geospatial-visualization

# Geospatial Visualization Skill

## Overview

Master geographic and mapping visualizations with GeoViews and spatial data handling. This skill covers creating interactive maps, analyzing geographic data, and visualizing spatial relationships.

## Dependencies

- geoviews >= 1.11.0
- geopandas >= 0.10.0
- shapely >= 1.8.0
- cartopy >= 0.20.0 (optional)
- pyproj >= 3.0.0

## Core Capabilities

### 1. Basic Geographic Visualization

GeoViews extends HoloViews with geographic support:

```python
import geoviews as gv
import geopandas as gpd
from geoviews import tile_providers as gvts

# Load geographic data
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Basic map visualization
world_map = gv.Polygons(world, vdims=['name', 'pop_est']).opts(
    title='World Population',
    height=600,
    width=800,
    tools=['hover']
)

# Add tile layer background
tiled = gvts.ESRI.apply.opts(
    alpha=0.4,
    xaxis=None,
    yaxis=None
) * world_map
```

### 2. Point Data on Maps

```python
# Create point features
cities_data = {
    'city': ['New York', 'Los Angeles', 'Chicago'],
    'latitude': [40.7128, 34.0522, 41.8781],
    'longitude': [-74.0060, -118.2437, -87.6298],
    'population': [8337000, 3990456, 2693976]
}

cities_gdf = gpd.GeoDataFrame(
    cities_data,
    geometry=gpd.points_from_xy(cities_data['longitude'], cities_data['latitude']),
    crs='EPSG:4326'
)

# Visualize points
points = gv.Points(cities_gdf, kdims=['longitude', 'latitude'], vdims=['city', 'population'])
points = points.opts(
    size=gv.dim('population').norm(min=5, max=50),
    color='red',
    tools=['hover', 'box_select']
)

# With tile background
map_with_points = gvts.CartoDEM.apply.opts(alpha=0.5) * points
```

### 3. Choropleth Maps

```python
# Color regions by data value
choropleth = gv.Polygons(world, vdims=['name', 'pop_est']).opts(
    cmap='viridis',
    color=gv.dim('pop_est').norm(),
    colorbar=True,
    height=600,
    width=900,
    tools=['hover']
)

# Add