How would I build a real time flood map for Japan using Plotly Dash where I can actually see street names

đź“Š Plotly Python
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Ideally overlayed on Google Maps as that is the go-to map app here.
It is raining very heavily and when I looked up what the government has now, it is almost useless unless you are very familiar with the geography and your current location without a “my location” functionality.

Please feel free to comment or provide advice on how to get started and proceed.

I have one Plotly Dash app deployed: nyck33.pythonanywhere.com/ and another one that is not, also have done some map stuff for a coding test which I passed so not a complete beginner but not an expert either.

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This is what ChatGPT told me

Building a real-time flood risk map in Plotly Dash that shows flood risks overlayed on Google Maps or some other map with street names and other landmarks in Japan would involve several steps. Here is how you can build such a map using Python and Plotly Dash:

  1. Obtain the Flood Data: The first step is to obtain the flood data for Japan from reliable sources. You can use APIs provided by government agencies or get data from open-source repositories such as Open Flood Risk Map. Make sure that the data is in a format that can be easily ingested into Python.

  2. Install Required Libraries: To build a real-time dash application, you need to install several libraries such as Plotly, Dash, Pandas, Numpy, etc. Install these libraries using pip command, or if you’re using Anaconda distribution, you can install them using Conda.

  3. Create a New Project: Create a new Python project and import the required libraries into the project. Set up your virtual environment and create a new “app.py” file.

  4. Import Data into Python: Import the flood data into Python using Pandas. Clean the data and prepare it for visualization. You can also perform EDA (Exploratory Data Analysis) to get insights into the data.

  5. Create a Mapbox Account: Create a Mapbox account to get an API key. This API key will be used to access the Mapbox API, which provides street maps, satellite imagery, and terrain data.

  6. Set Up a Plotly Dash Application: Use the Dash framework to set up an application server. Define the layout of the application, including the HTML code, CSS styling, and the Plotly graph components.

  7. Add Callbacks: Add callbacks to the application that will trigger updates whenever there’s a change in the data or user inputs. These callbacks will enable the dashboard to support real-time updates.

  8. Build a Map Overlay: Build a map overlay using Plotly’s mapbox feature, which allows you to add layers and data points to the map. Use the flood data and the location data to create an overlay that displays the flood risk zones.

  9. Run the Application: Run the application on the server. You can use your local machine or host the application on cloud services such as Heroku.

  10. Visualize the Flood Risk Map: Once the application is running, visit the URL in your web browser to see your real-time flood risk map. The map will display flood risk zones overlaid on a map with street names and other landmarks in Japan.

These are the high-level steps involved in building a real-time flood risk map in Plotly Dash that shows flood risks overlayed on Google Maps or some other map with street names and other landmarks in Japan. However, please note that this is not a comprehensive guide and several implementation details may vary depending on your specific project requirements.

Please let me know if there are any errors or better ways for any or all steps.