[DOCS] Fix grammar and spelling

.github/workflows/docker-build.yml

@@ -47,7 +47,7 @@ jobs:
         path: ~/.m2
         key: ${{ runner.os }}-m2-${{ hashFiles('**/pom.xml') }}
         restore-keys: ${{ runner.os }}-m2
-    - name: Setup docker (missing on MacOS)
+    - name: Setup docker (missing on macOS)
       if: runner.os == 'macos'
       run: |
         brew install docker

docs/api/snowflake/vector-data/Function.md

@@ -626,7 +626,7 @@ Result:
 
 ## ST_ConcaveHull
 
-Introduction: Return the Concave Hull of polgyon A, with alpha set to pctConvex[0, 1] in the Delaunay Triangulation method, the concave hull will not contain a hole unless allowHoles is set to true
+Introduction: Return the Concave Hull of polygon A, with alpha set to pctConvex[0, 1] in the Delaunay Triangulation method, the concave hull will not contain a hole unless allowHoles is set to true
 
 Format: `ST_ConcaveHull (A:geometry, pctConvex:float)`
 
@@ -641,7 +641,7 @@ FROM polygondf
 
 ## ST_ConvexHull
 
-Introduction: Return the Convex Hull of polgyon A
+Introduction: Return the Convex Hull of polygon A
 
 Format: `ST_ConvexHull (A:geometry)`
 

docs/api/sql/Raster-operators.md

@@ -450,7 +450,7 @@ SELECT RS_GeoReferrence(ST_MakeEmptyRaster(1, 3, 4, 100.0, 200.0,2.0, -3.0, 0.1,
 
 ### RS_GeoTransform
 
-Introduction: Returns an array of parameters that represent the GeoTranformation of the raster. The array contains the following values:
+Introduction: Returns an array of parameters that represent the GeoTransformation of the raster. The array contains the following values:
 
 - 0: pixel width along west-east axis (x axis)
 - 1: pixel height along north-south axis (y axis)

docs/api/sql/Visualization_SedonaKepler.md

@@ -41,7 +41,7 @@ A map config can be passed optionally to apply pre-apply customizations to the m
 
 ### **Adding SedonaDataFrame to a map object using SedonaKepler.add_df**
 
-SedonaKepler exposes a add_df API with the following signature:
+SedonaKepler exposes an add_df API with the following signature:
 
 ```python
 add_df(map, df: SedonaDataFrame, name: str='unnamed')

docs/community/develop.md

@@ -74,7 +74,7 @@ Append the submodule folder to `Working Directory`. For example, `sedona/sql`.
 
 ![](../image/ide-java-9.png)
 
-Re-run the test case. Do NOT right click the test case to re-run. Instead, click the button as shown in the figure below.
+Re-run the test case. Do NOT right-click the test case to re-run. Instead, click the button as shown in the figure below.
 
 ![](../image/ide-java-10.png)
 

docs/community/release-manager.md

@@ -40,7 +40,7 @@ JAVA_HOME="${JAVA_HOME:-$(/usr/libexec/java_home -v 1.8)}" exec "/usr/local/Cell
 
 ### 2. Prepare Secret GPG key
 
-1. Install GNUGPG if it was not installed before. On Mac: `brew install gnupg gnupg2`
+1. Install GNUPG if it was not installed before. On Mac: `brew install gnupg gnupg2`
 2. Generate a secret key. It must be RSA4096 (4096 bits long).
    * Run `gpg --full-generate-key`. If not work, run `gpg --default-new-key-algo rsa4096 --gen-key`
    * At the prompt, specify the kind of key you want: Select `RSA`, then press `enter`

docs/community/rule.md

@@ -16,7 +16,7 @@ Code contributions should include the following:
 * Unit Tests to demonstrate code correctness and allow this to be maintained going forward.  In the case of bug fixes the unit test should demonstrate the bug in the absence of the fix (if any).  Unit Tests can be JUnit test or Scala test. Some Sedona functions need to be tested in both Scala and Java.
 * Updates on corresponding Sedona documentation if necessary.
 
-Code contributions must include a Apache 2.0 license header at the top of each file.
+Code contributions must include an Apache 2.0 license header at the top of each file.
 
 ## Develop a document contribution
 

docs/community/vote.md

@@ -1,6 +1,6 @@
 # Vote a Sedona release
 
-This page is for Sedona community to vote a Sedona release. The script below is tested on MacOS.
+This page is for Sedona community to vote a Sedona release. The script below is tested on macOS.
 
 In order to vote a Sedona release, you must provide your checklist including the following minimum requirement:
 

docs/setup/emr.md

@@ -1,4 +1,4 @@
-We recommend Sedona-1.3.1-incuabting and above for EMR. In the tutorial, we use AWS Elastic MapReduce (EMR) 6.9.0. It has the following applications installed: Hadoop 3.3.3, JupyterEnterpriseGateway 2.6.0, Livy 0.7.1, Spark 3.3.0.
+We recommend Sedona-1.3.1-incubating and above for EMR. In the tutorial, we use AWS Elastic MapReduce (EMR) 6.9.0. It has the following applications installed: Hadoop 3.3.3, JupyterEnterpriseGateway 2.6.0, Livy 0.7.1, Spark 3.3.0.
 
 !!!tip
 	Wherobots Cloud provides a free tool to deploy Apache Sedona to AWS EMR. Please sign up [here](https://www.wherobots.services/).
@@ -33,11 +33,11 @@ sudo python3 -m pip install pydeck==0.8.0
 sudo python3 -m pip install attrs matplotlib descartes apache-sedona=={{ sedona.current_version }}
 ```
 
-When you create a EMR cluster, in the `bootstrap action`, specify the location of this script.
+When you create an EMR cluster, in the `bootstrap action`, specify the location of this script.
 
 ## Add software configuration
 
-When you create a EMR cluster, in the software configuration, add the following content:
+When you create an EMR cluster, in the software configuration, add the following content:
 
 ```bash
 [

docs/setup/install-python.md

@@ -10,7 +10,7 @@ You need to install necessary packages if your system does not have them install
 
 ### Install sedona
 
-* Installing from PyPI repositories. You can find the latest Sedona Python on [PyPI](https://pypi.org/project/apache-sedona/). [There is an known issue in Sedona v1.0.1 and earlier versions](release-notes.md#known-issue).
+* Installing from PyPI repositories. You can find the latest Sedona Python on [PyPI](https://pypi.org/project/apache-sedona/). [There is a known issue in Sedona v1.0.1 and earlier versions](release-notes.md#known-issue).
 
 ```bash
 pip install apache-sedona

docs/setup/release-notes.md

@@ -565,7 +565,7 @@ Sedona 1.4.0 is compiled against, Spark 3.3 / Flink 1.12, Java 8.
 ### Behavior change
 
 * **Sedona Flink** Sedona Flink no longer outputs any LinearRing type geometry. All LinearRing are changed to LineString.
-* **Sedona Spark** Join optimization strategy changed. Sedona no longer optimizes spatial join when use a spatial predicate together with a equijoin predicate. By default, it prefers equijoin whenever possible. SedonaConf adds a config option called `sedona.join.optimizationmode`, it can be configured as one of the following values:
+* **Sedona Spark** Join optimization strategy changed. Sedona no longer optimizes spatial join when use a spatial predicate together with an equijoin predicate. By default, it prefers equijoin whenever possible. SedonaConf adds a config option called `sedona.join.optimizationmode`, it can be configured as one of the following values:
 	* `all`: optimize all joins having spatial predicate in join conditions. This was the behavior of Apache Sedona prior to 1.4.0.
 	* `none`: disable spatial join optimization.
 	* `nonequi`: only enable spatial join optimization on non-equi joins. This is the default mode.

docs/setup/snowflake/install.md

@@ -77,7 +77,7 @@ In this case, we will choose the option `Create Worksheet from SQL File`.
 
 ![](../../image/snowflake/snowflake-6.png)
 
-In the worksheet, choose `SEDONA_TEST` as the database, and `PUBLIC` as the schema. The SQL script should be in the worksheet. Then right click the worksheet and choose `Run All`. Snowflake will take 3 minutes to create Sedona's functions.
+In the worksheet, choose `SEDONA_TEST` as the database, and `PUBLIC` as the schema. The SQL script should be in the worksheet. Then right-click the worksheet and choose `Run All`. Snowflake will take 3 minutes to create Sedona's functions.
 
 ![](../../image/snowflake/snowflake-7.png)
 

docs/tutorial/rdd.md

@@ -24,7 +24,7 @@ Sedona-core provides three special SpatialRDDs: PointRDD, PolygonRDD, and LineSt
 
 ### Create a generic SpatialRDD
 
-A generic SpatialRDD is not typed to a certain geometry type and open to more scenarios. It allows an input data file contains mixed types of geometries. For instance, a WKT file contains three types gemetries ==LineString==, ==Polygon== and ==MultiPolygon==.
+A generic SpatialRDD is not typed to a certain geometry type and open to more scenarios. It allows an input data file contains mixed types of geometries. For instance, a WKT file contains three types geometries ==LineString==, ==Polygon== and ==MultiPolygon==.
 
 #### From WKT/WKB
 
@@ -295,7 +295,7 @@ To retrieve the UserData field, use the following code:
 
 A spatial range query takes as input a range query window and an SpatialRDD and returns all geometries that have specified relationship with the query window.
 
-Assume you now have an SpatialRDD (typed or generic). You can use the following code to issue an Spatial Range Query on it.
+Assume you now have a SpatialRDD (typed or generic). You can use the following code to issue a Spatial Range Query on it.
 
 ==spatialPredicate== can be set to `SpatialPredicate.INTERSECTS` to return all geometries intersect with query window. Supported spatial predicates are:
 
@@ -525,9 +525,9 @@ To utilize a spatial index in a spatial range query, use the following code:
 
 ## Write a Spatial KNN Query
 
-A spatial K Nearnest Neighbor query takes as input a K, a query point and an SpatialRDD and finds the K geometries in the RDD which are the closest to he query point.
+A spatial K Nearest Neighbor query takes as input a K, a query point and a SpatialRDD and finds the K geometries in the RDD which are the closest to the query point.
 
-Assume you now have an SpatialRDD (typed or generic). You can use the following code to issue an Spatial KNN Query on it.
+Assume you now have a SpatialRDD (typed or generic). You can use the following code to issue a Spatial KNN Query on it.
 
 === "Scala"
 
@@ -658,7 +658,7 @@ To utilize a spatial index in a spatial KNN query, use the following code:
 
 A spatial join query takes as input two Spatial RDD A and B. For each geometry in A, finds the geometries (from B) covered/intersected by it. A and B can be any geometry type and are not necessary to have the same geometry type.
 
-Assume you now have two SpatialRDDs (typed or generic). You can use the following code to issue an Spatial Join Query on them.
+Assume you now have two SpatialRDDs (typed or generic). You can use the following code to issue a Spatial Join Query on them.
 
 === "Scala"
 
@@ -878,7 +878,7 @@ A distance join query takes as input two Spatial RDD A and B and a distance. For
 
 If you don't want to transform your data and are ok with sacrificing the query accuracy, you can use an approximate degree value for distance. Please use [this calculator](https://lucidar.me/en/online-unit-converter-length-to-angle/convert-degrees-to-meters/#online-converter).
 
-Assume you now have two SpatialRDDs (typed or generic). You can use the following code to issue an Distance Join Query on them.
+Assume you now have two SpatialRDDs (typed or generic). You can use the following code to issue a Distance Join Query on them.
 
 === "Scala"
 

docs/tutorial/snowflake/sql.md

@@ -350,7 +350,7 @@ Sedona implements over 200 geospatial vector and raster functions, which are muc
 * [ST_Multi](../../api/snowflake/vector-data/Function.md#st_multi)
 * [ST_NumGeometries](../../api/snowflake/vector-data/Function.md#st_numgeometries)
 * [ST_ReducePrecision](../../api/snowflake/vector-data/Function.md#st_reduceprecision)
-* [ST_SubdivdeExplode](../../api/snowflake/vector-data/Function.md#st_subdivideexplode)
+* [ST_SubdivideExplode](../../api/snowflake/vector-data/Function.md#st_subdivideexplode)
 
 You can click the links above to learn more about these functions. More functions can be found in [SedonaSQL API](../../api/snowflake/vector-data/Function.md).