How to Use Plotly for GIS Data Visualization in Python

Interactive geospatial visualization is important for working with geospatial data, understanding geographic phenomena, and creating cutting-edge GIS products. While traditional maps are good for use in reports and publications, interactive maps permit users to zoom in, pan around, inspect features, filter information, and discover spatial relationships.

Plotly is a robust Python library used to create interactive visualizations such as maps, graphs, and dashboards. When used with GeoPandas, Shapely, Pandas, GeoJSON, and GIS data formats, Plotly provides flexible options for visualizing GIS data.

Plotly for GIS Data

What Is Plotly?

Plotly is an open-source visualization tool that allows the creation of graphics that are of interactive and publication quality. The most commonly used version of Plotly is the one found in Python, utilized through plotly.express and plotly.graph_objects, which allows the generation of various types of visualizations, including:

• Scatter plots

• Line charts

• Bar charts

• Heatmaps

• Choropleth maps

• Bubble maps

• 3D scatter plot

• Surface plots

• Interactive dashboards

• Geographic maps

Plotly is very useful in GIS since it generates maps from coordinates and geometries in GeoJSON format.

The typical process of doing geospatial visualization in Python is pretty much like this:

GIS Data → GeoPandas → Data Preparation → Plotly → Interactive Map

This makes it ideal for exploratory spatial analysis and web-based visualization.

Why Use Plotly for GIS Visualization?

When using geospatial data, Plotly offers many benefits compared to traditional static graphing software.

1. Interactive maps

Users are able to zoom, pan, hover on certain areas, and view specific geospatial information.

2. Python-based process

Plotly can easily be used with Python geospatial libraries, including:

• GeoPandas

• Shapely

• Rasterio

• Fiona

• PyProj

• Pandas

• NumPy

3. Visualization in the browser

Plotly creates interactive HTML-based visualizations that can be used on a website, in a notebook, or in an application.

4. Data-based visualization

With the help of GIS attributes, it is easy to control:

• Size of a point

• Color of a point

• Color of a polygon

• Opacity of features

• Interesting information when just hovering

• Types of symbols

5. Support for different mapping methods

Plotly can be used in geoviz in different ways, for example, using Mapbox maps or new Maplibre-based mapping technology.

Installing Plotly and GIS Libraries

Install Plotly and the most commonly used Python geospatial libraries with:

pip install plotly geopandas pandas shapely pyproj

You can verify the Plotly installation with:

import plotly
print(plotly.__version__)

For a typical GIS visualization project, the Python environment may contain:

import pandas as pd
import geopandas as gpd
import plotly.express as px
import plotly.graph_objects as go

Understanding the Plotly GIS Data Workflow

Before creating an interactive map, geospatial data usually needs to be converted into a structure that Plotly can understand.

For vector GIS data, the workflow generally consists of five steps:

1. Load the spatial dataset.

2. Validate and clean the geometries.

3. Reproject the data when necessary.

4. Extract geographic coordinates or convert geometries to GeoJSON.

5. Pass the processed data to Plotly.

For example:

import geopandas as gpd

gdf = gpd.read_file("cities.shp")

print(gdf.head())
print(gdf.crs)

The resulting GeoDataFrame contains both attribute columns and a geometry column.

Using GeoPandas with Plotly

GeoPandas is one of the most important Python libraries for vector GIS processing.

A GeoDataFrame combines:

• Geometry

• Coordinate Reference System

• Attribute data

Load a GeoPackage, Shapefile, or other supported vector dataset:

import geopandas as gpd

gdf = gpd.read_file("roads.gpkg")

print(gdf.columns)
print(gdf.crs)

For point features, coordinates can be extracted using:

gdf["longitude"] = gdf.geometry.x
gdf["latitude"] = gdf.geometry.y

You can then pass these columns to Plotly:

fig = px.scatter_map(
    gdf,
    lat="latitude",
    lon="longitude",
    hover_data=gdf.columns,
    zoom=5
)

fig.show()

This creates a direct GeoPandas-to-Plotly visualization pipeline.

Understanding GeoJSON in Plotly

GeoJSON is a JSON-based format designed for representing geographic features.

A GeoJSON FeatureCollection commonly contains:

{
  "type": "FeatureCollection",
  "features": []
}

Each feature can contain:

• Geometry

• Properties

• Feature identifiers

Plotly uses GeoJSON properties to associate geographic shapes with attribute records.

For example:

fig = px.choropleth(
    df,
    geojson=geojson_data,
    locations="id",
    featureidkey="properties.id",
    color="value"
)

Here:

• geojson defines the geographic boundaries.

• locations identifies records in the DataFrame.

• featureidkey specifies where the corresponding identifier exists in the GeoJSON.

• color determines the attribute used to style the polygons.

Correct identifier matching is critical when creating Plotly GIS maps.

Plotting Polygon Data from GeoPandas

GeoPandas can convert polygon geometries into GeoJSON:

geojson_data = gdf.__geo_interface__

Alternatively:

import json

geojson_data = json.loads(gdf.to_json())

You can then use the resulting GeoJSON with Plotly.

For example:

fig = px.choropleth(
    gdf,
    geojson=geojson_data,
    locations="region_id",
    featureidkey="properties.region_id",
    color="population"
)

fig.update_geos(fitbounds="locations", visible=False)

fig.show()

This is a common technique for creating interactive polygon maps with GeoPandas and Plotly.

Coordinate Reference Systems and Plotly

Coordinate Reference Systems, or CRS, are fundamental to GIS.

A GeoDataFrame might use:

EPSG:4326

which represents geographic coordinates using longitude and latitude.

Check the CRS with:

print(gdf.crs)

For web-based interactive maps, converting geographic data to WGS 84 is often useful:

gdf = gdf.to_crs(epsg=4326)

Then verify:

print(gdf.crs)

You should understand the difference between:

• Geographic CRS

• Projected CRS

• Datum

• Coordinate units

• Axis order

before visualizing spatial data.

Important GIS considerations

Do not blindly convert every dataset to EPSG:4326 for analysis.

Projected coordinate systems are generally more appropriate for:

• Distance calculations

• Buffering

• Area calculations

• Spatial measurements

EPSG:4326 is commonly used for geographic visualization because coordinates are expressed as longitude and latitude.

A good workflow is:

Project → Analyze → Transform to geographic coordinates → Visualize

Adding Multiple GIS Layers

Real GIS applications rarely contain only one layer.

A typical map may include:

• Administrative boundaries

• Roads

• Buildings

• Points of interest

• Survey locations

• Satellite-derived observations

Plotly allows multiple traces to be combined into one figure.

For example:

fig = px.scatter_map(
    points,
    lat="latitude",
    lon="longitude",
    color="category",
    zoom=5
)

fig.update_layout(
    map_style="open-street-map"
)

fig.show()

Additional layers can be added using Plotly traces.

This enables the construction of multi-layer interactive GIS visualizations.

When working with Python for GIS data visualization, Plotly emerges as a strong candidate for different spatial datasets and transforming them into interactive maps with visualizations. The key strength of Plotly is in its combination with the likes of GeoPandas, Shapely, Pandas, GeoJSON, Rasterio, and various geospatial libraries. This gives it the scope for performing a plethora of tasks, from basic point visualization to advanced applications in UAV, LiDAR, remote sensing, and others.

In creating a successful Plotly visualization, it is not enough to just create an interactive map. It is also important to create a production-ready workflow that addresses aspects like spatial reference system (CRS) management, geometry validity, data volume, spatial aggregation, mapping performance, attribute design, and data formats.

For medium and small datasets, Plotly is a very efficient method that gives users fairly flexible interactive GIS visualization. For large systems, however, Plotly shines the most when used as a visualization tool in a broader system built with heavily optimized spatial processing and cloud-based geospatial solutions.

To learn more about Plotly and its geospatial capabilities, click here.

For more information or any questions regarding Plotly, please don't hesitate to contact us at

Email: info@geowgs84.com

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