02_03/03_Flooding_Germany_Exposed_Buildings.ipynb

+%% Cell type:markdown id:fb053094-8052-4532-bfd1-1bd1dc249d9f tags:
+
+### 📖Flooding in Germany: assessing damage by detecting affected buildings
+
+%% Cell type:markdown id:75d6c38a-3124-42f4-8e3d-e7174d6c02c1 tags:
+
+In this chapter, you will assess the damage of a flood by detecting the affected buildings in the area using the ohsome package.
+
+%% Cell type:code id:24b70612 tags:
+
+``` python
+# import necessary packages
+from ohsome import OhsomeClient
+import pandas as pd
+import json
+import requests
+```
+
+%% Cell type:code id:6c5c26b9-52a6-4a01-8d19-da1955a163f2 tags:
+
+``` python
+client = OhsomeClient()
+```
+
+%% Cell type:markdown id:37d34fc4 tags:
+
+Use the following code if the data is not stored on your local machine. If you already have it, you can skip this step.
+
+%% Cell type:code id:743e2ef8 tags:
+
+``` python
+# You can convert a shapefile to a geojson for example in QGIS
+# The AOI Geojson file needs to be a Feature Collection, meaning it contains several Polygons.
+# You can also create  geojson files with https://geojson.io/#map=2/20.0/0.0
+# For more Information about how to use boundaries in ohsome, see : https://docs.ohsome.org/ohsome-api/v1/boundaries.html
+# Open the file and extract the content as a string (text)
+AOI_path = 'https://git.rwth-aachen.de/nfdi4earth/edutrain/content/extern/analysis-of-urban-transformation-processes-part-2/-/raw/main/Data/Flooding_Germany.geojson?ref_type=heads&inline=false' # Provide the path to your geojson file
+response = requests.get(AOI_path)
+aoi_json = response.json()
+json_aoi_txt = json.dumps(aoi_json)
+json_aoi_txt = json.dumps(aoi_json)
+print(json_aoi_txt[:500]) # print the first 500 characters of the geojson string
+```
+
+%% Output
+
+    {"type": "FeatureCollection", "name": "Flooding_Germany_202107_FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84"}}, "features": [{"type": "Feature", "properties": {"id": 1}, "geometry": {"type": "MultiPolygon", "coordinates": [[[[6.642126184000062, 49.84587318906443], [6.642126184000062, 49.84585135700007], [6.642119816298699, 49.84585135700007], [6.642100365000033, 49.84578466700003], [6.642076571000076, 49.84558003300003], [6.642036353000037, 49
+
+%% Cell type:markdown id:86077ff7 tags:
+
+Alternatively, if you prefer to store the data locally [(download)](https://git.rwth-aachen.de/nfdi4earth/edutrain/content/extern/analysis-of-urban-transformation-processes-part-2/-/raw/main/Data/Flooding_Germany.geojson?ref_type=heads&inline=false) or if you already have it, you can follow the step below:
+
+%% Cell type:code id:42ff8ce7 tags:
+
+``` python
+# You can convert a shapefile to a geojson for example in QGIS
+# The AOI Geojson file needs to be a Feature Collection, meaning it contains several Polygons.
+# You can also create  geojson files with https://geojson.io/#map=2/20.0/0.0
+# For more Information about how to use boundaries in ohsome, see : https://docs.ohsome.org/ohsome-api/v1/boundaries.html
+# Open the file and extract the content as a string (text)
+AOI_path= R"path to your geojson file" # Provide the path to your geojson file
+with open(AOI_path) as jsonAOI:
+    aoi_json = json.load(jsonAOI)
+json_aoi_txt = json.dumps(aoi_json)
+print(json_aoi_txt[:500]) # print the first 500 characters of the geojson string
+```
+
+%% Output
+
+    {"type": "FeatureCollection", "name": "Flooding_Germany_202107_FeatureCollection", "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84"}}, "features": [{"type": "Feature", "properties": {"id": 1}, "geometry": {"type": "MultiPolygon", "coordinates": [[[[6.642126184000062, 49.84587318906443], [6.642126184000062, 49.84585135700007], [6.642119816298699, 49.84585135700007], [6.642100365000033, 49.84578466700003], [6.642076571000076, 49.84558003300003], [6.642036353000037, 49
+
+%% Cell type:markdown id:d97da475 tags:
+
+### 📖Count buildings affected by flooding
+
+%% Cell type:code id:532a11f7 tags:
+
+``` python
+# Time of your investigation
+time = "2021-06-15"
+
+# Filter for a desired variable
+fltr = "building=* and type:way"
+
+response = client.elements.count.post(bpolys=json_aoi_txt, time=time, filter=fltr)
+response.as_dataframe()
+```
+
+%% Cell type:markdown id:e26b4fd0 tags:
+
+### 📖Download all Buildings in the affected area
+
+%% Cell type:code id:5700244b tags:
+
+``` python
+response = client.elements.geometry.post(bpolys=json_aoi_txt, time=time, filter=fltr)
+response.to_json("./Affected_buildings.json")
+```