.60820.451 0006SInstitutional1.20821.151 0007AAgriculture0.06650.051 0008VVacant0.000 0 changing columns during FS assignment TAZ727679 1045600112570 105172132650 106998205632 107009987 PECAS SPACE TYPES: 72= Multifamily68= Industry 76= SingleFamily83= Institutional 79= Office65= Agriculture 82= Retail0 = Vacant Figure 2. Floorspace Synthesizer: Floorspace Inventory and Parcel Geodatabase FI - Floorspace inventory

10. FS - Floorspace Synthesizer Output shape file (Initial runs) Calibration Strategies and adjustments Output shape file (Calibrated Targets) Scoring System: Level 1: assign a score from match column Level 2: score – penalty function (FAR) Level 3: final penalty (based on space) MCT - Match Coefficient Table Figure 3. Floorspace Synthesizer Scoring System, Output Files, Calibration Strategies and Calibrated Targets

11. Score Look up attributes for suitability Penalty and bonus for already assigned space Penalty when FAR gets too high

12. Simplified Example HouseApartmentOffice IDQuantityScoreIDQuantityScoreIDQuantityScore 509.88309.981908.24 409.541609.56307.68 709.351507.891407.46 108.74107.85407.40 1008.621907.851107.27 608.35707.841206.73 1508.291006.701806.15 808.251106.13205.87 1307.49405.55505.14 306.781705.10904.99 205.84505.041504.61 1705.38803.73803.60 1605.311403.682002.63 2005.091303.41602.51 904.342002.511002.14 1203.92601.871602.07 1401.971201.791300.75 1101.09201.411700.58 1800.861801.04100.25 1900.24900.82700.03

13. Simplified Example HouseApartmentOffice IDQuantityScoreIDQuantityScoreIDQuantityScore 550010.88309.981908.24 409.541609.56307.68 709.351507.891407.46 108.74107.85407.40 1008.621907.851107.27 608.35707.841206.73 1508.291006.701806.15 808.251106.13205.87 1307.49405.55504.64 306.781705.10904.99 205.84504.541504.61 1705.38803.73803.60 1605.311403.682002.63 2005.091303.41602.51 904.342002.511002.14 1203.92601.871602.07 1401.971201.791300.75 1101.09201.411700.58 1800.861801.04100.25 1900.24900.82700.03

14. Simplified Example HouseApartmentOffice IDQuantityScoreIDQuantityScoreIDQuantityScore 550010.88309.981908.24 409.541609.56307.68 709.351507.891407.46 108.74107.85407.40 1008.621907.851107.27 608.35707.841206.73 1508.291006.701806.15 808.251106.13205.87 1307.49405.55904.99 306.781705.10504.64 205.84504.541504.61 1705.38803.73803.60 1605.311403.682002.63 2005.091303.41602.51 904.342002.511002.14 1203.92601.871602.07 1401.971201.791300.75 1101.09201.411700.58 1800.861801.04100.25 1900.24900.82700.03

15. Simplified Example HouseApartmentOffice IDQuantityScoreIDQuantityScoreIDQuantityScore 550010.88350010.98195009.24 409.541609.561407.46 709.351507.89407.40 108.74107.851107.27 1008.62707.84307.18 608.351907.351206.73 1508.291006.701806.15 808.251106.13205.87 1307.49405.55904.99 306.281705.10504.64 205.84504.541504.61 1705.38803.73803.60 1605.311403.682002.63 2005.091303.41602.51 904.342002.511002.14 1203.92601.871602.07 1401.971201.791300.75 1101.09201.411700.58 1800.861801.04100.25 190-0.26900.82700.03

16. Simplified Example HouseApartmentOffice IDQuantityScoreIDQuantityScoreIDQuantityScore 425009.153100010.98195009.24 525009.501609.561407.46 709.351507.89407.40 108.74107.851107.27 1008.62707.84307.18 608.351907.351206.73 1508.291006.701806.15 808.251106.13205.87 1307.491705.10904.99 306.28405.051504.61 205.84504.54504.14 1705.38803.73803.60 1605.311403.682002.63 2005.091303.41602.51 904.342002.511002.14 1203.92601.871602.07 1401.971201.791300.75 1101.09201.411700.58 1800.861801.04100.25 190-0.26900.82700.03

17. Simplified Example HouseApartmentOffice IDQuantityScoreIDQuantityScoreIDQuantityScore 525009.503150010.981910009.24 425009.151609.561407.46 725008.971507.891107.27 108.74107.85307.18 1008.621907.35406.90 608.35707.341206.73 1508.291006.701806.15 808.251106.13205.87 1307.491705.10904.99 306.28405.051504.61 205.84504.54504.14 1705.38803.73803.60 1605.311403.682002.63 2005.091303.41602.51 904.342002.511002.14 1203.92601.871602.07 1401.971201.791300.75 1101.09201.411700.58 1800.861801.04100.25 190-0.26900.8270-0.47

18. Simplified Example HouseApartmentOffice IDQuantityScoreIDQuantityScoreIDQuantityScore 430008.743200010.981915009.24 535008.671609.561407.46 1008.621507.891107.27 730008.55107.35307.18 125008.361907.35406.90 608.35707.341206.73 1508.291006.701806.15 808.251106.13205.87 1307.491705.10904.99 306.28405.051504.61 205.84504.54504.14 1705.38803.73803.60 1605.311403.682002.63 2005.091303.41602.51 904.342002.511002.14 1203.92601.871602.07 1401.971201.791300.75 1101.09201.411700.58 1800.861801.0410-0.25 190-0.26900.8270-0.47

19. Simplified Example HouseApartmentOffice IDQuantityScoreIDQuantityScoreIDQuantityScore 105009.623200010.981915009.24 430008.741609.561407.46 535008.671507.891107.27 730008.55107.35307.18 125008.361907.35406.90 608.35707.341206.73 1508.291006.201806.15 808.251106.13205.87 1307.491705.10904.99 306.28405.051504.61 205.84504.54504.14 1705.38803.73803.60 1605.311403.682002.63 2005.091303.41602.51 904.342002.511602.07 1203.92601.871001.64 1401.971201.791300.75 1101.09201.411700.58 1800.861801.0410-0.25 190-0.26900.8270-0.47

20. The synthesizer was correct in assigning residential space to parcels that had been observed to have agriculture land; but it had no information to identify which of the “observed agricultural” parcels it should use Figure 4. Example of parcels with agriculture assigned as single family

21. 3. Major results and improvements

22. Implications / Conclusions Data are wrong – And when they are right, are inconsistent in other ways Theory helps identify inconsistencies – Strong theoretical model also needs system for dealing with inconsistencies Incremental model data improvement program

23. Implications / Conclusions Scoring system identified best possible parcels to hold compromise space quantity – Scores based on observed parcel attributes Comparing assigned vs observed type/intensity showed TAZ level inconsistencies. – Tracked to incorrect/suspect data and odd places like airports Correct problems, accept inconsistencies, or modify scoring to put buildings in better locations