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The following commit(s) were added to refs/heads/master by this push:
     new e70fba6109 [DOCS] Fix spelling (#1812)
e70fba6109 is described below

commit e70fba61098bde0ae7c01f93599bd3fc9655bd70
Author: John Bampton <[email protected]>
AuthorDate: Mon Feb 17 08:38:17 2025 +1000

    [DOCS] Fix spelling (#1812)
    
    * [DOCS] Fix spelling
    
    * Fix word casings
    
    * Fix more spelling
    
    * Fix typos
---
 docs/setup/azure-synapse-analytics.md                          |  6 +++---
 docs/tutorial/sql.md                                           | 10 +++++-----
 mkdocs.yml                                                     |  2 +-
 .../core/spatialPartitioning/GenericUniquePartitioner.java     |  8 ++++----
 .../java/org/apache/sedona/core/spatialRDD/SpatialRDD.java     |  2 +-
 5 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/docs/setup/azure-synapse-analytics.md 
b/docs/setup/azure-synapse-analytics.md
index 1fb268b9f5..985eb0890f 100644
--- a/docs/setup/azure-synapse-analytics.md
+++ b/docs/setup/azure-synapse-analytics.md
@@ -42,7 +42,7 @@ From Maven
 
 - 
[geotools-wrapper-1.6.1-28.2.jar](https://mvnrepository.com/artifact/org.datasyslab/geotools-wrapper/1.6.1-28.2)
 
-From PyPi
+From PyPI
 
 - 
[rasterio-1.4.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl](https://files.pythonhosted.org/packages/cd/ad/2d3a14e5a97ca827a38d4963b86071267a6cd09d45065cd753d7325699b6/rasterio-1.4.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl)
 
@@ -225,7 +225,7 @@ pip install -r requirements.txt > pip-output.txt
 grep Downloading pip-output.txt
 ```
 
-**This will be the list of packages you need to locate and download from PyPi**
+**This will be the list of packages you need to locate and download from PyPI**
 
 Example output
 
@@ -243,6 +243,6 @@ Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 
kB)
 - upload packages to workspace
 - add packages to your (clean!) Spark pool
 
-Pay careful attention to errors reported back from Synpase and troubleshoot to 
resolve conflicts.
+Pay careful attention to errors reported back from Synapse and troubleshoot to 
resolve conflicts.
 
 Note: We didn't have issues with Sedona 1.6.0 on Spark 3.4, but Sedona 1.6.1 
and supporting packages had a conflict around `numpy` which requires us to 
download a specific version and add it to the packages list. `numpy` was not 
listed in the output of the grep.
diff --git a/docs/tutorial/sql.md b/docs/tutorial/sql.md
index 009a21c87e..b51628b26c 100644
--- a/docs/tutorial/sql.md
+++ b/docs/tutorial/sql.md
@@ -1651,18 +1651,18 @@ You can use `StructuredAdapter` and the 
`spatialRDD.spatialPartitioningWithoutDu
 === "Scala"
 
        ```scala
-       spatialRDD.spatialParitioningWithoutDuplicates(GridType.KDBTREE)
+       spatialRDD.spatialPartitioningWithoutDuplicates(GridType.KDBTREE)
        // Specify the desired number of partitions as 10, though the actual 
number may vary
-       // spatialRDD.spatialParitioningWithoutDuplicates(GridType.KDBTREE, 10)
+       // spatialRDD.spatialPartitioningWithoutDuplicates(GridType.KDBTREE, 10)
        var spatialDf = StructuredAdapter.toSpatialPartitionedDf(spatialRDD, 
sedona)
        ```
 
 === "Java"
 
        ```java
-       spatialRDD.spatialParitioningWithoutDuplicates(GridType.KDBTREE)
+       spatialRDD.spatialPartitioningWithoutDuplicates(GridType.KDBTREE)
        // Specify the desired number of partitions as 10, though the actual 
number may vary
-       // spatialRDD.spatialParitioningWithoutDuplicates(GridType.KDBTREE, 10)
+       // spatialRDD.spatialPartitioningWithoutDuplicates(GridType.KDBTREE, 10)
        Dataset<Row> spatialDf = 
StructuredAdapter.toSpatialPartitionedDf(spatialRDD, sedona)
        ```
 
@@ -1673,7 +1673,7 @@ You can use `StructuredAdapter` and the 
`spatialRDD.spatialPartitioningWithoutDu
 
        spatialRDD.spatialPartitioningWithoutDuplicates(GridType.KDBTREE)
        # Specify the desired number of partitions as 10, though the actual 
number may vary
-       # spatialRDD.spatialParitioningWithoutDuplicates(GridType.KDBTREE, 10)
+       # spatialRDD.spatialPartitioningWithoutDuplicates(GridType.KDBTREE, 10)
        spatialDf = StructuredAdapter.toSpatialPartitionedDf(spatialRDD, sedona)
        ```
 
diff --git a/mkdocs.yml b/mkdocs.yml
index eed5a32ab5..82073f4ab5 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -44,7 +44,7 @@ nav:
           - Install on AWS Glue: setup/glue.md
           - Install on Microsoft Fabric: setup/fabric.md
           - Set up Spark cluster manually: setup/cluster.md
-          - Install on Azure Synpase Analytics: 
setup/azure-synapse-analytics.md
+          - Install on Azure Synapse Analytics: 
setup/azure-synapse-analytics.md
       - Install with Apache Flink:
           - Install Sedona Scala/Java: setup/flink/install-scala.md
       - Install with Snowflake:
diff --git 
a/spark/common/src/main/java/org/apache/sedona/core/spatialPartitioning/GenericUniquePartitioner.java
 
b/spark/common/src/main/java/org/apache/sedona/core/spatialPartitioning/GenericUniquePartitioner.java
index 214446d6dd..1f65ca627e 100644
--- 
a/spark/common/src/main/java/org/apache/sedona/core/spatialPartitioning/GenericUniquePartitioner.java
+++ 
b/spark/common/src/main/java/org/apache/sedona/core/spatialPartitioning/GenericUniquePartitioner.java
@@ -49,19 +49,19 @@ public class GenericUniquePartitioner extends 
SpatialPartitioner {
     // and return the partition with the minimum ID. This ensures that given 
the same
     // (parent) partitioner, the output partitions from this method will be 
consistent.
     Iterator<Tuple2<Integer, Geometry>> it = parent.placeObject(spatialObject);
-    int minParitionId = Integer.MAX_VALUE;
+    int minPartitionId = Integer.MAX_VALUE;
     Geometry minGeometry = null;
     while (it.hasNext()) {
       Tuple2<Integer, Geometry> value = it.next();
-      if (value._1() < minParitionId) {
-        minParitionId = value._1();
+      if (value._1() < minPartitionId) {
+        minPartitionId = value._1();
         minGeometry = value._2();
       }
     }
 
     HashSet<Tuple2<Integer, Geometry>> out = new HashSet<Tuple2<Integer, 
Geometry>>();
     if (minGeometry != null) {
-      out.add(new Tuple2<Integer, Geometry>(minParitionId, minGeometry));
+      out.add(new Tuple2<Integer, Geometry>(minPartitionId, minGeometry));
     }
 
     return out.iterator();
diff --git 
a/spark/common/src/main/java/org/apache/sedona/core/spatialRDD/SpatialRDD.java 
b/spark/common/src/main/java/org/apache/sedona/core/spatialRDD/SpatialRDD.java
index b8b46ae35e..3d5f6e662f 100644
--- 
a/spark/common/src/main/java/org/apache/sedona/core/spatialRDD/SpatialRDD.java
+++ 
b/spark/common/src/main/java/org/apache/sedona/core/spatialRDD/SpatialRDD.java
@@ -162,7 +162,7 @@ public class SpatialRDD<T extends Geometry> implements 
Serializable {
     return true;
   }
 
-  public boolean spatialParitioningWithoutDuplicates(GridType gridType) throws 
Exception {
+  public boolean spatialPartitioningWithoutDuplicates(GridType gridType) 
throws Exception {
     int numPartitions = this.rawSpatialRDD.rdd().partitions().length;
     spatialPartitioningWithoutDuplicates(gridType, numPartitions);
     return true;

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