diff --git a/dev/api/index.html b/dev/api/index.html index 76c2151..2b7b2da 100644 --- a/dev/api/index.html +++ b/dev/api/index.html @@ -1,2 +1,2 @@ -API · DGGS.jl
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DGGS.AbstractGrid — Type

A set of points defining a grid as a voronoi partition. Points must be geographical coordinates (lon, lat) and stored as a KDTree in the filed data.

source
DGGS.CellCube — Method

Import geographical data cube into a DGGS

Transforms a data cube with spatial index dimensions longitude and latitude into a data cube with the cell id as a single spatial index dimension. Re-gridding is done using the average value of all geographical coordinates belonging to a particular cell defined by the grid specification grid_spec.

source
DGGS.GeoCube — Method

Export cell data cube into a traditional geographical one

Transforms a data cube with one spatial index dimensions, i. e., the cell id, into a traditional geographical data cube with two spatial index dimensions longitude and latitude. Values are taken from the nearest cell.

source
DGGS.Grid — Method

Create a Grid using coordinates of center points describing a voronoi partition. center_points must have one point per column with 2 rows for longitude and latitude, respectiveley.

source
DGGS.get_children_cell_ids — Method

Get the cell ids of the children associated to each cell id of the parent level

Note that a child often has multiple parents. The cells at different resolutions are only in rectangular pyramids perfectly nested. Returns a Dict with parent cell id as keys and children cell ids as values

source
DGGS.get_cube_pyramid — Method

Get a cell data cube pyramid

Calculates a stack of cell data cubes with incrementally lower levels based on the same data as provided by cell_cube. Cell values are combined according to the provided aggregate_function.

source
DGGS.knn — Method

Get cell ids of k nearest neighbors arround a cell

source
+API · DGGS.jl
Edit on GitHub
DGGS.AbstractGrid — Type

A set of points defining a grid as a voronoi partition. Points must be geographical coordinates (lon, lat) and stored as a KDTree in the filed data.

source
DGGS.CellCube — Method

Import geographical data cube into a DGGS

Transforms a data cube with spatial index dimensions longitude and latitude into a data cube with the cell id as a single spatial index dimension. Re-gridding is done using the average value of all geographical coordinates belonging to a particular cell defined by the grid specification grid_spec.

source
DGGS.GeoCube — Method

Export cell data cube into a traditional geographical one

Transforms a data cube with one spatial index dimensions, i. e., the cell id, into a traditional geographical data cube with two spatial index dimensions longitude and latitude. Values are taken from the nearest cell.

source
DGGS.Grid — Method

Create a Grid using coordinates of center points describing a voronoi partition. center_points must have one point per column with 2 rows for longitude and latitude, respectiveley.

source
DGGS.get_children_cell_ids — Method

Get the cell ids of the children associated to each cell id of the parent level

Note that a child often has multiple parents. The cells at different resolutions are only in rectangular pyramids perfectly nested. Returns a Dict with parent cell id as keys and children cell ids as values

source
DGGS.get_cube_pyramid — Method

Get a cell data cube pyramid

Calculates a stack of cell data cubes with incrementally lower levels based on the same data as provided by cell_cube. Cell values are combined according to the provided aggregate_function.

source
DGGS.knn — Method

Get cell ids of k nearest neighbors arround a cell

source
diff --git a/dev/background/index.html b/dev/background/index.html index e62d56b..36fcd91 100644 --- a/dev/background/index.html +++ b/dev/background/index.html @@ -1,2 +1,2 @@ -Background · DGGS.jl
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The background behind Discrete Global Grid Systems (DGGS)

A Discrete Global Grid Systems (DGGS) tessellate the surface of the earth with hierarchical cells of equal area. This minimizes distortion and loading time of large geospatial datasets, which is crucial in spatial statistics and building Machine Learning models.

Why to use a DGGS

  • multi resolutions
  • chunking and compression
  • spherical properties
  • equal area

DGGS creation

  1. Take a platonic solid (e.g. icosahedron)
  2. Blow it up so that it s size fits the radius of the earth
  3. Chose a rotation of the polyhedron relative to the (e.g. those used in the Dymaxion projection so that the evrtices of the polyhedrons with high distortions are in the oceans)
  4. Tessellate the faces of the polyhedron (e.g. triangles, diamonds, or hexagons). One must introduce 12 pentagons at the vertices of the polyhedron to enable a tesselation of the 3D surface with hexagons. Polygons are regular on the faces of the polyhedron but may be distorted after re-projection to the sphere.
  5. Redo the tessellation with increasing resolutions forming a grid system

By Justin Kunimune - Own work, Public Domain, https://commons.wikimedia.org/w/index.php?curid=65694588

Projections

  • ISEA: Icosahedral Snyder Equal Area projection Snyder 1992. Distortions are low in area but high in shape.
  • FULLER: Icosahedral Dymaxion projection of Fuller in 1975. Distortions are moderate in both area and shape. More details at Gregory et al. 2008

Grid cells

DGGS cells are optimized towards having equal area leading into an irregular grid in geographical lon/lat space Any grid, including those used in a DGGS, can be represented as a Voronoi partition of the cell center coordinates These coordinates are stored in a KD-Tree (Similar in python xarrys extension Xoak)

It is important to give nearby cells a similar cell id. This allows faster indexing and loading times, because the chunking is based on cell id. The cell id encodes the 1D (memory address and cell id) curve to traverse the the 2D surface (geographical coordinates). These space-filling curves are often recursively defined fractals. Solutions for the 2-D plane are the Hilbert and Gosper curve for rectangular and hexagonal grids, respectively. The Superfund grid is a solution for the sphere surface.

+Background · DGGS.jl
Edit on GitHub

The background behind Discrete Global Grid Systems (DGGS)

A Discrete Global Grid Systems (DGGS) tessellate the surface of the earth with hierarchical cells of equal area. This minimizes distortion and loading time of large geospatial datasets, which is crucial in spatial statistics and building Machine Learning models.

Why to use a DGGS

  • multi resolutions
  • chunking and compression
  • spherical properties
  • equal area

DGGS creation

  1. Take a platonic solid (e.g. icosahedron)
  2. Blow it up so that it s size fits the radius of the earth
  3. Chose a rotation of the polyhedron relative to the (e.g. those used in the Dymaxion projection so that the evrtices of the polyhedrons with high distortions are in the oceans)
  4. Tessellate the faces of the polyhedron (e.g. triangles, diamonds, or hexagons). One must introduce 12 pentagons at the vertices of the polyhedron to enable a tesselation of the 3D surface with hexagons. Polygons are regular on the faces of the polyhedron but may be distorted after re-projection to the sphere.
  5. Redo the tessellation with increasing resolutions forming a grid system

By Justin Kunimune - Own work, Public Domain, https://commons.wikimedia.org/w/index.php?curid=65694588

Projections

  • ISEA: Icosahedral Snyder Equal Area projection Snyder 1992. Distortions are low in area but high in shape.
  • FULLER: Icosahedral Dymaxion projection of Fuller in 1975. Distortions are moderate in both area and shape. More details at Gregory et al. 2008

Grid cells

DGGS cells are optimized towards having equal area leading into an irregular grid in geographical lon/lat space Any grid, including those used in a DGGS, can be represented as a Voronoi partition of the cell center coordinates These coordinates are stored in a KD-Tree (Similar in python xarrys extension Xoak)

It is important to give nearby cells a similar cell id. This allows faster indexing and loading times, because the chunking is based on cell id. The cell id encodes the 1D (memory address and cell id) curve to traverse the the 2D surface (geographical coordinates). These space-filling curves are often recursively defined fractals. Solutions for the 2-D plane are the Hilbert and Gosper curve for rectangular and hexagonal grids, respectively. The Superfund grid is a solution for the sphere surface.

diff --git a/dev/develop/index.html b/dev/develop/index.html index e6ec1bb..a46f141 100644 --- a/dev/develop/index.html +++ b/dev/develop/index.html @@ -10,4 +10,4 @@ grid.data # ball tree of geo coordinates grid.index - + diff --git a/dev/index.html b/dev/index.html index f584fec..3848e8b 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,4 +1,4 @@ Home · DGGS.jl
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DGGS.jl: A Discrete Global Grid System for Julia

Documentation for DGGS.

DGGS.jl is a Julia Package for scalable geospatial analysis using Discrete Global Grid Systems (DGGS), which tessellate the surface of the earth with hierarchical cells of equal area, minimizing distortion and loading time of large geospatial datasets, which is crucial in spatial statistics and building Machine Learning models.

Get Started

Install DGGS.jl:

using Pkg
 Pkg.add("DGGS")

Create a simple grid:

using DGGS
-grid = create_toy_grid()

API

+grid = create_toy_grid()

API

diff --git a/dev/search/index.html b/dev/search/index.html index 8c8bbfa..3351799 100644 --- a/dev/search/index.html +++ b/dev/search/index.html @@ -1,2 +1,2 @@ -Search · DGGS.jl

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    +Search · DGGS.jl

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      diff --git a/dev/tutorial/index.html b/dev/tutorial/index.html index 25ffefa..cd402d6 100644 --- a/dev/tutorial/index.html +++ b/dev/tutorial/index.html @@ -90,4 +90,4 @@ Axes: lon Axis with 180 Elements from -179.0 to 179.0 lat Axis with 170 Elements from -79.5 to 89.5 -

      Plot the imported geo data cube:

      plot_map(geo_cube)

      Since this dataset is about ocean temperature, we do not have cells on the land area.

      +

      Plot the imported geo data cube:

      plot_map(geo_cube)

      Since this dataset is about ocean temperature, we do not have cells on the land area.