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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
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
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
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).
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
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.
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.
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.
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
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)
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
FiumicinoFiumicino RomeRome
Traffic Zones
463 463 ZonesZones
Comparing with Suddivisioni Toponomastiche
Travel Zones Suddivisioni Toponomastiche
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
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
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
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.
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.
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
Choosing the semivariogram model
The direction is important: we use an
AnisotropicAnisotropic variogram
model
Geostatistical Geostatistical Analyst Analyst
extensionextension
Making the prediction
Coordinates of the airport
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
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
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)
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
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
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
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
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
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
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
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
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
Tank YouTank You
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
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
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
Some variables of the base year
GridcellsGridcellsDBDB
Some variables of the base year
GridcellsGridcellsDBDB
Some variables of the base year
GridcellsGridcellsDBDB
Some variables of the base year
GridcellsGridcellsDBDB
Some variables of the base year
JobsJobsDBDB
Some variables of the base year
JobsJobsDBDB
Some variables of the base year
JobsJobsDBDB
Some variables of the base year
JobsJobsDBDB
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
• 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.
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)