In [1]:
%load_ext autoreload
%autoreload 2
if 'sim' not in globals():
import os; os.chdir('../example')
import urbansim.sim.simulation as sim
from activitysim.defaults import variables
import models
In [2]:
sim.run(["auto_ownership_simulate"])
Running model 'auto_ownership_simulate'
Choices:
2 39441
1 32330
3 14788
0 8397
4 5044
dtype: int64
Time to execute model 'auto_ownership_simulate': 10.16s
Total time to execute: 10.16s
In [16]:
sim.get_table("land_use").to_frame().describe()
Out[16]:
DISTRICT
SD
county_id
total_households
HHPOP
TOTPOP
EMPRES
SFDU
MFDU
HHINCQ1
...
COLLPTE
TOPOLOGY
TERMINAL
ZERO
hhlds
sftaz
gqpop
employment_density
household_density
density_index
count
1454.000000
1454.000000
1454.000000
1454.000000
1454.000000
1454.000000
1454.000000
1454.000000
1454.000000
1454.000000
...
1454.000000
1454.000000
1454.000000
1454
1454.000000
1454.000000
1454.000000
1454.000000
1454.000000
1453.000000
mean
14.908528
14.908528
3.835626
1793.688446
4816.408528
4917.978680
2168.684319
1122.798487
670.889959
508.134801
...
166.744054
2.063274
1.630505
0
1793.688446
727.500000
101.570151
9.596395
6.008186
2.279554
std
8.701078
8.701078
2.040153
961.021405
2686.029808
2690.352928
1211.109335
854.895353
717.261660
378.753528
...
1234.717238
0.926842
0.879441
0
961.021405
419.877958
393.886676
45.067313
8.565908
3.945717
min
1.000000
1.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
...
0.000000
1.000000
0.904320
0
0.000000
1.000000
-1.000000
0.000000
0.000000
0.000000
25%
8.000000
8.000000
3.000000
1200.250000
3288.250000
3384.500000
1460.500000
602.000000
144.500000
257.000000
...
0.000000
1.000000
1.167372
0
1200.250000
364.250000
5.000000
0.877829
1.910701
0.550232
50%
15.000000
15.000000
4.000000
1681.500000
4504.500000
4577.000000
2016.000000
1034.000000
460.000000
434.000000
...
0.000000
2.000000
1.323075
0
1681.500000
727.500000
18.000000
2.158701
3.939122
1.289224
75%
20.750000
20.750000
5.000000
2259.750000
6033.750000
6098.500000
2735.500000
1496.000000
907.750000
674.750000
...
0.000000
3.000000
1.632443
0
2259.750000
1090.750000
71.000000
5.492696
6.693238
2.337577
max
34.000000
34.000000
9.000000
12542.000000
39671.000000
40020.000000
16799.000000
12413.000000
4920.000000
3754.000000
...
19570.523440
3.000000
7.310200
0
12542.000000
1454.000000
7810.000000
877.564767
90.891304
46.360371
8 rows × 44 columns
In [3]:
sim.get_table("households").to_frame().describe()
Out[3]:
TAZ
SERIALNO
PUMA5
income
PERSONS
HHT
UNITTYPE
NOC
BLDGSZ
TENURE
...
bucketBin
originalPUMA
hmultiunit
num_young_adults
drivers
num_children
num_adolescents
income_in_thousands
num_young_children
num_college_age
count
2732722.000000
2732722.000000
2732722.000000
2732722.000000
2732722.000000
2732722.000000
2732722.000000
2732722.000000
2732722.000000
2732722.000000
...
2732722.000000
2732722.000000
2732722.000000
2732722.000000
2732722.000000
2732722.000000
2732722.000000
2732722.000000
2732722.000000
2732722.000000
mean
751.134174
4923397.817272
2168.537679
77670.162133
2.581065
2.640939
0.076833
0.468948
3.528608
1.893538
...
4.492757
2168.537679
0.401827
0.396906
2.062626
0.359349
0.059565
77.670162
0.159090
0.223251
std
430.938788
2857497.525312
516.128215
81405.085003
1.605801
2.065958
0.365144
0.917211
2.516513
1.010827
...
2.871945
516.128215
0.490267
0.728451
1.117948
0.764597
0.254209
81.405085
0.462614
0.581043
min
1.000000
20.000000
1000.000000
-20000.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
...
0.000000
1000.000000
0.000000
0.000000
0.000000
0.000000
0.000000
-20.000000
0.000000
0.000000
25%
372.000000
2463830.000000
2104.000000
26500.000000
1.000000
1.000000
0.000000
0.000000
2.000000
1.000000
...
2.000000
2104.000000
0.000000
0.000000
1.000000
0.000000
0.000000
26.500000
0.000000
0.000000
50%
762.000000
4901786.000000
2303.000000
58000.000000
2.000000
1.000000
0.000000
0.000000
2.000000
2.000000
...
4.000000
2303.000000
0.000000
0.000000
2.000000
0.000000
0.000000
58.000000
0.000000
0.000000
75%
1144.000000
7361646.000000
2410.000000
100000.000000
4.000000
4.000000
0.000000
1.000000
5.000000
3.000000
...
7.000000
2410.000000
1.000000
1.000000
2.000000
0.000000
0.000000
100.000000
0.000000
0.000000
max
1454.000000
9999899.000000
2714.000000
1968504.000000
25.000000
7.000000
2.000000
12.000000
10.000000
4.000000
...
9.000000
2714.000000
1.000000
9.000000
25.000000
9.000000
5.000000
1968.504000
8.000000
24.000000
8 rows × 53 columns
In [ ]:
Content source: osPlanning/activitysim
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