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new 16e3ce600b [DOCS] Added links to spatial computing, Spatial R and
Spatial Python (#1675)
16e3ce600b is described below
commit 16e3ce600be88c4c5f0190914126ad9f53e47768
Author: Amir Tallap <[email protected]>
AuthorDate: Wed Nov 6 02:28:26 2024 +0200
[DOCS] Added links to spatial computing, Spatial R and Spatial Python
(#1675)
* Added links to spatial computing, Spatial R and Spatial Python
* Added links to spatial computing, Spatial R and Spatial Python
---
README.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/README.md b/README.md
index 5a352f80c9..efb3b39dd6 100644
--- a/README.md
+++ b/README.md
@@ -34,8 +34,8 @@ Join the Sedona monthly community office hour: [Google
Calendar](https://calenda
## What is Apache Sedona?
-Apache Sedona™ is a spatial computing engine that enables developers to easily
process spatial data at any scale within modern cluster computing systems such
as [Apache Spark](https://spark.apache.org/) and [Apache
Flink](https://flink.apache.org/).
-Sedona developers can express their spatial data processing tasks in [Spatial
SQL](https://carto.com/spatial-sql), Spatial Python or Spatial R. Internally,
Sedona provides spatial data loading, indexing, partitioning, and query
processing/optimization functionality that enable users to efficiently analyze
spatial data at any scale.
+Apache Sedona™ is a [spatial
computing](https://en.wikipedia.org/wiki/Spatial_computing) engine that enables
developers to easily process spatial data at any scale within modern cluster
computing systems such as [Apache Spark](https://spark.apache.org/) and [Apache
Flink](https://flink.apache.org/).
+Sedona developers can express their spatial data processing tasks in [Spatial
SQL](https://carto.com/spatial-sql), [Spatial
Python](https://docs.scipy.org/doc/scipy/reference/spatial.html) or [Spatial
R](https://r-spatial.org/). Internally, Sedona provides spatial data loading,
indexing, partitioning, and query processing/optimization functionality that
enable users to efficiently analyze spatial data at any scale.
