diff --git a/challenge-4/racehd/FL-disaster-vulnerabilities.ipynb b/challenge-4/racehd/FL-disaster-vulnerabilities.ipynb new file mode 100644 index 0000000..6471438 --- /dev/null +++ b/challenge-4/racehd/FL-disaster-vulnerabilities.ipynb @@ -0,0 +1 @@ +{"cells":[{"source":"For now this is a relatively basic geospatial map for some natural disaster data. Feel free to borrow code or expand upon. \n\nThe cloud version of this notebook can be found at: https://www.datacamp.com/datalab/w/a8b8f744-32e9-4226-9680-73a301221667/edit","metadata":{},"cell_type":"markdown","id":"532928bb-535e-47b9-86f6-035c6cf7af75"},{"source":"import folium\nfrom folium import FeatureGroup, LayerControl\nfrom folium.plugins import HeatMap, MiniMap\nfrom branca.colormap import LinearColormap\nfrom folium.features import GeoJsonTooltip\nimport pandas as pd\nimport geopandas as gpd","metadata":{"executionCancelledAt":null,"executionTime":50,"lastExecutedAt":1727135086402,"lastExecutedByKernel":"487da37a-06ca-4081-82e4-b512be64a950","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"import folium\nfrom folium import FeatureGroup, LayerControl\nfrom folium.plugins import HeatMap, MiniMap\nfrom branca.colormap import LinearColormap\nfrom folium.features import GeoJsonTooltip\nimport pandas as pd\nimport geopandas as 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geoid Lat ... s2503_c05_024e s2503_c05_024m\n0 12001000202 29.646160 ... 1136 171\n1 12001000301 29.668004 ... 865 86\n2 12001000400 29.679476 ... 1297 405\n3 12001000700 29.628043 ... 979 255\n4 12001000806 29.639118 ... 971 43\n\n[5 rows x 389 columns]","text/html":"
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geoidLatLongeoid_yearstatecountystate_fips_codecounty_fips_codeb19083_001eb19083_001meconomic_distress_pop_aggeconomic_distress_simple_agginvestment_areasopzoneb23025_002eb23025_002mb23025_004eb23025_004mb23025_005eb23025_005mb23025_006eb23025_006ms1701_c03_001es1701_c03_001ms1701_c03_002es1701_c03_002ms1701_c03_003es1701_c03_003ms1701_c03_004es1701_c03_004ms1701_c03_006es1701_c03_006ms1701_c03_007es1701_c03_007ms1701_c03_008es1701_c03_008ms1701_c03_009es1701_c03_009ms1701_c03_010es1701_c03_010m...s0101_c02_028ms0101_c04_028es0101_c04_028ms0101_c06_028es0101_c06_028ms0101_c02_029es0101_c02_029ms0101_c04_029es0101_c04_029ms0101_c06_029es0101_c06_029ms0101_c02_030es0101_c02_030ms0101_c04_030es0101_c04_030ms0101_c06_030es0101_c06_030ms0101_c02_031es0101_c02_031ms0101_c04_031es0101_c04_031ms0101_c06_031es0101_c06_031mloan_amountmedian_mortgage_amountmedian_prop_valuemedian_sba504_loan_amountmedian_sba7a_loan_amountnum_mortgagenum_mortgage_denialsnum_mortgage_originatednumber_of_sba504_loansnumber_of_sba7a_loansqcts2503_c01_024es2503_c01_024ms2503_c03_024es2503_c03_024ms2503_c05_024es2503_c05_024m
01200100020229.646160-82.33295320201211210.59560.0799YESYESYES118904531837458534302175.39.9100.095.0-666666666.0-222222222.0100.095.075.69.976.59.958.828.254.042.561.751.3...1.72.72.63.62.42.71.61.92.23.22.42.11.61.21.92.62.40.00.90.02.60.01.46543.333333205000.0325000.0NaN150000.036.04.023.0NaN5.0011001235635421136171
11200100030129.668004-82.33141820201211210.40890.0654YESYESYES02263458212445513910301522.79.713.921.140.651.611.818.825.89.333.313.513.49.524.014.920.317.2...5.613.86.115.98.014.05.413.05.915.17.710.13.99.85.010.55.62.41.81.11.64.03.444290.000000165000.0225000.0351500.0915000.083.015.040.04.02.008526881514186586
21200100040029.679476-82.30812120201211210.39280.0724YESYESYES12958768269870826020902120.49.841.018.968.941.636.818.510.98.711.813.39.89.020.811.227.213.6...5.120.28.616.15.414.44.414.16.814.74.812.63.711.25.113.84.64.42.35.03.53.93.26940.000000135000.0205000.0121000.01471000.0284.060.0117.05.05.00961110909671297405
31200100070029.628043-82.29578220201211210.52040.0995YESYESYES13098622285760024120202129.913.636.025.348.947.031.722.231.513.248.225.225.511.020.510.217.111.1...5.823.36.425.77.021.76.019.56.323.67.219.35.617.16.021.26.75.52.54.52.96.33.174871.000000135000.0185000.0140000.0NaN293.062.0104.01.0NaN086911081879979255
41200100080629.639118-82.33220720201211210.54660.0649YESYESYES014313261337313949501551.013.536.628.216.430.053.437.856.013.860.213.636.624.42.64.80.027.1...4.59.97.711.67.18.93.96.56.111.46.98.63.86.26.111.06.84.61.42.13.47.15.37990.000000125000.0265000.0250000.0NaN18.03.06.01.0NaN196038-666666666-22222222297143
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5 rows × 389 columns

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"},"metadata":{},"execution_count":243}],"execution_count":243},{"source":"# geojson from arcgis https://www.arcgis.com/home/item.html?id=3c164274a80748dda926a046525da610\n# url = 'https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/USA_Counties_Generalized_Boundaries/FeatureServer/0/query?outFields=*&where=1%3D1&f=geojson' # exceededTransferLimit\ttrue, refining query\nurl = 'https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/USA_Counties_Generalized_Boundaries/FeatureServer/0/query?outFields=*&where=STATE_ABBR%3D%27FL%27&f=geojson'\n\n# Read the GeoJSON data into a GeoDataFrame\ncounties = gpd.read_file(url)\n\n# Print or inspect the counties data\ncounties.head()","metadata":{"executionCancelledAt":null,"executionTime":100,"lastExecutedAt":1727135088846,"lastExecutedByKernel":"487da37a-06ca-4081-82e4-b512be64a950","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"# geojson from arcgis https://www.arcgis.com/home/item.html?id=3c164274a80748dda926a046525da610\n# url = 'https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/USA_Counties_Generalized_Boundaries/FeatureServer/0/query?outFields=*&where=1%3D1&f=geojson' # exceededTransferLimit\ttrue, refining query\nurl = 'https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/USA_Counties_Generalized_Boundaries/FeatureServer/0/query?outFields=*&where=STATE_ABBR%3D%27FL%27&f=geojson'\n\n# Read the GeoJSON data into a GeoDataFrame\ncounties = gpd.read_file(url)\n\n# Print or inspect the counties data\ncounties.head()"},"cell_type":"code","id":"eac14537-cb3c-4176-b268-d4821cafa85f","outputs":[{"output_type":"execute_result","data":{"text/plain":" OBJECTID ... geometry\n0 318 ... POLYGON ((-82.40526 29.48510, -82.55477 29.481...\n1 319 ... POLYGON ((-82.04629 30.13971, -82.14505 30.140...\n2 320 ... MULTIPOLYGON (((-85.38475 30.57410, -85.38210 ...\n3 321 ... POLYGON ((-82.04629 30.13971, -82.04367 29.723...\n4 322 ... MULTIPOLYGON (((-80.78566 28.78519, -80.76242 ...\n\n[5 rows x 13 columns]","text/html":"
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OBJECTIDNAMESTATE_NAMESTATE_FIPSFIPSSQMIPOPULATIONPOP_SQMISTATE_ABBRCOUNTY_FIPSShape__AreaShape__Lengthgeometry
0318Alachua CountyFlorida1212001968.81278468287.4FL0010.2355562.085381POLYGON ((-82.40526 29.48510, -82.55477 29.481...
1319Baker CountyFlorida1212003588.972825948.0FL0030.1440741.683327POLYGON ((-82.04629 30.13971, -82.14505 30.140...
2320Bay CountyFlorida1212005765.78175216228.8FL0050.1791503.572319MULTIPOLYGON (((-85.38475 30.57410, -85.38210 ...
3321Bradford CountyFlorida1212007300.492830394.2FL0070.0702441.364875POLYGON ((-82.04629 30.13971, -82.04367 29.723...
4322Brevard CountyFlorida12120091051.77606612576.8FL0090.2420085.141660MULTIPOLYGON (((-80.78566 28.78519, -80.76242 ...
\n
"},"metadata":{},"execution_count":244}],"execution_count":244},{"source":"# Define the features you want to visualize and their corresponding column names\nfeature_list = [\n \n {\n 'name': 'Energy Burden',\n 'column': 'energy_burden_percentile',\n 'enabled': False \n },\n {\n 'name': 'Expected Agricultural Loss due to Natural Hazards Heatmap',\n 'column': 'expected_agricultural_loss_rate_natural_hazards_risk_index_percentile',\n 'enabled': False \n },\n {\n 'name': 'Expected Building Loss due to Natural Hazards Heatmap',\n 'column': 'expected_building_loss_rate_natural_hazards_risk_index_percentile',\n 'enabled': False \n },\n {\n 'name': 'Expected Population Loss due to Natural Hazards Heatmap',\n 'column': 'expected_population_loss_rate_natural_hazards_risk_index_percentile',\n 'enabled': False \n },\n {\n 'name': 'Risk of Fire in 30 years Heatmap',\n 'column': 'share_of_properties_at_risk_of_fire_in_30_years_percentile',\n 'enabled': False \n },\n {\n 'name': 'Risk of Flood in 30 years Heatmap',\n 'column': 'share_of_properties_at_risk_of_flood_in_30_years_percentile',\n 'enabled': False \n }\n]\n\n# Prepare required columns\nrequired_columns = {'Lat', 'Lon', 'geoid'} | {f['column'] for f in feature_list}\nrequired_columns = [col['column'] if isinstance(col, dict) else col for col in required_columns]\n\n# clean and reduce data\ndf_clean = df.dropna(subset=required_columns)\ndf_clean_reduced = df_clean[required_columns]\n# df_clean_reduced.head()","metadata":{"executionCancelledAt":null,"executionTime":53,"lastExecutedAt":1727135088900,"lastExecutedByKernel":"487da37a-06ca-4081-82e4-b512be64a950","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"# Define the features you want to visualize and their corresponding column names\nfeature_list = [\n \n {\n 'name': 'Energy Burden',\n 'column': 'energy_burden_percentile',\n 'enabled': False \n },\n {\n 'name': 'Expected Agricultural Loss due to Natural Hazards Heatmap',\n 'column': 'expected_agricultural_loss_rate_natural_hazards_risk_index_percentile',\n 'enabled': False \n },\n {\n 'name': 'Expected Building Loss due to Natural Hazards Heatmap',\n 'column': 'expected_building_loss_rate_natural_hazards_risk_index_percentile',\n 'enabled': False \n },\n {\n 'name': 'Expected Population Loss due to Natural Hazards Heatmap',\n 'column': 'expected_population_loss_rate_natural_hazards_risk_index_percentile',\n 'enabled': False \n },\n {\n 'name': 'Risk of Fire in 30 years Heatmap',\n 'column': 'share_of_properties_at_risk_of_fire_in_30_years_percentile',\n 'enabled': False \n },\n {\n 'name': 'Risk of Flood in 30 years Heatmap',\n 'column': 'share_of_properties_at_risk_of_flood_in_30_years_percentile',\n 'enabled': False \n }\n]\n\n# Prepare required columns\nrequired_columns = {'Lat', 'Lon', 'geoid'} | {f['column'] for f in feature_list}\nrequired_columns = [col['column'] if isinstance(col, dict) else col for col in required_columns]\n\n# clean and reduce data\ndf_clean = df.dropna(subset=required_columns)\ndf_clean_reduced = df_clean[required_columns]\n# df_clean_reduced.head()"},"cell_type":"code","id":"521da123-5eba-4b04-bc78-d3afa4bf5774","outputs":[],"execution_count":245},{"source":"# Create base map\nm = folium.Map(location=[df_clean_reduced['Lat'].mean(), df_clean_reduced['Lon'].mean()], zoom_start=7)\n\n# Create shared color scale for percentile data\nshared_color_scale = LinearColormap(\n colors=['green', 'yellow', 'red'],\n vmin=0,\n vmax=100,\n caption=\"Percentile\"\n)\n\n# Function to create grid\ndef create_grid(df, feature_column, grid_size=0.1):\n df['lat_bin'] = np.floor(df['Lat'] / grid_size) * grid_size\n df['lon_bin'] = np.floor(df['Lon'] / grid_size) * grid_size\n grid = df.groupby(['lat_bin', 'lon_bin']).agg({\n feature_column: 'mean',\n 'geoid': lambda x: x.iloc[0] # Take the first geoid in the group\n }).reset_index()\n return grid\n\n# Function to get county name from geoid\ndef get_county_name(geoid):\n county = counties[counties['FIPS'] == str(geoid)[:5]]\n return county['NAME'].iloc[0] if not county.empty else \"Unknown County\"\n\n# Iterate over features\nfor feature in feature_list:\n # Create grid\n grid = create_grid(df_clean_reduced, feature['column'])\n \n # Create feature group\n feature_group = folium.FeatureGroup(name=feature['name'], show=feature['enabled'])\n \n # Add grid cells to feature group\n for _, row in grid.iterrows():\n value = row[feature['column']]\n county_name = get_county_name(row['geoid'])\n \n popup_text = f\"\"\"\n {feature['name']}
\n County: {county_name}
\n Percentile: {value:.2f}
\n Geoid: {row['geoid']}\n \"\"\"\n \n folium.CircleMarker(\n location=[row['lat_bin'], row['lon_bin']],\n radius=10,\n popup=folium.Popup(popup_text, max_width=300),\n tooltip=f\"{feature['name']} Percentile: {value:.2f}\",\n color=shared_color_scale(value),\n fill=True,\n fillColor=shared_color_scale(value),\n fillOpacity=0.7,\n weight=0\n ).add_to(feature_group)\n \n feature_group.add_to(m)\n\n# Add county boundaries\nfolium.GeoJson(\n counties,\n name='County Boundaries',\n style_function=lambda feature: {\n 'fillColor': 'transparent',\n 'color': 'black',\n 'weight': 1,\n 'fillOpacity': 0.7,\n },\n tooltip=folium.GeoJsonTooltip(\n fields=['NAME'],\n aliases=['County:'],\n localize=True\n )\n).add_to(m)\n\n# Add shared color scale legend to map\nshared_color_scale.add_to(m)\n\n# Add layer control\nfolium.LayerControl().add_to(m)\n\n# Add custom JavaScript to toggle legend visibility\nlegend_toggle_js = \"\"\"\n\n\"\"\"\n\n# Add the custom JavaScript to the map\nm.get_root().html.add_child(folium.Element(legend_toggle_js))\n","metadata":{"executionCancelledAt":null,"executionTime":2440,"lastExecutedAt":1727135091340,"lastExecutedByKernel":"487da37a-06ca-4081-82e4-b512be64a950","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"# Create base map\nm = folium.Map(location=[df_clean_reduced['Lat'].mean(), df_clean_reduced['Lon'].mean()], zoom_start=7)\n\n# Create shared color scale for percentile data\nshared_color_scale = LinearColormap(\n colors=['green', 'yellow', 'red'],\n vmin=0,\n vmax=100,\n caption=\"Percentile\"\n)\n\n# Function to create grid\ndef create_grid(df, feature_column, grid_size=0.1):\n df['lat_bin'] = np.floor(df['Lat'] / grid_size) * grid_size\n df['lon_bin'] = np.floor(df['Lon'] / grid_size) * grid_size\n grid = df.groupby(['lat_bin', 'lon_bin']).agg({\n feature_column: 'mean',\n 'geoid': lambda x: x.iloc[0] # Take the first geoid in the group\n }).reset_index()\n return grid\n\n# Function to get county name from geoid\ndef get_county_name(geoid):\n county = counties[counties['FIPS'] == str(geoid)[:5]]\n return county['NAME'].iloc[0] if not county.empty else \"Unknown County\"\n\n# Iterate over features\nfor feature in feature_list:\n # Create grid\n grid = create_grid(df_clean_reduced, feature['column'])\n \n # Create feature group\n feature_group = folium.FeatureGroup(name=feature['name'], show=feature['enabled'])\n \n # Add grid cells to feature group\n for _, row in grid.iterrows():\n value = row[feature['column']]\n county_name = get_county_name(row['geoid'])\n \n popup_text = f\"\"\"\n {feature['name']}
\n County: {county_name}
\n Percentile: {value:.2f}
\n Geoid: {row['geoid']}\n \"\"\"\n \n folium.CircleMarker(\n location=[row['lat_bin'], row['lon_bin']],\n radius=10,\n popup=folium.Popup(popup_text, max_width=300),\n tooltip=f\"{feature['name']} Percentile: {value:.2f}\",\n color=shared_color_scale(value),\n fill=True,\n fillColor=shared_color_scale(value),\n fillOpacity=0.7,\n weight=0\n ).add_to(feature_group)\n \n feature_group.add_to(m)\n\n# Add county boundaries\nfolium.GeoJson(\n counties,\n name='County Boundaries',\n style_function=lambda feature: {\n 'fillColor': 'transparent',\n 'color': 'black',\n 'weight': 1,\n 'fillOpacity': 0.7,\n },\n tooltip=folium.GeoJsonTooltip(\n fields=['NAME'],\n aliases=['County:'],\n localize=True\n )\n).add_to(m)\n\n# Add shared color scale legend to map\nshared_color_scale.add_to(m)\n\n# Add layer control\nfolium.LayerControl().add_to(m)\n\n# Add custom JavaScript to toggle legend visibility\nlegend_toggle_js = \"\"\"\n\n\"\"\"\n\n# Add the custom JavaScript to the map\nm.get_root().html.add_child(folium.Element(legend_toggle_js))\n"},"cell_type":"code","id":"441fde7d-57cc-42d5-bbfc-5c6650811755","outputs":[{"output_type":"execute_result","data":{"text/plain":""},"metadata":{},"execution_count":246}],"execution_count":246},{"source":"## Map Notes:\n- Toggle a layer to view\n- Datapoints are clickable for additional info. ","metadata":{},"cell_type":"markdown","id":"8752dc52-882f-4628-8456-04caa3484129"},{"source":"m","metadata":{"executionCancelledAt":null,"executionTime":4370,"lastExecutedAt":1727135180952,"lastExecutedByKernel":"487da37a-06ca-4081-82e4-b512be64a950","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"m"},"cell_type":"code","id":"1a801d0d-6eda-4d71-ae2d-4a10e7568f36","outputs":[{"output_type":"execute_result","data":{"text/plain":"","text/html":"
Make this Notebook Trusted to load map: File -> Trust Notebook
"},"metadata":{},"execution_count":248}],"execution_count":248}],"metadata":{"colab":{"name":"Welcome to DataCamp Workspaces.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file