Probability Count Summary

Probability Count-Summary Method

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

Returns an array of ProbabilityCountSummary product for the given property IDs. Only property IDs are accepted. Optionally creates a csv file.

Arguments:

• search_items: list/file of SearchItems, property parcels to retrieve probability count-summary 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_count_summary on a list with 2 FSIDs
python -m firststreet -p probability.get_count_summary -i 190836953;392804911

# Call probability.get_count_summary on a file of SearchItems
python -m firststreet -p probability.get_count_summary -f sample.txt

Example (Python File):

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

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

# Call probability.get_count_summary on a file of SearchItems
probability_count_summary = fs.probability.get_count_summary("sample.txt", csv=True)

Probability Count-Summary Objects

ProbabilityCountSummary Object

Key	Type	Description	Example
fsid	int	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
neighborhood	array of dict	The count of flooding to a building footprint within the neighborhood broken down by the year of annual risk. The low, mid, high count is returned as a dict within the associated year. Available years within the array of dict are 2020 and 2050.	See Location Dictionary Below
city	array of dict	The count of flooding to a building footprint within the city broken down by the year of annual risk. The low, mid, high count is returned as a dict within the associated year. Available years within the array of dict are 2020 and 2050.	See Location Dictionary Below
zcta	array of dict	The count of flooding to a building footprint within the zcta broken down by the year of annual risk. The low, mid, high count is returned as a dict within the associated year. Available years within the array of dict are 2020 and 2050.	See Location Dictionary Below
tract	array of dict	The count of flooding to a building footprint within the tract broken down by the year of annual risk. The low, mid, high count is returned as a dict within the associated year. Available years within the array of dict are 2020 and 2050.	See Location Dictionary Below
county	array of dict	The count of flooding to a building footprint within the county broken down by the year of annual risk. The low, mid, high count is returned as a dict within the associated year. Available years within the array of dict are 2020 and 2050.	See Location Dictionary Below
cd	array of dict	The count of flooding to a building footprint within the cd broken down by the year of annual risk. The low, mid, high count is returned as a dict within the associated year. Available years within the array of dict are 2020 and 2050.	See Location Dictionary Below
state	array of dict	The count of flooding to a building footprint within the state broken down by the year of annual risk. The low, mid, high count is returned as a dict within the associated year. Available years within the array of dict are 2020 and 2050.	See Location Dictionary Below

Location Dictionary

Key	Type	Description	Example
fsid	int	First Street ID (FSID) is a unique identifier assigned to each location.	392804911
name	string	The name of the location	'North Newport News'
count	array of dict	A collection of Count Data	See Below

Count Data

Key	Type	Description	Example
year	int	The year (2020 or 2050) the probability was calculated for.	2020
data	Array of Dict	A collection of Probability Count Data	See below

Probability Count Data

Key	Type	Description	Example
low	int	The total count of properties that exist in that return period and depth bin, based on the low scenario of the RCP 4.5 emissions curve.	125
mid	int	The total count of properties that exist in that return period and depth bin, based on the mid scenario of the RCP 4.5 emissions curve.	150
high	int	The total count of properties that exist in that return period and depth bin, based on the high scenario of the RCP 4.5 emissions curve.	175