Rename geospatial_analyzer.py to geospatial_visualization.py

geospatial_analyzer.py

@@ -1,47 +0,0 @@
-import streamlit as st
-import pandas as pd
-import plotly.express as px
-
-class GeospatialAnalyzer:
-    def analyze_geospatial_data(self, df):
-        lat_columns = [col for col in df.columns if 'lat' in col.lower()]
-        lon_columns = [col for col in df.columns if 'lon' in col.lower()]
-        
-        if len(lat_columns) > 0 and len(lon_columns) > 0:
-            lat_column = st.selectbox("Select latitude column", lat_columns)
-            lon_column = st.selectbox("Select longitude column", lon_columns)
-            
-            map_type = st.selectbox("Select map type", ["Scatter Mapbox", "Density Mapbox"])
-            
-            if map_type == "Scatter Mapbox":
-                self.create_scatter_mapbox(df, lat_column, lon_column)
-            elif map_type == "Density Mapbox":
-                self.create_density_mapbox(df, lat_column, lon_column)
-        else:
-            st.write("No latitude and longitude columns found in the dataset.")
-
-    def create_scatter_mapbox(self, df, lat_column, lon_column):
-        st.subheader("Scatter Mapbox")
-        
-        color_column = st.selectbox("Select color column (optional)", ["None"] + df.columns.tolist())
-        size_column = st.selectbox("Select size column (optional)", ["None"] + df.columns.tolist())
-        
-        fig = px.scatter_mapbox(df, 
-                                lat=lat_column, 
-                                lon=lon_column, 
-                                color=None if color_column == "None" else color_column,
-                                size=None if size_column == "None" else size_column,
-                                zoom=3)
-        fig.update_layout(mapbox_style="open-street-map")
-        st.plotly_chart(fig)
-
-    def create_density_mapbox(self, df, lat_column, lon_column):
-        st.subheader("Density Mapbox")
-        
-        fig = px.density_mapbox(df, 
-                                lat=lat_column, 
-                                lon=lon_column, 
-                                zoom=3,
-                                radius=10)
-        fig.update_layout(mapbox_style="open-street-map")
-        st.plotly_chart(fig)

geospatial_visualization.py

@@ -0,0 +1,54 @@
+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