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[1]:
import libpysal
[2]:
libpysal.examples.fetch_all()
downloading dataset from https://s3.amazonaws.com/geoda/data/guerry.zip to /home/jovyan/pysal_data
Extracting files....
downloading dataset from https://github.com/sjsrey/rio_grande_do_sul/archive/master.zip to /home/jovyan/pysal_data
Extracting files....
downloading dataset from https://s3.amazonaws.com/geoda/data/ncovr.zip to /home/jovyan/pysal_data
Extracting files....
downloading dataset from https://github.com/sjsrey/nyc_bikes/archive/master.zip to /home/jovyan/pysal_data
Extracting files....
downloading dataset from https://s3.amazonaws.com/geoda/data/SacramentoMSA2.zip to /home/jovyan/pysal_data
Extracting files....
downloading dataset from https://s3.amazonaws.com/geoda/data/south.zip to /home/jovyan/pysal_data
Extracting files....
downloading dataset from https://github.com/sjsrey/taz/archive/master.zip to /home/jovyan/pysal_data
Extracting files....
[3]:
from libpysal.examples import nat
[4]:
nat.fetch_nat()
already exists, not downloading
[5]:
from os import environ
[6]:
environ.get("PYSALDATA")
[6]:
'/home/jovyan/pysal_data'
[7]:
from libpysal.examples import south
[8]:
sd = south.fetch_south()
already exists, not downloading
[9]:
from libpysal.examples import guerry
[10]:
guerry.fetch_guerry()
already exists, not downloading
[11]:
libpysal.examples.explain('guerry')

guerry
======

Andre-Michel Guerry data on "moral statistics" 1930 crime, suicide, literacy and other “moral statistics” in 1830s France.

- Observations = 85
- Variables = 23
- Years = 1915-1934
- Support = polygon

Files
-----
Guerry.dbf  Guerry_documentation.html  Guerry.geojson  Guerry.prj  Guerry.shp  Guerry.shx  README.md


Variables
---------

dept, code_de   Department ID: Standard numbers for the departments
region  Region of France (‘N’=’North’, ‘S’=’South’, ‘E’=’East’, ‘W’=’West’, ‘C’=’Central’). Corsica is coded as NA.
dprtmnt         Department name: Departments are named according to usage in 1830, but without accents. A factor with levels Ain Aisne Allier … Vosges Yonne
crm_prs         Population per Crime against persons.   A2. Compte général, 1825-1830
crm_prp         Population per Crime against property.  Compte général, 1825-1830
litercy         Percent of military conscripts who can read and write.  A2
donatns         Donations to the poor.  A2. Bulletin des lois
infants         Population per illegitimate birth.      A2. Bureau des Longitudes, 1817-1821
suicids         Population per suicide.         A2. Compte général, 1827-1830
maincty         Size of principal city (‘1:Sm’, ‘2:Med’, ‘3:Lg’), used as a surrogate for population density. Large refers to the top 10, small to the bottom 10; all the rest are classed Medium.      A1. An ordered factor with levels: 1:Sm < 2:Med < 3:Lg
wealth  Per capita tax on personal property. A ranked index based on taxes on personal and movable property per inhabitant.     A1
commerc         Commerce and Industry, measured by the rank of the number of patents / population.      A1
clergy  Distribution of clergy, measured by the rank of the number of Catholic priests in active service population.    A1. Almanach officiel du clergy, 1829
crim_prn        Crimes against parents, measured by the rank of the ratio of crimes against parents to all crimes – Average for the years 1825-1830.    A1. Compte général
infntcd         Infanticides per capita. A ranked ratio of number of infanticides to population – Average for the years 1825-1830.      A1. Compte général
dntn_cl         Donations to the clergy. A ranked ratio of the number of bequests and donations inter vivios to population – Average for the years 1815-1824.   A1. Bull. des lois, ordunn. d’autorisation
lottery         Per capita wager on Royal Lottery. Ranked ratio of the proceeds bet on the royal lottery to population — Average for the years 1822-1826.       A1. Compte rendu par le ministre des finances
desertn         Military desertion, ratio of number of young soldiers accused of desertion to the force of the military contingent, minus the deficit produced by the insufficiency of available billets – Average of the years 1825-1827.      A1. Compte du ministère du guerre, 1829 état V
instrct         Instruction. Ranks recorded from Guerry’s map of Instruction. Note: this is inversely related to Literacy
Prsttts         Number of prostitutes registered in Paris from 1816 to 1834, classified by the department of their birth        Parent-Duchatelet (1836), De la prostitution en Paris
distanc         Distance to Paris (km). Distance of each department centroid to the centroid of the Seine (Paris)       Calculated from department centroids
area    Area (1000 km^2).       Angeville (1836)
pop1831         Population in 1831, in 1000s    Taken from Angeville (1836), Essai sur la Statistique de la Population français
Details

Note that most of the variables (e.g., Crime_pers) are scaled so that more is “better”.

Values for the quantitative variables displayed on Guerry’s maps were taken from Table A2 in the English translation of Guerry (1833) by Whitt and Reinking. Values for the ranked variables were taken from Table A1, with some corrections applied. The maximum is indicated by rank 1, and the minimum by rank 86.
Sources

Angeville, A. (1836). Essai sur la Statistique de la Population française Paris: F. Doufour.

Guerry, A.-M. (1833). Essai sur la statistique morale de la France Paris: Crochard. English translation: Hugh P. Whitt and Victor W. Reinking, Lewiston, N.Y. : Edwin Mellen Press, 2002.

Parent-Duchatelet, A. (1836). De la prostitution dans la ville de Paris, 3rd ed, 1857, p. 32, 36
References

Dray, S. and Jombart, T. (2011). A Revisit Of Guerry’s Data: Introducing Spatial Constraints In Multivariate Analysis. The Annals of Applied Statistics, Vol. 5, No. 4, 2278-2299., DOI: 10.1214/10-AOAS356.

Brunsdon, C. and Dykes, J. (2007). Geographically weighted visualization: interactive graphics for scale-varying exploratory analysis. Geographical Information Science Research Conference (GISRUK 07), NUI Maynooth, Ireland, April, 2007.

Friendly, M. (2007). A.-M. Guerry’s Moral Statistics of France: Challenges for Multivariable Spatial Analysis. Statistical Science, 22, 368-399.

Friendly, M. (2007). Data from A.-M. Guerry, Essay on the Moral Statistics of France (1833).
See Also

The Guerry package for maps of France: gfrance and related data.

Prepared by Center for Spatial Data Science. Last updated July 3, 2017. Data provided “as is,” no warranties.




[12]:
libpysal.examples.get_path('Guerry.geojson')
[12]:
'/home/jovyan/pysal_data/guerry/Guerry.geojson'
[13]:
libpysal.examples.explain('south')

south
=====

Homicides and selected socio-economic characteristics for Southern U.S. counties.
---------------------------------------------------------------------------------

- Observations = 1,412
- Variables = 69
- Years = 1960-90s
- Support = polygon

Files
-----
south.gdb     README.md  south.dbf      south.gpkg  south.kml  south.mif  south.shp  south.sqlite
codebook.pdf  south.csv  south.geojson  south.html  south.mid  south.prj  south.shx  south.xlsx

Variables
---------
NAME    county name
STATE_NAME      state name
STATE_FIPS      state fips code (character)
CNTY_FIPS       county fips code (character)
FIPS    combined state and county fips code (character)
STFIPS  state fips code (numeric)
COFIPS  county fips code (numeric)
FIPSNO  fips code as numeric variable
SOUTH   dummy variable for Southern counties (South = 1)
HR**    homicide rate per 100,000 (1960, 1970, 1980, 1990)
HC**    homicide count, three year average centered on 1960, 1970, 1980, 1990
PO**    county population, 1960, 1970, 1980, 1990
RD**    resource deprivation 1960, 1970, 1980, 1990 (principal component, see Codebook for details)
PS**    population structure 1960, 1970, 1980, 1990 (principal component, see Codebook for details)
UE**    unemployment rate 1960, 1970, 1980, 1990
DV**    divorce rate 1960, 1970, 1980, 1990 (% males over 14 divorced)
MA**    median age 1960, 1970, 1980, 1990
POL**   log of population 1960, 1970, 1980, 1990
DNL**   log of population density 1960, 1970, 1980, 1990
MFIL**  log of median family income 1960, 1970, 1980, 1990
FP**    % families below poverty 1960, 1970, 1980, 1990 (see Codebook for details)
BLK**   % black 1960, 1970, 1980, 1990
GI**    Gini index of family income inequality 1960, 1970, 1980, 1990
FH**    % female headed households 1960, 1970, 1980, 1990

[14]:
libpysal.examples.get_path('south.shp')
[14]:
'/home/jovyan/pysal_data/south/south.shp'
[15]:
libpysal.examples.get_path('missing.shp')
missing.shp not found.
[16]:
pth = libpysal.examples.get_path('south.shp')
[17]:
pth
[17]:
'/home/jovyan/pysal_data/south/south.shp'
[18]:
import geopandas as gpd
[19]:
df = gpd.read_file(pth)
[20]:
df.head()
[20]:
NAME STATE_NAME STATE_FIPS CNTY_FIPS FIPS STFIPS COFIPS FIPSNO SOUTH HR60 ... BLK90 GI59 GI69 GI79 GI89 FH60 FH70 FH80 FH90 geometry
0 Hancock West Virginia 54 029 54029 54 29 54029 1 1.682864 ... 2.557262 0.223645 0.295377 0.332251 0.363934 9.981297 7.8 9.785797 12.604552 POLYGON ((-80.6280517578125 40.39815902709961,...
1 Brooke West Virginia 54 009 54009 54 9 54009 1 4.607233 ... 0.748370 0.220407 0.318453 0.314165 0.350569 10.929337 8.0 10.214990 11.242293 POLYGON ((-80.52625274658203 40.16244888305664...
2 Ohio West Virginia 54 069 54069 54 69 54069 1 0.974132 ... 3.310334 0.272398 0.358454 0.376963 0.390534 15.621643 12.9 14.716681 17.574021 POLYGON ((-80.52516937255859 40.02275085449219...
3 Marshall West Virginia 54 051 54051 54 51 54051 1 0.876248 ... 0.546097 0.227647 0.319580 0.320953 0.377346 11.962834 8.8 8.803253 13.564159 POLYGON ((-80.52446746826172 39.72112655639648...
4 New Castle Delaware 10 003 10003 10 3 10003 1 4.228385 ... 16.480294 0.256106 0.329678 0.365830 0.332703 12.035714 10.7 15.169480 16.380903 POLYGON ((-75.77269744873047 39.38300704956055...

5 rows × 70 columns

[21]:
from libpysal.examples.sacramento2 import fetch_sacramento2
[22]:
fetch_sacramento2()
already exists, not downloading
[23]:
libpysal.examples.explain('sacramento2')

sacramento2
===========

2000 Census Tract Data for Sacramento MSA
-----------------------------------------

- Observations = 83
- Variables = 66
- Years = 1998, 2001
- Support = polygon

Files
-----
 SacramentoMSA2.gdb       SacramentoMSA2.kml   SacramentoMSA2.shp
 README.md                SacramentoMSA2.mid   SacramentoMSA2.shx
 SacramentoMSA2.csv       SacramentoMSA2.mif   SacramentoMSA2.sqlite
 SacramentoMSA2.dbf       SacramentoMSA2.prj   SacramentoMSA2.xlsx
 SacramentoMSA2.geojson   SacramentoMSA2.sbn  'Variable Info for Zip Code File.pdf'
 SacramentoMSA2.gpkg      SacramentoMSA2.sbx

Variables
---------
ZIP ZIP code
PO_NAME         Name of ZIP code area
STATE   STATE
MSA     MSA
CBSA_CODE       CBSA code
MAN98   1998 total manufacturing establishments (MSA)
MAN98_12        1998 total manufacturing establishments, 1-9 employees (MSA)
MAN98_39        1998 total manufacturing establishments 10+ employees (MSA)
MAN01   2001 total manufacturing establishments (MSA)
MAN01_12        2001 total manufacturing establishments, 1-9 employees (MSA)
MAN01_39        2001 total manufacturing establishments, 10+ employees (MSA)
MAN98US         1998 total manufacturing establishments (US)
MAN98US12       1998 total manufacturing establishments, 1-9 employees (US)
MAN98US39       1998 total manufacturing establishments 10+ employees (US)
MAN01US         2001 total manufacturing establishments (US)
MAN01US_12      2001 total manufacturing establishments, 1-9 employees (US)
MAN01US_39      2001 total manufacturing establishments, 10+ employees (US)
OFF98   1998 total office establishments (MSA)
OFF98_12        1998 total office establishments, 1-9 employees (MSA)
OFF98_39        1998 total office establishments, 10+ employees (MSA)
OFF01   2001 total office establishments (MSA)
OFF01_12        2001 total office establishments, 1-9 employees (MSA)
OFF01_39        2001 total office establishments, 10+ employees (MSA)
OFF98US         1998 total office establishments (US)
OFF98US12       1998 total office establishments, 1-9 employees (US)
OFF98US39       1998 total office establishments, 10+ employees (US)
OFF01US         2001 total office establishments (US)
OFFUS01_12      2001 total office establishments, 1-9 employees (US)
OFFUS01_39      2001 total office establishments, 10+ employees (US)
INFO98  1998 total information establishments (MSA)
INFO98_12       1998 total information establishments, 1-9 employees (MSA)
INFO98_39       1998 total information establishments, 10+ employees (MSA)
INFO01  2001 total information establishments (MSA)
INFO01_12       2001 total information establishments, 1-9 employees (MSA)
INFO01_39       2001 total information establishments, 10+ employees (MSA)
INFO98US        1998 total information establishments (US)
INFO98US12      1998 total information establishments, 1-9 employees (US)
INFO98US39      1998 total information establishments, 10+ employees (US)
INFO01US        2001 total information establishments (US)
INFO01US_1      2001 total information establishments, 1-9 employees (US)
INFO01US_3      2001 total information establishments, 10+ employees (US)
INDEX   Index
NUMSEC  Number of sectors represented in ZIP code
EST98   Total establishments in ZIP code, 1998
EST01   Total establishments in ZIP code, 2001
PCTNGE  National growth effect, percent (N)
PCTIME  Industry mix effect, percent (M)
PCTCSE  Competitive shift effect, percent (S)
PCTGRO  Percent growth establishments, 1998-2001 (R)
ID      Unique ZIP code ID for ID variables in weights matrix creation window

Source: US Census Bureau, 2000 Census (Summary File 3). Extracted from http://factfinder.census.gov in April 2004.

[24]:
libpysal.examples.get_path("10740.shx")
[24]:
'/home/jovyan/libpysal/examples/10740/10740.shx'
[25]:
from libpysal.examples.nyc_bikes import fetch_bikes
[26]:
fetch_bikes()
already exists, not downloading
[27]:
libpysal.examples.get_path('nyct2010.shp')
[27]:
'/home/jovyan/pysal_data/nyc_bikes/nyct2010.shp'
[28]:
from libpysal.examples.rio_grande_do_sul import fetch_rio
fetch_rio()
already exists, not downloading
[29]:
libpysal.examples.get_path('map_RS_BR.shp')
[29]:
'/home/jovyan/pysal_data/rio_grande_do_sul/map_RS_BR.shp'
[ ]:
from libpysal.examples.taz import fetch_taz
fetch_taz()
[ ]:
libpysal.examples.get_path('taz.dbf')
[ ]: