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A Conditionally Parametric Probit Model of Micro-Data Land Use in Chicago Daniel McMillen Maria Soppelsa.

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Presentation on theme: "A Conditionally Parametric Probit Model of Micro-Data Land Use in Chicago Daniel McMillen Maria Soppelsa."— Presentation transcript:

1 A Conditionally Parametric Probit Model of Micro-Data Land Use in Chicago Daniel McMillen Maria Soppelsa

2 Overview Residential v. Commercial/Industrial Land Use in Chicago, 2010 A conditionally parametric (CPAR) approach produces smooth estimates over space Target points chosen using an adaptive decision tree approach (Loader, 1999) Interpolation from 182 target points to all 583,063 individual parcels in the data set

3 Estimation Procedures Case (1992). Special From for W McMillen (1992). EM Algorithm Pinkse and Slade (1998). GMM for spatial error model. LeSage (2000). Bayesian approach Klier and McMillen (2007). Linearized version of GMM probit/logit for spatial AR model.

4 GMM Probit

5 Linearized GMM Probit

6 CPAR Probit

7 Spatial AR v. LWR

8 Data Individual parcels in Chicago, 2010 Major Classes: 1.Vacant Land (33,139) 2.Residential, 6 units or fewer (728,541, 539,975 after geocoding) 3.Multi-Family Residential (11,529) 4.Non-Profit (316) 5.Commercial and Industrial (50,508, 43,088 after geocoding) 6.“Incentive Classes” (1,487)

9 Explanatory Variables Distance from parcel centroid to: 1.CBD 2.Lake Michigan 3.EL line 4.EL stop 5.Rail line 6.Major street 7.Park 8.Highway

10 Rogers Park

11 Descriptive Statistics VariableMeanStd. Dev.MinMax Residential Lot Distance from CBD Distance from Lake Michigan Distance from EL Line Distance from EL Stop Distance from Rail Line Distance from Major Street Distance from Park Distance from Highway

12 Probit Models, Probability Residential Standard ProbitCPAR Probit VariableCoef.Std. ErrorMeanStd. Dev. Intercept Distance from CBD Distance from Lake Michigan Distance from EL Line Distance from EL Stop Distance from Rail Line Distance from Major Street Distance from Park Distance from Highway Log-likelihood Pseudo-R

13 Probability of Residential Land Use: Standard Probit

14 Probability of Residential Land Use: CPAR Probit, 10% Window Size

15 Difference, CPAR Probability – Standard Probit Probability

16 Kernel Density Estimates for CPAR Coefficients

17 LWR Estimates of CPAR Coefficients

18 Marginal Probabilities

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26 Rogers Park

27 Rogers Park, n = 3,193 StandardGMMCPAR CoefStd. Err.CoefStd. Err.MeanStd. dev. Intercept CBD Lake Michigan EL Line EL Stop Rail Line Major Street Park Highway Metra Stop ρ pseudo-R

28 Correlations, Predicted Probabilities StandardGMMCPAR Standard GMM0.571 CPAR

29 Standard Probit Probabilities

30 CPAR Probit Probabilities

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32 Standard Probit: Southwest

33 CPAR – Standard: Southwest

34 Standard Probit: Southeast

35 CPAR – Standard: Southeast

36 Standard Probit: Northwest

37 CPAR – Standard: Northwest

38 Standard Probit: Northeast

39 CPAR – Standard: Southeast


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