create_distance_zonal_stats[source]

create_distance_zonal_stats(aoi:GeoDataFrame, data:GeoDataFrame, max_distance:float, aggregations:List[typing.Dict[str, typing.Any]]=[], distance_col:str='nearest')

Computes zonal stats based on nearest matching data geometry within max_distance. Note that setting a too high max_distance (or None) will incur a performance cost.

	Type	Default	Details
aoi	GeoDataFrame		Area of interest for which zonal stats are to be computed for
data	GeoDataFrame		Source gdf of region/areas containing data to compute zonal stats from
max_distance	float		max distance to compute distance for (the larger the slower the join), set to None for no limit
aggregations	typing.List[typing.Dict[str, typing.Any]]	None	aggregations
distance_col	str	nearest	column name of the distance column, set to None if not wanted in results

Test data

Simple squares

Given an aoi (simple_aoi) and geodataframe containing sample data (simple_data)

simple_aoi

	geometry
0	POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...
1	POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...
2	POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...

simple_data

	geometry	population	internet_speed
0	POLYGON ((0.250 0.000, 0.250 1.000, 1.250 1.00...	100	20.0
1	POLYGON ((1.250 0.000, 1.250 1.000, 2.250 1.00...	200	10.0
2	POLYGON ((2.250 0.000, 2.250 1.000, 3.250 1.00...	300	5.0

We also have simple point data which do not intersect with our AOIs.

simple_point_data

	geometry	population	internet_speed
0	POINT (0.500 3.000)	100	20.0
1	POINT (0.500 4.000)	600	120.0
2	POINT (0.500 5.000)	1100	220.0
3	POINT (0.500 6.000)	1600	320.0
4	POINT (0.500 7.000)	2100	420.0
5	POINT (1.500 3.000)	200	10.0
6	POINT (1.500 4.000)	700	110.0
7	POINT (1.500 5.000)	1200	210.0
8	POINT (1.500 6.000)	1700	310.0
9	POINT (1.500 7.000)	2200	410.0
10	POINT (2.500 3.000)	300	5.0
11	POINT (2.500 4.000)	800	105.0
12	POINT (2.500 5.000)	1300	205.0
13	POINT (2.500 6.000)	1800	305.0
14	POINT (2.500 7.000)	2300	405.0

ax = plt.axes()
ax = simple_data.plot(
    ax=ax, color=["orange", "brown", "purple"], edgecolor="yellow", alpha=0.4
)
ax = simple_aoi.plot(ax=ax, facecolor="none", edgecolor=["r", "g", "b"])
ax = simple_point_data.plot(ax=ax)

The red,green,blue outlines are the 3 regions of interest (aoi) while the orange,brown, purple areas are the data areas.The blue dots are data which do not intersect our AOIs.

%%time
results = create_distance_zonal_stats(
    simple_aoi,
    simple_point_data,
    max_distance=7,
    aggregations=[
        dict(func="count"),
        dict(func="sum", column="population"),
        dict(func="mean", column="internet_speed"),
    ],
)

CPU times: user 32.1 ms, sys: 11 ms, total: 43.1 ms
Wall time: 36 ms

results

	geometry	index_count	population_sum	internet_speed_mean	nearest
0	POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...	1	100	20.0	2.0
1	POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...	1	200	10.0	2.0
2	POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...	1	300	5.0	2.0

%%time
results2 = create_distance_zonal_stats(
    simple_aoi,
    simple_data,
    max_distance=1,
    aggregations=[
        dict(func="count"),
        dict(func="sum", column="population"),
        dict(func="mean", column="internet_speed"),
    ],
)

CPU times: user 38.5 ms, sys: 0 ns, total: 38.5 ms
Wall time: 35 ms

results2

	geometry	index_count	population_sum	internet_speed_mean	nearest
0	POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...	1	100	20.0	0.0
1	POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...	2	300	15.0	0.0
2	POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...	2	500	7.5	0.0