Using python to plot 'Gridded' map - python-3.x

Question

I would like to know how I can create a gridded map of a country(i.e. Singapore) with resolution of 200m x 200m squares. (50m or 100m is ok too)
I would then use the 'nearest neighbour' technique to assign a rainfall data and colour code to each square based on the nearest rainfall station's data.
[I have the latitude,longitude & rainfall data for all the stations for each date.]
Then, I would like to store the data in an Array for each 'gridded map' (i.e. from 1-Jan-1980 to 31-Dec-2021)
Can this be done using python?
P.S Below is a 'simple' version I did as an example to how the 'gridded' map should look like for 1 particular day.
https://i.stack.imgur.com/9vIeQ.png
Thank you so much!

Answer 1

Can this be done using python? YES
I have previously provided a similar answer binning spatial dataframe. Reference that also for concepts
you have noted that you are working with Singapore geometry and rainfall data. To setup an answer I have sourced this data from government sources
for purpose on answer I have used 2kmx2km grid so when plotting to demonstrate answer resource utilisation is reduced
core concept: create a grid of box polygons that cover the total bounds of the geometry. Note it's important to use UTM CRS here so that bounds in meters make sense. Once boxes are created remove boxes that are within total bounds but do not intersect with actual geometry
next create a geopandas dataframe of rainfall data. Use longitude and latitude of weather station to create points
final step, join_nearest() grid geometry with rainfall data geometry and data
clearly this final data frame gdf_grid_rainfall is a data frame, which is effectively an array. You can use as an array as you please ...
have provided a folium and plotly interactive visualisations that demonstrate clearly solution is working
solution
Dependent on data sourcing
# number of meters
STEP = 2000
a, b, c, d = gdf_sg.to_crs(gdf_sg.estimate_utm_crs()).total_bounds
# create a grid for Singapore
gdf_grid = gpd.GeoDataFrame(
geometry=[
shapely.geometry.box(minx, miny, maxx, maxy)
for minx, maxx in zip(np.arange(a, c, STEP), np.arange(a, c, STEP)[1:])
for miny, maxy in zip(np.arange(b, d, STEP), np.arange(b, d, STEP)[1:])
],
crs=gdf_sg.estimate_utm_crs(),
).to_crs(gdf_sg.crs)
# restrict grid to only squares that intersect with Singapore geometry
gdf_grid = (
gdf_grid.sjoin(gdf_sg)
.pipe(lambda d: d.groupby(d.index).first())
.set_crs(gdf_grid.crs)
.drop(columns=["index_right"])
)
# geodataframe of weather station locations and rainfall by date
gdf_rainfall = gpd.GeoDataFrame(
df_stations.merge(df, on="id")
.assign(
geometry=lambda d: gpd.points_from_xy(
d["location.longitude"], d["location.latitude"]
)
)
.drop(columns=["location.latitude", "location.longitude"]),
crs=gdf_sg.crs,
)
# weather station to nearest grid
gdf_grid_rainfall = gpd.sjoin_nearest(gdf_grid, gdf_rainfall).drop(
columns=["Description", "index_right"]
)
# does it work? let's visualize with folium
gdf_grid_rainfall.loc[lambda d: d["Date"].eq("20220622")].explore("Rainfall (mm)", height=400, width=600)
data sourcing
import requests, itertools, io
from pathlib import Path
import urllib
from zipfile import ZipFile
import fiona.drvsupport
import geopandas as gpd
import numpy as np
import pandas as pd
import shapely.geometry
# get official Singapore planning area geometry
url = "https://geo.data.gov.sg/planning-area-census2010/2014/04/14/kml/planning-area-census2010.zip"
f = Path.cwd().joinpath(urllib.parse.urlparse(url).path.split("/")[-1])
if not f.exists():
r = requests.get(url, stream=True, headers={"User-Agent": "XY"})
with open(f, "wb") as fd:
for chunk in r.iter_content(chunk_size=128):
fd.write(chunk)
zfile = ZipFile(f)
zfile.extractall(f.stem)
fiona.drvsupport.supported_drivers['KML'] = 'rw'
gdf_sg = gpd.read_file(
[_ for _ in Path.cwd().joinpath(f.stem).glob("*.kml")][0], driver="KML"
)
# get data about Singapore weather stations
df_stations = pd.json_normalize(
requests.get("https://api.data.gov.sg/v1/environment/rainfall").json()["metadata"][
"stations"
]
)
# dates to get data from weather.gov.sg
dates = pd.date_range("20220601", "20220730", freq="MS").strftime("%Y%m")
df = pd.DataFrame()
# fmt: off
bad = ['S100', 'S201', 'S202', 'S203', 'S204', 'S205', 'S207', 'S208',
'S209', 'S211', 'S212', 'S213', 'S214', 'S215', 'S216', 'S217',
'S218', 'S219', 'S220', 'S221', 'S222', 'S223', 'S224', 'S226',
'S227', 'S228', 'S229', 'S230', 'S900']
# fmt: on
for stat, month in itertools.product(df_stations["id"], dates):
if not stat in bad:
try:
df_ = pd.read_csv(
io.StringIO(
requests.get(
f"http://www.weather.gov.sg/files/dailydata/DAILYDATA_{stat}_{month}.csv"
).text
)
).iloc[:, 0:5]
except pd.errors.ParserError as e:
bad.append(stat)
print(f"failed {stat} {month}")
df = pd.concat([df, df_.assign(id=stat)])
df["Rainfall (mm)"] = pd.to_numeric(
df["Daily Rainfall Total (mm)"], errors="coerce"
)
df["Date"] = pd.to_datetime(df[["Year","Month","Day"]]).dt.strftime("%Y%m%d")
df = df.loc[:,["id","Date","Rainfall (mm)", "Station"]]
visualisation using plotly animation
import plotly.express as px
# reduce dates so figure builds in sensible time
gdf_px = gdf_grid_rainfall.loc[
lambda d: d["Date"].isin(
gdf_grid_rainfall["Date"].value_counts().sort_index().index[0:15]
)
]
px.choropleth_mapbox(
gdf_px,
geojson=gdf_px.geometry,
locations=gdf_px.index,
color="Rainfall (mm)",
hover_data=gdf_px.columns[1:].tolist(),
animation_frame="Date",
mapbox_style="carto-positron",
center={"lat":gdf_px.unary_union.centroid.y, "lon":gdf_px.unary_union.centroid.x},
zoom=8.5
).update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0, "pad": 4})