Area Zonal Stats

generate vector zonal stat features for area data

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source

create_area_zonal_stats

 create_area_zonal_stats (aoi:geopandas.geodataframe.GeoDataFrame,
                          data:geopandas.geodataframe.GeoDataFrame,
                          aggregations:List[Dict[str,Any]]=[],
                          include_intersect=True, fix_min=True)

	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
aggregations	List	[]	List of agg specs, with each agg spec applied to a data column
include_intersect	bool	True	Add column ‘intersect_area_sum’ w/ch computes total area of data areas intersecting aoi
fix_min	bool	True	Set min to zero if there are areas in aoi w/ch do not containing any intersecting area from the data.

Test data

Simple squares

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

simple_aoi

	geometry
0	POLYGON ((0 0, 0 1, 1 1, 1 0, 0 0))
1	POLYGON ((1 0, 1 1, 2 1, 2 0, 1 0))
2	POLYGON ((2 0, 2 1, 3 1, 3 0, 2 0))
3	POLYGON ((3 0, 3 1, 4 1, 4 0, 3 0))
4	POLYGON ((4 0, 4 1, 5 1, 5 0, 4 0))

simple_data

	geometry	population	internet_speed
0	POLYGON ((0.25 0, 0.25 1, 1.25 1, 1.25 0, 0.25...	100	20.0
1	POLYGON ((1.25 0, 1.25 1, 2.25 1, 2.25 0, 1.25...	200	10.0
2	POLYGON ((2.25 0, 2.25 1, 3.25 1, 3.25 0, 2.25...	300	5.0

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", "orange", "purple"]
)

The red,green,blue, orange and purple outlines are the 5 regions of interest (aoi) while the orange,brown, purple areas are the data areas.

empty_aoi_results = create_area_zonal_stats(simple_aoi, simple_data)

empty_aoi_results

	geometry	intersect_area_sum
0	POLYGON ((0 0, 0 1, 1 1, 1 0, 0 0))	0.75
1	POLYGON ((1 0, 1 1, 2 1, 2 0, 1 0))	1.00
2	POLYGON ((2 0, 2 1, 3 1, 3 0, 2 0))	1.00
3	POLYGON ((3 0, 3 1, 4 1, 4 0, 3 0))	0.25
4	POLYGON ((4 0, 4 1, 5 1, 5 0, 4 0))	0.00

simple_aoi_results = create_area_zonal_stats(
    simple_aoi,
    simple_data,
    [
        dict(func="count", output="sample_count"),
        dict(func=["sum", "count"], column="population"),
        dict(func=["mean", "max", "min", "std"], column="internet_speed"),
    ],
)

CPU times: user 11.3 ms, sys: 1.23 ms, total: 12.5 ms
Wall time: 12.9 ms

simple_aoi_results

	geometry	intersect_area_sum	sample_count	population_sum	population_count	internet_speed_mean	internet_speed_max	internet_speed_min	internet_speed_std
0	POLYGON ((0 0, 0 1, 1 1, 1 0, 0 0))	0.75	1.0	75.0	1.0	15.000	20.0	0.0	NaN
1	POLYGON ((1 0, 1 1, 2 1, 2 0, 1 0))	1.00	2.0	175.0	2.0	6.250	20.0	10.0	7.071068
2	POLYGON ((2 0, 2 1, 3 1, 3 0, 2 0))	1.00	2.0	275.0	2.0	3.125	10.0	5.0	3.535534
3	POLYGON ((3 0, 3 1, 4 1, 4 0, 3 0))	0.25	1.0	75.0	1.0	1.250	5.0	0.0	NaN
4	POLYGON ((4 0, 4 1, 5 1, 5 0, 4 0))	0.00	NaN	NaN	NaN	NaN	NaN	0.0	NaN

simple_aoi_results.population_sum.sum(axis=None)

np.float64(600.0)

corrected_aoi_results = create_area_zonal_stats(
    simple_aoi,
    simple_data,
    [
        dict(func=["sum", "count"], column="population"),
        dict(
            func=["mean", "imputed_mean", "raw_max", "raw_min", "raw_std"],
            column="internet_speed",
            output=[
                "internet_speed_mean",
                "internet_speed_imputed_mean",
                "internet_speed_max",
                "internet_speed_min",
                "internet_speed_std",
            ],
        ),
    ],
    fix_min=False,
)

CPU times: user 11.7 ms, sys: 849 µs, total: 12.5 ms
Wall time: 12.1 ms

corrected_aoi_results

	geometry	intersect_area_sum	population_sum	population_count	internet_speed_mean	internet_speed_imputed_mean	internet_speed_max	internet_speed_min	internet_speed_std
0	POLYGON ((0 0, 0 1, 1 1, 1 0, 0 0))	0.75	75.0	1.0	15.000	20.000	20.0	20.0	NaN
1	POLYGON ((1 0, 1 1, 2 1, 2 0, 1 0))	1.00	175.0	2.0	6.250	6.250	20.0	10.0	7.071068
2	POLYGON ((2 0, 2 1, 3 1, 3 0, 2 0))	1.00	275.0	2.0	3.125	3.125	10.0	5.0	3.535534
3	POLYGON ((3 0, 3 1, 4 1, 4 0, 3 0))	0.25	75.0	1.0	1.250	5.000	5.0	5.0	NaN
4	POLYGON ((4 0, 4 1, 5 1, 5 0, 4 0))	0.00	NaN	NaN	NaN	NaN	NaN	NaN	NaN

aois_no_nas = create_area_zonal_stats(
    simple_aoi,
    simple_data,
    [
        dict(func=["sum", "count"], column="population", fillna=[True, True]),
        dict(
            func=["mean", "imputed_mean", "raw_max", "raw_min", "raw_std"],
            column="internet_speed",
            output=[
                "internet_speed_mean",
                "internet_speed_imputed_mean",
                "internet_speed_max",
                "internet_speed_min",
                "internet_speed_std",
            ],
            fillna=[True, True, True, True, True],
        ),
    ],
    fix_min=False,
)

CPU times: user 12.1 ms, sys: 679 µs, total: 12.8 ms
Wall time: 12.3 ms

aois_no_nas

	geometry	intersect_area_sum	population_sum	population_count	internet_speed_mean	internet_speed_imputed_mean	internet_speed_max	internet_speed_min	internet_speed_std
0	POLYGON ((0 0, 0 1, 1 1, 1 0, 0 0))	0.75	75.0	1.0	15.000	20.000	20.0	20.0	0.000000
1	POLYGON ((1 0, 1 1, 2 1, 2 0, 1 0))	1.00	175.0	2.0	6.250	6.250	20.0	10.0	7.071068
2	POLYGON ((2 0, 2 1, 3 1, 3 0, 2 0))	1.00	275.0	2.0	3.125	3.125	10.0	5.0	3.535534
3	POLYGON ((3 0, 3 1, 4 1, 4 0, 3 0))	0.25	75.0	1.0	1.250	5.000	5.0	5.0	0.000000
4	POLYGON ((4 0, 4 1, 5 1, 5 0, 4 0))	0.00	0.0	0.0	0.000	0.000	0.0	0.0	0.000000