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import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
class GeospatialVisualizer:
def __init__(self):
self.world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
def visualize(self, data, location_column, value_column):
# Merge data with world map
merged = self.world.merge(data, how='left', left_on=['name'], right_on=[location_column])
# Create the plot
fig, ax = plt.subplots(figsize=(15, 10))
merged.plot(column=value_column, ax=ax, legend=True,
legend_kwds={'label': value_column, 'orientation': 'horizontal'},
missing_kwds={'color': 'lightgrey'})
# Customize the plot
ax.set_title(f'{value_column} by Country')
ax.axis('off')
return fig
def create_choropleth(self, data, location_column, value_column):
# Merge data with world map
merged = self.world.merge(data, how='left', left_on=['name'], right_on=[location_column])
# Create the choropleth map
fig, ax = plt.subplots(figsize=(15, 10))
merged.plot(column=value_column, ax=ax, legend=True,
legend_kwds={'label': value_column, 'orientation': 'horizontal'},
cmap='YlOrRd', missing_kwds={'color': 'lightgrey'})
# Customize the plot
ax.set_title(f'Choropleth Map: {value_column} by Country')
ax.axis('off')
return fig
def create_bubble_map(self, data, lat_column, lon_column, size_column):
# Create a GeoDataFrame from the data
gdf = gpd.GeoDataFrame(data, geometry=gpd.points_from_xy(data[lon_column], data[lat_column]))
# Create the bubble map
fig, ax = plt.subplots(figsize=(15, 10))
self.world.plot(ax=ax, color='lightgrey')
gdf.plot(ax=ax, markersize=data[size_column]/data[size_column].max()*100, alpha=0.5)
# Customize the plot
ax.set_title(f'Bubble Map: {size_column} by Location')
ax.axis('off')
return fig |