In [1]:
import seaborn as sns
import metapack as mp
import pandas as pd
import numpy as np
import rowgenerators as rg
import matplotlib.pyplot as plt
from IPython.display import display
%matplotlib inline
sns.set_context('notebook')
mp.jupyter.init()
In [21]:
pkg = mp.jupyter.open_package()
#pkg = mp.jupyter.open_source_package()
pkg
Out[21]:
LA County Homeless Survey
economicrt.org-homeless_survey-2011e2017-4 Last Update: 2019-01-25T04:15:29
Data from Demographic Surveys and HMIS Records 2011 to 2017
This dataset consists of records of in-person surveys of homeless individuals
in Los Angeles county, from 2011 to 2017, inclusive. The original data is
collected from annual surveys that are part of the annual Point In Time Count
of homeless individuqals conducted by the Los Angeles Homeless Services
Authority. The data is published by The Economic Roundtable.
The data in this package is significantly altered from the source file to make
analysis easier. Changes include:
- Added 'raceeth' field, which recodes the very many race/ethnicity values to a much simpler set that hamonizes with US Census categories.
- Added 'sex' field, which recodes the 'gender' field.
- Broke out the type+year combination in 'survey_year' into two values.
The Race/Ethnicity categories are:
- hisp: Hispanic or Latino, of any race
- nhwhite: Non hispanic white
- black: Non hispanic Black or African American
- aian: Non hispanic American Indian / Alaskan Native
- asian: Non hispanic Asian
- nhopi: Non hispanic Native Hawaiian / Other Pacific Islander
- other: Other race or multiple races
Versions
- Initial Version
- Added schema descriptions
- Broke out the survey_year into two fields, survey_type and year
- Improved Metadata
Documentation Links
- Codebook Codebook for Multi-Year Data Set
- Methodology white paper
- Los Angeles County Homeless Count Data Library Source records from Los Angeles continuum of care homeless counts, 2009 to 2017
Contacts
- Publisher The Economic Roundtable
- Creator Los Angeles Homeless Services Authority
- Wrangler Eric Busboom, Civic Knowledge
Resources
- homeless_survey. Homeless survey data, with recoded race, ethnicity, and sex
References
- homeless_survey_source. Unprocessed Source file
In [22]:
df = pkg.resource('homeless_survey').dataframe()
In [4]:
t1 = pd.pivot_table(df[df.sex!='U'], index='raceeth',columns='sex', values='mental_illness',
margins=True, aggfunc='count')
t1.loc[:,'All']
Out[4]:
raceeth
aian 804
asian 749
black 31692
hisp 19543
nhopi 355
nhwhite 15009
other 3745
All 71897
Name: All, dtype: int64
In [5]:
t2 = pd.pivot_table(df[df.sex!='U'], index='raceeth',columns='sex', values='mental_illness',
margins=True, aggfunc='sum')
t2
Out[5]:
sex
F
M
All
raceeth
aian
109
163
272
asian
91
114
205
black
3125
5634
8759
hisp
1501
2369
3870
nhopi
39
57
96
nhwhite
1625
3140
4765
other
391
660
1051
All
6881
12137
19018
In [6]:
t2/t1
Out[6]:
sex
F
M
All
raceeth
aian
0.386525
0.312261
0.338308
asian
0.366935
0.227545
0.273698
black
0.301845
0.264024
0.276379
hisp
0.241902
0.177613
0.198025
nhopi
0.330508
0.240506
0.270423
nhwhite
0.368899
0.296115
0.317476
other
0.334474
0.256211
0.280641
All
0.302063
0.247104
0.264517
In [7]:
pd.pivot_table(df[df.sex!='U'], index='raceeth',columns='sex', values='mental_illness',
margins=True, aggfunc='mean')
Out[7]:
sex
F
M
All
raceeth
aian
0.386525
0.312261
0.338308
asian
0.366935
0.227545
0.273698
black
0.301845
0.264024
0.276379
hisp
0.241902
0.177613
0.198025
nhopi
0.330508
0.240506
0.270423
nhwhite
0.368899
0.296115
0.317476
other
0.334474
0.256211
0.280641
All
0.302063
0.247104
0.264517
In [8]:
tracts = rg.geoframe('census://CA/140')
tracts.plot()
Out[8]:
<matplotlib.axes._subplots.AxesSubplot at 0x10b9794a8>
In [9]:
tj = tracts.merge(df, on='geoid')
tj.head()
Out[9]:
statefp
countyfp
tractce
geoid
name
namelsad
mtfcc
funcstat
aland
awater
...
physical_disability
mental_illness
alcohol_abuse
drug_abuse
drug_alcohol_history
hiv_positive
part_time
full_time
unemployed_looking
unemployed_not_looking
0
06
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5 rows × 45 columns
In [10]:
len(tj)
Out[10]:
40513
In [11]:
df['n'] = 1
t = df.groupby('geoid').sum()
t['mi_rate'] = t.mental_illness / t['n']
t2 = tracts.set_index('geoid').join(t[['mi_rate', 'n']], how='right')
t2['z'] = np.log(t2['n']/t2['n'].std())
t2.plot(column='z', figsize=(12,12))
Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0x12d1b1898>
In [12]:
t2.z.describe()
Out[12]:
count 942.000000
mean -3.124980
std 1.539776
min -5.338555
25% -4.239943
50% -3.259114
75% -2.119680
max 3.022685
Name: z, dtype: float64
In [13]:
t2.to_csv('lacount.csv')
In [18]:
t2
Out[18]:
statefp
countyfp
tractce
name
namelsad
mtfcc
funcstat
aland
awater
intptlat
intptlon
geometry
mi_rate
n
z
geoid
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7
-3.392645
14000US06037980009
06
037
980009
9800.09
Census Tract 9800.09
G5020
S
20912227.0
356888.0
+34.1276072
-118.2963873
POLYGON ((-118.337072 34.141602, -118.336165 3...
0.500000
2
-4.645408
14000US06037980014
06
037
980014
9800.14
Census Tract 9800.14
G5020
S
6478839.0
973261.0
+33.7824778
-118.2384201
POLYGON ((-118.260881 33.768504, -118.260288 3...
0.355932
59
-1.261018
14000US06037980015
06
037
980015
9800.15
Census Tract 9800.15
G5020
S
5188936.0
220674.0
+33.7732472
-118.2888391
POLYGON ((-118.310477 33.766853, -118.310228 3...
0.333333
12
-2.853649
14000US06037980021
06
037
980021
9800.21
Census Tract 9800.21
G5020
S
6380469.0
565373.0
+34.2700470
-118.3813338
POLYGON ((-118.401829 34.265091, -118.401779 3...
0.333333
3
-4.239943
14000US06037980023
06
037
980023
9800.23
Census Tract 9800.23
G5020
S
2863946.0
2355864.0
+34.2367861
-118.6324389
POLYGON ((-118.648695 34.231201, -118.648031 3...
0.000000
1
-5.338555
14000US06037980024
06
037
980024
9800.24
Census Tract 9800.24
G5020
S
7998854.0
288250.0
+34.1803242
-118.4883751
POLYGON ((-118.518491 34.183893, -118.518487 3...
0.274194
124
-0.518274
14000US06037980026
06
037
980026
9800.26
Census Tract 9800.26
G5020
S
13858036.0
35273.0
+34.2734829
-118.2633094
POLYGON ((-118.351732 34.280342, -118.351731 3...
0.000000
12
-2.853649
14000US06037980028
06
037
980028
9800.28
Census Tract 9800.28
G5020
S
17299747.0
2367417.0
+33.9421429
-118.4173296
POLYGON ((-118.452459 33.943151, -118.446436 3...
0.125000
8
-3.259114
14000US06037980031
06
037
980031
9800.31
Census Tract 9800.31
G5020
S
12492183.0
13434637.0
+33.7390903
-118.2585391
POLYGON ((-118.291048 33.753779, -118.290498 3...
0.555556
9
-3.141331
942 rows × 15 columns
In [23]:
df
Out[23]:
geoid
survey_year
survey_type
year
birth_year
age
gender
sex
ethnicity
race_full
...
physical_disability
mental_illness
alcohol_abuse
drug_abuse
drug_alcohol_history
hiv_positive
part_time
full_time
unemployed_looking
unemployed_not_looking
0
NaN
Unsheltered 2011
unsheltered
2011
1993.0
18.0
Female
F
European American
White
...
0
0
0.0
0.0
1
0
0.0
0.0
1.0
0.0
1
NaN
Unsheltered 2011
unsheltered
2011
1964.0
46.0
Female
F
African American
Black-African-American
...
0
1
0.0
0.0
0
0
0.0
0.0
1.0
0.0
2
NaN
Unsheltered 2011
unsheltered
2011
1956.0
55.0
Male
M
European American
White
...
1
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
3
NaN
Unsheltered 2011
unsheltered
2011
1960.0
50.0
Male
M
European American
White
...
0
0
1.0
0.0
1
0
0.0
0.0
1.0
0.0
4
NaN
Unsheltered 2011
unsheltered
2011
1979.0
31.0
Male
M
Latino
BLANK
...
0
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
5
NaN
Unsheltered 2011
unsheltered
2011
1952.0
59.0
Male
M
Unknown
BLANK
...
0
0
1.0
0.0
1
0
0.0
0.0
1.0
0.0
6
NaN
Unsheltered 2011
unsheltered
2011
1988.0
22.0
Male
M
African American
Black-African-American
...
0
0
0.0
1.0
1
0
0.0
0.0
1.0
0.0
7
NaN
Unsheltered 2011
unsheltered
2011
1961.0
49.0
Male
M
African American
Black-African-American
...
0
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
8
NaN
Unsheltered 2011
unsheltered
2011
1981.0
29.0
Male
M
European American
White
...
0
0
0.0
0.0
1
0
0.0
0.0
1.0
0.0
9
NaN
Unsheltered 2011
unsheltered
2011
1956.0
55.0
Male
M
Latino
BLANK
...
0
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
10
NaN
Unsheltered 2011
unsheltered
2011
1992.0
18.0
Male
M
Latino
BLANK
...
0
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
11
NaN
Unsheltered 2011
unsheltered
2011
1989.0
21.0
Male
M
Latino
BLANK
...
0
0
0.0
1.0
1
0
0.0
0.0
1.0
0.0
12
NaN
Unsheltered 2011
unsheltered
2011
1989.0
21.0
Male
M
Latino
BLANK
...
1
1
1.0
1.0
1
0
0.0
0.0
1.0
0.0
13
NaN
Unsheltered 2011
unsheltered
2011
1984.0
26.0
Male
M
Latino
BLANK
...
0
0
0.0
1.0
1
0
0.0
0.0
1.0
0.0
14
NaN
Unsheltered 2011
unsheltered
2011
1983.0
27.0
Male
M
Latino
BLANK
...
0
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
15
NaN
Unsheltered 2011
unsheltered
2011
1982.0
28.0
Male
M
Latino
BLANK
...
0
0
1.0
1.0
1
0
0.0
0.0
1.0
0.0
16
NaN
Unsheltered 2011
unsheltered
2011
1981.0
29.0
Male
M
Latino
BLANK
...
0
0
1.0
1.0
1
0
0.0
0.0
1.0
0.0
17
NaN
Unsheltered 2011
unsheltered
2011
1981.0
29.0
Male
M
Latino
BLANK
...
0
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
18
NaN
Unsheltered 2011
unsheltered
2011
1980.0
30.0
Male
M
Latino
BLANK
...
0
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
19
NaN
Unsheltered 2011
unsheltered
2011
1979.0
31.0
Male
M
Latino
BLANK
...
0
0
1.0
0.0
1
0
0.0
0.0
1.0
0.0
20
NaN
Unsheltered 2011
unsheltered
2011
1979.0
31.0
Male
M
Latino
BLANK
...
0
0
0.0
0.0
1
0
0.0
0.0
1.0
0.0
21
NaN
Unsheltered 2011
unsheltered
2011
1978.0
32.0
Male
M
Latino
BLANK
...
0
0
0.0
1.0
1
0
0.0
0.0
1.0
0.0
22
NaN
Unsheltered 2011
unsheltered
2011
1973.0
37.0
Female
F
Latino
BLANK
...
0
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
23
NaN
Unsheltered 2011
unsheltered
2011
1974.0
37.0
Male
M
Unknown
BLANK
...
0
0
0.0
1.0
1
0
0.0
0.0
1.0
0.0
24
NaN
Unsheltered 2011
unsheltered
2011
1973.0
37.0
Male
M
Latino
BLANK
...
0
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
25
NaN
Unsheltered 2011
unsheltered
2011
1972.0
38.0
Male
M
Latino
BLANK
...
0
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
26
NaN
Unsheltered 2011
unsheltered
2011
1971.0
39.0
Male
M
Latino
BLANK
...
0
0
1.0
0.0
1
0
0.0
0.0
1.0
0.0
27
NaN
Unsheltered 2011
unsheltered
2011
1970.0
40.0
Male
M
Latino
BLANK
...
0
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
28
NaN
Unsheltered 2011
unsheltered
2011
1969.0
41.0
Unknown
U
Latino
BLANK
...
0
0
0.0
0.0
1
0
0.0
0.0
1.0
0.0
29
NaN
Unsheltered 2011
unsheltered
2011
1969.0
41.0
Female
F
Latino
BLANK
...
0
0
0.0
0.0
0
0
0.0
0.0
1.0
0.0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
73123
14000US06037206300
Sheltered 2017
sheltered
2017
1972.0
45.0
Male
M
African American
African American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73124
14000US06037206300
Sheltered 2017
sheltered
2017
1965.0
52.0
Female
F
Latino
European American
...
1
1
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73125
14000US06037206300
Sheltered 2017
sheltered
2017
1981.0
36.0
Male
M
African American
African American
...
1
1
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73126
14000US06037208610
Sheltered 2017
sheltered
2017
1994.0
23.0
Female
F
Latino
European American
...
0
0
0.0
0.0
0
0
1.0
0.0
0.0
0.0
73127
14000US06037208610
Sheltered 2017
sheltered
2017
2010.0
7.0
Male
M
Latino
Refused
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73128
14000US06037208610
Sheltered 2017
sheltered
2017
1995.0
22.0
Female
F
Latino
Refused
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73129
14000US06037208610
Sheltered 2017
sheltered
2017
1995.0
22.0
Female
F
African American
African American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73130
14000US06037208610
Sheltered 2017
sheltered
2017
2017.0
0.0
Female
F
African American
African American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73131
14000US06037208610
Sheltered 2017
sheltered
2017
1997.0
20.0
Female
F
Other Ethnicity
Multi-Race
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73132
14000US06037208610
Sheltered 2017
sheltered
2017
2016.0
1.0
Male
M
Latino
Multi-Race
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73133
14000US06037601401
Sheltered 2017
sheltered
2017
1983.0
34.0
Male
M
African American
African American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73134
14000US06037206300
Sheltered 2017
sheltered
2017
1967.0
50.0
Unknown
U
Unknown
Null
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73135
14000US06037408301
Sheltered 2017
sheltered
2017
1953.0
64.0
Unknown
U
Unknown
Null
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73136
14000US06037206300
Sheltered 2017
sheltered
2017
1960.0
57.0
Unknown
U
Unknown
Null
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73137
14000US06037232500
Sheltered 2017
sheltered
2017
1988.0
29.0
Female
F
African American
African American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73138
14000US06037232500
Sheltered 2017
sheltered
2017
2012.0
5.0
Male
M
African American
African American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73139
14000US06037234502
Sheltered 2017
sheltered
2017
NaN
NaN
Unknown
U
Unknown
Null
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73140
14000US06037234502
Sheltered 2017
sheltered
2017
1995.0
22.0
Male
M
African American
African American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73141
14000US06037209102
Sheltered 2017
sheltered
2017
1979.0
38.0
Male
M
Unknown
Refused
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73142
14000US06037206300
Sheltered 2017
sheltered
2017
1966.0
51.0
Female
F
European American
European American
...
1
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73143
14000US06037208302
Sheltered 2017
sheltered
2017
1963.0
54.0
Female
F
African American
African American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73144
14000US06037900504
Sheltered 2017
sheltered
2017
1975.0
42.0
Male
M
African American
African American
...
1
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73145
14000US06037208302
Sheltered 2017
sheltered
2017
1977.0
40.0
Female
F
African American
African American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73146
14000US06037208302
Sheltered 2017
sheltered
2017
1952.0
65.0
Unknown
U
Unknown
Null
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73147
14000US06037235201
Sheltered 2017
sheltered
2017
1955.0
62.0
Female
F
European American
European American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73148
14000US06037503105
Sheltered 2017
sheltered
2017
1996.0
21.0
Female
F
Latino
Don't Know
...
0
0
0.0
0.0
0
0
1.0
0.0
0.0
0.0
73149
14000US06037191000
Sheltered 2017
sheltered
2017
2003.0
14.0
Female
F
Latino
European American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73150
14000US06037503105
Sheltered 2017
sheltered
2017
1994.0
23.0
Female
F
African American
African American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73151
14000US06037206200
Sheltered 2017
sheltered
2017
1965.0
52.0
Male
M
African American
African American
...
0
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73152
14000US06037206300
Sheltered 2017
sheltered
2017
1957.0
60.0
Male
M
African American
African American
...
1
0
0.0
0.0
0
0
0.0
0.0
0.0
0.0
73153 rows × 33 columns
In [24]:
df.head().T
Out[24]:
0
1
2
3
4
geoid
NaN
NaN
NaN
NaN
NaN
survey_year
Unsheltered 2011
Unsheltered 2011
Unsheltered 2011
Unsheltered 2011
Unsheltered 2011
survey_type
unsheltered
unsheltered
unsheltered
unsheltered
unsheltered
year
2011
2011
2011
2011
2011
birth_year
1993
1964
1956
1960
1979
age
18
46
55
50
31
gender
Female
Female
Male
Male
Male
sex
F
F
M
M
M
ethnicity
European American
African American
European American
European American
Latino
race_full
White
Black-African-American
White
White
BLANK
race_recode
European American
African American
European American
European American
Unknown
raceeth
nhwhite
black
nhwhite
nhwhite
hisp
veteran
0
0
0
1
0
chronic_time
0
0
1
1
0
chronic_condition
0
1
1
1
0
chronic
0
0
1
1
0
adult_with_child
0
0
0
0
0
times_homeless_3yrs
2 to 3 times
unknown
1 time
1 time
2 to 3 times
times_homeless_past_year
1 time
2 to 3 times
1 time
4 or more times
1 time
current_stint_duration
4-11 months
4-11 months
12+ months
12+ months
1-3 months
spa
4
6
4
6
6
census_tract
NaN
NaN
NaN
NaN
NaN
physical_sexual_abuse
0
0
0
0
0
physical_disability
0
0
1
0
0
mental_illness
0
1
0
0
0
alcohol_abuse
0
0
0
1
0
drug_abuse
0
0
0
0
0
drug_alcohol_history
1
0
0
1
0
hiv_positive
0
0
0
0
0
part_time
0
0
0
0
0
full_time
0
0
0
0
0
unemployed_looking
1
1
1
1
1
unemployed_not_looking
0
0
0
0
0
In [29]:
df.groupby(['sex', 'adult_with_child']).mean()['drug_abuse'].unstack() * 100.0
Out[29]:
adult_with_child
0
1
sex
F
11.638346
7.314815
M
13.938345
5.454545
U
14.843007
0.000000
In [ ]:
Content source: CivicKnowledge/metatab-packages
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