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How to create interactive maps in Bokeh

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by vinitha@openweaver.com dot icon Updated: Aug 23, 2023

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Bokeh library is a Python library to create interactive data visualization. People use it to create web-based visualizations. 


It offers various tools to build simple interactive plots, line charts, and maps. Modern web browsers can display that. The concept involves combining geographical data with interactive capabilities. Users can explore and interact with the map's content. They can also add broken effects to specific map elements. 


Here's how you can create interactive maps with Bokeh:  

  • Data Preparation: Gather the geographical map data you want to display on the map. This could be latitude and longitude coordinates data value. It represents points, lines, or polygons on the map.  
  • Install Bokeh: Install the Bokeh library in Python using pip: pip install Bokeh.   
  • Import Libraries: Import necessary functionality in your Python script.  
  • Create the Map Plot: Use Bokeh's mapping tools to create a plot showing your geographical data on a map.  
  • Add Interactivity: Use Bokeh's interactive features to enhance the map. Users can interact with the map by zooming and adding tooltip variables.  
  • Incorporate Bokeh Effects: It can enhance specific regions or map elements.  
  • Display the Map: Display the map in a web browser window using Bokeh's server capabilities.  

 

With Bokeh's interactive features, you can create informative map visualizations using geographical data. Adding a creative and aesthetic touch can make the map look attractive.  


Bokeh is the library that allows for interactive map visualizations in Python code. Other popular options include Folium and Plotly, each with unique features. Depending on your specific requirements, you may explore these alternatives as well.  

Fig: Preview of the output that you will get on running this code from your IDE

Code

In this solution we are using bokeh library

How to make an interactive bokeh plot?

PythondotLines of Code : 18dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
from bokeh.layouts import column
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure, curdoc

p = figure(x_range=[0,100],y_range=[0,100],tools='tap')

source = ColumnDataSource(data={'x':[10, 20, 30, 40, 50],'y':[60, 70, 20, 40, 50]})

p.circle(x='x',y='y',size=20,source=source)

def foo(attr,old,new):
    new_source_data = {'x':source.data['x']+[60],'y':source.data['y']+[60]}
    source.data.update(new_source_data)

source.on_change('selected',foo)

curdoc().add_root(column(p))

Instructions


Follow the steps carefully to get the output easily.


  1. Download and Install the PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install bokeh - pip install bokeh
  4. Create a new Python file on your IDE.
  5. Copy the snippet using the 'copy' button and paste it into your Python file.
  6. Add show(p) in last line of code
  7. Run the current file to generate the output.


I hope you found this useful.


I found this code snippet by searching for ' how to create interactive maps in Bokeh' in Kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. PyCharm Community Edition 2023.1
  2. The solution is created in Python 3.11.1 Version
  3. bokeh 3.1.1 Version



Using this solution, we can create interactive maps in Bokeh with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to create interactive maps in bokeh.

Dependent Library


bokehby bokeh

Python doticonstar image 17667 doticonVersion:Currentdoticon
License: Permissive (BSD-3-Clause)

Interactive Data Visualization in the browser, from Python

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            bokehby bokeh

            Python doticon star image 17667 doticonVersion:Currentdoticon License: Permissive (BSD-3-Clause)

            Interactive Data Visualization in the browser, from Python
            Support
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                    Reuse

                      You can search for any dependent library on Kandi like ' bokeh'.

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                      FAQ:  

                      1. What is a visualization library, and how does it relate to Bokeh interactive maps?  

                      A visualization library is a software tool or framework. It provides the necessary components for creating visual representations of data. These libraries enable developers to generate charts, graphs, maps, and other visualizations. It helps to convey insights and patterns present in their data.  


                      Bokeh is an example of a visualization library. A Python library that specializes in creating interactive data visualizations for the web. It allows you to build various visualizations, including plots, charts, and maps.  

                       

                      2. Can we use a point plot to create static and interactive maps?  

                      Depending on your chosen tools, you can use point plots to create static and interactive maps. Point plots are a way to show data by putting each point on a graph or map. It represents specific data values. 

                       

                      You can make maps with points using different libraries and methods. 

                      • Static Maps with Point Plots  
                      • Interactive Maps with Point Plots  

                       

                      3. How do I create an interactive Bokeh map with my data?  

                      Here's a basic step to create an interactive Bokeh map:  

                      • Install Bokeh  
                      • Import Libraries  
                      • Prepare Your Data  
                      • Create a Plot  
                      • Add Data to the Plot  
                      • Add Interactive Features  
                      • Show and Save the Map  
                      • Run Your Script  

                       

                      4. How have Bokeh's interactive maps changed how we visualize information?  

                      Bokeh interactive maps have transformed the visualization space to present spatial data. Bokeh interactive maps have changed how we make visualizations. 

                      • Enhanced Interactivity  
                      • Dynamic Data Exploration  
                      • Storytelling and Engagement:  
                      • Data Communication  
                      • User-Centric Experiences  

                       

                      5. What are some benefits of Geodata Exploration and Visualization using Bokeh?  

                      Exploring and showing geodata has many benefits that help share spatial information effectively. Here are some key advantages of using Bokeh for geodata exploration and visualization:  

                      • Interactivity  
                      • Enhanced Insights  
                      • Dynamic Filtering  
                      • Customization  
                      • Widgets and Controls  

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