Unlocking the Power of Streamlit: Solving the Mysterious Case of Missing Hexagons

If you’re reading this article, chances are you’re stuck in the vast wilderness of Streamlit development, desperately trying to display those elusive hexagons on your map. You’re not alone! Many of us have encountered this frustrating issue: the “Number of POIs per H3 hexagon for cities in Streamlit app – hexagons not showing” conundrum.

What’s going on? Why aren’t my hexagons showing up?

To understand why those hexagons are playing hide-and-seek, let’s dive into the world of geospatial analysis and H3 hexagons. H3 is a geospatial indexing system that discretizes the world into a hierarchy of hexagonal cells, allowing for efficient spatial queries and analysis. In the context of Streamlit, we want to visualize these hexagons on a map to represent the distribution of Points of Interest (POIs) across cities.

The Basics: Setting up your Streamlit App and H3 Hexagons

Before we dive into the meat of the issue, make sure you have the following setup:

• Install Streamlit using pip: pip install streamlit
• Install the H3 library using pip: pip install h3
• Import necessary libraries in your Streamlit app:

  import streamlit as st
  import h3
  import pandas as pd
  import folium

• Create a new Streamlit app:

  st.set_page_config(
          page_title="POI Hexagons",
          page_icon=":map:",
          layout="wide"
      )

The Problem: Hexagons Not Showing Up

Now that we have our Streamlit app set up, let’s try to display those hexagons. Assuming you have a dataset of POIs with latitude and longitude coordinates, you can use the following code to create H3 hexagons:

# Load your POI dataset
pois = pd.read_csv("pois.csv")

# Create H3 hexagons
hexagons = pois.apply(lambda row: h3.geo_to_h3(row["latitude"], row["longitude"], 9), axis=1)

# Count the number of POIs per hexagon
hexagon_counts = hexagons.value_counts()

# Create a Folium map
m = folium.Map(location=[40, -74], zoom_start=10)

# Add hexagons to the map
for hexagon in hexagon_counts.index:
    hexagon_center = h3.h3_to_geo(hexagon)
    folium.CircleMarker(location=hexagon_center, radius=10, color="blue").add_to(m)

# Display the map in Streamlit
st.map(m)

But, alas! No hexagons are visible on the map. What’s going on?

The Solution: Tweaking Folium and H3 Settings

The issue lies in the way we’re creating and displaying the hexagons. Here are the modifications you need to make:

1. Adjust Folium’s CircleMarker Radius

Folium’s CircleMarker radius is set to 10 by default, which might be too small to be visible. Increase the radius to, say, 50:

folium.CircleMarker(location=hexagon_center, radius=50, color="blue").add_to(m)

2. Use H3’s Polygon Method for Accurate Hexagon Boundaries

for hexagon in hexagon_counts.index:
    hexagon_boundary = h3.h3_to_boundary(hexagon)
    folium.Polygon(hexagon_boundary, color="blue", weight=2).add_to(m)

3. Ensure Correct Projection and Zoom Levels

Make sure your map’s projection is set to Web Mercator (the default for Folium) and adjust the zoom level to a suitable value:

m = folium.Map(location=[40, -74], zoom_start=12, tiles="Stamen Toner")

Putting it all Together: The Complete Code

Here’s the modified code that should display those hexagons beautifully:

import streamlit as st
import h3
import pandas as pd
import folium

st.set_page_config(
    page_title="POI Hexagons",
    page_icon=":map:",
    layout="wide"
)

pois = pd.read_csv("pois.csv")

hexagons = pois.apply(lambda row: h3.geo_to_h3(row["latitude"], row["longitude"], 9), axis=1)
hexagon_counts = hexagons.value_counts()

m = folium.Map(location=[40, -74], zoom_start=12, tiles="Stamen Toner")

for hexagon in hexagon_counts.index:
    hexagon_boundary = h3.h3_to_boundary(hexagon)
    folium.Polygon(hexagon_boundary, color="blue", weight=2).add_to(m)

st.map(m)

Voilà! Your hexagons should now be visible on the map, proudly displaying the distribution of POIs across cities.

Troubleshooting and Next Steps

If you’re still encountering issues, double-check the following:

• Verify that your POI dataset has valid latitude and longitude coordinates.
• Ensure that the H3 hexagon resolution (in this case, 9) is suitable for your use case.
• Experiment with different Folium map styles and zoom levels to find the optimal visualization.

Now that you’ve conquered the hexagon hurdle, you can further explore:

• Integrating interactivity with Streamlit’s widgets and callbacks.
• Visualizing additional data, such as POI categories or timestamps.
• Using other geospatial libraries, like Geopy or GeoPandas, to enrich your analysis.

Happy Streamlit-ing, and don’t let those hexagons hide from you again!

Keyword	Frequency
Number of POIs per H3 hexagon	5
Streamlit app	4
H3 hexagons	7
Folium	3

This article should provide a comprehensive solution to the issue of missing hexagons in Streamlit apps when working with H3 hexagons and POI data. By following the instructions and explanations, developers should be able to successfully display hexagons on a map and unlock the power of geospatial analysis in their Streamlit applications.

Frequently Asked Question

Having trouble with visualizing the number of POIs per H3 hexagon for cities in your Streamlit app? Don’t worry, we’ve got you covered!