In [7]:

    

import seaborn as sns
import metapack as mp
import pandas as pd
import geopandas as gpd
import rowgenerators as rg
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display 

%matplotlib inline
sns.set_context('notebook')
mp.jupyter.init()


    

In [22]:

    

#pkg = mp.jupyter.open_package()
pkg = mp.jupyter.open_source_package()
pkg


    

    
Out[22]:




Links between LA Tracts and Land Use Codes
sandiegodata.org-la_parcels-1 Last Update: 2019-01-18T19:19:31
__
Static files for links between LA parcels and LA tracts. 
See the notebooks directory for the code bits thatgenerated the files. They 
are slow and take a lot of memory, since there are about 2M parcels in the file
Contacts

Wrangler Eric Busboom, Civic Knowledge

Resources

 la-planning-code. 
 tract_to_parcels. 

References

la_land_use. Download page for the LA Land Use File

In [3]:

    

gdf = gpd.read_file('/Users/eric/Downloads/General_Plan_Land_Use__Los_Angeles/Los_Angeles_PL_SCAG.shp')


    

In [ ]:

    




    

In [4]:

    

gdf.SCAG_GP_CO.value_counts()


    

    
Out[4]:





1110 Single Family Residential                                                                              1367606
1120 Multi-Family Residential                                                                                308420
1140 Mixed Residential                                                                                        91688
1150 Rural Residential                                                                                        79266
1200 General Commercial                                                                                       39812
1600 Mixed Residential and Commercial                                                                         32545
1220 Retail and Commercial and Services                                                                       31682
1310 Light Industrial                                                                                         17866
1311 Light Manufacturing                                                                                      17707
7777 Specific Plan                                                                                            14879
1300 General Industrial                                                                                       13693
1240 Public Facilities                                                                                        11870
1500 Mixed Commercial and Industrial                                                                          10429
1800 Open Space and Recreation                                                                                 9740
1210 General Office Use                                                                                        7685
1221 Regional Shopping Center                                                                                  5991
1100 Residential                                                                                               5071
1320 Heavy Industrial                                                                                          4537
1321 Heavy Manufacturing                                                                                       3840
1850 Wildlife Preserves and Sanctuaries                                                                        2617
1410 Transportation                                                                                            2372
1880 Other Open Space and Recreation                                                                           2165
1830 State and National Parks and Recreation                                                                   1604
1820 Local Parks and Recreation                                                                                1596
1260 Education ?K-12                                                                                           1539
4000 Water                                                                                                     1087
8888 Undevelopable or Protected Land                                                                            761
1230 Other Commercial                                                                                           686
1430 Utility Facilities                                                                                         629
1870 Beach Parks                                                                                                428
3000 Vacant                                                                                                     337
1130 Mobile Homes and Trailer Parks                                                                             190
1270 Military Installations                                                                                     171
1420 Communication Facilities                                                                                   119
1400 Transportation, Communications, and Utilities\n1400 Transportation, Communications, and Utilities\n        112
1810 Golf Courses                                                                                                90
1233 Hotels and Motels                                                                                           59
1840 Cemeteries                                                                                                  37
1265 Education ?College                                                                                          25
1250 Special Use Facilities                                                                                      11
9999 Unknown                                                                                                      8
2000 Agriculture                                                                                                  8
1340 Wholesaling and Warehousing                                                                                  3
Name: SCAG_GP_CO, dtype: int64

In [5]:

    

gpco = gpd.GeoDataFrame({
    'objectid': gdf.OBJECTID,
    'SCAG_GP_CO': gdf.SCAG_GP_CO,
    'geometry': gdf.geometry.centroid
})


    

In [8]:

    

tracts = rg.geoframe('census://2017/5/CA/tract')
gpco.crs = tracts.crs # Just a guess

gptracts = gpd.sjoin(tracts, gpco)

gpt = gptracts.copy()
gpt['code'] = gpt.SCAG_GP_CO.apply(lambda v: int((v.split())[0]) if v else 0)


    

In [15]:

    

df = gpt[['geoid','code','OBJECTID']].merge(gdf, on='OBJECTID')
df = df[['geoid','OBJECTID','APN','CITY','code','ZONE_CODE','ACRES']]
df.columns = [c.lower() for c in df.columns]


    

In [20]:

    

df = df.merge(gpco[['objectid','geometry']], on='objectid')


    

In [24]:

    

!ls ../data/


    

    




la-planning-code.csv tract_to_parcels.csv

In [28]:

    

df.to_csv('../data/tract_to_parcels.csv')


    

In [27]:

    

df.index.name = 'index'
df.head()


    

    
Out[27]:







  

    

      

      
geoid
      
objectid
      
apn
      
city
      
code
      
zone_code
      
acres
      
geometry
    
    

      
index
      

      

      

      

      

      

      

      

    
  
  

    

      
0
      
14000US06037204920
      
205663
      
5191-012-009
      
Los Angeles
      
1120
      
R2
      
0.119355
      
POINT (-118.1997972661181 34.02204043785111)
    
    

      
1
      
14000US06037204920
      
205653
      
5191-011-008
      
Los Angeles
      
1120
      
R2
      
0.123901
      
POINT (-118.201096570206 34.02206941378159)
    
    

      
2
      
14000US06037204920
      
204574
      
5191-011-022
      
Los Angeles
      
1120
      
R2
      
0.119314
      
POINT (-118.2007983464134 34.02207176228492)
    
    

      
3
      
14000US06037204920
      
204573
      
5191-011-015
      
Los Angeles
      
1120
      
R2
      
0.104612
      
POINT (-118.1999064954099 34.02208907018797)
    
    

      
4
      
14000US06037204920
      
213146
      
5191-011-014
      
Los Angeles
      
1120
      
R2
      
0.119352
      
POINT (-118.2000169669352 34.02213571331608)
    
  

In [ ]: