notebook.community

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In [1]:



    


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

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



    











In [2]:



    


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



    


















    
Out[2]:







San Diego City IQ Assets and Locations


sandiego.gov-cityiq_objects-4 Last Update: 2019-02-18T01:17:23


All assets types extracted form the San Diego City Iq system


These datafile are extracts of the assets and locations from the San Diego
CityIQ system. Refer to the CityIQ developer
documentation for details about these data records. The data are
extracted using the cityiq Python package.
See the ExtractAssets.ipynb notebook for the extract process.


Versions


    

  1. Development version
    2. 

  2. Development version
    3. 

  3. First release
    4. 

  4. Added tract and community names
    5. 



Contacts



Resources


    

  •  assets. All assets
    • 

  •  locations. All locations: walkways, traffic lanes and parking zones
    • 



References


















In [3]:



    


pkg.resource('assets').dataframe().head()



    


















    
Out[3]:











  
    

      
      assetuid
      assettype
      parentassetuid
      mediatype
      events
      community_name
      tract_geoid
      roadsegid
      speed
      oneway
      abloaddr
      abhiaddr
      rd30full
      geometry
    

  
  
    

      0
      000223ee-a868-474b-abcb-12ff1bad00a3
      CAMERA
      131d67b7-daaf-4252-9202-5df347a6c2a3
      IMAGE,VIDEO
      NaN
      Downtown
      14000US00000006940
      1453.0
      20.0
      F
      500.0
      599.0
      05TH AVE
      POINT (-117.1600173 32.71143062)
    

    

      1
      0002e3bb-5a9a-4083-9a96-fad0d22877b9
      MIC
      e8beafbf-7836-48c3-bb14-440970eecd6d
      AUDIO
      NaN
      Downtown
      14000US00000006937
      3046.0
      20.0
      B
      400.0
      499.0
      16TH ST
      POINT (-117.1493049 32.70997207)
    

    

      2
      0003806e-e409-449f-aab1-fc95f6e88d4e
      CAMERA
      f91ec557-f03e-4189-8b83-5aa8d28fbf14
      IMAGE,VIDEO
      NaN
      Downtown
      14000US00000006940
      2270.0
      20.0
      B
      400.0
      499.0
      09TH AVE
      POINT (-117.1566029 32.70934863)
    

    

      3
      000a4ac7-224b-4fb7-a2e2-cd0592d2b2de
      CAMERA
      741ce488-72fa-4216-a65d-216f7bdf8b7d
      IMAGE,VIDEO
      NaN
      Downtown
      14000US00000006938
      3152.0
      20.0
      B
      900.0
      999.0
      17TH ST
      POINT (-117.148666 32.71585673)
    

    

      4
      000b2365-5309-422a-9be6-1b7127ca18db
      NODE
      NaN
      NaN
      NaN
      La Jolla
      14000US00000007220
      40722.0
      20.0
      B
      5300.0
      5349.0
      LA JOLLA HERMOSA AVE
      POINT (-117.264282290807 32.8105285177251)
    

  




















In [5]:



    


pkg.resource('locations').dataframe().head()



    


















    
Out[5]:











  
    

      
      locationuid
      locationtype
      parentlocationuid
      community_name
      tract_geoid
      roadsegid
      speed
      oneway
      abloaddr
      abhiaddr
      rd30full
      geometry
    

  
  
    

      0
      004361eb
      WALKWAY
      004361eb
      Downtown
      14000US00000006974
      40052.0
      35.0
      T
      2100.0
      2199.0
      KETTNER BLVD
      LINESTRING (-117.1706148251287 32.726548905684...
    

    

      1
      00456472
      TRAFFIC_LANE
      00456472
      Downtown
      14000US00000006974
      40052.0
      35.0
      T
      2100.0
      2199.0
      KETTNER BLVD
      LINESTRING (-117.1705245131968 32.726558517207...
    

    

      2
      0051796c
      WALKWAY
      0051796c
      Downtown
      14000US00000006974
      54946.0
      20.0
      F
      1600.0
      1699.0
      STATE ST
      LINESTRING (-117.1666581391174 32.722214661862...
    

    

      3
      00537bf3
      TRAFFIC_LANE
      00537bf3
      Downtown
      14000US00000007137
      54946.0
      20.0
      F
      1600.0
      1699.0
      STATE ST
      LINESTRING (-117.166581746989 32.7222178855665...
    

    

      4
      005f90ed
      WALKWAY
      005f90ed
      Downtown
      14000US00000006974
      54945.0
      20.0
      F
      1700.0
      1799.0
      STATE ST
      LINESTRING (-117.1665182431566 32.723810889902...
    

  




















In [ ]:

Content source: CivicKnowledge/metatab-packages

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