Basic RCFE Analysis


In [ ]:
# If you haven installed pandas and matplotlib, uncomment these and run this cell: 
#!pip install matplotlib
#!pip install pandas

In [3]:
import metatab
import pandas as pd

import matplotlib.pyplot
%matplotlib notebook
matplotlib.style.use('ggplot')

doc = metatab.open_package('..') # Assumes you started Jupyter in the same dir as the metadata file

# To run this from the package on the web
package_url='http://metatab.sandiegodata.org/zip/dss.ca.gov-residential_care_facilities-2017-1.zip'
doc = metatab.open_package(package_url)

doc


Out[3]:

California Residential Elder Care Facilities

dss.ca.gov-residential_care_facilities-2017-1

A list of licensed Residential Elder Care Facilities (RCFEs) from the California Department of Social Services.

HomepageCA DSS Care Facilities Home Page
Origindss.ca.gov

Resources

  1. facilities - data/facilities.csv

  2. age_income_tract - data/age_income_tract.csv

  3. geocoded-geographies - data/geocoded-geographies.csv


In [9]:
fac_resource = doc.resource('facilities')
fac = fac_resource.dataframe()

# Print out the errors, if there are any. The first version of the facilities file we downloaded was corrupt, with a
# lot of CSV formatting errors. We've asked for an update. 
for k,v in fac.metatab_errors.items():
    print(k, list(v)[0]) # Just print the first error on this field.


other_typea Failed to cast '07/07/2015, 01/27/2015, 01/09/2013, 09/10/2012' (<class 'rowpipe.valuetype.core.FailedValue'>) to int in 'other_typea': invalid literal for int() with base 10: '07/07/2015, 01/27/2015, 01/09/2013, 09/10/2012'
unfounded_allegations Failed to cast '07/27/2016' (<class 'rowpipe.valuetype.core.FailedValue'>) to int in 'unfounded_allegations': invalid literal for int() with base 10: '07/27/2016'
total_visits Failed to cast '98767, 87303(a)' (<class 'rowpipe.valuetype.core.FailedValue'>) to int in 'total_visits': invalid literal for int() with base 10: '98767, 87303(a)'
inspect_typeb Failed to cast '02/13/2015, 01/22/2015, 01/15/2015, 10/29/2014' (<class 'rowpipe.valuetype.core.FailedValue'>) to int in 'inspect_typeb': invalid literal for int() with base 10: '02/13/2015, 01/22/2015, 01/15/2015, 10/29/2014'
facility_capacity Failed to cast 'LICENSED' (<class 'rowpipe.valuetype.core.FailedValue'>) to int in 'facility_capacity': invalid literal for int() with base 10: 'LICENSED'
facility_zip Failed to cast '92683   0' (<class 'rowpipe.valuetype.core.FailedValue'>) to int in 'facility_zip': invalid literal for int() with base 10: '92683   0'
inspect_typea Failed to cast '10/24/2016' (<class 'rowpipe.valuetype.core.FailedValue'>) to int in 'inspect_typea': invalid literal for int() with base 10: '10/24/2016'

In [10]:
fac_resource


Out[10]:

facilities - data/RCFE02052017.csv

HeaderTypeDescription
facility_typetext
facility_numberinteger
facility_nametext
licenseetext
facility_administratortext
facility_telephone_numbertext
facility_addresstext
facility_citytext
facility_statetext
facility_zipinteger
county_nametext
regional_officeinteger
facility_capacityinteger
facility_statustext
license_first_datetext
closed_datetext
last_visit_datetext
inspection_visitsinteger
complaint_visitsinteger
other_visitsinteger
total_visitsinteger
citation_numberstext
poc_datestext
all_visit_datestext
inspection_visit_datestext
inspect_typeainteger
inspect_typebinteger
other_visit_datestext
other_typeainteger
other_typebinteger
complaint_type_ainteger
complaint_type_binteger
total_allegationsinteger
inconclusive_allegationsinteger
substantiated_allegationsinteger
unfounded_allegationsinteger
col41text

In [5]:
fac.T # A good way to get a view of all columns and their typical values.


Out[5]:
0 1 2 3 4 5 6 7 8 9 ... 11257 11258 11259 11260 11261 11262 11263 11264 11265 11266
facility_type RCFE-CONTINUING CARE RETIREMENT COMMUNITY RCFE-CONTINUING CARE RETIREMENT COMMUNITY RCFE-CONTINUING CARE RETIREMENT COMMUNITY RCFE-CONTINUING CARE RETIREMENT COMMUNITY RCFE-CONTINUING CARE RETIREMENT COMMUNITY RCFE-CONTINUING CARE RETIREMENT COMMUNITY RCFE-CONTINUING CARE RETIREMENT COMMUNITY RCFE-CONTINUING CARE RETIREMENT COMMUNITY RCFE-CONTINUING CARE RETIREMENT COMMUNITY RCFE-CONTINUING CARE RETIREMENT COMMUNITY ... RESIDENTIAL CARE ELDERLY RESIDENTIAL CARE ELDERLY RESIDENTIAL CARE ELDERLY RESIDENTIAL CARE ELDERLY RESIDENTIAL CARE ELDERLY RESIDENTIAL CARE ELDERLY RESIDENTIAL CARE ELDERLY RESIDENTIAL CARE ELDERLY RESIDENTIAL CARE ELDERLY RESIDENTIAL CARE ELDERLY
facility_number 15601302 216801686 330907913 197802543 421700369 191501662 191501668 565801963 374603618 374603617 ... 347004853 496803611 490111878 435202020 435202420 366413280 197607821 397004511 397004913 435294321
facility_name ACACIA CREEK - UNION CITY ALDERSLY ALTAVITA ATHERTON BAPTIST HOMES ATTERDAG VILLAGE OF SOLVANG BRETHREN HILLCREST HOMES BRITISH HOME IN CALIFORNIA LTD, THE BROOKDALE CAMARILLO BROOKDALE CARLSBAD BROOKDALE CARMEL VALLEY ... ZEPHYR COVE RESIDENTIAL CARE ZIMPHER RCFE ZIMPHER RESIDENTIAL CARE HOME ZION CARE HOME I ZION CARE HOME II ZION HOMECARE, INC ZION RESIDENTIAL CARE ZOSING CARE HOME ZOSING CARE HOME II ZUZZETTE'S RESIDENTIAL CARE HOME
licensee MASONIC HOMES CAL & ACACIA CREEK, MASONIC SEN LIV ALDERSLY/LIFE CARE SERVICES,LLC AIRFORCE VILLAGE WEST INC & ESKATON PROPERTIES... ATHERTON BAPTIST HOMES SOLVANG LUTHERAN HOME, INC. BRETHREN HILLCREST HOMES BRITISH HOME IN CALIFORNIA, LTD THE S-H OPCO CAMARILLO,LLC-BKD TWENTY-ONE MGT CO,INC S-H OPCO CARLSBAD LLC/BKD TWENTY-ONE MNGMNT CO... S-H OPCO CARMEL VALLEY LLC/BKD 21 MNGMNT CO INC ... ANDERSON, PAULINA ZIMPHER RCFE ZIMPHER, GREG A. & KAREN ZION CARE HOMES, INC. MARILOU A. PABLO ZION HOMECARE, INC ZION RESIDENTIAL CARE, INC. GENE VELASQUEZ ANGELICA P.VELASQUEZ ECHEVERRIA, ZUZZETTE M.
facility_administrator CHARLES MAJOR CLARA ALLEN BRUCE CAMERON MARY MONNIER CHRIS PARKER MATTHEW NEELEY MARLENE RAINEN VINCENT GONZAGA SASHA HIGHTOWER JASON MCDONALD ... ANDERSON, PAULINA BLANCAFLOR,JOSEPHINE BLANCAFLOR, JOSEPHINE D MARILOU A. PABLO PABLO, MARILOU LEONEL C. HONRADA ARTHUR AGUILAR ANGELICA VELASQUES VELASQUEZ, ANGELICA & GENE ECHEVERRIA, MARIA
facility_telephone_number (510) 441-3700 (415) 453-7425 (951) 697-2000 (626) 289-4178 (805) 688-3263 (909) 593-4917 (626) 355-7240 (805) 388-8086 (760) 720-9898 (858) 259-2222 ... (916) 393-3065 (707) 829-8539 (707) 829-8539 (408) 890-0084 (408) 926-9949 (909) 370-2271 (818) 620-2202 (916) 955-1033 (209) 955-1033 (408) 723-2334
facility_address 34400 MISSION BLVD. 326 MISSION AVENUE 17050 ARNOLD DRIVE 214 SOUTH ATLANTIC BLVD. 636 N ATTERDAG ROAD 2705 MOUNTAIN VIEW DRIVE 647 MANZANITA AVE 6000 SANTA ROSA ROAD 3140 EL CAMINO REAL 13101 HARTFIELD AVENUE ... 75 ZEPHYR COVE 476 EILEEN DR 476 EILEEN DRIVE 3592 PINE RIDGE WAY 14881 SAN PABLO AVENUE 2165 STEWART ST. 16654 SAN FERNANDO MISSION BLV 2777 WISTERIA LANE 2815 MIRASOL LANE 5103 CAMDEN AVENUE
facility_city UNION CITY SAN RAFAEL RIVERSIDE ALHAMBRA SOLVANG LA VERNE SIERRA MADRE CAMARILLO CARLSBAD SAN DIEGO ... SACRAMENTO SEBASTOPOL SEBASTOPOL SAN JOSE SAN JOSE COLTON GRANADA HILLS STOCKTON STOCKTON SAN JOSE
facility_state CA CA CA CA CA CA CA CA CA CA ... CA CA CA CA CA CA CA CA CA CA
facility_zip 94587 94901 92518 91801 93463 91750 91024 93012 92008 92130 ... 95831 95472 95472 95127 95127 92324 91344 95212 95212 95124
county_name ALAMEDA MARIN RIVERSIDE LOS ANGELES SANTA BARBARA LOS ANGELES LOS ANGELES VENTURA SAN DIEGO SAN DIEGO ... SACRAMENTO SONOMA SONOMA SANTA CLARA SANTA CLARA SAN BERNARDINO LOS ANGELES SAN JOAQUIN SAN JOAQUIN SANTA CLARA
regional_office 15 21 18 28 29 28 28 31 8 8 ... 27 21 21 26 26 18 31 27 27 26
facility_capacity 376 122 770 518 188 574 41 140 125 125 ... 4 6 6 5 6 6 6 6 6 6
facility_status LICENSED LICENSED LICENSED LICENSED LICENSED LICENSED LICENSED LICENSED LICENSED LICENSED ... LICENSED PENDING LICENSED CLOSED CLOSED CLOSED LICENSED LICENSED LICENSED CLOSED
license_first_date 5/20/2010 11/5/2004 12/22/1989 3/31/1999 5/28/1993 7/2/1976 5/1/1976 8/29/2014 8/29/2014 8/29/2014 ... 9/30/2011 None 10/22/1993 1/2/2008 None 10/9/2007 9/29/2009 1/29/2010 12/30/2011 4/21/2009
closed_date None None None None None None None None None None ... None None None 5/19/2014 8/29/2016 10/1/2013 None None None 4/8/2013
last_visit_date 4/3/2015 4/4/2016 1/18/2017 12/20/2016 1/26/2017 1/24/2017 10/14/2015 1/20/2017 10/20/2016 9/9/2016 ... 2/9/2016 3/7/2016 2/8/2016 5/19/2014 9/8/2016 10/3/2013 9/19/2016 6/10/2016 6/10/2016 6/19/2013
inspection_visits 1 2 1 1 1 1 1 0 0 0 ... 2 0 2 1 0 2 1 2 2 1
complaint_visits 0 0 3 2 0 2 5 7 8 5 ... 0 0 1 0 1 0 0 0 0 2
other_visits 0 0 4 1 5 4 0 3 7 11 ... 2 3 1 1 5 0 0 1 1 19
total_visits 1 2 8 4 6 7 6 10 15 16 ... 4 3 4 2 6 2 1 3 3 22
citation_numbers None None 87465(g), 87211(a)(1)(D) None None 87705(c)(3), 87705(f)(2), 87705(f)(1), 87303(e... 87468(a)(3), 87224(f), 87468(a)(3) 87468(a)(1), 87355(c), 87303(a), 87468(a)(2), ... 87465 a 5 , 87465 a 1 , 80072 (a) 1, 8, 87465,... 87465(g), 87464(d), 87464(d), 87456, 87456a(4)... ... 87465(h)(2) , 8765(h)(5), 1569.69, 87307(a) , ... None 87465(a)(5), 87465(e), 87309(a), 87303(a)(2), ... 87309(a), 87203 HS1569.44(a)(1), 87608(a)(5)(B), 87303(e)(5), ... None None 87705(c)(5), 87303 e2, 87506(a), 87303(c), 873... 87705(c)(5), 87465(e), 87303(c), 87506(a), 874... 87112 (a)(a), 87465(a)(5), 87464(f)(3), 87224(...
poc_dates None None 11/05/2013, 11/05/2013 None None 09/25/2014, 09/23/2014, 09/23/2014, 09/25/2014... 10/28/2015, 07/29/2013, 08/12/2015 01/23/2017, 12/05/2014, 04/15/2016, 04/08/2016... 08/31/2015, 05/15/2015, 02/20/2015, 02/19/2015... 12/02/2015, 12/02/2015, 12/22/2015, 12/02/2015... ... 02/10/2016, 02/23/2016, 02/23/2016, 08/01/2013... None 05/15/2013, 05/15/2013, 12/11/2015, 12/11/2015... 05/06/2013, 05/06/2013 06/09/2016, 05/06/2014, 05/09/2014, 05/06/2014... None None 05/25/2016, 03/28/2013, 05/31/2016, 05/31/2016... 05/25/2016, 05/31/2016, 05/31/2016, 05/31/2016... 06/19/2013, 02/23/2013, 02/23/2013, 02/23/2013...
all_visit_dates 04/03/2015 04/04/2016, 10/25/2012 01/18/2017, 10/12/2016, 10/12/2016, 03/22/2016... 12/20/2016, 02/16/2016, 03/25/2015, 02/29/2012 01/26/2017, 11/02/2015, 09/01/2015, 06/22/2015... 12/18/2015, 07/21/2015, 10/02/2014, 10/02/2014... 10/14/2015, 07/29/2015, 07/22/2013, 07/22/2013... 01/11/2017, 04/06/2016, 04/06/2016, 04/06/2016... 10/20/2016, 06/14/2016, 01/27/2016, 11/24/2015... 03/28/2016, 03/28/2016, 03/24/2016, 03/23/2016... ... 02/09/2016, 10/08/2013, 07/25/2013, 02/24/2012 03/07/2016, 02/08/2016, 02/08/2016 02/08/2016, 12/10/2015, 05/14/2013, 04/16/2013 04/29/2013, 05/19/2014 09/08/2016, 08/31/2016, 08/23/2016, 06/08/2016... 10/03/2013, 10/10/2012 09/19/2016 06/10/2016, 05/24/2016, 02/27/2013 06/10/2016, 05/24/2016, 02/27/2013 06/19/2013, 04/08/2013, 04/05/2013, 03/14/2013...
inspection_visit_dates 04/03/2015 04/04/2016, 10/25/2012 12/10/2012 02/29/2012 05/18/2012 09/23/2014 02/14/2013 None None None ... 02/09/2016, 07/25/2013 None 12/10/2015, 04/16/2013 04/29/2013 None 10/03/2013, 10/10/2012 09/19/2016 05/24/2016, 02/27/2013 05/24/2016, 02/27/2013 05/24/2012
inspect_typea 0 0 0 0 0 6 0 0 0 0 ... 1 0 9 0 0 0 0 2 1 0
inspect_typeb 0 0 0 0 0 0 0 0 0 0 ... 4 0 4 2 0 0 0 5 5 0
other_visit_dates None None 01/18/2017, 03/22/2016, 11/05/2013, 10/29/2013 03/25/2015 01/26/2017, 11/02/2015, 09/01/2015, 06/22/2015... 10/02/2014, 10/02/2014, 10/02/2014, 04/24/2014 None 01/11/2017, 12/03/2014, 08/26/2014 10/20/2016, 06/14/2016, 01/27/2016, 11/24/2015... 03/28/2016, 03/24/2016, 03/15/2016, 03/10/2016... ... 10/08/2013, 02/24/2012 03/07/2016, 02/08/2016, 02/08/2016 02/08/2016 05/19/2014 09/08/2016, 08/31/2016, 08/23/2016, 06/08/2016... None None 06/10/2016 06/10/2016 04/08/2013, 04/05/2013, 03/14/2013, 03/05/2013...
other_typea 0 0 0 0 0 0 0 1 4 1 ... 0 0 1 0 4 0 0 0 0 20
other_typeb 0 0 1 0 0 2 0 2 2 2 ... 0 0 0 0 6 0 0 0 0 4
complaint_type_a 0 0 1 0 0 0 2 0 2 5 ... 0 0 2 0 1 0 0 0 0 4
complaint_type_b 0 0 0 0 0 0 1 2 1 0 ... 0 0 1 0 0 0 0 0 0 0
total_allegations 0 0 3 7 0 1 5 10 12 7 ... 0 0 4 0 1 0 0 0 0 7
inconclusive_allegations 0 0 1 3 0 1 1 5 1 3 ... 0 0 0 0 0 0 0 0 0 1
substantiated_allegations 0 0 2 0 0 0 3 2 3 2 ... 0 0 1 0 1 0 0 0 0 6
unfounded_allegations 0 0 0 4 0 0 1 3 8 2 ... 0 0 3 0 0 0 0 0 0 0
col41 None None None None None None None None None None ... None None None None None None None None None None

37 rows × 11267 columns


In [6]:
# Save the facility addresses so we can send them to the Census geocoder
# https://www.census.gov/geo/maps-data/data/geocoder.html
sdfac = fac[fac.facility_city == 'SAN DIEGO'][['facility_number', 'facility_address', 
                                       'facility_city','facility_state', "facility_zip"]]


sdfac.to_csv('../data/sandiego.csv')

In [ ]: