Probability Cumulative

Probability Cumulative Method

probability.get_cumulative(
    search_items, 
    csv=False, 
    output_dir='/output'
)

Delivers the risk likelihood (% probability) for flooding to the building footprint broken down by depth threshold and cumulative year.

This method returns an array of ProbabilityCumulative product for the given property IDs. Only property IDs are accepted. Optionally creates a csv file.

(More information on the Probability Cumulative product can be found on the Probability Cumulative Page on the First Street Foundation API Data Dictionary)

Arguments:

• search_items: list/file of SearchItems, property parcels to retrieve probability cumulative for.
• csv: bool, whether to create a CSV for the retrieved data.
• output_dir: string, location to output the created CSV (if csv is True).

Example (Command Line):

Call probability.get_cumulative on a list with 2 FSIDs

python -m firststreet -p probability.get_cumulative -s 190836953;392804911

Call probability.get_cumulative on a lat/lng or address

python -m firststreet -p probability.get_cumulative -s 37.16314,-76.55782;38.50303,-106.72863
python -m firststreet -p probability.get_cumulative -s "247 Water Street, New York, New York";"135 East 46th Street New York, New York"

Call probability.get_cumulative on a file of SearchItems

python -m firststreet -p probability.get_cumulative -s sample_property.txt

Example (Python File):

# Contents of sample.py
# Create a `FirstStreet` object.  
import firststreet
fs = firststreet.FirstStreet("api-key")

# Call probability.get_cumulative on a list with 2 property FSIDs
probability_cumulative = fs.probability.get_cumulative(search_items=[190836953, 193139123])

# Call probability.get_cumulative on a lat/lng or address
probability_cumulative = fs.probability.get_cumulative(search_items=[(37.16314,-76.55782)], csv=True)
probability_cumulative = fs.probability.get_cumulative(search_items=["247 Water Street, New York, New York"], csv=True)

# Call probability.get_cumulative on a file of SearchItems
probability_cumulative = fs.probability.get_cumulative(search_items="sample_property.txt", csv=True)

Probability Cumulative Objects

ProbabilityCumulative Object

Key	Type	Description	Example
fsid	str	First Street ID (FSID) is a unique identifier assigned to each location.	392804911
valid_id	bool	Whether the input FSID returned valid data from the server.	True
cumulative	Array[dict]	The risk likelihood (% probability) for flooding to the building footprint broken down by depth threshold and cumulative year. The low, mid, high likelihood is returned as an array of dictionary within the associated threshold and cumulative year. Available depth thresholds include >0cm, >15cm, and >30cm, returned as an array of dictionary and are broken down by the following cumulative years within the array of dictionary - 2035, and 2050, where 2035=15 years and 2050=30 years.	See below

Cumulative Dictionary

Key	Type	Description	Example
year	int	The cumulative year (where 2020 = current annual risk, 2025 = risk over 5 years, 2030 = risk over 10 years, in 5 year increments up through 2050, where 2050 = risk over 30 years) the probability was calculated for.	2035
data	Array[dict]	A collection of Cumulative Data	See below

Cumulative Data

Key	Type	Description	Example
threshold	string	The depth threshold in centimeters (0, 15cm, or 30cm) that the probability was calculated for.	0
data	List	Threshold Data object	See below

Threshold Data

Key	Type	Description	Example
low	int	The likelihood (% probability) of flooding given as a value from 0-1 (rounded to two decimal places), at the specified depth, over the cumulative period of time, based on the low scenario of the RCP 4.5 emissions curve.	26
mid	int	The likelihood (% probability) of flooding given as a value from 0-1 (rounded to two decimal places), at the specified depth, over the cumulative period of time, based on the mid scenario of the RCP 4.5 emissions curve.	34
high	int	The likelihood (% probability) of flooding given as a value from 0-1 (rounded to two decimal places), at the specified depth, over the cumulative period of time, based on the high scenario of the RCP 4.5 emissions curve.	45