---
title: "API GDAL"
version: 1.10
locale: id
source: https://docs.djangoproject.com/id/1.10/ref/contrib/gis/gdal/
canonical: https://djangodocs.dev/id/1.10/ref/contrib/gis/gdal/
---
# API GDAL

[GDAL](http://www.gdal.org/) stands for **Geospatial Data Abstraction Library**,
and is a veritable "Swiss army knife" of GIS data functionality.  A subset
of GDAL is the [OGR](http://www.gdal.org/ogr_arch.html) Simple Features Library, which specializes
in reading and writing vector geographic data in a variety of standard
formats.

GeoDjango provides a high-level Python interface for some of the
capabilities of OGR, including the reading and coordinate transformation
of vector spatial data and minimal support for GDAL's features with respect
to raster (image) data.

> **Note**
>
> Although the module is named `gdal`, GeoDjango only supports some of the
> capabilities of OGR and GDAL's raster features at this time.

## Ikhtisar

### Data Contoh

The GDAL/OGR tools described here are designed to help you read in
your geospatial data, in order for most of them to be useful you have
to have some data to work with.  If you're starting out and don't yet
have any data of your own to use, GeoDjango tests contain a number of
simple data sets that you can use for testing. You can download them here:

```
$ wget https://raw.githubusercontent.com/django/django/master/tests/gis_tests/data/cities/cities.{shp,prj,shx,dbf}
$ wget https://raw.githubusercontent.com/django/django/master/tests/gis_tests/data/rasters/raster.tif
```

## Vector Data Source Objects

### `DataSource`

[`DataSource`](#django.contrib.gis.gdal.DataSource) adalah sebuah pembungkus untuk obyek sumber data OGR yang mendukung membaca data dari beragam dari bentuk berkas geospasial didukung-OGR dan sumber data menggunakan sederhana, konsisten antarmuka. Setiap sumber data diwakili oleh sebuah obyek [`DataSource`](#django.contrib.gis.gdal.DataSource) yang mengandung satu atau lebih lapisan data. Setiap lapisan, diwakili oleh [`Layer`](#django.contrib.gis.gdal.Layer) object, mengandung beberapa nomor dari fitur-fitur geografik ([`Feature`](#django.contrib.gis.gdal.Feature)), informasi tentang jenis dari fitur-fitur mengandung di lapisan itu (sebagai contoh titik, poligon, dll.), sama halnya nama-nama dan jenis-jenis dari bidang tambahan apapun ([`Field`](#django.contrib.gis.gdal.Field)) dari data yang mungkin terhubung dengan setiap fitur di lapisan itu.

#### `class DataSource(ds_input, encoding='utf-8')`

Pembangun untuk `DataSource` hanya membutuhkan satu parameter: jalur dari berkas anda ingin baca. Bagaimanapun, OGR juga mendukung beragam sumber data lebih rumit, termasuk basisdata, yang mungkin diakses dengan melewatkan string nama khusus daripada jalur. Untuk informasi lebih, lihat dokumentasi [OGR Vector Formats](http://www.gdal.org/ogr_formats.html). Sifat [`name`](#django.contrib.gis.gdal.DataSource.name) dari sebuah instance `DataSource` memberikan nama OGR dari sumber data pokok yang itu sedang gunakan.

Pilihan parameter `encoding` mengizinkan anda menentukan penyandian bukan-standar dari string di sumber. Ini khususnya berguna ketika anda mendapatkan pengecualian `DjangoUnicodeDecodeError` selagi membaca nilai bidang.

Sekali anda telah membuat `DataSource` anda, anda dapat menemukan seberapa banyak lapisan data itu kandung dengan mengakses sifat [`layer_count`](#django.contrib.gis.gdal.DataSource.layer_count), atau (setara) dengan menggunakan fungsi `len()`. Untuk informasi pada mengakses lapisan dari data mereka sendiri, lihat bagian lain:

```
>>> from django.contrib.gis.gdal import DataSource
>>> ds = DataSource('/path/to/your/cities.shp')
>>> ds.name
'/path/to/your/cities.shp'
>>> ds.layer_count                  # This file only contains one layer
1
```

#### `layer_count`

Mengembalikan sejumlah lapisan di sumber data.

#### `name`

Mengembalikan nama dari sumber data.

### `Lapisan`

#### `class Layer`

`Layer` adalah sebuah pembungkus untuk lapisan dari data di obyek `DataSource`. Anda tidak pernah membuat obyek `Layer` secara langsung. Sebagai gantinya, anda mengambil mereka dari obyek [`DataSource`](#django.contrib.gis.gdal.DataSource), yang pada dasarnya wadah standar Python dari obyek `Layer`. Sebagai contoh, anda dapat mengakses lapisan khusus dengan indeksnya (sebagai contoh `ds[0]` untuk mengakses lapisan pertama), atau anda dapat mengulang terhadap semua lapisan di wadah dalam perulangan `loop`. `Layer` itu sendiri bertindak sebagai sebuah wadah untuk fitur-fitur geometris.

Khususnya, semua fitur di lapisan yang diberikan mempunyai jenis geometri sama. Sifat [`geom_type`](#django.contrib.gis.gdal.Layer.geom_type) dari lapisan adalah sebuah [`OGRGeomType`](#django.contrib.gis.gdal.OGRGeomType) yang mencirikan jenis fitur. Kami dapat menggunakan itu untuk mencetak beberapa informasi dasar tentang setiap lapisan di [`DataSource`](#django.contrib.gis.gdal.DataSource):

```
>>> for layer in ds:
...     print('Layer "%s": %i %ss' % (layer.name, len(layer), layer.geom_type.name))
...
Layer "cities": 3 Points
```

Keluaran contoh adalah dari sumber data kota, dimuat diatas, yang ternyata mengandung satu lapisan, dipanggil `"cities"`, yang mengandung tida titik fitur. Untuk kemudahan, contoh-contoh diatas menganggap bahwa anda telah menyimpan lapisan itu di variabel `layer`:

```
>>> layer = ds[0]
```

#### `name`

Mengembalikan nama lapisan ini di sumber data.

```
>>> layer.name
'cities'
```

#### `num_feat`

Mengembalikan sejumlah fitur-fitur di lapisan. Sama seperti `len(layer)`:

```
>>> layer.num_feat
3
```

#### `geom_type`

Mengembalikan jenis geometri dari lapisan, sebagai sebuah obyek [`OGRGeomType`](#django.contrib.gis.gdal.OGRGeomType):

```
>>> layer.geom_type.name
'Point'
```

#### `num_fields`

Mengembalikan sejumlah bidang di lapisan, yaitu sejumlah bidang dari data terhubung dengan setiap fitur di lapisan:

```
>>> layer.num_fields
4
```

#### `fields`

Mengembalikan daftar nama dari setiap bidang di lapisan ini:

```
>>> layer.fields
['Name', 'Population', 'Density', 'Created']
```

Mengembalikan daftar dari jenis-jenis data dari setiap bidang di lapisan ini. Ini adalah subkelas dari `Field`, diobrolkan dibawah:

```
>>> [ft.__name__ for ft in layer.field_types]
['OFTString', 'OFTReal', 'OFTReal', 'OFTDate']
```

#### `field_widths`

Returns a list of the maximum field widths for each of the fields in this
layer:

```
>>> layer.field_widths
[80, 11, 24, 10]
```

#### `field_precisions`

Returns a list of the numeric precisions for each of the fields in this
layer. This is meaningless (and set to zero) for non-numeric fields:

```
>>> layer.field_precisions
[0, 0, 15, 0]
```

#### `extent`

Returns the spatial extent of this layer, as an [`Envelope`](#django.contrib.gis.gdal.Envelope) object:

```
>>> layer.extent.tuple
(-104.609252, 29.763374, -95.23506, 38.971823)
```

#### `srs`

Property that returns the [`SpatialReference`](#django.contrib.gis.gdal.SpatialReference) associated with this
layer:

```
>>> print(layer.srs)
GEOGCS["GCS_WGS_1984",
    DATUM["WGS_1984",
        SPHEROID["WGS_1984",6378137,298.257223563]],
    PRIMEM["Greenwich",0],
    UNIT["Degree",0.017453292519943295]]
```

If the [`Layer`](#django.contrib.gis.gdal.Layer) has no spatial reference information associated
with it, `None` is returned.

#### `spatial_filter`

Property that may be used to retrieve or set a spatial filter for this
layer. A spatial filter can only be set with an [`OGRGeometry`](#django.contrib.gis.gdal.OGRGeometry)
instance, a 4-tuple extent, or `None`. When set with something other than
`None`, only features that intersect the filter will be returned when
iterating over the layer:

```
>>> print(layer.spatial_filter)
None
>>> print(len(layer))
3
>>> [feat.get('Name') for feat in layer]
['Pueblo', 'Lawrence', 'Houston']
>>> ks_extent = (-102.051, 36.99, -94.59, 40.00) # Extent for state of Kansas
>>> layer.spatial_filter = ks_extent
>>> len(layer)
1
>>> [feat.get('Name') for feat in layer]
['Lawrence']
>>> layer.spatial_filter = None
>>> len(layer)
3
```

#### `get_fields()`

A method that returns a list of the values of a given field for each
feature in the layer:

```
>>> layer.get_fields('Name')
['Pueblo', 'Lawrence', 'Houston']
```

#### `get_geoms(geos=False)`

A method that returns a list containing the geometry of each feature in the
layer. If the optional argument `geos` is set to `True` then the
geometries are converted to [`GEOSGeometry`](/id/1.10/ref/contrib/gis/geos/#django.contrib.gis.geos.GEOSGeometry)
objects. Otherwise, they are returned as [`OGRGeometry`](#django.contrib.gis.gdal.OGRGeometry) objects:

```
>>> [pt.tuple for pt in layer.get_geoms()]
[(-104.609252, 38.255001), (-95.23506, 38.971823), (-95.363151, 29.763374)]
```

#### `test_capability(capability)`

Returns a boolean indicating whether this layer supports the given
capability (a string).  Examples of valid capability strings include:
`'RandomRead'`, `'SequentialWrite'`, `'RandomWrite'`,
`'FastSpatialFilter'`, `'FastFeatureCount'`, `'FastGetExtent'`,
`'CreateField'`, `'Transactions'`, `'DeleteFeature'`, and
`'FastSetNextByIndex'`.

### `Feature`

#### `class Feature`

`Feature` membungkus fitur OGR. Anda tidak pernah membuat obyek `Feature` secara langsung. Sebagai gantinya, anda mengambil mereka dari obyek [`Layer`](#django.contrib.gis.gdal.Layer). Setiap fitur terdiri dari sebuah geometri dan sekumpulan bidang mengandung sifat-sifat tambahan. Geometri dari sebuah bidang adalah dapat diakses melalui sifat `geom` nya, yang mengembalikan sebuah obyek [`OGRGeometry`](#django.contrib.gis.gdal.OGRGeometry). Sebuah `Feature` berperilaku seperti wadah Python standar untuk bidangnya, yang itu dikembalikan sebagai obyek [`Field`](#django.contrib.gis.gdal.Field): anda dapat mengakses sebuah bidang secara langsung berdasarkan indeks atau namanya, atau dapat berulang terhadap bidang-bidang fitur, sebagai contoh di sebuah perulangan `for`.

#### `geom`

Returns the geometry for this feature, as an `OGRGeometry` object:

```
>>> city.geom.tuple
(-104.609252, 38.255001)
```

#### `get`

A method that returns the value of the given field (specified by name)
for this feature, **not** a `Field` wrapper object:

```
>>> city.get('Population')
102121
```

#### `geom_type`

Returns the type of geometry for this feature, as an [`OGRGeomType`](#django.contrib.gis.gdal.OGRGeomType)
object. This will be the same for all features in a given layer and is
equivalent to the [`Layer.geom_type`](#django.contrib.gis.gdal.Layer.geom_type) property of the [`Layer`](#django.contrib.gis.gdal.Layer)
object the feature came from.

#### `num_fields`

Returns the number of fields of data associated with the feature. This will
be the same for all features in a given layer and is equivalent to the
[`Layer.num_fields`](#django.contrib.gis.gdal.Layer.num_fields) property of the [`Layer`](#django.contrib.gis.gdal.Layer) object the feature
came from.

#### `fields`

Returns a list of the names of the fields of data associated with the
feature. This will be the same for all features in a given layer and is
equivalent to the [`Layer.fields`](#django.contrib.gis.gdal.Layer.fields) property of the [`Layer`](#django.contrib.gis.gdal.Layer)
object the feature came from.

#### `fid`

Returns the feature identifier within the layer:

```
>>> city.fid
0
```

#### `layer_name`

Returns the name of the [`Layer`](#django.contrib.gis.gdal.Layer) that the feature came from. This
will be the same for all features in a given layer:

```
>>> city.layer_name
'cities'
```

#### `index`

A method that returns the index of the given field name. This will be the
same for all features in a given layer:

```
>>> city.index('Population')
1
```

### `Field`

#### `class Field`

#### `name`

Returns the name of this field:

```
>>> city['Name'].name
'Name'
```

#### `type`

Returns the OGR type of this field, as an integer. The `FIELD_CLASSES`
dictionary maps these values onto subclasses of `Field`:

```
>>> city['Density'].type
2
```

#### `type_name`

Returns a string with the name of the data type of this field:

```
>>> city['Name'].type_name
'String'
```

#### `value`

Returns the value of this field. The `Field` class itself returns the
value as a string, but each subclass returns the value in the most
appropriate form:

```
>>> city['Population'].value
102121
```

#### `width`

Mengembalikan lebar bidang ini:

```
>>> city['Name'].width
80
```

#### `precision`

Returns the numeric precision of this field. This is meaningless (and set
to zero) for non-numeric fields:

```
>>> city['Density'].precision
15
```

#### `as_double()`

Mengembalikan nilai dari bidang sebagai double (float):

```
>>> city['Density'].as_double()
874.7
```

#### `as_int()`

Mengembalikan nilai dari bidang sebagai integer:

```
>>> city['Population'].as_int()
102121
```

#### `as_string()`

Mengembalikan nilai dari bidang sebagai deretan kalimat:

```
>>> city['Name'].as_string()
'Pueblo'
```

#### `as_datetime()`

Returns the value of the field as a tuple of date and time components:

```
>>> city['Created'].as_datetime()
(c_long(1999), c_long(5), c_long(23), c_long(0), c_long(0), c_long(0), c_long(0))
```

### `Driver`

#### `class Driver(dr_input)`

The `Driver` class is used internally to wrap an OGR [`DataSource`](#django.contrib.gis.gdal.DataSource)
driver.

#### `driver_count`

Returns the number of OGR vector drivers currently registered.

## OGR Geometries

### `OGRGeometry`

Obyek-obyek [`OGRGeometry`](#django.contrib.gis.gdal.OGRGeometry) berbagi fungsi mirip dengan obyek [`GEOSGeometry`](/id/1.10/ref/contrib/gis/geos/#django.contrib.gis.geos.GEOSGeometry) dan pembungkus tipis disekitar perwakilan geometri internal OGR. Dengan demikian, mereka mengizinkan untuk lebih efektid mengakses ke data ketika menggunakan [`DataSource`](#django.contrib.gis.gdal.DataSource). Tidak seprti pasangan GEOS nya, [`OGRGeometry`](#django.contrib.gis.gdal.OGRGeometry) mendukung sistem acuan spasial dan perubahan kordinat:

```
>>> from django.contrib.gis.gdal import OGRGeometry
>>> polygon = OGRGeometry('POLYGON((0 0, 5 0, 5 5, 0 5))')
```

#### `class OGRGeometry(geom_input, srs=None)`

Obyek ini adalah sebuah pembungkus untuk kelas [OGR Geometry](http://www.gdal.org/classOGRGeometry.html). Obyek-obyek ini diinstasiasikan secara langsung dari parameter `geom_input` yang diberikan, yang mungkin berupa string mengandung WKT, HEX, GeoJSON, sebuah `buffer` mengandung data WKB, atau sebuah obyek [`OGRGeomType`](#django.contrib.gis.gdal.OGRGeomType). Obyek-obyek ini juga dikembalikan dari atribut [`Feature.geom`](#django.contrib.gis.gdal.Feature.geom), ketika membaca data vektor dari [`Layer`](#django.contrib.gis.gdal.Layer) (yaitu pada giliran bagian dari sebuah [`DataSource`](#django.contrib.gis.gdal.DataSource)).

#### `classmethod from_bbox(bbox)`

Constructs a [`Polygon`](#django.contrib.gis.gdal.Polygon) from the given bounding-box (a 4-tuple).

#### `__len__()`

Returns the number of points in a [`LineString`](#django.contrib.gis.gdal.LineString), the number of rings
in a [`Polygon`](#django.contrib.gis.gdal.Polygon), or the number of geometries in a
[`GeometryCollection`](#django.contrib.gis.gdal.GeometryCollection). Not applicable to other geometry types.

#### `__iter__()`

Iterates over the points in a [`LineString`](#django.contrib.gis.gdal.LineString), the rings in a
[`Polygon`](#django.contrib.gis.gdal.Polygon), or the geometries in a [`GeometryCollection`](#django.contrib.gis.gdal.GeometryCollection).
Not applicable to other geometry types.

#### `__getitem__()`

Returns the point at the specified index for a [`LineString`](#django.contrib.gis.gdal.LineString), the
interior ring at the specified index for a [`Polygon`](#django.contrib.gis.gdal.Polygon), or the geometry
at the specified index in a [`GeometryCollection`](#django.contrib.gis.gdal.GeometryCollection). Not applicable to
other geometry types.

#### `dimension`

Returns the number of coordinated dimensions of the geometry, i.e. 0
for points, 1 for lines, and so forth:

```
>> polygon.dimension
2
```

#### `coord_dim`

Returns or sets the coordinate dimension of this geometry. For example, the
value would be 2 for two-dimensional geometries.

#### `geom_count`

Returns the number of elements in this geometry:

```
>>> polygon.geom_count
1
```

#### `point_count`

Returns the number of points used to describe this geometry:

```
>>> polygon.point_count
4
```

#### `num_points`

Nama lain untuk [`point_count`](#django.contrib.gis.gdal.OGRGeometry.point_count).

#### `num_coords`

Nama lain untuk [`point_count`](#django.contrib.gis.gdal.OGRGeometry.point_count).

#### `geom_type`

Returns the type of this geometry, as an [`OGRGeomType`](#django.contrib.gis.gdal.OGRGeomType) object.

#### `geom_name`

Returns the name of the type of this geometry:

```
>>> polygon.geom_name
'POLYGON'
```

#### `area`

Returns the area of this geometry, or 0 for geometries that do not contain
an area:

```
>>> polygon.area
25.0
```

#### `envelope`

Returns the envelope of this geometry, as an [`Envelope`](#django.contrib.gis.gdal.Envelope) object.

#### `extent`

Returns the envelope of this geometry as a 4-tuple, instead of as an
[`Envelope`](#django.contrib.gis.gdal.Envelope) object:

```
>>> point.extent
(0.0, 0.0, 5.0, 5.0)
```

#### `srs`

Sifat ini mengendalikan acuan spasial untuk geometri ini, atau `None` jika tidak ada sisterm acuan spasia telah diberikan ke itu. Jika diberikan, mengakses sifat ini mengembalikan sebuah obyek [`SpatialReference`](#django.contrib.gis.gdal.SpatialReference). Itu mungkin disetel dengan obyek [`SpatialReference`](#django.contrib.gis.gdal.SpatialReference) lain, atau masukan apapun yang [`SpatialReference`](#django.contrib.gis.gdal.SpatialReference) terima. Contoh:

```
>>> city.geom.srs.name
'GCS_WGS_1984'
```

#### `srid`

Returns or sets the spatial reference identifier corresponding to
[`SpatialReference`](#django.contrib.gis.gdal.SpatialReference) of this geometry.  Returns `None` if
there is no spatial reference information associated with this
geometry, or if an SRID cannot be determined.

#### `geos`

Returns a [`GEOSGeometry`](/id/1.10/ref/contrib/gis/geos/#django.contrib.gis.geos.GEOSGeometry) object
corresponding to this geometry.

#### `gml`

Returns a string representation of this geometry in GML format:

```
>>> OGRGeometry('POINT(1 2)').gml
'<gml:Point><gml:coordinates>1,2</gml:coordinates></gml:Point>'
```

#### `hex`

Returns a string representation of this geometry in HEX WKB format:

```
>>> OGRGeometry('POINT(1 2)').hex
'0101000000000000000000F03F0000000000000040'
```

#### `json`

Returns a string representation of this geometry in JSON format:

```
>>> OGRGeometry('POINT(1 2)').json
'{ "type": "Point", "coordinates": [ 1.000000, 2.000000 ] }'
```

#### `kml`

Returns a string representation of this geometry in KML format.

#### `wkb_size`

Returns the size of the WKB buffer needed to hold a WKB representation
of this geometry:

```
>>> OGRGeometry('POINT(1 2)').wkb_size
21
```

#### `wkb`

Returns a `buffer` containing a WKB representation of this geometry.

#### `wkt`

Returns a string representation of this geometry in WKT format.

#### `ewkt`

Returns the EWKT representation of this geometry.

#### `clone()`

Returns a new [`OGRGeometry`](#django.contrib.gis.gdal.OGRGeometry) clone of this geometry object.

#### `close_rings()`

If there are any rings within this geometry that have not been closed,
this routine will do so by adding the starting point to the end:

```
>>> triangle = OGRGeometry('LINEARRING (0 0,0 1,1 0)')
>>> triangle.close_rings()
>>> triangle.wkt
'LINEARRING (0 0,0 1,1 0,0 0)'
```

#### `transform(coord_trans, clone=False)`

Transforms this geometry to a different spatial reference system. May take
a [`CoordTransform`](#django.contrib.gis.gdal.CoordTransform) object, a [`SpatialReference`](#django.contrib.gis.gdal.SpatialReference) object, or
any other input accepted by [`SpatialReference`](#django.contrib.gis.gdal.SpatialReference) (including spatial
reference WKT and PROJ.4 strings, or an integer SRID).

By default nothing is returned and the geometry is transformed in-place.
However, if the `clone` keyword is set to `True` then a transformed
clone of this geometry is returned instead.

#### `intersects(other)`

Returns `True` if this geometry intersects the other, otherwise returns
`False`.

#### `equals(other)`

Returns `True` if this geometry is equivalent to the other, otherwise
returns `False`.

#### `disjoint(other)`

Returns `True` if this geometry is spatially disjoint to (i.e. does
not intersect) the other, otherwise returns `False`.

#### `touches(other)`

Returns `True` if this geometry touches the other, otherwise returns
`False`.

#### `crosses(other)`

Returns `True` if this geometry crosses the other, otherwise returns
`False`.

#### `within(other)`

Returns `True` if this geometry is contained within the other, otherwise
returns `False`.

#### `contains(other)`

Returns `True` if this geometry contains the other, otherwise returns
`False`.

#### `overlaps(other)`

Returns `True` if this geometry overlaps the other, otherwise returns
`False`.

#### `boundary()`

The boundary of this geometry, as a new [`OGRGeometry`](#django.contrib.gis.gdal.OGRGeometry) object.

#### `convex_hull`

The smallest convex polygon that contains this geometry, as a new
[`OGRGeometry`](#django.contrib.gis.gdal.OGRGeometry) object.

#### `difference()`

Returns the region consisting of the difference of this geometry and
the other, as a new [`OGRGeometry`](#django.contrib.gis.gdal.OGRGeometry) object.

#### `intersection()`

Returns the region consisting of the intersection of this geometry and
the other, as a new [`OGRGeometry`](#django.contrib.gis.gdal.OGRGeometry) object.

#### `sym_difference()`

Returns the region consisting of the symmetric difference of this
geometry and the other, as a new [`OGRGeometry`](#django.contrib.gis.gdal.OGRGeometry) object.

#### `union()`

Returns the region consisting of the union of this geometry and
the other, as a new [`OGRGeometry`](#django.contrib.gis.gdal.OGRGeometry) object.

#### `tuple`

Returns the coordinates of a point geometry as a tuple, the
coordinates of a line geometry as a tuple of tuples, and so forth:

```
>>> OGRGeometry('POINT (1 2)').tuple
(1.0, 2.0)
>>> OGRGeometry('LINESTRING (1 2,3 4)').tuple
((1.0, 2.0), (3.0, 4.0))
```

#### `coords`

Sebuah nama lain untuk [`tuple`](#django.contrib.gis.gdal.OGRGeometry.tuple).

#### `class Point`

#### `x`

Returns the X coordinate of this point:

```
>>> OGRGeometry('POINT (1 2)').x
1.0
```

#### `y`

Returns the Y coordinate of this point:

```
>>> OGRGeometry('POINT (1 2)').y
2.0
```

#### `z`

Returns the Z coordinate of this point, or `None` if the point does not
have a Z coordinate:

```
>>> OGRGeometry('POINT (1 2 3)').z
3.0
```

#### `class LineString`

#### `x`

Returns a list of X coordinates in this line:

```
>>> OGRGeometry('LINESTRING (1 2,3 4)').x
[1.0, 3.0]
```

#### `y`

Returns a list of Y coordinates in this line:

```
>>> OGRGeometry('LINESTRING (1 2,3 4)').y
[2.0, 4.0]
```

#### `z`

Returns a list of Z coordinates in this line, or `None` if the line does
not have Z coordinates:

```
>>> OGRGeometry('LINESTRING (1 2 3,4 5 6)').z
[3.0, 6.0]
```

#### `class Polygon`

#### `shell`

Returns the shell or exterior ring of this polygon, as a `LinearRing`
geometry.

#### `exterior_ring`

Sebuah nama lain untuk [`shell`](#django.contrib.gis.gdal.Polygon.shell).

#### `centroid`

Returns a [`Point`](#django.contrib.gis.gdal.Point) representing the centroid of this polygon.

#### `class GeometryCollection`

#### `add(geom)`

Adds a geometry to this geometry collection. Not applicable to other
geometry types.

### `OGRGeomType`

#### `class OGRGeomType(type_input)`

This class allows for the representation of an OGR geometry type
in any of several ways:

```
>>> from django.contrib.gis.gdal import OGRGeomType
>>> gt1 = OGRGeomType(3)             # Using an integer for the type
>>> gt2 = OGRGeomType('Polygon')     # Using a string
>>> gt3 = OGRGeomType('POLYGON')     # It's case-insensitive
>>> print(gt1 == 3, gt1 == 'Polygon') # Equivalence works w/non-OGRGeomType objects
True True
```

#### `name`

Returns a short-hand string form of the OGR Geometry type:

```
>>> gt1.name
'Polygon'
```

#### `num`

Returns the number corresponding to the OGR geometry type:

```
>>> gt1.num
3
```

#### `django`

Returns the Django field type (a subclass of GeometryField) to use for
storing this OGR type, or `None` if there is no appropriate Django type:

```
>>> gt1.django
'PolygonField'
```

### `Envelope`

#### `class Envelope(*args)`

Represents an OGR Envelope structure that contains the minimum and maximum
X, Y coordinates for a rectangle bounding box. The naming of the variables
is compatible with the OGR Envelope C structure.

#### `min_x`

The value of the minimum X coordinate.

#### `min_y`

The value of the maximum X coordinate.

#### `max_x`

Nilai minimal kordinat Y.

#### `max_y`

Nilai maksimal kordinat Y.

#### `ur`

The upper-right coordinate, as a tuple.

#### `ll`

The lower-left coordinate, as a tuple.

#### `tuple`

A tuple representing the envelope.

#### `wkt`

A string representing this envelope as a polygon in WKT format.

#### `expand_to_include(*args)`

## Coordinate System Objects

### `SpatialReference`

#### `class SpatialReference(srs_input)`

Spatial reference objects are initialized on the given `srs_input`,
which may be one of the following:

- OGC Well Known Text (WKT) (a string)
- EPSG code (integer or string)
- PROJ.4 string
- A shorthand string for well-known standards (`'WGS84'`, `'WGS72'`,
  `'NAD27'`, `'NAD83'`)

Contoh:

```
>>> wgs84 = SpatialReference('WGS84') # shorthand string
>>> wgs84 = SpatialReference(4326) # EPSG code
>>> wgs84 = SpatialReference('EPSG:4326') # EPSG string
>>> proj4 = '+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs '
>>> wgs84 = SpatialReference(proj4) # PROJ.4 string
>>> wgs84 = SpatialReference("""GEOGCS["WGS 84",
DATUM["WGS_1984",
     SPHEROID["WGS 84",6378137,298.257223563,
         AUTHORITY["EPSG","7030"]],
     AUTHORITY["EPSG","6326"]],
 PRIMEM["Greenwich",0,
     AUTHORITY["EPSG","8901"]],
 UNIT["degree",0.01745329251994328,
     AUTHORITY["EPSG","9122"]],
 AUTHORITY["EPSG","4326"]]""") # OGC WKT
```

#### `__getitem__(target)`

Returns the value of the given string attribute node, `None` if the node
doesn't exist. Can also take a tuple as a parameter, (target, child), where
child is the index of the attribute in the WKT. For example:

```
>>> wkt = 'GEOGCS["WGS 84", DATUM["WGS_1984, ... AUTHORITY["EPSG","4326"]]')
>>> srs = SpatialReference(wkt) # could also use 'WGS84', or 4326
>>> print(srs['GEOGCS'])
WGS 84
>>> print(srs['DATUM'])
WGS_1984
>>> print(srs['AUTHORITY'])
EPSG
>>> print(srs['AUTHORITY', 1]) # The authority value
4326
>>> print(srs['TOWGS84', 4]) # the fourth value in this wkt
0
>>> print(srs['UNIT|AUTHORITY']) # For the units authority, have to use the pipe symbol.
EPSG
>>> print(srs['UNIT|AUTHORITY', 1]) # The authority value for the units
9122
```

#### `attr_value(target, index=0)`

The attribute value for the given target node (e.g. `'PROJCS'`).
The index keyword specifies an index of the child node to return.

#### `auth_name(target)`

Returns the authority name for the given string target node.

#### `auth_code(target)`

Returns the authority code for the given string target node.

#### `clone()`

Returns a clone of this spatial reference object.

#### `identify_epsg()`

This method inspects the WKT of this `SpatialReference` and will add EPSG
authority nodes where an EPSG identifier is applicable.

#### `from_esri()`

Morphs this SpatialReference from ESRI's format to EPSG

#### `to_esri()`

Morphs this SpatialReference to ESRI's format.

#### `validate()`

Checks to see if the given spatial reference is valid, if not
an exception will be raised.

#### `import_epsg(epsg)`

Import spatial reference from EPSG code.

#### `import_proj(proj)`

Import spatial reference from PROJ.4 string.

#### `import_user_input(user_input)`

#### `import_wkt(wkt)`

Import spatial reference from WKT.

#### `import_xml(xml)`

Import spatial reference from XML.

#### `name`

Returns the name of this Spatial Reference.

#### `srid`

Returns the SRID of top-level authority, or `None` if undefined.

#### `linear_name`

Returns the name of the linear units.

#### `linear_units`

Returns the value of the linear units.

#### `angular_name`

Returns the name of the angular units."

#### `angular_units`

Returns the value of the angular units.

#### `units`

Returns a 2-tuple of the units value and the units name and will
automatically determines whether to return the linear or angular units.

#### `ellipsoid`

Returns a tuple of the ellipsoid parameters for this spatial reference:
(semimajor axis, semiminor axis, and inverse flattening).

#### `semi_major`

Returns the semi major axis of the ellipsoid for this spatial reference.

#### `semi_minor`

Returns the semi minor axis of the ellipsoid for this spatial reference.

#### `inverse_flattening`

Returns the inverse flattening of the ellipsoid for this spatial reference.

#### `geographic`

Returns `True` if this spatial reference is geographic (root node is
`GEOGCS`).

#### `local`

Returns `True` if this spatial reference is local (root node is
`LOCAL_CS`).

#### `projected`

Returns `True` if this spatial reference is a projected coordinate system
(root node is `PROJCS`).

#### `wkt`

Returns the WKT representation of this spatial reference.

#### `pretty_wkt`

Returns the 'pretty' representation of the WKT.

#### `proj`

Returns the PROJ.4 representation for this spatial reference.

#### `proj4`

Nama lain untuk [`SpatialReference.proj`](#django.contrib.gis.gdal.SpatialReference.proj).

#### `xml`

Returns the XML representation of this spatial reference.

### `CoordTransform`

#### `class CoordTransform(source, target)`

Represents a coordinate system transform. It is initialized with two
[`SpatialReference`](#django.contrib.gis.gdal.SpatialReference), representing the source and target coordinate
systems, respectively. These objects should be used when performing the same
coordinate transformation repeatedly on different geometries:

```
>>> ct = CoordTransform(SpatialReference('WGS84'), SpatialReference('NAD83'))
>>> for feat in layer:
...     geom = feat.geom # getting clone of feature geometry
...     geom.transform(ct) # transforming
```

## Raster Data Objects

### `GDALRaster`

[`GDALRaster`](#django.contrib.gis.gdal.GDALRaster) is a wrapper for the GDAL raster source object that
supports reading data from a variety of GDAL-supported geospatial file
formats and data sources using a simple, consistent interface.  Each
data source is represented by a [`GDALRaster`](#django.contrib.gis.gdal.GDALRaster) object which contains
one or more layers of data named bands.  Each band, represented by a
[`GDALBand`](#django.contrib.gis.gdal.GDALBand) object, contains georeferenced image data. For example, an RGB
image is represented as three bands: one for red, one for green, and one for
blue.

> **Note**
>
> For raster data there is no difference between a raster instance and its
> data source. Unlike for the Geometry objects, [`GDALRaster`](#django.contrib.gis.gdal.GDALRaster) objects are
> always a data source. Temporary rasters can be instantiated in memory
> using the corresponding driver, but they will be of the same class as file-based
> raster sources.

#### `class GDALRaster(ds_input, write=False)`

The constructor for `GDALRaster` accepts two parameters. The first parameter
defines the raster source, it is either a path to a file or spatial data with
values defining the properties of a new raster (such as size and name). If the
input is a file path, the second parameter specifies if the raster should
be opened with write access. If the input is raw data, the parameters `width`,
`height`, and `srid` are required. The following example shows how rasters
can be created from different input sources (using the sample data from the
GeoDjango tests, see also the [Data Contoh](#gdal-sample-data) section):

```
>>> from django.contrib.gis.gdal import GDALRaster
>>> rst = GDALRaster('/path/to/your/raster.tif', write=False)
>>> rst.name
'/path/to/your/raster.tif'
>>> rst.width, rst.height            # This file has 163 x 174 pixels
(163, 174)
>>> rst = GDALRaster({'srid': 4326, 'width': 1, 'height': 2, 'datatype': 1
...                   'bands': [{'data': [0, 1]}]}) # Creates in-memory raster
>>> rst.srs.srid
4326
>>> rst.width, rst.height
(1, 2)
>>> rst.bands[0].data()
array([[0, 1]], dtype=int8)
```

> **Changed in Django 1.9**
>
> `GDALRaster` objects can now be instantiated directly from raw data.
> Setters have been added for the following properties: `srs`,
> `geotransform`, `origin`, `scale`, and `skew`.

#### `name`

The name of the source which is equivalent to the input file path or the name
provided upon instantiation.

```
>>> GDALRaster({'width': 10, 'height': 10, 'name': 'myraster', 'srid': 4326}).name
'myraster'
```

#### `driver`

The name of the GDAL driver used to handle the input file. For `GDALRaster`s created
from a file, the driver type is detected automatically. The creation of rasters from
scratch is a in-memory raster by default (`'MEM'`), but can be altered as
needed. For instance, use `GTiff` for a `GeoTiff` file. For a list of file types,
see also the [GDAL Raster Formats](http://www.gdal.org/formats_list.html) list.

An in-memory raster is created through the following example:

```
>>> GDALRaster({'width': 10, 'height': 10, 'srid': 4326}).driver.name
'MEM'
```

A file based GeoTiff raster is created through the following example:

```
>>> import tempfile
>>> rstfile = tempfile.NamedTemporaryFile(suffix='.tif')
>>> rst = GDALRaster({'driver': 'GTiff', 'name': rstfile.name, 'srid': 4326,
...                   'width': 255, 'height': 255, 'nr_of_bands': 1})
>>> rst.name
'/tmp/tmp7x9H4J.tif'           # The exact filename will be different on your computer
>>> rst.driver.name
'GTiff'
```

#### `width`

The width of the source in pixels (X-axis).

```
>>> GDALRaster({'width': 10, 'height': 20, 'srid': 4326}).width
10
```

#### `height`

The height of the source in pixels (Y-axis).

```
>>> GDALRaster({'width': 10, 'height': 20, 'srid': 4326}).height
20
```

#### `srs`

The spatial reference system of the raster, as a
[`SpatialReference`](#django.contrib.gis.gdal.SpatialReference) instance. The SRS can be changed by
setting it to an other [`SpatialReference`](#django.contrib.gis.gdal.SpatialReference) or providing any input
that is accepted by the [`SpatialReference`](#django.contrib.gis.gdal.SpatialReference) constructor.

```
>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326})
>>> rst.srs.srid
4326
>>> rst.srs = 3086
>>> rst.srs.srid
3086
```

#### `srid`

> **New in Django 1.10**

The Spatial Reference System Identifier (SRID) of the raster. This
property is a shortcut to getting or setting the SRID through the
[`srs`](#django.contrib.gis.gdal.GDALRaster.srs) attribute.

```
>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326})
>>> rst.srid
4326
>>> rst.srid = 3086
>>> rst.srid
3086
>>> rst.srs.srid  # This is equivalent
3086
```

#### `geotransform`

The affine transformation matrix used to georeference the source, as a
tuple of six coefficients which map pixel/line coordinates into
georeferenced space using the following relationship:

```
Xgeo = GT(0) + Xpixel*GT(1) + Yline*GT(2)
Ygeo = GT(3) + Xpixel*GT(4) + Yline*GT(5)
```

The same values can be retrieved by accessing the [`origin`](#django.contrib.gis.gdal.GDALRaster.origin)
(indices 0 and 3), [`scale`](#django.contrib.gis.gdal.GDALRaster.scale) (indices 1 and 5) and [`skew`](#django.contrib.gis.gdal.GDALRaster.skew)
(indices 2 and 4) properties.

Awalnya adalah `[0.0, 1.0, 0.0, 0.0, 0.0, -1.0]`.

```
>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326})
>>> rst.geotransform
[0.0, 1.0, 0.0, 0.0, 0.0, -1.0]
```

#### `origin`

Coordinates of the top left origin of the raster in the spatial
reference system of the source, as a point object with `x` and `y`
members.

```
>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326})
>>> rst.origin
[0.0, 0.0]
>>> rst.origin.x = 1
>>> rst.origin
[1.0, 0.0]
```

#### `scale`

Pixel width and height used for georeferencing the raster, as a as a
point object with `x` and `y`  members. See [`geotransform`](#django.contrib.gis.gdal.GDALRaster.geotransform)
for more information.

```
>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326})
>>> rst.scale
[1.0, -1.0]
>>> rst.scale.x = 2
>>> rst.scale
[2.0, -1.0]
```

#### `skew`

Skew coefficients used to georeference the raster, as a point object
with `x` and `y`  members. In case of north up images, these
coefficients are both `0`.

```
>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326})
>>> rst.skew
[0.0, 0.0]
>>> rst.skew.x = 3
>>> rst.skew
[3.0, 0.0]
```

#### `extent`

Extent (boundary values) of the raster source, as a 4-tuple
`(xmin, ymin, xmax, ymax)` in the spatial reference system of the
source.

```
>>> rst = GDALRaster({'width': 10, 'height': 20, 'srid': 4326})
>>> rst.extent
(0.0, -20.0, 10.0, 0.0)
>>> rst.origin.x = 100
>>> rst.extent
(100.0, -20.0, 110.0, 0.0)
```

#### `bands`

List of all bands of the source, as [`GDALBand`](#django.contrib.gis.gdal.GDALBand) instances.

```
>>> rst = GDALRaster({"width": 1, "height": 2, 'srid': 4326,
...                   "bands": [{"data": [0, 1]}, {"data": [2, 3]}]})
>>> len(rst.bands)
2
>>> rst.bands[1].data()
array([[ 2.,  3.]], dtype=float32)
```

#### `warp(ds_input, resampling='NearestNeighbour', max_error=0.0)`

> **New in Django 1.9**

Returns a warped version of this raster.

The warping parameters can be specified through the `ds_input`
argument. The use of `ds_input` is analogous to the corresponding
argument of the class constructor. It is a dictionary with the
characteristics of the target raster. Allowed dictionary key values are
width, height, SRID, origin, scale, skew, datatype, driver, and name
(filename).

By default, the warp functions keeps most parameters equal to the
values of the original source raster, so only parameters that should be
changed need to be specified. Note that this includes the driver, so
for file-based rasters the warp function will create a new raster on
disk.

The only parameter that is set differently from the source raster is the
name. The default value of the the raster name is the name of the source
raster appended with `'_copy' + source_driver_name`. For file-based
rasters it is recommended to provide the file path of the target raster.

The resampling algorithm used for warping can be specified with the
`resampling` argument. The default is `NearestNeighbor`, and the
other allowed values are `Bilinear`, `Cubic`, `CubicSpline`,
`Lanczos`, `Average`, and `Mode`.

The `max_error` argument can be used to specify the maximum error
measured in input pixels that is allowed in approximating the
transformation. The default is 0.0 for exact calculations.

For users familiar with `GDAL`, this function has a similar
functionality to the `gdalwarp` command-line utility.

For example, the warp function can be used for aggregating a raster to
the double of its original pixel scale:

```
>>> rst = GDALRaster({
...     "width": 6, "height": 6, "srid": 3086,
...     "origin": [500000, 400000],
...     "scale": [100, -100],
...     "bands": [{"data": range(36), "nodata_value": 99}]
... })
>>> target = rst.warp({"scale": [200, -200], "width": 3, "height": 3})
>>> target.bands[0].data()
array([[  7.,   9.,  11.],
       [ 19.,  21.,  23.],
       [ 31.,  33.,  35.]], dtype=float32)
```

#### `transform(srid, driver=None, name=None, resampling='NearestNeighbour', max_error=0.0)`

> **New in Django 1.9**

Returns a transformed version of this raster with the specified SRID.

This function transforms the current raster into a new spatial reference
system that can be specified with an `srid`. It calculates the bounds
and scale of the current raster in the new spatial reference system and
warps the raster using the [`warp`](#django.contrib.gis.gdal.GDALRaster.warp) function.

By default, the driver of the source raster is used and the name of the
raster is the original name appended with
`'_copy' + source_driver_name`. A different driver or name can be
specified with the `driver` and `name` arguments.

The default resampling algorithm is `NearestNeighbour` but can be
changed using the `resampling` argument. The default maximum allowed
error for resampling is 0.0 and can be changed using the `max_error`
argument. Consult the [`warp`](#django.contrib.gis.gdal.GDALRaster.warp) documentation for detail
on those arguments.

```
>>> rst = GDALRaster({
...     "width": 6, "height": 6, "srid": 3086,
...     "origin": [500000, 400000],
...     "scale": [100, -100],
...     "bands": [{"data": range(36), "nodata_value": 99}]
... })
>>> target = rst.transform(4326)
>>> target.origin
[-82.98492744885776, 27.601924753080144]
```

### `GDALBand`

#### `class GDALBand`

`GDALBand` instances are not created explicitly, but rather obtained
from a [`GDALRaster`](#django.contrib.gis.gdal.GDALRaster) object, through its [`bands`](#django.contrib.gis.gdal.GDALRaster.bands)
attribute. The GDALBands contain the actual pixel values of the raster.

#### `description`

The name or description of the band, if any.

#### `width`

The width of the band in pixels (X-axis).

#### `height`

The height of the band in pixels (Y-axis).

#### `pixel_count`

> **New in Django 1.9**

The total number of pixels in this band. Is equal to `width * height`.

#### `statistics(refresh=False, approximate=False)`

> **New in Django 1.10**

Compute statistics on the pixel values of this band. The return value
is a tuple with the following structure:
`(minimum, maximum, mean, standard deviation)`.

If the `approximate` argument is set to `True`, the statistics may
be computed based on overviews or a subset of image tiles.

If the `refresh` argument is set to `True`, the statistics will be
computed from the data directly, and the cache will be updated with the
result.

If a persistent cache value is found, that value is returned. For
raster formats using Persistent Auxiliary Metadata (PAM) services, the
statistics might be cached in an auxiliary file. In some cases this
metadata might be out of sync with the pixel values or cause values
from a previous call to be returned which don't reflect the value of
the `approximate` argument. In such cases, use the `refresh`
argument to get updated values and store them in the cache.

For empty bands (where all pixel values are "no data"), all statistics
are returned as `None`.

The statistics can also be retrieved directly by accessing the
[`min`](#django.contrib.gis.gdal.GDALBand.min), [`max`](#django.contrib.gis.gdal.GDALBand.max), [`mean`](#django.contrib.gis.gdal.GDALBand.mean), and [`std`](#django.contrib.gis.gdal.GDALBand.std) properties.

#### `min`

The minimum pixel value of the band (excluding the "no data" value).

#### `max`

The maximum pixel value of the band (excluding the "no data" value).

#### `mean`

> **New in Django 1.10**

The mean of all pixel values of the band (excluding the "no data"
value).

#### `std`

> **New in Django 1.10**

The standard deviation of all pixel values of the band (excluding the
"no data" value).

#### `nodata_value`

The "no data" value for a band is generally a special marker value used
to mark pixels that are not valid data. Such pixels should generally not
be displayed, nor contribute to analysis operations.

To delete an existing "no data" value, set this property to `None`
(requires GDAL ≥ 2.1).

> **Changed in Django 1.9**
>
> This property can now be set as well.

> **Changed in Django 1.10**
>
> The "no data" value can now be deleted by setting the
> `nodata_value` attribute to `None`.

#### `datatype(as_string=False)`

The data type contained in the band, as an integer constant between 0
(Unknown) and 11. If `as_string` is `True`, the data type is
returned as a string with the following possible values:
`GDT_Unknown`, `GDT_Byte`, `GDT_UInt16`, `GDT_Int16`,
`GDT_UInt32`, `GDT_Int32`, `GDT_Float32`, `GDT_Float64`,
`GDT_CInt16`, `GDT_CInt32`, `GDT_CFloat32`, and `GDT_CFloat64`.

#### `data(data=None, offset=None, size=None, shape=None)`

> **New in Django 1.9**

The accessor to the pixel values of the `GDALBand`. Returns the complete
data array if no parameters are provided. A subset of the pixel array can
be requested by specifying an offset and block size as tuples.

If NumPy is available, the data is returned as NumPy array. For performance
reasons, it is highly recommended to use NumPy.

Data is written to the `GDALBand` if the `data` parameter is provided.
The input can be of one of the following types - packed string, buffer, list,
array, and NumPy array. The number of items in the input should normally
correspond to the total number of pixels in the band, or to the number
of pixels for a specific block of pixel values if the `offset` and
`size` parameters are provided.

If the number of items in the input is different from the target pixel
block, the `shape` parameter must be specified. The shape is a tuple
that specifies the width and height of the input data in pixels. The
data is then replicated to update the pixel values of the selected
block. This is useful to fill an entire band with a single value, for
instance.

Sebagai contoh:

```
>>> rst = GDALRaster({'width': 4, 'height': 4, 'srid': 4326, 'datatype': 1, 'nr_of_bands': 1})
>>> bnd = rst.bands[0]
>>> bnd.data(range(16))
>>> bnd.data()
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [12, 13, 14, 15]], dtype=int8)
>>> bnd.data(offset=(1, 1), size=(2, 2))
array([[ 5,  6],
       [ 9, 10]], dtype=int8)
>>> bnd.data(data=[-1, -2, -3, -4], offset=(1, 1), size=(2, 2))
>>> bnd.data()
array([[ 0,  1,  2,  3],
       [ 4, -1, -2,  7],
       [ 8, -3, -4, 11],
       [12, 13, 14, 15]], dtype=int8)
>>> bnd.data(data='\x9d\xa8\xb3\xbe', offset=(1, 1), size=(2, 2))
>>> bnd.data()
array([[  0,   1,   2,   3],
       [  4, -99, -88,   7],
       [  8, -77, -66,  11],
       [ 12,  13,  14,  15]], dtype=int8)
>>> bnd.data([1], shape=(1, 1))
>>> bnd.data()
array([[1, 1, 1, 1],
       [1, 1, 1, 1],
       [1, 1, 1, 1],
       [1, 1, 1, 1]], dtype=uint8)
>>> bnd.data(range(4), shape=(1, 4))
array([[0, 0, 0, 0],
       [1, 1, 1, 1],
       [2, 2, 2, 2],
       [3, 3, 3, 3]], dtype=uint8)
```

> **Changed in Django 1.10**
>
> The `shape` parameter and the ability to replicate data input when
> setting `GDALBand` data was added.

## Pengaturan

### `GDAL_LIBRARY_PATH`

A string specifying the location of the GDAL library.  Typically,
this setting is only used if the GDAL library is in a non-standard
location (e.g., `/home/john/lib/libgdal.so`).