Using the loss-calculator

For this tutorial you need to install the packages below. Also the software packages losscalculator and databaselibfrom GFZ are needed.

In [1]:

# uncomment to install the packages
#!pip install matplotlib folium mapclassify geopandas
#!pip install databaselib exposurelib
#!pip install git+https://git.gfz-potsdam.de/globaldynamicexposure/loss-calculator@docs-patch

# uncomment to install the packages #!pip install matplotlib folium mapclassify geopandas #!pip install databaselib exposurelib #!pip install git+https://git.gfz-potsdam.de/globaldynamicexposure/loss-calculator@docs-patch

In [2]:

from exposurelib import SpatiaLiteExposure
from shapely import from_wkt
import geopandas

from exposurelib import SpatiaLiteExposure from shapely import from_wkt import geopandas

Setup db for calculations¶

Open a new database connection

In [3]:

db = SpatiaLiteExposure('data/losscalculator.db')

db = SpatiaLiteExposure('data/losscalculator.db')

In [4]:

try:
    db.connect()
    db.create_tables()
    print('attach database')
    db.connection.executescript(
        f"""
        ATTACH DATABASE 'data/2023-011_Schorlemmer-et-al_ALB.Albania.db' AS source;
        INSERT INTO Taxonomy SELECT * FROM source.Taxonomy;
        INSERT INTO Entity SELECT * FROM source.Entity;
        INSERT INTO Asset SELECT * FROM source.Asset
        """
    )
    db.connection.commit()
    db.close()
except Exception as e:
    print("Delete existing dataset if Integrity Error - InitSpatialMetaData()")
    print(e)

try: db.connect() db.create_tables() print('attach database') db.connection.executescript( f""" ATTACH DATABASE 'data/2023-011_Schorlemmer-et-al_ALB.Albania.db' AS source; INSERT INTO Taxonomy SELECT * FROM source.Taxonomy; INSERT INTO Entity SELECT * FROM source.Entity; INSERT INTO Asset SELECT * FROM source.Asset """ ) db.connection.commit() db.close() except Exception as e: print("Delete existing dataset if Integrity Error - InitSpatialMetaData()") print(e)

attach database

Calculate damage loss¶

In [5]:

!damagecalculator damage \
    -e data/losscalculator.db \
    -f data/fragility_ESRM20_various_IM.xml \
    -g data/ground_motion_field.csv \
    -t data/esrm20_exposure_vulnerability_mapping.csv \
    -S losscalculator

!damagecalculator damage \ -e data/losscalculator.db \ -f data/fragility_ESRM20_various_IM.xml \ -g data/ground_motion_field.csv \ -t data/esrm20_exposure_vulnerability_mapping.csv \ -S losscalculator

INFO:root:Start time: 2025-03-17 11:44:48.425698
INFO:root:Entity 1000 computed
INFO:root:Entity 2000 computed
INFO:root:Entity 3000 computed
INFO:root:Entity 4000 computed
INFO:root:Damage view view_damage_building_1 created
INFO:root:Damage view view_damage_residual_1 created
INFO:root:Damage view view_damage_tile_1 created
INFO:root:Execution time: 0:00:22.452799

In [6]:

db = SpatiaLiteExposure('data/losscalculator.db')
db.connect()
sql_query = f"""
    SELECT
        quadkey,
        ST_AsText(geometry),
        no_damage_damage AS no_damage,
        slight_damage,
        moderate_damage,
        extensive_damage,
        complete_damage
    FROM view_damage_tile_1
    """

db.cursor.execute(sql_query)
res = db.cursor.fetchall()
description = db.cursor.description
df_dict = {
    "quadkey": [],
    "geometry": [],
    "no_damage": [],
    "slight_damage": [],
    "moderate_damage": [],
    "extensive_damage": [],
    "complete_damage": []
}

db = SpatiaLiteExposure('data/losscalculator.db') db.connect() sql_query = f""" SELECT quadkey, ST_AsText(geometry), no_damage_damage AS no_damage, slight_damage, moderate_damage, extensive_damage, complete_damage FROM view_damage_tile_1 """ db.cursor.execute(sql_query) res = db.cursor.fetchall() description = db.cursor.description df_dict = { "quadkey": [], "geometry": [], "no_damage": [], "slight_damage": [], "moderate_damage": [], "extensive_damage": [], "complete_damage": [] }

InitSpatiaMetaData() error:"table spatial_ref_sys already exists"

In [7]:

for quadkey, geom , nd, sd, md, ed, cd in res:
    df_dict["quadkey"].append(quadkey)
    df_dict["geometry"].append(from_wkt(geom))
    df_dict["no_damage"].append(nd)
    df_dict["slight_damage"].append(sd)
    df_dict["moderate_damage"].append(md)
    df_dict["extensive_damage"].append(ed)
    df_dict["complete_damage"].append(cd)

for quadkey, geom , nd, sd, md, ed, cd in res: df_dict["quadkey"].append(quadkey) df_dict["geometry"].append(from_wkt(geom)) df_dict["no_damage"].append(nd) df_dict["slight_damage"].append(sd) df_dict["moderate_damage"].append(md) df_dict["extensive_damage"].append(ed) df_dict["complete_damage"].append(cd)

Check results at map¶

In [11]:

gdf = geopandas.GeoDataFrame(df_dict, crs='EPSG:4326')
gdf.iloc[:, 2:] = gdf.iloc[:, 2:].round(2)
gdf.explore('slight_damage')

gdf = geopandas.GeoDataFrame(df_dict, crs='EPSG:4326') gdf.iloc[:, 2:] = gdf.iloc[:, 2:].round(2) gdf.explore('slight_damage')

Out[11]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Get descriptive statistics¶

In [ ]:

damage_columns = ["no_damage", "slight_damage", "moderate_damage", "extensive_damage", "complete_damage"]
damage_stats = gdf[damage_columns].describe().T

damage_stats["total"] = gdf[damage_columns].sum()
damage_stats

damage_columns = ["no_damage", "slight_damage", "moderate_damage", "extensive_damage", "complete_damage"] damage_stats = gdf[damage_columns].describe().T damage_stats["total"] = gdf[damage_columns].sum() damage_stats

Out[ ]:

	count	mean	std	min	25%	50%	75%	max	total
no_damage	4409.0	19.713218	15.310294	0.92	11.41	17.32	23.74	295.61	86915.58
slight_damage	4409.0	1.443178	1.122570	0.05	0.81	1.25	1.75	22.17	6362.97
moderate_damage	4409.0	0.207757	0.164187	0.01	0.11	0.18	0.25	3.08	916.00
extensive_damage	4409.0	0.060581	0.048772	0.00	0.03	0.05	0.07	0.87	267.10
complete_damage	4409.0	0.054695	0.044742	0.00	0.03	0.05	0.07	0.75	241.15