python Point-In-Polygon操作。 根据网格内的点将网格数据与点数据连接起来

Question

I want to know how to select values from the xarray DataArray based on the location ( geo_df.geometry ) and time ( geo_df.plant_date & geo_df.cut_date ) of rows in the geopandas GeoDataFrame . I want to join them as 'features' in an output GeoDataFrame .

My datasets:

Packages I'm using:

import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
from shapely import geometry
import xarray as xr

I have a geodataframe storing lat/lon POINTS which corresponds to households. The index column is the id of the households.

geo_df.head()

Out[]:
  crop_name     xxx     cut_date plant_date                       geometry
0   SORGHUM  0.061029 2011-11-10 2011-11-10 POINT (37.89087631 14.35381619)
1    MILLET -0.104342 2011-10-19 2011-10-19 POINT (37.89087631 14.35381619)
2   SORGHUM -0.031697 2013-11-26 2013-11-26 POINT (37.89087631 14.35381619)

I have an xarray object storing GRIDDED vegetation health data (NDVI).

ndvi_df = xr.open_dataset(geo_data_dir+ndvi_dir).ndvi

Out[]: <xarray.DataArray 'ndvi' (time: 212, lat: 200, lon: 220)>
[9328000 values with dtype=float32]
Coordinates:
  * lon      (lon) float32 35.024994 35.074997 35.125 35.174988 35.22499 ...
  * lat      (lat) float32 14.974998 14.924995 14.875 14.824997 14.775002 ...
  * time     (time) datetime64[ns] 2000-02-14 2000-03-16 2000-04-15 ...
Attributes:
    long_name:   Normalized Difference Vegetation Index
    units:       1
    _fillvalue:  -3000

I have a geodataframe storing a POLYGON which corresponds to a country.

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
ethiopia = world.loc[world["name"] == "Ethiopia"]

Visual Summary:

My datasets plotted on top of one another look as follows (plotted annually for demonstration purposes).

(ndvi_df.loc[f'{year}-01-16T00:00:00.000000000':f'{year}-12-16T00:00:00.000000000']
 .mean(dim='time')
 .plot(cmap='gist_earth_r', vmin=-0.1, vmax=1)
)

ax = plt.gca()

ethiopia.plot(alpha=0.2, color='black', ax=ax)

(geo_df
 .loc[ (lsms_geo_1["cut_date"] > f'{year}-01-01') & (lsms_geo_1["cut_date"] < f'{year+1}-01-01') ]
 .plot(markersize=6 ,ax=ax, color="#FEF731")
)
ax.set_title(f'{year} Mean NDVI and Households')
plt.show()

Ideal Output:

I want as an output, a geodataframe with extra columns telling me the NDVI values in the PRECEDING MONTHS for the pixel which the households are inside.

The index column is the id of the households.

like this:

  crop_name     xxx     cut_date plant_date                       geometry  ndvi_month_0  ndvi_month_1  ndvi_month_2
0   SORGHUM  0.061029 2011-11-10 2011-11-10 POINT (37.89087631 14.35381619)          0.3           0.3           0.3
1    MILLET -0.104342 2011-10-19 2011-10-19 POINT (37.89087631 14.35381619)          0.6           0.6           0.6
2   SORGHUM -0.031697 2013-11-26 2013-11-26 POINT (37.89087631 14.35381619)          0.1           0.1           0.1

I would also like to know how to subset my data in xarray object by using the geodataframe polygon ethiopia .

(reposted on GIS Stack Exchange here )

Answer 1

So, after help from @om_henners here there is a working solution to this question.

The following function can be applied to the geopandas.GeoDataFrame object. It will select the preceding 12 months and select the NEAREST value for that lat,lon point in the GeoDataFrame .

def geo_var_for_point(row, geovar_df, geovar_name):
    """
      Return a pandas series of geovariable values (NDVI or LST) which will be 
        indexed by the time index.

      Usage:
      -----
      `geo_df.apply(ndvi_for_point, axis=1, **{"geovar_df":ndvi_df})`

      Arguments:
      ---------
      :df (geopandas.GeoDataFrame) : dataframe with `geometry` and `cut_date` cols
      :geovar_df (xarray.DataArray): the geographic variable you want information from
      :geovar_name (str): how to label to columns with the correct geovariable

      Returns:
      -------
      :(pd.Series) : series object of geo_Var values for the 12 months prior to cut_date

      Variables:
      ---------
      :point (shapely.Point): geometry of the point (x, y coords)
      :cut_date (pd.datetime): the date at which the crop was cut
      :start_date (pd.datetime): the first month to select geovars from
    """
    # get the times
    cut_date = row['cut_date']
    start_date = cut_date - pd.DateOffset(months=12)

    # subset the geovar dataframe by time
    limited_geovar = geovar_df.loc[start_date: cut_date]

    # get the location
    point = row['geometry']

    # select the values from the xarray.DataArray for that location
    series = limited_geovar.sel(lat=point.y, lon=point.x, method='nearest').to_series()

    # create the output with columns labelled
    columns = [f"{geovar_name}_month_t-{i}" for i in np.arange(len(series))]
    return pd.Series(series.values , index=columns)

This function can be applied like so:

ndvi_extract = geo_df.head().apply(geo_var_for_point, axis=1, **{"geovar_df":ndvi_df, "geovar_name": "ndvi"})

Which returns:

  ndvi_month_t-0  ndvi_month_t-1  ndvi_month_t-2  ndvi_month_t-3  ndvi_month_t-4  ndvi_month_t-5  ndvi_month_t-6  ndvi_month_t-7  ndvi_month_t-8  ndvi_month_t-9  ndvi_month_t-10 ndvi_month_t-11
0         0.3141          0.2559          0.2287          0.2056          0.1993          0.2015          0.1970          0.2187          0.2719          0.3669           0.4647          0.3563
1         0.3141          0.2559          0.2287          0.2056          0.1993          0.2015          0.1970          0.2187          0.2719          0.3669           0.4647          0.3563
2         0.2257          0.2065          0.1967          0.1949          0.1878          0.1861          0.1987          0.2801          0.4338          0.5667           0.4209          0.2880
3         0.2866          0.2257          0.2065          0.1967          0.1949          0.1878          0.1861          0.1987          0.2801          0.4338           0.5667          0.4209
4         0.4044          0.2866          0.2257          0.2065          0.1967          0.1949          0.1878          0.1861          0.1987          0.2801           0.4338          0.5667

Which can then be concatenated to the original dataframe:

pd.concat([geo_df.head(), ndvi_extract.head()], axis=1)

This will return a geopandas.GeoDataFrame with the geovariable values for that point from the gridded product.