Geo Visualizer

Create interactive HTML maps from geographic data using Folium.

使用Folium从地理数据创建交互式HTML地图。

Features

功能特性

Markers: Plot points with custom icons, popups, and tooltips
Heatmaps: Visualize density/intensity data
Choropleth: Color regions by data values
Routes/Lines: Draw paths between points
Circles/Areas: Show radius-based coverage
Layer Control: Toggle multiple layers
Clustering: Auto-cluster dense markers

标记点：使用自定义图标、弹出窗口和提示框标注点位
热力图：可视化密度/强度数据
分级着色图：根据数据值为区域着色
路线/线条：绘制点位间的路径
圆形/区域：展示基于半径的覆盖范围
图层控制：切换多个图层
聚类功能：对密集标记点自动聚类

Quick Start

快速开始

python

from geo_visualizer import GeoVisualizer

python

from geo_visualizer import GeoVisualizer

Simple marker map

viz = GeoVisualizer() viz.add_markers([ {"lat": 40.7128, "lon": -74.0060, "name": "New York"}, {"lat": 34.0522, "lon": -118.2437, "name": "Los Angeles"} ]) viz.save("cities.html")

From CSV

viz = GeoVisualizer() viz.from_csv("locations.csv", lat_col="latitude", lon_col="longitude") viz.save("map.html")

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viz = GeoVisualizer() viz.from_csv("locations.csv", lat_col="latitude", lon_col="longitude") viz.save("map.html")

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CLI Usage

CLI 使用方法

bash

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bash

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Plot markers from CSV

python geo_visualizer.py --input locations.csv --lat latitude --lon longitude --output map.html

Add heatmap

python geo_visualizer.py --input data.csv --lat lat --lon lng --heatmap --output heat.html

With clustering

python geo_visualizer.py --input stores.csv --lat lat --lon lon --cluster --output stores.html

Choropleth map

python geo_visualizer.py --geojson states.geojson --data stats.csv --key state --value population --output choropleth.html

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python geo_visualizer.py --geojson states.geojson --data stats.csv --key state --value population --output choropleth.html

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API Reference

API 参考

GeoVisualizer Class

GeoVisualizer 类

python

class GeoVisualizer:
    def __init__(self, center=None, zoom=10, tiles="OpenStreetMap")

    # Data loading
    def from_csv(self, filepath, lat_col, lon_col, **kwargs) -> 'GeoVisualizer'
    def from_dataframe(self, df, lat_col, lon_col, **kwargs) -> 'GeoVisualizer'
    def from_geojson(self, filepath) -> 'GeoVisualizer'

    # Markers
    def add_marker(self, lat, lon, popup=None, tooltip=None, icon=None, color="blue")
    def add_markers(self, locations: list, name_col=None, popup_cols=None)
    def cluster_markers(self, enabled=True) -> 'GeoVisualizer'

    # Layers
    def add_heatmap(self, points=None, weight_col=None, radius=15) -> 'GeoVisualizer'
    def add_choropleth(self, geojson, data, key_on, value_col, **kwargs) -> 'GeoVisualizer'
    def add_route(self, points, color="blue", weight=3) -> 'GeoVisualizer'
    def add_circle(self, lat, lon, radius_m, color="blue", fill=True)

    # Output
    def save(self, filepath) -> str
    def get_html(self) -> str
    def fit_bounds(self) -> 'GeoVisualizer'

python

class GeoVisualizer:
    def __init__(self, center=None, zoom=10, tiles="OpenStreetMap")

    # Data loading
    def from_csv(self, filepath, lat_col, lon_col, **kwargs) -> 'GeoVisualizer'
    def from_dataframe(self, df, lat_col, lon_col, **kwargs) -> 'GeoVisualizer'
    def from_geojson(self, filepath) -> 'GeoVisualizer'

    # Markers
    def add_marker(self, lat, lon, popup=None, tooltip=None, icon=None, color="blue")
    def add_markers(self, locations: list, name_col=None, popup_cols=None)
    def cluster_markers(self, enabled=True) -> 'GeoVisualizer'

    # Layers
    def add_heatmap(self, points=None, weight_col=None, radius=15) -> 'GeoVisualizer'
    def add_choropleth(self, geojson, data, key_on, value_col, **kwargs) -> 'GeoVisualizer'
    def add_route(self, points, color="blue", weight=3) -> 'GeoVisualizer'
    def add_circle(self, lat, lon, radius_m, color="blue", fill=True)

    # Output
    def save(self, filepath) -> str
    def get_html(self) -> str
    def fit_bounds(self) -> 'GeoVisualizer'

Marker Options

标记点选项

python

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Custom icons

viz.add_marker(lat, lon, icon="fa-coffee", color="red")

With popup content

viz.add_marker(lat, lon, popup="<b>Store #123</b><br>Open 9-5")

From CSV with popup columns

viz.from_csv("stores.csv", lat_col="lat", lon_col="lon") viz.add_markers(viz.data, popup_cols=["name", "address", "phone"])

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viz.from_csv("stores.csv", lat_col="lat", lon_col="lon") viz.add_markers(viz.data, popup_cols=["name", "address", "phone"])

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Heatmap Options

热力图选项

python

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Basic heatmap

viz.add_heatmap()

Weighted heatmap (e.g., by sales volume)

viz.add_heatmap(weight_col="sales", radius=20, blur=15, max_zoom=12)

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viz.add_heatmap(weight_col="sales", radius=20, blur=15, max_zoom=12)

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Choropleth Maps

分级着色图

python

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Color regions by data

viz.add_choropleth( geojson="us-states.geojson", data=state_data, key_on="feature.properties.name", # GeoJSON property value_col="population", fill_color="YlOrRd", # Color scale legend_name="Population" )

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viz.add_choropleth( geojson="us-states.geojson", data=state_data, key_on="feature.properties.name", # GeoJSON property value_col="population", fill_color="YlOrRd", # Color scale legend_name="Population" )

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Tile Layers

瓦片图层

Available base maps:

```
OpenStreetMap
```
(default)
```
CartoDB positron
```
(light, minimal)
```
CartoDB dark_matter
```
(dark theme)
```
Stamen Terrain
```
(terrain features)
```
Stamen Toner
```
(high contrast B&W)

python

viz = GeoVisualizer(tiles="CartoDB positron")

可用的基础地图：

```
OpenStreetMap
```
（默认）
```
CartoDB positron
```
（浅色极简风格）
```
CartoDB dark_matter
```
（深色主题）
```
Stamen Terrain
```
（地形特征）
```
Stamen Toner
```
（高对比度黑白风格）

python

viz = GeoVisualizer(tiles="CartoDB positron")

Example Workflows

示例工作流

Store Locator Map

门店定位地图

python

viz = GeoVisualizer()
viz.from_csv("stores.csv", lat_col="lat", lon_col="lon")
viz.add_markers(viz.data, popup_cols=["name", "address", "hours"])
viz.cluster_markers(True)
viz.fit_bounds()
viz.save("store_locator.html")

python

viz = GeoVisualizer()
viz.from_csv("stores.csv", lat_col="lat", lon_col="lon")
viz.add_markers(viz.data, popup_cols=["name", "address", "hours"])
viz.cluster_markers(True)
viz.fit_bounds()
viz.save("store_locator.html")

Sales Heatmap

销售热力图

python

viz = GeoVisualizer(tiles="CartoDB dark_matter")
viz.from_csv("sales.csv", lat_col="lat", lon_col="lon")
viz.add_heatmap(weight_col="revenue", radius=25)
viz.save("sales_heat.html")

python

viz = GeoVisualizer(tiles="CartoDB dark_matter")
viz.from_csv("sales.csv", lat_col="lat", lon_col="lon")
viz.add_heatmap(weight_col="revenue", radius=25)
viz.save("sales_heat.html")

Delivery Route

配送路线

python

viz = GeoVisualizer()
stops = [(40.7, -74.0), (40.8, -73.9), (40.75, -73.95)]
viz.add_route(stops, color="blue", weight=4)
for i, (lat, lon) in enumerate(stops):
    viz.add_marker(lat, lon, popup=f"Stop {i+1}")
viz.save("route.html")

python

viz = GeoVisualizer()
stops = [(40.7, -74.0), (40.8, -73.9), (40.75, -73.95)]
viz.add_route(stops, color="blue", weight=4)
for i, (lat, lon) in enumerate(stops):
    viz.add_marker(lat, lon, popup=f"Stop {i+1}")
viz.save("route.html")

Output

输出结果

HTML: Interactive map viewable in any browser
Auto-fit: Automatically zooms to show all data
Responsive: Works on mobile devices

HTML：可在任意浏览器中查看的交互式地图
自动适配：自动缩放以展示所有数据
响应式设计：支持移动设备

Dependencies

依赖项

folium>=0.14.0
pandas>=2.0.0
branca>=0.6.0

folium>=0.14.0
pandas>=2.0.0
branca>=0.6.0

geo-visualizer

Original

Translation

Geo Visualizer

Geo Visualizer

Features

功能特性

Quick Start

快速开始

Simple marker map

Simple marker map

From CSV

From CSV

CLI Usage

CLI 使用方法

Plot markers from CSV

Plot markers from CSV

Add heatmap

Add heatmap

With clustering

With clustering

Choropleth map

Choropleth map

API Reference

API 参考

GeoVisualizer Class

GeoVisualizer 类

Marker Options

标记点选项

Custom icons

Custom icons

With popup content

With popup content

From CSV with popup columns

From CSV with popup columns

Heatmap Options

热力图选项

Basic heatmap

Basic heatmap

Weighted heatmap (e.g., by sales volume)

Weighted heatmap (e.g., by sales volume)

Choropleth Maps

分级着色图

Color regions by data

Color regions by data

Tile Layers

瓦片图层

Example Workflows

示例工作流

Store Locator Map

门店定位地图

Sales Heatmap

销售热力图

Delivery Route

配送路线

Output

输出结果

Dependencies

依赖项