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knaaptime/UrbanSim test 1

Created January 17, 2014 20:27
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first UrbanSim run with sample data
Raw
UrbanSim test 1
Fri Jan 17 15:21:50 2014
Running buildings.json
Fetching buildings
Fetching parcels
Fetching modify_table
Fetching jobs
Fetching modify_table
Fetching modify_table
Specifying model in 0.965123
Finished executing in 0.965149 seconds
Running hhlds.json
Fetching households
Fetching modify_table
Specifying model in 0.837692
Finished executing in 0.839741 seconds
Running jobs.json
Specifying model in 0.342408
Finished executing in 0.343521 seconds
Running zones.json
Fetching zones
Fetching modify_table
Fetching travel_data
Fetching modify_table
Specifying model in 0.862982
Finished executing in 0.863011 seconds
Running rsh.json
Done merging land use and choosers in 0.077251
Finished specifying in 0.173613 seconds
Specifying model in 0.129268
Estimating hedonic for 1 with 297376 observations
historic new year_built ln_parcel_acres \
count 297376.000000 297376.000000 297376.000000 2.973760e+05
mean 0.070934 0.365013 1980.371378 4.129644e-01
std 0.256715 0.481435 19.725919 7.159198e-01
min 0.000000 0.000000 1790.000000 2.149406e-14
25% 0.000000 0.000000 1967.000000 1.334722e-01
50% 0.000000 0.000000 1984.000000 1.765473e-01
75% 0.000000 1.000000 1996.000000 3.790199e-01
max 1.000000 1.000000 2012.000000 1.126495e+01
ln_sqft_per_unit ln_average_income ln_population_in_range \
count 297376.000000 297376.000000 297376.000000
mean 7.366880 10.959103 11.884783
std 0.409320 1.167037 1.437795
min 4.615120 0.000000 0.000000
25% 7.029973 10.866889 11.707925
50% 7.346655 11.080326 12.381000
75% 7.630947 11.304999 12.800691
max 12.288689 11.819008 13.235408
ln_time_to_downtown const
count 297376.000000 297376
mean 3.340930 1
std 0.463769 0
min 2.051068 1
25% 3.031756 1
50% 3.228030 1
75% 3.745288 1
max 4.637399 1
[8 rows x 9 columns]
OLS Regression Results
==================================================================================
Dep. Variable: unit_price_residential R-squared: 0.491
Model: OLS Adj. R-squared: 0.491
Method: Least Squares F-statistic: 3.579e+04
Date: Fri, 17 Jan 2014 Prob (F-statistic): 0.00
Time: 15:21:55 Log-Likelihood: -1.6818e+05
No. Observations: 297376 AIC: 3.364e+05
Df Residuals: 297367 BIC: 3.365e+05
Df Model: 8
==========================================================================================
coef std err t P>|t| [95.0% Conf. Int.]
------------------------------------------------------------------------------------------
historic 0.0024 0.004 0.578 0.563 -0.006 0.011
new -0.0614 0.003 -22.693 0.000 -0.067 -0.056
year_built 0.0043 8.66e-05 49.795 0.000 0.004 0.004
ln_parcel_acres 0.0381 0.001 30.304 0.000 0.036 0.041
ln_sqft_per_unit 0.9179 0.002 447.874 0.000 0.914 0.922
ln_average_income 0.0821 0.001 120.506 0.000 0.081 0.083
ln_population_in_range -0.0114 0.001 -11.432 0.000 -0.013 -0.009
ln_time_to_downtown -0.1591 0.003 -47.906 0.000 -0.166 -0.153
const -3.3623 0.165 -20.318 0.000 -3.687 -3.038
==============================================================================
Omnibus: 140602.129 Durbin-Watson: 0.751
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2822495.447
Skew: 1.804 Prob(JB): 0.00
Kurtosis: 17.655 Cond. No. 4.20e+05
==============================================================================
Warnings:
[1] The condition number is large, 4.2e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
Specifying model in 0.002574
Estimating hedonic for 2 with 4711 observations
historic new ln_average_income const
count 4711.000000 4711.000000 4711.000000 4711
mean 0.167268 0.060921 10.171841 1
std 0.373255 0.239211 2.354934 0
min 0.000000 0.000000 0.000000 1
25% 0.000000 0.000000 10.445782 1
50% 0.000000 0.000000 10.637335 1
75% 0.000000 0.000000 10.947579 1
max 1.000000 1.000000 11.685293 1
[8 rows x 4 columns]
OLS Regression Results
==================================================================================
Dep. Variable: unit_price_residential R-squared: 0.038
Model: OLS Adj. R-squared: 0.037
Method: Least Squares F-statistic: 61.61
Date: Fri, 17 Jan 2014 Prob (F-statistic): 4.59e-39
Time: 15:21:55 Log-Likelihood: -5937.2
No. Observations: 4711 AIC: 1.188e+04
Df Residuals: 4707 BIC: 1.191e+04
Df Model: 3
=====================================================================================
coef std err t P>|t| [95.0% Conf. Int.]
-------------------------------------------------------------------------------------
historic 0.1676 0.034 4.987 0.000 0.102 0.233
new 0.0101 0.052 0.193 0.847 -0.093 0.113
ln_average_income 0.0652 0.005 12.314 0.000 0.055 0.076
const 10.3836 0.055 188.025 0.000 10.275 10.492
==============================================================================
Omnibus: 905.683 Durbin-Watson: 1.580
Prob(Omnibus): 0.000 Jarque-Bera (JB): 5470.965
Skew: -0.779 Prob(JB): 0.00
Kurtosis: 8.044 Cond. No. 47.0
==============================================================================
Finished executing in 0.731019 seconds
Running zones2.json
Traceback (most recent call last):
File "run_json.py", line 12, in <module>
for arg in args: misc.run_model(arg,dset,estimate=1)
File "/Users/knaaptime/anaconda/lib/python2.7/site-packages/urbansim-0.1.0-py2.7.egg/synthicity/utils/misc.py", line 18, in run_model
if estimate: model.estimate(dset,config,2010,show=show,variables=variables)
File "/Users/knaaptime/anaconda/lib/python2.7/site-packages/urbansim-0.1.0-py2.7.egg/synthicity/urbansim/minimodel.py", line 7, in estimate
simulate(dataset,config,year,show,variables)
File "/Users/knaaptime/anaconda/lib/python2.7/site-packages/urbansim-0.1.0-py2.7.egg/synthicity/urbansim/minimodel.py", line 14, in simulate
_tbl_ = spec(_tbl_,config,dset=dataset,newdf=False)
File "/Users/knaaptime/anaconda/lib/python2.7/site-packages/urbansim-0.1.0-py2.7.egg/synthicity/urbansim/modelspec.py", line 70, in spec
est_data[varname] = calcvar(segment,config,dset,varname)
File "/Users/knaaptime/anaconda/lib/python2.7/site-packages/urbansim-0.1.0-py2.7.egg/synthicity/urbansim/modelspec.py", line 49, in calcvar
ret = eval(expression).astype('float')
File "<string>", line 1, in <module>
File "/Users/knaaptime/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 298, in __getattr__
(type(self).__name__, attr))
AttributeError: 'DataFrameGroupBy' object has no attribute 'residential_sales_price'
Closing remaining open files: /Users/knaaptime/urbansim/data/mrcog.h5... done
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