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> Use this file to discover all available pages before exploring further.

# GeoSeries.is_valid_coverage

> Return whether the geometry column forms a valid polygonal coverage.

Return whether the geometry column forms a valid polygonal coverage.

A coverage is valid when polygon interiors do not overlap and shared
boundaries are edge-matched. Geometries without polygonal components
and missing geometries are ignored. A positive `gap_width`
additionally detects narrow gaps up to that width.

## Signature

```python theme={"system"}
is_valid_coverage(*, gap_width=0.0) -> bool
```

## Parameters

<ParamField path="gap_width" type="float" default="0.0">
  Finite, non-negative maximum width of gaps to report as coverage
  invalidities.
</ParamField>

## Returns

<ResponseField name="return" type="bool">
  `True` when no invalid coverage edges are found.
</ResponseField>

<Note>
  This method discovers and validates neighbouring polygons on Spark
  executors, then reduces the invalid-edge results to one Python
  `bool`.
</Note>

**See also**

* `GeoSeries.invalid_coverage_edges`: return invalid edges for each input
* `geometry`

## Examples

```python theme={"system"}
>>> from shapely.geometry import box
>>> from sedona.spark.geopandas import GeoSeries
>>> coverage = GeoSeries([box(0, 0, 1, 1), box(1, 0, 2, 1)])
>>> coverage.is_valid_coverage()
True
```

Overlapping polygon interiors make a coverage invalid:

```python theme={"system"}
>>> overlapping = GeoSeries(
...     [box(0, 0, 1, 1), box(0.5, 0, 1.5, 1)]
... )
>>> overlapping.is_valid_coverage()
False
```

A positive gap width can also detect narrow gaps:

```python theme={"system"}
>>> separated = GeoSeries(
...     [box(0, 0, 1, 1), box(1.1, 0, 2.1, 1)]
... )
>>> separated.is_valid_coverage(gap_width=0.2)
False
```


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