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Simone Di Zio University G. d’Annunzio Pescara, Italy

Rome UrbanSIM. Simone Di Zio University G. d’Annunzio Pescara, Italy. ETH Zürich, March 17/18th 2008. Grid Cells size: 250 x 250 mt Number of Grid Cells: 23933; 1498 Km 2 Base Year: 1991. Municipality of Rome. 2005 We started the implementation of UrbanSIM.

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Simone Di Zio University G. d’Annunzio Pescara, Italy

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

  2. Grid Cells size: 250 x 250 mt • Number of Grid Cells: 23933; • 1498 Km2 • Base Year: 1991 Municipality of Rome 2005 We started the implementation of UrbanSIM

  3. Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization Rome Critical points UrbanSIM Desirable improvements Critical Points during the implementation on ROME

  4. CORINE MEDASEis sufficiently detailed but unfortunately it is available only for a portion of the study area. CORINEis 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. MEDASE Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization 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).

  5. Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization Starting from two different lists of categories we created a unique final classification of the Land Use

  6. Before 1991 Changes in the administration of the Study Area. Municipality of Rome Problems in collecting data for the construction of the Base Year DB. 1498 Km2 After 1991 Municipality of Rome Municipality of Fiumicino

  7. Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization 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.

  8. Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization Two different lists of plan type. Problems in comparing e reclassifying the two different data.

  9. Jobs DB Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization • DATA SOURCES • The ISTAT was very late in releasing the 2001 census data. • In 2005 (September) we had only four economic sectors. (Industry, Trade, Service, Institution) DATA SOURCES

  10. Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization 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)

  11. Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization TRAVEL DATA 2. Once again the data were available only for Rome and not for Fiumicino Fiumicino Missing Data Rome

  12. Rome Fiumicino Traffic Zones 463 Zones

  13. Comparing with Suddivisioni Toponomastiche Travel Zones Suddivisioni Toponomastiche

  14. New Traffic Zones Reconstruction of the Traffic Zones 8 new Zones for the Municipality of Fiumicino 463 + 8 = 471 Traffic Zones

  15. Sik k Traffic Zones Data – Travel Times Sik = f(xi) = f (xi1,…,xin) Geostatistical Approach 463 + 8 = 471 Traffic Zones

  16. Tkj k Traffic Zones Data – Travel Times Tkj= f(xj) = f (x1j,…,xnj) Geostatistical Approach 463 + 8 = 471 Traffic Zones

  17. 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.  • Fit a model: the model quantifies the spatial autocorrelation in the data. Kriging principal steps • Determine the kriging weights: using the autocorrelation values from the variogram model the weights are estimated • Make the prediction: from the kriging weights for the measured values, we can calculate a prediction for the location with the unknown value.

  18. Anisotropy Travel times are strongly related to the road network. In our model we must consider also the influence of different directions in estimating the surface. Anisotropy is a characteristic of a random process that shows higher autocorrelation in one direction than another.

  19. From the CBD to the Airport Example • We need to estimate • Sik • Where • Sik= f(xi)=f(xi1,…,xin) • i = CBD • k = Fiumicino Airport CBD i k Fiumicino airport

  20. The direction is important: we use an Anisotropic variogram model Choosing the semivariogram model Geostatistical Analyst extension

  21. Coordinates of the airport Making the prediction

  22. Final Prediction Map CBD We have used this map to predict missing data on Rome

  23. 1991 2001 Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization CENSUS TRACTS The National Institute of Statistics (ISTAT), from 1991 to 2001 changed the census tracts.

  24. House Price = L + (S*C) L = Residential Land Value S = Surface of the House (in mq) C= Construction Cost per mq (S*C)= Residential improvement value Residential Land Value: L = House Price - (S*C) Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization RESIDENTIAL LAND VALUE We don’t have data

  25. Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization HOUSE PRICE In 1991 we have data only for some Suddivisioni Toponomastiche

  26. RESIDENTIAL LAND VALUES HOUSE PRICES HOUSE PRICE • RECONSTRUCTION OF MISSING DATA • We 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.

  27. UrbanSIM 3 UrbanSIM 4 Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization 2007

  28. Data Availability Data completeness Data Availability UrbanSIM User Interface External Models User Inputs Data homogeneity User Interface Base Year automation Estimation Calibration automation Understanding and solving simulation errors UrbanSIM CORE (Simulations) Transfers of data among different softwares ASCII OutputFiles Data Store User Interface User Interface GIS Visualization ESTIMATION AND CALIBRATION Now we have problems with the calibration of some models Some Results Number of hh Number of jobs

  29. HOUSEHOLD LOCATION CHOICHE MODEL household_location_choice_model_coefficients EMPLOYMENT LOCATION CHOICHE MODEL – home based home_based_employment_location_choice_model_coefficients

  30. RESIDENTIAL LAND SHARE MODEL residential_land_share_model_coefficients DEVELOPMENT LOCATION CHOICHE MODEL – industrial industrial_development_location_choice_model_coefficients

  31. EMPLOYMENT LOCATION CHOICHE MODEL - industrial industrial_employment_location_choice_model_coefficients LAND PRICE MODEL land_price_model_coefficients

  32. EMPLOYMENT LOCATION CHOICHE MODEL - commercial commercial_employment_location_choice_model_coefficients

  33. DEVELOPMENT LOCATION CHOICHE MODEL – commercial commercial_development_location_choice_model_coefficients DEVELOPMENT LOCATION CHOICHE MODEL – residential residential_development_location_choice_model_coefficients

  34. Tank You

  35. Land Use - Medase

  36. Land Use - Corine

  37. Some variables of the base year We are in an early stage of the UrbanSim implementation. We show some variables of the base year 1991.

  38. Some variables of the base year Gridcells DB

  39. Some variables of the base year Gridcells DB

  40. Some variables of the base year Gridcells DB

  41. Some variables of the base year Gridcells DB

  42. Some variables of the base year Jobs DB

  43. Some variables of the base year Jobs DB

  44. Some variables of the base year Jobs DB

  45. Some variables of the base year Jobs DB

  46. Interpolation Methods There are two main groupings of interpolation techniques deterministic interpolation geostatistical interpolation Based on statistical models that include autocorrelation. These techniques have the capability of producing prediction surfaces, and also provide some measure of the accuracy of these predictions. The weights are based not only on the distance, but also on the overall spatial arrangement among the measured points. A deterministic interpolation technique applies a mathematical formula 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 weight that is derived from the distance the sample point is from the processing location. KRIGING IDW(Inverse Distance Weighted)

  47. How well the model predicts the value? The plot shows that kriging is predicting well. • One advantage of the kriging is that it provides some measure of the accuracy of the prediction. • Cross-validation and validation make an informed decision as the model provides the best predictions.

  48. formula di Eyal: House Price = (L + (S*C))* DL = Residential Land Value S= superficie della casa in mq. C= costo di costruzione al mq la 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 diventa House Price = L + (S*C)Dalla quale possiamo ricavarci il Residential Land Value: L = House Price - (S*C)

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