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Simone Di Zio Simone Di Zio University G. d’Annunzio University G. d’Annunzio Pescara, Italy Pescara, Italy ETH Zürich, ETH Zürich, March 17/18th 2008 March 17/18th 2008 Rome UrbanSIM Rome UrbanSIM
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Page 1: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Simone Di ZioSimone Di ZioUniversity G. d’AnnunzioUniversity G. d’Annunzio

Pescara, ItalyPescara, Italy

ETH Zürich,ETH Zürich, March 17/18th 2008March 17/18th 2008

Rome UrbanSIMRome UrbanSIM

Page 2: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Municipality of Rome

• Grid Cells size: 250 x 250 mtGrid Cells size: 250 x 250 mt

• Number of Grid Cells: 23933;Number of Grid Cells: 23933;

• 1498 Km1498 Km22

• Base Year: 1991Base Year: 1991

2005

We started the implementation of UrbanSIM

Page 3: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

Rome Critical points

Desirable improvements

UrbanSIM

Critical Points during the implementation on ROME

Page 4: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

MEDASEMEDASE

CORINECORINE

MEDASE is sufficiently detailed but unfortunately it is available only for a portion of the study area.

CORINE is available for the whole M.A. but is not much detailed and, especially in the centre of the city, is not sufficient for distinguish features in a spatial resolution of 250mt.

Land Use Data are available from two different sources.

1. MEDASE project, from CNR (Italian National Research Council).

2. CORINE programme (Coordination of Information on the Environment).

Page 5: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

Starting from two different lists of categories we created a unique final classification of the Land Use

Page 6: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Before 1991

Municipality of Rome

1498 Km2

After 1991

Municipality of Fiumicino

Municipality of Rome

Changes in the administration of the Study Area.

Problems in collecting data for the construction of the Base Year DB.

Page 7: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

The City Master Plan was available in GIS format only for the Rome Municipality.

For the Municipality of Fiumicino we obtained only an old version on paper.

Page 8: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

Problems in comparing e reclassifying the two different data.

Two different lists of plan type.

Page 9: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

ISTAT, Italian National Institute of StatisticsISTAT, Italian National Institute of Statistics - National Census of the Population 1991, 2001. - National Census of the Industry 1991, 2001.

Municipality of Rome Municipality of Rome - STA, Agency for the Mobility of Rome - Risorse per Roma (Resources for Rome)

CRESME ResearchCRESME Research

BIRBIR - Real Estate Stock of Rome

Bank of ItalyBank of Italy - Survey on Household Income and Wealth 1991

CNRCNR - National Research Council, MEDASE

CORINECORINE programme

DATA SOURCES

DATA SOURCES

1. The ISTAT was very late in releasing the 2001 census data.

2. In 2005 (September) we had only four economic sectors. (Industry, Trade, Service, Institution)

Jobs DB

Page 10: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

TRAVEL DATA

1. We have had many problems in acquiring travel data.

A first version was available only in 2006 (March - April)

Municipality of Rome - STA, Agency for the Mobility of Rome - Risorse per Roma (Resources for Rome)

Page 11: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

FiumicinoFiumicinoMissing DataMissing Data RomeRome

TRAVEL DATA2. Once again the data were available only for Rome and not for Fiumicino

Page 12: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

FiumicinoFiumicino RomeRome

Traffic Zones

463 463 ZonesZones

Page 13: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Comparing with Suddivisioni Toponomastiche

Travel Zones Suddivisioni Toponomastiche

Page 14: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

New Traffic Zones

463 + 8 = 471 Traffic Zones463 + 8 = 471 Traffic Zones

8 new Zones for 8 new Zones for the Municipality the Municipality of Fiumicinoof Fiumicino

Reconstruction of the Traffic ZonesReconstruction of the Traffic Zones

Page 15: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

DESTINATION

ORIGIN 1 2 3 4 5 6 … … 462 463 1001 1002 1003 1004 1005 1006 1007 1008

1 0 12.7 11.5 11.5 13 12.6 … … 15.1 15

2 12.9 0 12.8 11.8 11.3 12.2 … … 13.9 13.4

3 11.8 12 0 12.2 12.3 11.7 … … 15.6 15.4

4 11.5 11.6 12.4 0 12.2 12.8 … … 13.8 13.7

5 13.1 11.4 12.4 12.2 0 11 … … 13.9 13.4

6 13.6 12.2 11.9 12.7 11.7 0 … … 14.8 14.4

… … … … … … … … … … … … … … … … … … …

… … … … … … … … … … … … … … … … … … …

462 13.1 13.7 14.1 13.3 14.9 15.3 … … 0 11.7

463 13.6 14.1 14.5 13.7 13.9 15.4 … … 11.4 0

1001 … …

1002 … …

1003 … …

1004 … …

1005 … …

1006 … …

1007 … …

1008 … …

Traffic Zones Data – Travel Times

463 + 8 = 471 Traffic Zones463 + 8 = 471 Traffic Zones

SSik

kk

Sik = f(xi) = f (xi1,…,xin)

Geostatistical ApproachGeostatistical Approach

Page 16: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

DESTINATION

ORIGIN 1 2 3 4 5 6 … … 462 463 1001 1002 1003 1004 1005 1006 1007 1008

1 0 12.7 11.5 11.5 13 12.6 … … 15.1 15

2 12.9 0 12.8 11.8 11.3 12.2 … … 13.9 13.4

3 11.8 12 0 12.2 12.3 11.7 … … 15.6 15.4

4 11.5 11.6 12.4 0 12.2 12.8 … … 13.8 13.7

5 13.1 11.4 12.4 12.2 0 11 … … 13.9 13.4

6 13.6 12.2 11.9 12.7 11.7 0 … … 14.8 14.4

… … … … … … … … … … … … … … … … … … …

… … … … … … … … … … … … … … … … … … …

462 13.1 13.7 14.1 13.3 14.9 15.3 … … 0 11.7

463 13.6 14.1 14.5 13.7 13.9 15.4 … … 11.4 0

1001 … …

1002 … …

1003 … …

1004 … …

1005 … …

1006 … …

1007 … …

1008 … …

463 + 8 = 471 Traffic Zones463 + 8 = 471 Traffic Zones

TTkj

kkTkj= f(xj) = f (x1j,…,xnj)

Traffic Zones Data – Travel Times

Geostatistical ApproachGeostatistical Approach

Page 17: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Kriging principal steps

4.4. Make the prediction:Make the prediction: from the kriging weights for the measured values, we can calculate a prediction for the location with the unknown value.

)()(ˆ1

0 i

N

ii sXsX

hss

ssh

hji

ji XXn

2

)(

1)(ˆ2

3.3. Determine the kriging weights:Determine the kriging weights: using the autocorrelation values from the variogram model the weights are estimated i

1.1. Calculate the empirical variogram:Calculate the empirical variogram: pairs that are close in distance should have a smaller difference than those farther away from one another. The extent to which this assumption is true is examined in the empirical variogram.

2.2. Fit a model:Fit a model: the model quantifies the spatial autocorrelation in the data.

Page 18: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Anisotropy

AnisotropyAnisotropy is a characteristic of a random process that shows higher autocorrelation in one direction than another.

Travel times are strongly related to the road network. In our model we must consider also the influence of different influence of different directionsdirections in estimating the surface.

Page 19: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

From the CBD to the Airport

We need to estimateSik

Where

• Sik= f(xi)=f(xi1,…,xin)

• i = CBD

• k = Fiumicino Airport

CBDCBDii

Fiumicino airport

kk

ExampleExample

Page 20: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Choosing the semivariogram model

The direction is important: we use an

AnisotropicAnisotropic variogram

model

Geostatistical Geostatistical Analyst Analyst

extensionextension

Page 21: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Making the prediction

Coordinates of the airport

Page 22: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Legend

Ordinary Kriging

travel time from CBD

0.000000 - 9.739294

9.739294 - 15.616524

15.616524 - 19.163170

19.163170 - 21.303413

21.303413 - 22.594954

22.594954 - 23.374342

23.374342 - 24.665880

24.665880 - 26.806126

26.806126 - 30.352772

30.352772 - 36.230000

CBDCBD

Final Prediction Map

We have used this map to predict missing data on Rome

Page 23: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

CENSUS TRACTSCENSUS TRACTS

The National Institute of Statistics (ISTAT), from 1991 to 2001 changed the census tracts.

19911991

20012001

Page 24: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

RESIDENTIAL RESIDENTIAL LAND VALUE LAND VALUE

We don’t have data

House Price = L + (S*C)House Price = L + (S*C)

L L = Residential Land Value

S S = Surface of the House (in mq)

CC = Construction Cost per mq

(S*C)(S*C) = Residential improvement value

Residential Land Value:

L = House Price - (S*C)L = House Price - (S*C)

Page 25: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

HOUSE PRICEHOUSE PRICE

In 1991 we have data only for some Suddivisioni Toponomastiche

Page 26: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

RECONSTRUCTION RECONSTRUCTION OF MISSING DATAOF MISSING DATAWe considered separately the core and the rest of the MA.

• Out of the core there is homogeneity in the area. We considered simply a mean value.

• In the CORE we have used the IDW (Inverse Distance Weighted) in order to estimate missing values.

HOUSE HOUSE PRICESPRICES

RESIDENTIAL RESIDENTIAL LAND VALUESLAND VALUES

HOUSE PRICEHOUSE PRICE

Page 27: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

20072007

UrbanSIM 3UrbanSIM 3

UrbanSIM 4UrbanSIM 4

Page 28: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

User Interface

Data Availabilit

y

External Models User Inputs

Data Store

GIS Visualization

UrbanSIM CORE(Simulations)

Base Year

ASCIIOutput Files

UrbanSIMUser

Interface

Data Availabilit

y

Estimation Calibration automationautomation

Data homogeneit

y

User Interface

User Interface

Data completenes

s

Understanding and solving

simulation errors

Transfers of data among

different softwares

ESTIMATION AND ESTIMATION AND CALIBRATIONCALIBRATION

Now we have problems with the calibration of some models

Number of hhNumber of hh

Some Some ResultsResults

Number of jobsNumber of jobs

Page 29: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

HOUSEHOLD LOCATION CHOICHE MODELhousehold_location_choice_model_coefficients

coefficient_name:S68 estimate:f8 standard_error:f8 sub_ t_statistic:f8 cost_to_income_ratio -1.87381 6.070282 -2 -0.308686644 income_and_year_built -2.33E-09 0.000000 -2 -1.078903437 percent_high_income_households_within_walking_distance_if_low_income -0.04343 0.001485 -2 -29.24147797 percent_low_income_households_within_walking_distance_if_high_income -0.0341 0.001579 -2 -21.59424973 percent_minority_households_within_walking_distance_if_minority 1.485609 0.079542 -2 18.67713547 residential_units_when_household_has_children -0.00034 0.000010 -2 -35.36430359 young_household_in_high_density_residential 0.953642 0.047803 -2 19.94939423 young_household_in_mixed_use 1.08067 0.046608 -2 23.18641853

EMPLOYMENT LOCATION CHOICHE MODEL – home basedhome_based_employment_location_choice_model_coefficients

coefficient_name:S7 estimate:f8 standard_error:f8 sub_model_id:i4 t_statistic:f8 BLE_SEW 0.845846057 0.002255060 -2 375.0879517 BLTLV -0.651483297 0.002016162 -2 -323.1303406 BLWAP_1 -0.49182874 0.010428486 -2 -47.16204453 BPLIW 0.017649969 0.000821047 -2 21.49691391 BPMIW -0.091851585 0.001370531 -2 -67.01899719 BTT_CBD -0.014823494 0.000397665 -2 -37.27632523

Page 30: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

RESIDENTIAL LAND SHARE MODELresidential_land_share_model_coefficients

coefficient_name:S20 estimate:f8 standard_error:f8 sub_model_id:i4 t_statistic:f8 autogenvar0 0.050503977 0.008062160 -2 6.264323711 constant -0.36647287 0.032711033 -2 -11.20334148 ln_residential_units 0.321727097 0.010537968 -2 30.53027916

DEVELOPMENT LOCATION CHOICHE MODEL – industrialindustrial_development_location_choice_model_coefficients

coefficient_name:S7 estimate:f8 standard_error:f8 sub_model_id:i4 t_statistic:f8 LDEVSFI -0.376998812 0.029252842 1 -12.88759613 LDUW -0.076945327 0.083654918 1 -0.919794381 LSFIW 0.264217347 0.083532847 1 3.163035393 LV 0.057993043 0.081951573 1 0.707650185 PIW 0.002510386 0.009806764 1 0.255985171

Page 31: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

EMPLOYMENT LOCATION CHOICHE MODEL - industrialindustrial_employment_location_choice_model_coefficients

coefficient_name:S7 estimate:f8 standard_error:f8 sub_model_id:i4 t_statistic:f8 BLTV -0.000163587 0.001647327 1 -0.099304698 BLTV 1 0 2 0 BLTV 1 0 3 0 BLTV 1 0 4 0 BART 1 0 5 0 BLTV 1 0 5 0 BLTV 1 0 6 0 BLE_SAW 1 0 7 0 BLE_SEW 1 0 7 0 BLTV 1 0 7 0

LAND PRICE MODELland_price_model_coefficients

coefficient_name:S9 estimate:f8 standard_error:f8 sub_model_id:i4 BWET 1 0 -2 LN_IMPVAL 1 0 -2 constant 1 0 -2

Page 32: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

EMPLOYMENT LOCATION CHOICHE MODEL - commercialcommercial_employment_location_choice_model_coefficients

coefficient_name:S7 estimate:f8 standard_error:f8 sub_model_id:i4 BART 1 0 1 BLE_REW 1 0 1 BLE_SAW 1 0 1 BLE_SEW 1 0 1 BLNRSFW 1 0 1 BLTV 1 0 1 BART 1 0 2 BLE_BW 1 0 2 BLE_REW 1 0 2 BLE_SAW 1 0 2 BLNRSFW 1 0 2 BLSFCW 1 0 2 BLTV 1 0 2 BLWAP_1 1 0 2 BLE_REW 1 0 3 BLTV 1 0 3 BART 1 0 4 BLE_BW 1 0 4 BLE_REW 1 0 4 BLE_SAW 1 0 4 BLNRSFW 1 0 4 BLSFCW 1 0 4 BLWAP_1 1 0 4 BART 1 0 5 BHWY 1 0 5 BLE_BW 1 0 5 BLE_SAW 1 0 5

Page 33: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

DEVELOPMENT LOCATION CHOICHE MODEL – commercialcommercial_development_location_choice_model_coefficients

coefficient_name:S7 estimate:f8 standard_error:f8 sub_model_id:i4 ART 1 0 -2 BLTLV 1 0 -2 BLWAP_1 1 0 -2 LDEVSFC 1 0 -2 LE_W 1 0 -2 O_UGB 1 0 -2 PRW 1 0 -2 TT_CBD 1 0 -2

DEVELOPMENT LOCATION CHOICHE MODEL – residentialresidential_development_location_choice_model_coefficients

coefficient_name:S7 estimate:f8 standard_error:f8 sub_model_id:i4 BLIMP 1 0 1 LE_W 1 0 1 O_UGB 1 0 1 PRW 1 0 1 SFC_0 1 0 1 TT_CBD 1 0 1 UNIT_35 1 0 1

Page 34: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Tank YouTank You

Page 35: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Land Use - Medase

Code Description Code Description90 Internal Water 1 Water

6 Archaeological areas 51 Public Space - archaeological24 Airport and linked areas 52 Public Space - Airport26 Areas with bathing structures 53 Public Space - Beaches

0 Open Space 60 Open Space41 Green urban areas 54 Public Space - green urban42 Open space, non built-up 61 Open Space - non built-up22 Rail area 7 Roads91 Areas for other uses 55 Public Space - other uses

7 Historical military buildings 81 Residential - continuous11 Saturated residential area, existing at 1870 81 Residential - continuous12 Saturated residential area, modern 81 Residential - continuous13 Saturated residential area, modern 1870-1950 81 Residential - continuous14 Saturated residential area, contemporary 81 Residential - continuous15 Non saturated residential area, contemporary 82 Residential - discontinuous16 Little built-up area in non urbanized areas 82 Residential - discontinuous17 Non saturated residential area, lotting 82 Residential - discontinuous18 Discontinuous houses 82 Residential - discontinuous21 Other urban services 82 Residential - discontinuous31 Industrial build area 91 Industrial33 active or inactive quarry 92 Industrial - extraction

5 Transforming areas 63 Open Space - construction23 Areas with sports ground 57 Public Space - sports ground

Medase Final Classification

Page 36: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Land Use - Corine

Code Description Code Description511 Water courses 1 Water512 Water bodies 4 Wetland411 Inland marshes 4 Wetland124 Airports 52 Public Space - Airport331 Beaches, dunes, and sand plains 53 Public Space - Beaches141 Green urban areas 54 Public Space - green urban334 Burnt areas 61 Open Space - non built-up

2.. Agricultural areas 62 Open Space - agricultural122 Road and rail networks and assoc. land 7 Roads111 Continuous urban fabric 81 Residential - continuous111 Continuous urban fabric 81 Residential - continuous111 Continuous urban fabric 81 Residential - continuous111 Continuous urban fabric 81 Residential - continuous111 Continuous urban fabric 81 Residential - continuous112 Discontinuous urban fabric 82 Residential - discontinuous112 Discontinuous urban fabric 82 Residential - discontinuous112 Discontinuous urban fabric 82 Residential - discontinuous112 Discontinuous urban fabric 82 Residential - discontinuous112 Discontinuous urban fabric 82 Residential - discontinuous121 Industrial or commercial units 91 Industrial123 Port areas 56 Public Space - port areas131 Mineral extraction sites 92 Industrial - extraction133 Construction sites 63 Open Space - construction142 Sport and leisure facilities 57 Public Space - sports ground

Corine Final Classification

Page 37: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

We are in an early stage of the UrbanSim We are in an early stage of the UrbanSim implementation. implementation.

We show some variables of the base year 1991.We show some variables of the base year 1991.

Some variables of the base year

Page 38: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Some variables of the base year

GridcellsGridcellsDBDB

Page 39: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Some variables of the base year

GridcellsGridcellsDBDB

Page 40: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Some variables of the base year

GridcellsGridcellsDBDB

Page 41: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Some variables of the base year

GridcellsGridcellsDBDB

Page 42: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Some variables of the base year

JobsJobsDBDB

Page 43: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Some variables of the base year

JobsJobsDBDB

Page 44: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Some variables of the base year

JobsJobsDBDB

Page 45: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

Some variables of the base year

JobsJobsDBDB

Page 46: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.
Page 47: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

There are two main groupings of interpolation techniques

Interpolation Methods

deterministicdeterministic interpolation

geostatisticalgeostatistical interpolation

A deterministic interpolation technique applies a mathematical a mathematical formula to the sample pointsformula to the sample points. The idea is to multiply the values of the points that fall within a specified neighborhood from the processing cell by a weightweight that is derived from the distancefrom the distance the sample point is from the processing location.

Based on statistical models that include autocorrelationautocorrelation. These techniques have the capability of producing prediction surfacesprediction surfaces, and also provide some measure of the measure of the accuracyaccuracy of these predictions. The weightsweights are based not only on the distanceon the distance, but also on the overall spatial arrangementoverall spatial arrangement among the measured points.

IDWIDW (Inverse Distance Weighted) KRIGINGKRIGING

Page 48: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

• One advantage of the kriging is that it provides some measure of the accuracy of measure of the accuracy of the predictionthe prediction.

• Cross-validation and validation make an informed decision as the model provides the best predictions.

How well the model predicts the value?

The plot shows that kriging is predicting well.

Page 49: Simone Di Zio University G. d’Annunzio Pescara, Italy ETH Zürich,March 17/18th 2008 ETH Zürich, March 17/18th 2008 Rome UrbanSIM.

formula di Eyal: House Price = (L + (S*C))* DL = Residential Land ValueS= superficie della casa in mq.C= costo di costruzione al mqla possiamo riscrivere così:

House Price = D*L + D*(S*C)Allora, in mancanza di informazioni sul profitto, pensavo di mettere

D=1, nel senso che inglobiamo il profitto nel costo di costruzione che abbiamo preso su internet, dall’ordine degli architetti.

Se sei daccordo la formula diventaHouse Price = L + (S*C)

Dalla quale possiamo ricavarci il Residential Land Value: L = House Price - (S*C)


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