In [1]:
%matplotlib inline
import metapack as mp
In [23]:
doc = mp.jupyter.open_package()
doc
Out[23]:
San Diego Point In Time County 2010
rtfhsd.org-pitc-2010-1 Last Update: 2018-09-13T16:43:56
Point data for locations of homeless for the 2010 PiTC count in San Diego county.
Contacts
- Wrangler Eric Busboom Civic Knowledge
Resources
- pitc Datafile. Point in time count geo data for 2010
In [24]:
pitc = doc.resource('pitc').geoframe()
In [4]:
pitc.plot()
Out[4]:
<matplotlib.axes._subplots.AxesSubplot at 0x103354e80>
In [5]:
pitc.head()
Out[5]:
id
tract
observe
municiplty
locaccurac
geometry
0
0
14000US607319405000000000
Handbuilt Structure
Vista
NaN
POINT (-117.2783206487761 33.1931401603591)
1
1
14000US607319405000000000
Handbuilt Structure
Vista
NaN
POINT (-117.2755271030352 33.19106585259205)
2
2
14000US607319405000000000
Handbuilt Structure
Vista
NaN
POINT (-117.2729841130781 33.19476218891354)
3
3
14000US607319406000000000
Handbuilt Structure
Vista
NaN
POINT (-117.2613263882209 33.19486242999563)
4
4
14000US607319406000000000
Handbuilt Structure
Vista
NaN
POINT (-117.260782966759 33.19454916397248)
In [25]:
pitc = doc.resource('pitc')\
.geoframe()\
.groupby(['tract','observe'])\
.geometry.count().to_frame()\
.unstack(-1)\
.fillna(0)
pitc.columns = ['handbuilt','individual','vehicle']
pitc['total'] = pitc.sum(axis=1)
pitc
Out[25]:
handbuilt
individual
vehicle
total
tract
14000US60730000000000
0.0
1.0
0.0
1.0
14000US6073100000000000
0.0
6.0
3.0
9.0
14000US60731000000000000
0.0
0.0
3.0
3.0
14000US607310001000000000
0.0
3.0
2.0
5.0
14000US607310003000000000
0.0
0.0
1.0
1.0
14000US607310005000000000
0.0
1.0
14.0
15.0
14000US607310009000000000
0.0
0.0
1.0
1.0
14000US607310010000000000
0.0
2.0
4.0
6.0
14000US607310011000000000
0.0
4.0
2.0
6.0
14000US607310013000000000
4.0
8.0
3.0
15.0
14000US607310014000000000
0.0
1.0
0.0
1.0
14000US607310015000000000
0.0
18.0
3.0
21.0
14000US607310103000000000
8.0
9.0
1.0
18.0
14000US607310104000000000
0.0
0.0
1.0
1.0
14000US607310106000000000
0.0
0.0
5.0
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0.0
0.0
3.0
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14000US607310112000000000
0.0
0.0
1.0
1.0
14000US607310200000000000
0.0
6.0
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6.0
14000US607310300000000000
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5.0
14000US607311500000000000
1.0
1.0
10.0
12.0
...
...
...
...
...
14000US60738505000000000
1.0
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6.0
14000US60738506000000000
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14000US60738511000000000
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14000US60739102000000000
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14000US60739104000000000
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0.0
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1.0
334 rows × 4 columns
In [ ]:
Content source: CivicKnowledge/metatab-packages
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