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app.py
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app.py
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import geopandas as gpd
import rasterio
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import silhouette_score
import requests
import matplotlib.pyplot as plt
# Step 1: Load GIS Data and Satellite Imagery
city_shapefile = "path_to_city_shapefile.shp"
satellite_image = "path_to_satellite_image.tif"
city_data = gpd.read_file(city_shapefile)
image_data = rasterio.open(satellite_image)
# Step 2: Green Space Mapping and Urban Landscape Identification
# Assuming the satellite image has multiple bands (RGB, NIR, etc.)
# Apply segmentation or clustering techniques to identify green spaces
# For simplicity, let's use K-Means clustering on NDVI (Normalized Difference Vegetation Index)
red_band = image_data.read(3)
nir_band = image_data.read(4)
ndvi = (nir_band - red_band) / (nir_band + red_band)
ndvi = ndvi.reshape(-1, 1)
scaler = StandardScaler()
ndvi_scaled = scaler.fit_transform(ndvi)
# Determine optimal number of clusters using silhouette score
silhouette_scores = []
for n_clusters in range(2, 11):
kmeans = KMeans(n_clusters=n_clusters, random_state=0)
cluster_labels = kmeans.fit_predict(ndvi_scaled)
silhouette_scores.append(silhouette_score(ndvi_scaled, cluster_labels))
optimal_clusters = silhouette_scores.index(max(silhouette_scores)) + 2
kmeans = KMeans(n_clusters=optimal_clusters, random_state=0)
cluster_labels = kmeans.fit_predict(ndvi_scaled)
# Assign cluster labels to the city's polygons
city_data['green_space_cluster'] = cluster_labels
# Step 3: Air Quality Analysis
# Assuming you have access to AQI data for each city area
aqi_data = {
'area1': [AQI_values],
'area2': [AQI_values],
# ...
}
# Step 4: Efficient Urban Planning
# Identify areas with low AQI and high green space clusters
low_pollution_clusters = city_data[city_data['mean_AQI'] < threshold_AQI]['green_space_cluster']
high_green_space_clusters = city_data.groupby('green_space_cluster')['mean_AQI'].mean().sort_values().index
# Select areas that satisfy both criteria
target_neighborhoods = low_pollution_clusters.intersection(high_green_space_clusters)
# Visualize the results
fig, ax = plt.subplots(figsize=(10, 8))
city_data.plot(column='green_space_cluster', cmap='Set2', ax=ax, legend=True)
ax.set_title("Green Space Clusters")
plt.show()