104 point style

Plotting point data with custom styles

Uncomment the following line to install leafmap if needed.

In [1]:

# %pip install -U leafmap

# %pip install -U leafmap

In [2]:

from leafmap import leafmap

from leafmap import leafmap

Load GeoJSON data¶

In [3]:

url = (
    "https://github.com/opengeos/datasets/releases/download/world/world_cities.geojson"
)

url = ( "https://github.com/opengeos/datasets/releases/download/world/world_cities.geojson" )

In [4]:

m = leafmap.Map()
point_style = {
    "radius": 5,
    "color": "red",
    "fillOpacity": 0.8,
    "fillColor": "blue",
    "weight": 3,
}
hover_style = {"fillColor": "yellow", "fillOpacity": 1.0}
m.add_geojson(
    url, point_style=point_style, hover_style=hover_style, layer_name="World Cities"
)
m

m = leafmap.Map() point_style = { "radius": 5, "color": "red", "fillOpacity": 0.8, "fillColor": "blue", "weight": 3, } hover_style = {"fillColor": "yellow", "fillOpacity": 1.0} m.add_geojson( url, point_style=point_style, hover_style=hover_style, layer_name="World Cities" ) m

Load GoeDataFrame¶

In [5]:

import geopandas as gpd

import geopandas as gpd

In [6]:

gdf = gpd.read_file(url)

gdf = gpd.read_file(url)

In [7]:

m = leafmap.Map()
point_style = {
    "radius": 5,
    "color": "red",
    "fillOpacity": 0.8,
    "fillColor": "blue",
    "weight": 3,
}
hover_style = {"fillColor": "yellow", "fillOpacity": 1.0}
m.add_gdf(
    gdf, point_style=point_style, hover_style=hover_style, layer_name="World Cities"
)
m

m = leafmap.Map() point_style = { "radius": 5, "color": "red", "fillOpacity": 0.8, "fillColor": "blue", "weight": 3, } hover_style = {"fillColor": "yellow", "fillOpacity": 1.0} m.add_gdf( gdf, point_style=point_style, hover_style=hover_style, layer_name="World Cities" ) m

Load Random Data¶

In [8]:

import geopandas, pandas as pd, numpy as np
import random

import geopandas, pandas as pd, numpy as np import random

In [9]:

# Function to generate random coordinates within latitude and longitude bounds
def random_coordinates(n, lat_min=-90, lat_max=90, lon_min=-180, lon_max=180):
    """Generates n random latitude/longitude coordinates.

    Args:
        n (int): The number of coordinates to generate.
        lat_min (float): Minimum latitude. Defaults to -90.
        lat_max (float): Maximum latitude. Defaults to 90.
        lon_min (float): Minimum longitude. Defaults to -180.
        lon_max (float): Maximum longitude. Defaults to 180.

    Returns:
        pandas.DataFrame: A DataFrame containing 'Longitude' and 'Latitude' columns.
    """

    latitudes = [random.uniform(lat_min, lat_max) for _ in range(n)]
    longitudes = [random.uniform(lon_min, lon_max) for _ in range(n)]
    return pd.DataFrame({"Longitude": longitudes, "Latitude": latitudes})


numpoints = 1000

# Generate random coordinates across the globe
df = random_coordinates(numpoints)

# Add a 'Conc' column (optional, for demonstration)
df["Conc"] = np.random.randn(numpoints) + 17  # Example data

# Create GeoDataFrame
gdf = geopandas.GeoDataFrame(
    df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude), crs="EPSG:4326"
)

m = leafmap.Map()  # Start with a low zoom to show the global distribution

# Add the GeoDataFrame to the map
m.add_gdf(
    gdf,
    hover_style={"fillColor": "yellow", "fillOpacity": 1.0},
    point_style={
        "radius": 5,
        "color": "red",
        "fillColor": "red",
        "fillOpacity": 0.5,
        "opacity": 0.5,
    },
)

m

# Function to generate random coordinates within latitude and longitude bounds def random_coordinates(n, lat_min=-90, lat_max=90, lon_min=-180, lon_max=180): """Generates n random latitude/longitude coordinates. Args: n (int): The number of coordinates to generate. lat_min (float): Minimum latitude. Defaults to -90. lat_max (float): Maximum latitude. Defaults to 90. lon_min (float): Minimum longitude. Defaults to -180. lon_max (float): Maximum longitude. Defaults to 180. Returns: pandas.DataFrame: A DataFrame containing 'Longitude' and 'Latitude' columns. """ latitudes = [random.uniform(lat_min, lat_max) for _ in range(n)] longitudes = [random.uniform(lon_min, lon_max) for _ in range(n)] return pd.DataFrame({"Longitude": longitudes, "Latitude": latitudes}) numpoints = 1000 # Generate random coordinates across the globe df = random_coordinates(numpoints) # Add a 'Conc' column (optional, for demonstration) df["Conc"] = np.random.randn(numpoints) + 17 # Example data # Create GeoDataFrame gdf = geopandas.GeoDataFrame( df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude), crs="EPSG:4326" ) m = leafmap.Map() # Start with a low zoom to show the global distribution # Add the GeoDataFrame to the map m.add_gdf( gdf, hover_style={"fillColor": "yellow", "fillOpacity": 1.0}, point_style={ "radius": 5, "color": "red", "fillColor": "red", "fillOpacity": 0.5, "opacity": 0.5, }, ) m