generating pecas base year built form for clayton county in atlanta trb innovations in travel...
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Generating PECAS Base Year Built Form for Clayton County in Atlanta
TRB Innovations in Travel Modeling 2014
Geraldine J. FuenmayorHBA Specto IncorporatedUniversity of [email protected]; [email protected]
John E. AbrahamHBA Specto [email protected]
John Douglas HuntHBA Specto IncorporatedUniversity of [email protected]
Wei WangAtlanta Regional [email protected]
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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
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PECAS
AA - Economic Interactions Module
SD - Space Development
Module
EconomySize
Ren
ts
Time t Time t + 1
Locations/Interactions
SpaceInventor
y
Travel Conditions
AA - Economic Interactions Module
EconomySize
Economy size forecast
(REMI)
Transport demand model
Economy size forecast
(REMI)
Transport demand model
Locations/Interactions
Economic Conditions
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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
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EmploymentPopulationLocations
Input Output Economic
RelationshipsTransport 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 RentsModeled Space Rents
Employment and floorspace calibration
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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.
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Clayton County
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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
ObsservedFAR
Assigned space
Assigned FAR Built
0001 H Multifamily 2.30 72 2.22 1
0002 L Single family 0.80 76 0.77 1
0003 O Office 1.80 79 1.9 1
0004 R Retail 2.20 83 2.16 1
0005 D Industrial 0.60 82 0.45 1
0006 S Institutional 1.20 82 1.15 1
0007 A Agriculture 0.06 65 0.05 1
0008 V Vacant 0.00 0 0.00 0
changing columns during FS assignment
TAZ 72 76 79
104 5600 11257 0
105 1721 3265 0
106 9982 0 5632
107 0 0 9987
PECAS SPACE TYPES:
72= Multifamily 68= Industry
76= SingleFamily 83= Institutional
79= Office 65= Agriculture
82= Retail 0 = Vacant
MCT - Match Coefficient Tablefieldname pecastype fieldvalue fartarget match idbuilt 65 0 0.1 -4 1built 68 0 0.3 -4 2built 72 0 0.8 -4 3built 79 1 0 0 4built 82 1 0 0 5built 83 1 0 0 6observed_pecas_type 76 A 0 -0.2 7observed_pecas_type 76 D 0 -0.2 8observed_pecas_type 76 H 0 -0.2 9observed_pecas_type 76 L 0 4 10observed_pecas_type 76 M 0 -0.2 11observed_pecas_type 76 O 0 -0.2 12observed_pecas_type 76 R 0 -0.2 13observed_pecas_type 76 S 0 -0.2 14
FI - Floorspace inventory PG - Parcel Geodatabase
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PG - Parcel Geodatabase
Parcel ID
Observed Pecas type
Observed Pecas type description
ObservedFAR
Assigned space
Assigned FAR
Built
0001 H Multifamily 2.30 72 2.22 1
0002 L Single family 0.80 76 0.77 1
0003 O Office 1.80 79 1.9 1
0004 R Retail 2.20 83 2.16 1
0005 D Industrial 0.60 82 0.45 1
0006 S Institutional 1.20 82 1.15 1
0007 A Agriculture 0.06 65 0.05 1
0008 V Vacant 0.00 0 0.00 0
changing columns during
FS assignmentTAZ 72 76 79
104 5600 11257 0
105 1721 3265 0
106 9982 0 5632
107 0 0 9987
PECAS SPACE TYPES:72= Multifamily 68= Industry76= SingleFamily 83= Institutional
79= Office 65= Agriculture82= Retail 0 = Vacant
Figure 2. Floorspace Synthesizer: Floorspace Inventory and Parcel Geodatabase
FI - Floorspace inventory
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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 Tablefieldname pecastype fieldvalue fartarget match idbuilt 65 0 0.1 -4 1built 68 0 0.3 -4 2built 72 0 0.8 -4 3built 79 1 0 0 4built 82 1 0 0 5built 83 1 0 0 6observed_pecas_type 76 A 0 -0.2 7observed_pecas_type 76 D 0 -0.2 8observed_pecas_type 76 H 0 -0.2 9observed_pecas_type 76 L 0 4 10observed_pecas_type 76 M 0 -0.2 11observed_pecas_type 76 O 0 -0.2 12observed_pecas_type 76 R 0 -0.2 13observed_pecas_type 76 S 0 -0.2 14
Figure 3. Floorspace Synthesizer Scoring System, Output Files, Calibration Strategies and Calibrated Targets
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ScoreLook up attributes for
suitability
Penalty and bonus for
already assigned space
Penalty when FAR gets too
high
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Simplified ExampleHouse Apartment Office
ID Quantity Score ID Quantity Score ID Quantity Score5 0 9.88 3 0 9.98 19 0 8.244 0 9.54 16 0 9.56 3 0 7.687 0 9.35 15 0 7.89 14 0 7.461 0 8.74 1 0 7.85 4 0 7.40
10 0 8.62 19 0 7.85 11 0 7.276 0 8.35 7 0 7.84 12 0 6.73
15 0 8.29 10 0 6.70 18 0 6.158 0 8.25 11 0 6.13 2 0 5.87
13 0 7.49 4 0 5.55 5 0 5.143 0 6.78 17 0 5.10 9 0 4.992 0 5.84 5 0 5.04 15 0 4.61
17 0 5.38 8 0 3.73 8 0 3.6016 0 5.31 14 0 3.68 20 0 2.6320 0 5.09 13 0 3.41 6 0 2.51
9 0 4.34 20 0 2.51 10 0 2.1412 0 3.92 6 0 1.87 16 0 2.0714 0 1.97 12 0 1.79 13 0 0.7511 0 1.09 2 0 1.41 17 0 0.5818 0 0.86 18 0 1.04 1 0 0.2519 0 0.24 9 0 0.82 7 0 0.03
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Simplified ExampleHouse Apartment Office
ID Quantity Score ID Quantity Score ID Quantity Score5 500 10.88 3 0 9.98 19 0 8.244 0 9.54 16 0 9.56 3 0 7.687 0 9.35 15 0 7.89 14 0 7.461 0 8.74 1 0 7.85 4 0 7.40
10 0 8.62 19 0 7.85 11 0 7.276 0 8.35 7 0 7.84 12 0 6.73
15 0 8.29 10 0 6.70 18 0 6.158 0 8.25 11 0 6.13 2 0 5.87
13 0 7.49 4 0 5.55 5 0 4.643 0 6.78 17 0 5.10 9 0 4.992 0 5.84 5 0 4.54 15 0 4.61
17 0 5.38 8 0 3.73 8 0 3.6016 0 5.31 14 0 3.68 20 0 2.6320 0 5.09 13 0 3.41 6 0 2.51
9 0 4.34 20 0 2.51 10 0 2.1412 0 3.92 6 0 1.87 16 0 2.0714 0 1.97 12 0 1.79 13 0 0.7511 0 1.09 2 0 1.41 17 0 0.5818 0 0.86 18 0 1.04 1 0 0.2519 0 0.24 9 0 0.82 7 0 0.03
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Simplified ExampleHouse Apartment Office
ID Quantity Score ID Quantity Score ID Quantity Score5 500 10.88 3 0 9.98 19 0 8.244 0 9.54 16 0 9.56 3 0 7.687 0 9.35 15 0 7.89 14 0 7.461 0 8.74 1 0 7.85 4 0 7.40
10 0 8.62 19 0 7.85 11 0 7.276 0 8.35 7 0 7.84 12 0 6.73
15 0 8.29 10 0 6.70 18 0 6.158 0 8.25 11 0 6.13 2 0 5.87
13 0 7.49 4 0 5.55 9 0 4.993 0 6.78 17 0 5.10 5 0 4.642 0 5.84 5 0 4.54 15 0 4.61
17 0 5.38 8 0 3.73 8 0 3.6016 0 5.31 14 0 3.68 20 0 2.6320 0 5.09 13 0 3.41 6 0 2.51
9 0 4.34 20 0 2.51 10 0 2.1412 0 3.92 6 0 1.87 16 0 2.0714 0 1.97 12 0 1.79 13 0 0.7511 0 1.09 2 0 1.41 17 0 0.5818 0 0.86 18 0 1.04 1 0 0.2519 0 0.24 9 0 0.82 7 0 0.03
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Simplified ExampleHouse Apartment Office
ID Quantity Score ID Quantity Score ID Quantity Score5 500 10.88 3 500 10.98 19 500 9.244 0 9.54 16 0 9.56 14 0 7.467 0 9.35 15 0 7.89 4 0 7.401 0 8.74 1 0 7.85 11 0 7.27
10 0 8.62 7 0 7.84 3 0 7.186 0 8.35 19 0 7.35 12 0 6.73
15 0 8.29 10 0 6.70 18 0 6.158 0 8.25 11 0 6.13 2 0 5.87
13 0 7.49 4 0 5.55 9 0 4.993 0 6.28 17 0 5.10 5 0 4.642 0 5.84 5 0 4.54 15 0 4.61
17 0 5.38 8 0 3.73 8 0 3.6016 0 5.31 14 0 3.68 20 0 2.6320 0 5.09 13 0 3.41 6 0 2.51
9 0 4.34 20 0 2.51 10 0 2.1412 0 3.92 6 0 1.87 16 0 2.0714 0 1.97 12 0 1.79 13 0 0.7511 0 1.09 2 0 1.41 17 0 0.5818 0 0.86 18 0 1.04 1 0 0.2519 0 -0.26 9 0 0.82 7 0 0.03
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Simplified ExampleHouse Apartment Office
ID Quantity Score ID Quantity Score ID Quantity Score4 2500 9.15 3 1000 10.98 19 500 9.245 2500 9.50 16 0 9.56 14 0 7.467 0 9.35 15 0 7.89 4 0 7.401 0 8.74 1 0 7.85 11 0 7.27
10 0 8.62 7 0 7.84 3 0 7.186 0 8.35 19 0 7.35 12 0 6.73
15 0 8.29 10 0 6.70 18 0 6.158 0 8.25 11 0 6.13 2 0 5.87
13 0 7.49 17 0 5.10 9 0 4.993 0 6.28 4 0 5.05 15 0 4.612 0 5.84 5 0 4.54 5 0 4.14
17 0 5.38 8 0 3.73 8 0 3.6016 0 5.31 14 0 3.68 20 0 2.6320 0 5.09 13 0 3.41 6 0 2.51
9 0 4.34 20 0 2.51 10 0 2.1412 0 3.92 6 0 1.87 16 0 2.0714 0 1.97 12 0 1.79 13 0 0.7511 0 1.09 2 0 1.41 17 0 0.5818 0 0.86 18 0 1.04 1 0 0.2519 0 -0.26 9 0 0.82 7 0 0.03
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Simplified ExampleHouse Apartment Office
ID Quantity Score ID Quantity Score ID Quantity Score5 2500 9.50 3 1500 10.98 19 1000 9.244 2500 9.15 16 0 9.56 14 0 7.467 2500 8.97 15 0 7.89 11 0 7.271 0 8.74 1 0 7.85 3 0 7.18
10 0 8.62 19 0 7.35 4 0 6.906 0 8.35 7 0 7.34 12 0 6.73
15 0 8.29 10 0 6.70 18 0 6.158 0 8.25 11 0 6.13 2 0 5.87
13 0 7.49 17 0 5.10 9 0 4.993 0 6.28 4 0 5.05 15 0 4.612 0 5.84 5 0 4.54 5 0 4.14
17 0 5.38 8 0 3.73 8 0 3.6016 0 5.31 14 0 3.68 20 0 2.6320 0 5.09 13 0 3.41 6 0 2.51
9 0 4.34 20 0 2.51 10 0 2.1412 0 3.92 6 0 1.87 16 0 2.0714 0 1.97 12 0 1.79 13 0 0.7511 0 1.09 2 0 1.41 17 0 0.5818 0 0.86 18 0 1.04 1 0 0.2519 0 -0.26 9 0 0.82 7 0 -0.47
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Simplified ExampleHouse Apartment Office
ID Quantity Score ID Quantity Score ID Quantity Score4 3000 8.74 3 2000 10.98 19 1500 9.245 3500 8.67 16 0 9.56 14 0 7.46
10 0 8.62 15 0 7.89 11 0 7.277 3000 8.55 1 0 7.35 3 0 7.181 2500 8.36 19 0 7.35 4 0 6.906 0 8.35 7 0 7.34 12 0 6.73
15 0 8.29 10 0 6.70 18 0 6.158 0 8.25 11 0 6.13 2 0 5.87
13 0 7.49 17 0 5.10 9 0 4.993 0 6.28 4 0 5.05 15 0 4.612 0 5.84 5 0 4.54 5 0 4.14
17 0 5.38 8 0 3.73 8 0 3.6016 0 5.31 14 0 3.68 20 0 2.6320 0 5.09 13 0 3.41 6 0 2.51
9 0 4.34 20 0 2.51 10 0 2.1412 0 3.92 6 0 1.87 16 0 2.0714 0 1.97 12 0 1.79 13 0 0.7511 0 1.09 2 0 1.41 17 0 0.5818 0 0.86 18 0 1.04 1 0 -0.2519 0 -0.26 9 0 0.82 7 0 -0.47
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Simplified ExampleHouse Apartment Office
ID Quantity Score ID Quantity Score ID Quantity Score10 500 9.62 3 2000 10.98 19 1500 9.24
4 3000 8.74 16 0 9.56 14 0 7.465 3500 8.67 15 0 7.89 11 0 7.277 3000 8.55 1 0 7.35 3 0 7.181 2500 8.36 19 0 7.35 4 0 6.906 0 8.35 7 0 7.34 12 0 6.73
15 0 8.29 10 0 6.20 18 0 6.158 0 8.25 11 0 6.13 2 0 5.87
13 0 7.49 17 0 5.10 9 0 4.993 0 6.28 4 0 5.05 15 0 4.612 0 5.84 5 0 4.54 5 0 4.14
17 0 5.38 8 0 3.73 8 0 3.6016 0 5.31 14 0 3.68 20 0 2.6320 0 5.09 13 0 3.41 6 0 2.51
9 0 4.34 20 0 2.51 16 0 2.0712 0 3.92 6 0 1.87 10 0 1.6414 0 1.97 12 0 1.79 13 0 0.7511 0 1.09 2 0 1.41 17 0 0.5818 0 0.86 18 0 1.04 1 0 -0.2519 0 -0.26 9 0 0.82 7 0 -0.47
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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
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3. Major results and improvements
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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
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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