Generating PECAS Base Year Built Form for Clayton County in Atlanta

TRB Innovations in Travel Modeling 2014

Geraldine J. FuenmayorHBA Specto IncorporatedUniversity of Calgarygjfuemay@ucalgary.ca; gfm@hbaspecto.com

John E. AbrahamHBA Specto Incorporatedjea@hbaspecto.com

John Douglas HuntHBA Specto IncorporatedUniversity of Calgaryjdhunt@ucalgary.ca

Wei WangAtlanta Regional Commissionwwang@atlantaregional.com

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

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

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

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

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.

Clayton County

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

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

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

ScoreLook up attributes for

suitability

Penalty and bonus for

already assigned space

Penalty when FAR gets too

high

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

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

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

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

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

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

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

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

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

3. Major results and improvements

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

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