14 maidenhead
Maidenhead key features¶
You can try out vgridpandas by using the cloud-computing platforms below without having to install anything on your computer:
Install vgridpandas¶
In [1]:
Copied!
# %pip install vgridpandas
# %pip install vgridpandas
Latlong to Maidenhead¶
In [2]:
Copied!
import pandas as pd
from vgridpandas import maidenheadpandas
df = pd.read_csv('https://github.com/uber-web/kepler.gl-data/raw/master/nyctrips/data.csv')
df = df.head(100)
df = df.rename({'pickup_longitude': 'lon', 'pickup_latitude': 'lat'}, axis=1)[['lon', 'lat', 'passenger_count']]
resolution = 3
df = df.maidenhead.latlon2maidenhead(resolution)
df.head()
import pandas as pd
from vgridpandas import maidenheadpandas
df = pd.read_csv('https://github.com/uber-web/kepler.gl-data/raw/master/nyctrips/data.csv')
df = df.head(100)
df = df.rename({'pickup_longitude': 'lon', 'pickup_latitude': 'lat'}, axis=1)[['lon', 'lat', 'passenger_count']]
resolution = 3
df = df.maidenhead.latlon2maidenhead(resolution)
df.head()
Out[2]:
| lon | lat | passenger_count | maidenhead_res | |
|---|---|---|---|---|
| maidenhead | ||||
| FN30as | -73.993896 | 40.750111 | 1 | 3 |
| FN30ar | -73.976425 | 40.739811 | 1 | 3 |
| FN30as | -73.968704 | 40.754246 | 5 | 3 |
| FN30bs | -73.863060 | 40.769581 | 5 | 3 |
| FN30as | -73.945541 | 40.779423 | 1 | 3 |
Maidenhead to geo boundary¶
In [3]:
Copied!
df = df.maidenhead.maidenhead2geo()
df
df = df.maidenhead.maidenhead2geo()
df
Out[3]:
| lon | lat | passenger_count | maidenhead_res | geometry | |
|---|---|---|---|---|---|
| maidenhead | |||||
| FN30as | -73.993896 | 40.750111 | 1 | 3 | POLYGON ((-74 40.75, -73.91667 40.75, -73.9166... |
| FN30ar | -73.976425 | 40.739811 | 1 | 3 | POLYGON ((-74 40.70833, -73.91667 40.70833, -7... |
| FN30as | -73.968704 | 40.754246 | 5 | 3 | POLYGON ((-74 40.75, -73.91667 40.75, -73.9166... |
| FN30bs | -73.863060 | 40.769581 | 5 | 3 | POLYGON ((-73.91667 40.75, -73.83333 40.75, -7... |
| FN30as | -73.945541 | 40.779423 | 1 | 3 | POLYGON ((-74 40.75, -73.91667 40.75, -73.9166... |
| ... | ... | ... | ... | ... | ... |
| FN30bs | -73.866035 | 40.770744 | 1 | 3 | POLYGON ((-73.91667 40.75, -73.83333 40.75, -7... |
| FN30ar | -73.994415 | 40.724907 | 1 | 3 | POLYGON ((-74 40.70833, -73.91667 40.70833, -7... |
| FN30ar | -73.994217 | 40.734909 | 1 | 3 | POLYGON ((-74 40.70833, -73.91667 40.70833, -7... |
| FN20xr | -74.014938 | 40.710232 | 1 | 3 | POLYGON ((-74.08333 40.70833, -74 40.70833, -7... |
| FN30ar | -73.998070 | 40.735664 | 1 | 3 | POLYGON ((-74 40.70833, -73.91667 40.70833, -7... |
100 rows × 5 columns
Maidenhead point binning¶
In [4]:
Copied!
from vgridpandas import maidenheadpandas
import geopandas as gpd
# df = pd.read_csv("https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/csv/dist1_pois.csv")
df = gpd.read_file("https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/dist1_pois.geojson")
resolution = 4
stats = "count"
df_bin = df.maidenhead.maidenheadbin(resolution=resolution, stats = stats,
# numeric_column="confidence",
# category_column="category",
return_geometry=True)
df_bin.plot(
column=stats, # numeric column to base the colors on
cmap='Spectral_r', # color scheme (matplotlib colormap)
legend=True,
linewidth=0.2 # boundary width (optional)
)
from vgridpandas import maidenheadpandas
import geopandas as gpd
# df = pd.read_csv("https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/csv/dist1_pois.csv")
df = gpd.read_file("https://raw.githubusercontent.com/opengeoshub/vopendata/refs/heads/main/shape/dist1_pois.geojson")
resolution = 4
stats = "count"
df_bin = df.maidenhead.maidenheadbin(resolution=resolution, stats = stats,
# numeric_column="confidence",
# category_column="category",
return_geometry=True)
df_bin.plot(
column=stats, # numeric column to base the colors on
cmap='Spectral_r', # color scheme (matplotlib colormap)
legend=True,
linewidth=0.2 # boundary width (optional)
)
Out[4]:
<Axes: >
