OpenLayers Clustering¶

This notebook demonstrates OpenLayers' built-in point clustering feature.

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# %pip install anymap-ts

# %pip install anymap-ts

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import random
from anymap_ts import OpenLayersMap

import random from anymap_ts import OpenLayersMap

Generate Sample Data¶

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random.seed(42)
features = []

clusters = [
    {"center": [-122.4, 37.8], "name": "SF Area", "count": 150},
    {"center": [-118.2, 34.05], "name": "LA Area", "count": 200},
    {"center": [-73.98, 40.75], "name": "NYC Area", "count": 250},
    {"center": [-87.63, 41.88], "name": "Chicago Area", "count": 120},
    {"center": [-95.37, 29.76], "name": "Houston Area", "count": 100},
    {"center": [-112.07, 33.45], "name": "Phoenix Area", "count": 80},
]

for cluster in clusters:
    cx, cy = cluster["center"]
    for i in range(cluster["count"]):
        features.append(
            {
                "type": "Feature",
                "geometry": {
                    "type": "Point",
                    "coordinates": [
                        cx + random.gauss(0, 0.5),
                        cy + random.gauss(0, 0.5),
                    ],
                },
                "properties": {
                    "region": cluster["name"],
                    "value": random.randint(1, 100),
                },
            }
        )

point_data = {"type": "FeatureCollection", "features": features}
print(f"Generated {len(features)} points across {len(clusters)} regions")

random.seed(42) features = [] clusters = [ {"center": [-122.4, 37.8], "name": "SF Area", "count": 150}, {"center": [-118.2, 34.05], "name": "LA Area", "count": 200}, {"center": [-73.98, 40.75], "name": "NYC Area", "count": 250}, {"center": [-87.63, 41.88], "name": "Chicago Area", "count": 120}, {"center": [-95.37, 29.76], "name": "Houston Area", "count": 100}, {"center": [-112.07, 33.45], "name": "Phoenix Area", "count": 80}, ] for cluster in clusters: cx, cy = cluster["center"] for i in range(cluster["count"]): features.append( { "type": "Feature", "geometry": { "type": "Point", "coordinates": [ cx + random.gauss(0, 0.5), cy + random.gauss(0, 0.5), ], }, "properties": { "region": cluster["name"], "value": random.randint(1, 100), }, } ) point_data = {"type": "FeatureCollection", "features": features} print(f"Generated {len(features)} points across {len(clusters)} regions")

Basic Clustering¶

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m = OpenLayersMap(center=[-98, 38], zoom=4, height="600px")
m.add_basemap("OpenStreetMap")
m.add_cluster_layer(
    point_data,
    name="clusters",
    distance=50,
)
m

m = OpenLayersMap(center=[-98, 38], zoom=4, height="600px") m.add_basemap("OpenStreetMap") m.add_cluster_layer( point_data, name="clusters", distance=50, ) m

Custom Cluster Colors¶

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m = OpenLayersMap(center=[-98, 38], zoom=4, height="600px")
m.add_basemap("CartoDB.Positron")
m.add_cluster_layer(
    point_data,
    name="custom-clusters",
    distance=60,
    min_distance=25,
    cluster_color="rgba(231, 76, 60, 0.7)",
    point_color="rgba(231, 76, 60, 0.9)",
    text_color="#fff",
)
m

m = OpenLayersMap(center=[-98, 38], zoom=4, height="600px") m.add_basemap("CartoDB.Positron") m.add_cluster_layer( point_data, name="custom-clusters", distance=60, min_distance=25, cluster_color="rgba(231, 76, 60, 0.7)", point_color="rgba(231, 76, 60, 0.9)", text_color="#fff", ) m

Tight Clustering¶

Use a smaller distance to show more individual points.

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m = OpenLayersMap(center=[-98, 38], zoom=4, height="600px")
m.add_basemap("CartoDB.DarkMatter")
m.add_cluster_layer(
    point_data,
    name="tight-clusters",
    distance=25,
    min_distance=10,
    cluster_color="rgba(46, 204, 113, 0.8)",
    point_color="rgba(46, 204, 113, 1.0)",
)
m

m = OpenLayersMap(center=[-98, 38], zoom=4, height="600px") m.add_basemap("CartoDB.DarkMatter") m.add_cluster_layer( point_data, name="tight-clusters", distance=25, min_distance=10, cluster_color="rgba(46, 204, 113, 0.8)", point_color="rgba(46, 204, 113, 1.0)", ) m