Geohash layer

This example aggregates public NYC 311 requests into geohash cells and renders their density with GeohashLayer. Brighter cells received fewer reports; darker red cells received more.

The data comes from NYC Open Data's 311 Service Requests dataset.

Dependencies¶

Install uv and then launch this notebook with:

uvx juv run examples/geohash-layer.ipynb

In [ ]:

# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "geohash2>=1.1",
#     "lonboard>=0.16.0",
#     "matplotlib>=3.11.1",
#     "palettable>=3.3.3",
#     "pandas>=3.0.5",
#     "pyarrow>=25.0.1",
#     "pygeohash>=3.3.1",
#     "requests>=2.34.2",
# ]
# ///

# /// script # requires-python = ">=3.12" # dependencies = [ # "geohash2>=1.1", # "lonboard>=0.16.0", # "matplotlib>=3.11.1", # "palettable>=3.3.3", # "pandas>=3.0.5", # "pyarrow>=25.0.1", # "pygeohash>=3.3.1", # "requests>=2.34.2", # ] # ///

In [ ]:

import pandas as pd
import pygeohash
import requests
from matplotlib.colors import LogNorm
from palettable.colorbrewer.sequential import YlOrRd_9

from lonboard import GeohashLayer, Map
from lonboard.basemap import CartoStyle, MaplibreBasemap
from lonboard.colormap import apply_continuous_cmap
from lonboard.view_state import MapViewState

import pandas as pd import pygeohash import requests from matplotlib.colors import LogNorm from palettable.colorbrewer.sequential import YlOrRd_9 from lonboard import GeohashLayer, Map from lonboard.basemap import CartoStyle, MaplibreBasemap from lonboard.colormap import apply_continuous_cmap from lonboard.view_state import MapViewState

Download and aggregate the data¶

In [ ]:

API_URL = "https://data.cityofnewyork.us/resource/erm2-nwe9.json"
PARAMS = {
    "$select": "latitude, longitude, complaint_type, borough",
    "$where": (
        "created_date >= '2025-01-01T00:00:00' AND "
        "created_date < '2025-01-08T00:00:00' AND "
        "latitude IS NOT NULL AND longitude IS NOT NULL"
    ),
    "$order": "created_date ASC",
    "$limit": 10_000,
}

try:
    response = requests.get(API_URL, params=PARAMS, timeout=30)
    response.raise_for_status()
except requests.RequestException as exc:
    raise RuntimeError(
        "Unable to download NYC 311 data. Please try again later.",
    ) from exc

requests_df = pd.DataFrame(response.json())
if requests_df.empty:
    raise RuntimeError("The NYC 311 query returned no requests.")

requests_df = requests_df.astype({"latitude": "float64", "longitude": "float64"})
requests_df["geohash"] = [
    pygeohash.encode(latitude, longitude, precision=6)
    for latitude, longitude in zip(
        requests_df["latitude"],
        requests_df["longitude"],
        strict=True,
    )
]


def aggregate_cell_data(df: pd.DataFrame) -> pd.Series:
    top_complaint = (
        df["complaint_type"].mode()[0] if not df["complaint_type"].empty else "N/A"
    )
    return pd.Series(
        {
            "request_count": len(df),
            "primary_borough": df["borough"].mode()[0]
            if "borough" in df.columns
            else "Unspecified",
            "top_complaint": top_complaint,
            "top_complaint_count": (df["complaint_type"] == top_complaint).sum(),
        },
    )


cells = requests_df.groupby("geohash", as_index=False).apply(aggregate_cell_data)
cells.head()

API_URL = "https://data.cityofnewyork.us/resource/erm2-nwe9.json" PARAMS = { "$select": "latitude, longitude, complaint_type, borough", "$where": ( "created_date >= '2025-01-01T00:00:00' AND " "created_date < '2025-01-08T00:00:00' AND " "latitude IS NOT NULL AND longitude IS NOT NULL" ), "$order": "created_date ASC", "$limit": 10_000, } try: response = requests.get(API_URL, params=PARAMS, timeout=30) response.raise_for_status() except requests.RequestException as exc: raise RuntimeError( "Unable to download NYC 311 data. Please try again later.", ) from exc requests_df = pd.DataFrame(response.json()) if requests_df.empty: raise RuntimeError("The NYC 311 query returned no requests.") requests_df = requests_df.astype({"latitude": "float64", "longitude": "float64"}) requests_df["geohash"] = [ pygeohash.encode(latitude, longitude, precision=6) for latitude, longitude in zip( requests_df["latitude"], requests_df["longitude"], strict=True, ) ] def aggregate_cell_data(df: pd.DataFrame) -> pd.Series: top_complaint = ( df["complaint_type"].mode()[0] if not df["complaint_type"].empty else "N/A" ) return pd.Series( { "request_count": len(df), "primary_borough": df["borough"].mode()[0] if "borough" in df.columns else "Unspecified", "top_complaint": top_complaint, "top_complaint_count": (df["complaint_type"] == top_complaint).sum(), }, ) cells = requests_df.groupby("geohash", as_index=False).apply(aggregate_cell_data) cells.head()

Out[ ]:

	geohash	request_count	primary_borough	top_complaint	top_complaint_count
0	dr5nqr	1	STATEN ISLAND	Noise - Commercial	1
1	dr5nqw	1	STATEN ISLAND	Noise - Residential	1
2	dr5nqx	1	STATEN ISLAND	Illegal Parking	1
3	dr5nqz	1	STATEN ISLAND	Damaged Tree	1
4	dr5nw9	1	STATEN ISLAND	Building/Use	1

Explore neighborhood-scale demand¶

Hover over a cell to inspect its geohash and request count.

In [ ]:

color_scale = LogNorm(
    vmin=cells["request_count"].min(),
    vmax=cells["request_count"].max(),
)

layer = GeohashLayer.from_pandas(
    cells,
    get_geohash=cells["geohash"],
    get_fill_color=apply_continuous_cmap(
        color_scale(cells["request_count"]),
        YlOrRd_9,
        alpha=0.88,
    ),
    get_line_color=[35, 12, 8, 160],
    line_width_min_pixels=0.75,
    pickable=True,
)

map_ = Map(
    layer,
    basemap=MaplibreBasemap(style=CartoStyle.DarkMatter),
    view_state=MapViewState(longitude=-73.96, latitude=40.73, zoom=10),
    height=700,
    show_tooltip=True,
    show_side_panel=False,
    picking_radius=5,
)
map_

color_scale = LogNorm( vmin=cells["request_count"].min(), vmax=cells["request_count"].max(), ) layer = GeohashLayer.from_pandas( cells, get_geohash=cells["geohash"], get_fill_color=apply_continuous_cmap( color_scale(cells["request_count"]), YlOrRd_9, alpha=0.88, ), get_line_color=[35, 12, 8, 160], line_width_min_pixels=0.75, pickable=True, ) map_ = Map( layer, basemap=MaplibreBasemap(style=CartoStyle.DarkMatter), view_state=MapViewState(longitude=-73.96, latitude=40.73, zoom=10), height=700, show_tooltip=True, show_side_panel=False, picking_radius=5, ) map_

Out[ ]:

VBox(children=(<lonboard._map.Map object at 0x11ce1a360>, VBox(children=(ErrorOutput(), ErrorOutput(), ErrorOu…