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%load_ext autoreload
%autoreload 2

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 
from collections import deque
from tqdm import tqdm_notebook as tqdm

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


    

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


    

    
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San Diego Parking Time Series
sandiego.gov-cityiq_parking-2 Last Update: 2019-02-16T04:47:36
15 minute interval parking utilization for 1600 parking zones in San Diego city.
This datasets is compiled from parking events scraped from the San Diego CityIQ
smart streetmap system, via the cityiq Python
package. The dataset is compiled from PKIN
and PKOUT events between the dates of Sept 2018 and Feb 2019 for the whole SaN
Diego system.
The dataset is heavily processed to eliminate duplicate events because there
are many spurious events, but an excess of PKIN events. When computing the
number of cars parked in all parking zones, the excess of PKIN events results
in about 60,000 extra cars per month. These issues are explored in an Jupyter
Notebook
The records in this dataset referece parking zones. More information, including geographic positions, are avialble in the CityIQ Objects dataset.
Processing
These data were produced with these programs: 
$ pip install cityiq
$ ciq_config -w
$ # Edit .cityiq-config.yaml with client-id and secret
# Scrape PKIN and PKOUT from Sept 2018 to present
$ $ ciq_events -s -e PKIN -e PKOUT -t 20190901
# Split event dump in to event-location csv files
$ ciq_events -S
# Deduplicate and normalize
$ ciq_events -n

The last step, deduplication and normalization, involves these steps: 

Group events by event type, location and 1 second period and select only 1
  record from each group 
Collect runs of a events of one type and select only
  the first record of the run, up to a run of 4 minutes long 
For each location, compute the cumulative sum of in and outs ( calculating
  the number of cars in the zone ) then create a rolling 2-day average.
  Subtract off the average.

The third step is demonstrated in this image:

The blue line is the original utilization for a single location, showing the
larger number of PKIN events than PKOUT events. The red line is the 2-day
rolling average, and the green line is after subtracting the 2-dat rolling
average.
In the final dataset, the data for the blue line is in the  cs column, which is created from the cumulative sum of the delta column. The green line is the data in the cs_norm column, which is differentiated to create the delta_normcolumn. 
For most purpuses you should use cs_norm and delta_norm.
Contacts

Wrangler Eric Busboom, Civic Knowledge

Resources

 parking_events. Parking events

References

parking_events. Parking events
assets. Data package with metadata about the parking zone locations.
locations. Data package with metadata about the parking zone locations.

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import datetime
import statsmodels.formula.api as sm
from cityiq import Config, CityIq
from cityiq.scrape import EventScraper
tz = datetime.datetime.now(datetime.timezone.utc).astimezone().tzinfo
from pathlib import Path

config = Config() # Assumes ~/.city-iq.yaml

c = CityIq(config) # Maybe will need this later

# Scrape events from Sept 2018
start_time = datetime.datetime(2019, 1, 1, 0, 0, tzinfo = tz)

s = EventScraper(config, start_time,  ['PKIN', 'PKOUT'])


    

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from cityiq.clean_events import clean_events
locations = ['2byc1udh1jajlnw7b2b'] #,'jvfh0jtj8e8jlqxk5xi']


    

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df = clean_events(s)
df.sum()


    

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len(df)


    

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list(s.iterate_splits(locations=))


    

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t = df.index.min()
t.year, t.month


    

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df.plot.scatter(x='delta', y='delta_norm')


    

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