User-defined functions: summarizing noise data

This example works with a model of sound levels for the United States produced by the US National Park Service. We want to compute the average noise level for each municipality in Hawaii, but the standard mean operation provided by exactextract is not appropriate for the logarithmic decibel scale used for sound levels.

To avoid working with large data files, we use the sound level grid provided for Hawaii, along with the TIGER shapefile of 2023 municipal boundaries (“county subdivisions”) published by the US Census Bureau:

sound_fname = 'hawaii/HI_L50dBA_sumDay_exi.tif'
municipalities_fname = 'hawaii/tl_2023_15_cousub.zip'

The sound boundaries are published in UTM 4N (meters), while the municipal boundaries use NAD 1983 geographic coordinates. Rather than pass the filenames to exact_extract, we will first load the datasets and reproject the municipal boundaries to the UTM coordinate system used by the sound grid.

from exactextract import exact_extract
from matplotlib import pyplot
import rasterio
import rasterio.plot
import geopandas as gpd

sound_ds = rasterio.open(sound_fname)
municipalities = gpd.read_file(municipalities_fname).to_crs(sound_ds.crs)

fig, ax = pyplot.subplots()
ax.set_xlim([0.3e6, 1.0e6])
ax.set_ylim([2.0e6, 2.5e6])

rasterio.plot.show(sound_ds, ax=ax)
municipalities.plot(ax=ax, facecolor='none', edgecolor='black', linewidth=0.2)

<Axes: >

The following function can be used to compute the mean sound levels:

import numpy as np

def mean_sound_db(values, coverage):
    # transform values, take average, transform result
    values = np.power(10, values)
    mean = np.average(values, weights=coverage)
    return np.log10(mean)

The user-defined function can now be used in the same way as a built-in summary operation. Unsurprisingly, we see that Honolulu is the loudest municipality in Hawaii.

results = exact_extract(sound_ds, municipalities, mean_sound_db, include_cols=['GEOID', 'NAME'], output='pandas')
results.sort_values(by=['mean_sound_db'], ascending=False)

	GEOID	NAME	mean_sound_db
11	1500390810	Honolulu	50.403354
33	1500390270	Ewa	49.420535
34	1500990900	Kahului	49.416380
32	1500393420	Wahiawa	49.414049
13	1500391800	Koolaupoko	48.554184
5	1500993060	Puunene	47.855280
37	1500993870	Wailuku	47.531631
21	1500190630	Hilo	47.491786
3	1500792160	Lihue	47.213052
42	1500993330	Spreckelsville	47.006436
12	1500393600	Waianae	46.891559
30	1500992250	Makawao-Paia	46.169428
7	1500791080	Kapaa	46.048761
28	1500792970	Puhi-Hanamaulu	45.986987
4	1500793780	Wailua-Anahola	45.556824
29	1500990360	Haiku-Pauwela	45.062474
19	1500192610	North Kona	44.449442
31	1500993690	Waihee-Waikapu	44.227507
41	1500991980	Lahaina	43.614811
10	1500393510	Waialua	43.020145
1	1500790180	Eleele-Kalaheo	42.995253
9	1500391710	Koolauloa	42.980143
2	1500791260	Kaumakani-Hanapepe	42.904206
39	1500991530	Kihei	42.618990
6	1500790540	Hanalei	42.488026
27	1500791620	Koloa-Poipu	42.177167
35	1500991890	Kula	42.043837
26	1500791440	Kekaha-Waimea	41.944628
14	1500190720	Honokaa-Kukuihaele	41.780611
40	1500990090	East Molokai	41.377057
22	1500191350	Keaau-Mountain View	41.020687
20	1500193150	South Kohala	40.443613
15	1500192520	North Kohala	40.338913
25	1500192880	Papaikou-Wailea	39.910005
16	1500192790	Pahoa-Kalapana	39.805841
36	1500992070	Lanai	39.660176
17	1500193240	South Kona	39.441267
24	1500192700	Paauhau-Paauilo	39.416518
43	1500993960	West Molokai	39.159257
38	1500990450	Hana	39.091584
0	1500590990	Kalawao	38.723797
23	1500192430	North Hilo	38.371793
18	1500191170	Kau	37.412745
8	1500792340	Niihau	35.633805