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

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

3. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g and solving simulation errors Transfers of data among different softwares Rome Critical points Desirable improvements UrbanSIM Critical Points during the implementation on ROME

4. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g and solving simulation errors Transfers of data among different softwares MEDASE CORINE 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).

5. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g 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

6. Before 1991 Municipality of Rome 1498 Km 2 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.

7. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g 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.

8. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g 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.

9. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g and solving simulation errors Transfers of data among different softwares ISTAT, Italian National Institute of Statistics ISTAT, 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 Research BIR BIR - Real Estate Stock of Rome Bank of Italy Bank of Italy - Survey on Household Income and Wealth 1991 CNR CNR - National Research Council, MEDASE CORINE CORINE programme 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

10. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g 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)

11. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g and solving simulation errors Transfers of data among different softwares Fiumicino Missing Data Rome TRAVEL DATA 2. Once again the data were available only for Rome and not for Fiumicino

12. Fiumicino Rome Traffic Zones 463 Zones

13. Comparing with Suddivisioni Toponomastiche Travel Zones Suddivisioni Toponomastiche

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

15. Traffic Zones Data – Travel Times 463 + 8 = 471 Traffic Zones S S ik k S ik = f(x i ) = f (x i1,…,x in ) Geostatistical Approach

16. 463 + 8 = 471 Traffic Zones T T kj k T kj = f(x j ) = f (x 1j,…,x nj ) Traffic Zones Data – Travel Times Geostatistical Approach

17. Kriging principal steps 4.Make the prediction: 4.Make the prediction: from the kriging weights for the measured values, we can calculate a prediction for the location with the unknown value. 3.Determine the kriging weights: 3.Determine the kriging weights: using the autocorrelation values from the variogram model the weights are estimated 1.Calculate the empirical variogram: 1.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.Fit a model: 2.Fit a model: the model quantifies the spatial autocorrelation in the data.

18. Anisotropy Anisotropy Anisotropy 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. influence of different directions In our model we must consider also the influence of different directions in estimating the surface.

19. From the CBD to the Airport We need to estimate S ik Where S ik = f(x i )=f(x i1,…,x in ) i = CBD k = Fiumicino Airport CBD i Fiumicino airport k Example

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

21. Making the prediction Coordinates of the airport

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

23. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g and solving simulation errors Transfers of data among different softwares CENSUS TRACTS The National Institute of Statistics (ISTAT), from 1991 to 2001 changed the census tracts. 1991 2001

24. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g and solving simulation errors Transfers of data among different softwares RESIDENTIAL LAND VALUE We don’t have data House Price = L + (S*C) L L = Residential Land Value S S = Surface of the House (in mq) C C = Construction Cost per mq (S*C) (S*C) = Residential improvement value Residential Land Value: L = House Price - (S*C)

25. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g and solving simulation errors Transfers of data among different softwares HOUSE PRICE In 1991 we have data only for some Suddivisioni Toponomastiche

26. 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. HOUSE PRICES RESIDENTIAL LAND VALUES HOUSE PRICE

27. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g and solving simulation errors Transfers of data among different softwares 2007 UrbanSIM 3 UrbanSIM 4

28. User Interface Data Availabilit y External ModelsUser Inputs Data Store GIS Visualization UrbanSIM CORE (Simulations) Base Year ASCII Output Files UrbanSIM User Interface Data Availabilit y EstimationCalibration automation Data homogeneity User Interface Data completenes s Understandin g and solving simulation errors Transfers of data among different softwares ESTIMATION AND CALIBRATION Now we have problems with the calibration of some models Number of hh Number of hh Some Results Number of jobs 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. We are in an early stage of the UrbanSim implementation. We show some variables of the base year 1991. Some variables of the base year

38. GridcellsDB

39. GridcellsDB

40. GridcellsDB

41. GridcellsDB

42. JobsDB

43. JobsDB

44. JobsDB

45. JobsDB

47. There are two main groupings of interpolation techniques Interpolation Methods deterministic deterministic interpolation geostatistical geostatistical interpolation a mathematical formula to the sample points weight from the distance 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. autocorrelation prediction surfaces measure of the accuracy weights on the distance overall spatial arrangement 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. IDW IDW (Inverse Distance Weighted) KRIGING

48. measure of the accuracy of the prediction 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. How well the model predicts the value? The plot shows that kriging is predicting well.

49. formula di Eyal: House Price = (L + (S*C))* D L = 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)