Retrofit strategy script

[longshuicy]

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Updated
Galveston_flood_zone_map.zip

There, through the NFHL ArcGIS Viewer, you can download the shapefiles related to Galveston County (I also attached the shapefiles here). Among them, I believe the "SFLD_HAZ_AR" should be the one you are looking for, as I saw the definition as below:
"The S_Fld_Haz_Ar table contains information about the flood hazards within the flood risk project area. These zones are used by FEMA to designate the SFHA and for insurance rating purposes. These data are the regulatory flood zones designated by FEMA."

Unique zone descriptions:
A: High-Risk Flood Area (No BFE)
AE: BFE-Determined High-Risk Flood Zone
VE: High-Risk Coastal Flood Zone with BFE
AO: River/Stream Flood Zone, 1-3 ft Depth
X: Low-to-Moderate Risk Flood Zones
OPEN WATER

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Research on loading the inventory to a database and querying
Design the query
Benchmark the performance

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1. Given the query then select matching inventory item
2. Assign retrofit key and value to it applicable item; linking to dfr3 mapping where has the retrofit key and value definition accordingly
3. Generate the retrofit strategy table that can be plugin into damage analysis

Notes:

• Gathering requirement. What are the typical criteria city planners interested in for each testbed

SLC
1 Strategy: Wood or Metal Deck Diaphragms
Selection criteria: URMM or URML + percentage % + council zone + housing type + etc
e.g. 20% random selected buildings in "residential zone", "multi-family", "built before year 1995", "URMM or URML" type -> retrofit.

Galveston
2 Strategies: raising first floor elevation ffe_elev by 5 feet, or by 10 feet
Selection criteria: Residential building type + within the flood zones
Residential building type: “wind archetype” 1,2,3,4,5,17
Flood zones --> there is a shapefile

• Need to design query syntax. Any existing library we can leverage (e.g. see how QGIS does things? Geopandas filter (lambda/apply)
• Benchmark performance
• Not necessarily in pyincore, can be moved to service, or other component

[longshuicy]

Notes on how to filter especially by zone shapefile:
Option 1: frontend join/filter using geojson. MIght crash browser with memory issue when it's SLC (x)
Option 2: create web service endpoint. Not enough time for that (x)
Option 3: have a separate datawolf flow runs pyincore function

As for the logic of filtering:

• Function has inputs of "building shapefile (id)", "additional zoning shapefile", filtering criteria
• Could use geopands do clipping, filter, etc

Diagram

[longshuicy]

@jonglee1 will double check on the requirement of retrofit criteria

[longshuicy]

Prototype is done. Will add below in a separate task:

proper logging (currently it uses "print")
command line inputs (argparse)
managing output files