[DOCS] Enable markdownlint rule MD022 (#1194)

https://github.com/DavidAnson/markdownlint/blob/main/doc/md022.md

.github/linters/.markdown-lint.yml

@@ -15,9 +15,6 @@ MD010: false
 # line-length - Line length
 MD013: false
 
-# blanks-around-headings - Headings should be surrounded by blank lines
-MD022: false
-
 # no-duplicate-heading - Multiple headings with the same content
 MD024: false
 

R/README.md

@@ -1,11 +1,13 @@
 
 # apache.sedona <img src="man/figures/logo.png" align="right" width="120"/>
+
 [Apache Sedona](https://sedona.apache.org/) is a cluster computing system for processing large-scale spatial data. Sedona extends existing cluster computing systems, such as Apache Spark and Apache Flink, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines.
 
 The apache.sedona R package exposes an interface to Apache Sedona through `{sparklyr}`
 enabling higher-level access through a `{dplyr}` backend and familiar R functions.
 
 ## Installation
+
 To use Apache Sedona from R, you just need to install the apache.sedona package; Spark dependencies are managed directly by the package.
 
 ```r
@@ -14,6 +16,7 @@ install.packages("apache.sedona")
 ```
 
 #### Development version
+
 To use the development version, you will need both the latest version of the package and of the Apache Sedona jars.
 
 To get the latest R package from GtiHub:

README.md

@@ -17,11 +17,13 @@
 * [Join the community](#join-the-community)
 
 ## 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 and Apache Flink. Sedona developers can express their spatial data processing tasks in 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.
 
 <img alt="Sedona Ecosystem" src="docs/image/sedona-ecosystem.png" width="800" class="center">
 
 ### Features
+
 Some of the key features of Apache Sedona include:
 
 * Support for a wide range of geospatial data formats, including GeoJSON, WKT, and ESRI Shapefile.
@@ -53,6 +55,7 @@ Apache Sedona is a widely used framework for working with spatial data, and it h
 This example loads NYC taxi trip records and taxi zone information stored as .CSV files on AWS S3 into Sedona spatial dataframes. It then performs spatial SQL query on the taxi trip datasets to filter out all records except those within the Manhattan area of New York. The example also shows a spatial join operation that matches taxi trip records to zones based on whether the taxi trip lies within the geographical extents of the zone. Finally, the last code snippet integrates the output of Sedona with GeoPandas and plots the spatial distribution of both datasets.
 
 #### Load NYC taxi trips and taxi zones data from CSV Files Stored on AWS S3
+
 ```python
 taxidf = sedona.read.format('csv').option("header","true").option("delimiter", ",").load("s3a://your-directory/data/nyc-taxi-data.csv")
 taxidf = taxidf.selectExpr('ST_Point(CAST(Start_Lon AS Decimal(24,20)), CAST(Start_Lat AS Decimal(24,20))) AS pickup', 'Trip_Pickup_DateTime', 'Payment_Type', 'Fare_Amt')
@@ -69,6 +72,7 @@ taxidf_mhtn = taxidf.where('ST_Contains(ST_PolygonFromEnvelope(-74.01,40.73,-73.
 ```
 
 #### Spatial Join between Taxi Dataframe and Zone Dataframe to Find taxis in each zone
+
 ```python
 taxiVsZone = sedona.sql('SELECT zone, zipcode, pickup, Fare_Amt FROM zoneDf, taxiDf WHERE ST_Contains(zone, pickup)')
 ```
@@ -88,6 +92,7 @@ zone.set_ylim(40.65, 40.9)
 
 taxi = taxiGpd.plot(ax=zone, alpha=0.01, color='red', zorder=3)
 ```
+
 ## Docker image
 
 We provide a Docker image for Apache Sedona with Python JupyterLab and a single-node cluster. The images are available on [DockerHub](https://hub.docker.com/r/apache/sedona)

docs/api/flink/Function.md

@@ -754,6 +754,7 @@ Output:
 ```
 POLYGON ((125 100, 20 40, 50 60, 175 150, 125 100))
 ```
+
 ## ST_ConvexHull
 
 Introduction: Return the Convex Hull of polygon A
@@ -1089,6 +1090,7 @@ POLYGON((0 0,0 5,5 0,0 0),(1 1,3 1,1 3,1 1))
 ```
 
 ## ST_Force3D
+
 Introduction: Forces the geometry into a 3-dimensional model so that all output representations will have X, Y and Z coordinates.
 An optionally given zValue is tacked onto the geometry if the geometry is 2-dimensional. Default value of zValue is 0.0
 If the given geometry is 3-dimensional, no change is performed on it.
@@ -1293,6 +1295,7 @@ To understand the cell statistics please refer to [H3 Doc](https://h3geo.org/doc
 H3 native fill functions doesn't guarantee full coverage on the shapes.
 
 ### Cover Polygon
+
 When fullCover = false, for polygon sedona will use [polygonToCells](https://h3geo.org/docs/api/regions#polygontocells).
 This can't guarantee full coverage but will guarantee no false positive.
 
@@ -1302,6 +1305,7 @@ This will lead to redundancy but can guarantee full coverage.
 Choose the option according to your use case.
 
 ### Cover LineString
+
 For the lineString, sedona will call gridPathCells(https://h3geo.org/docs/api/traversal#gridpathcells) per segment.
 From H3's documentation
 > This function may fail to find the line between two indexes, for example if they are very far apart. It may also fail when finding distances for indexes on opposite sides of a pentagon.
@@ -2550,6 +2554,7 @@ POLYGON ((8766047.980342899 17809098.336766362, 5122546.516721856 18580261.91252
 ```
 
 ## ST_Translate
+
 Introduction: Returns the input geometry with its X, Y and Z coordinates (if present in the geometry) translated by deltaX, deltaY and deltaZ (if specified)
 
 If the geometry is 2D, and a deltaZ parameter is specified, no change is done to the Z coordinate of the geometry and the resultant geometry is also 2D.

docs/api/flink/Predicate.md

@@ -179,6 +179,7 @@ false
 ```
 
 ## ST_OrderingEquals
+
 Introduction: Returns true if the geometries are equal and the coordinates are in the same order
 
 Format: `ST_OrderingEquals(A: geometry, B: geometry)`

docs/api/sql/Constructor.md

@@ -1,4 +1,5 @@
 ## Read ESRI Shapefile
+
 Introduction: Construct a DataFrame from a Shapefile
 
 Since: `v1.0.0`

docs/api/sql/Function.md

@@ -1097,6 +1097,7 @@ POLYGON((0 0,0 5,5 0,0 0),(1 1,3 1,1 3,1 1))
 ```
 
 ## ST_Force3D
+
 Introduction: Forces the geometry into a 3-dimensional model so that all output representations will have X, Y and Z coordinates.
 An optionally given zValue is tacked onto the geometry if the geometry is 2-dimensional. Default value of zValue is 0.0
 If the given geometry is 3-dimensional, no change is performed on it.
@@ -1301,6 +1302,7 @@ To understand the cell statistics please refer to [H3 Doc](https://h3geo.org/doc
 H3 native fill functions doesn't guarantee full coverage on the shapes.
 
 ### Cover Polygon
+
 When fullCover = false, for polygon sedona will use [polygonToCells](https://h3geo.org/docs/api/regions#polygontocells).
 This can't guarantee full coverage but will guarantee no false positive.
 
@@ -1310,6 +1312,7 @@ This will lead to redundancy but can guarantee full coverage.
 Choose the option according to your use case.
 
 ### Cover LineString
+
 For the lineString, sedona will call gridPathCells(https://h3geo.org/docs/api/traversal#gridpathcells) per segment.
 From H3's documentation
 > This function may fail to find the line between two indexes, for example if they are very far apart. It may also fail when finding distances for indexes on opposite sides of a pentagon.
@@ -2123,6 +2126,7 @@ Output:
 ```
 
 ## ST_NumPoints
+
 Introduction: Returns number of points in a LineString
 
 !!!note
@@ -2658,6 +2662,7 @@ POLYGON ((8766047.980342899 17809098.336766362, 5122546.516721856 18580261.91252
 ```
 
 ## ST_Translate
+
 Introduction: Returns the input geometry with its X, Y and Z coordinates (if present in the geometry) translated by deltaX, deltaY and deltaZ (if specified)
 
 If the geometry is 2D, and a deltaZ parameter is specified, no change is done to the Z coordinate of the geometry and the resultant geometry is also 2D.

docs/api/sql/Optimizer.md

@@ -7,6 +7,7 @@ Sedona Spatial operators fully supports Apache SparkSQL query optimizer. It has
 	Sedona join performance is heavily affected by the number of partitions. If the join performance is not ideal, please increase the number of partitions by doing `df.repartition(XXX)` right after you create the original DataFrame.
 
 ## Range join
+
 Introduction: Find geometries from A and geometries from B such that each geometry pair satisfies a certain predicate. Most predicates supported by SedonaSQL can trigger a range join.
 
 Spark SQL Example:
@@ -280,6 +281,7 @@ FROM lefts
 ```
 
 ## Regular spatial predicate pushdown
+
 Introduction: Given a join query and a predicate in the same WHERE clause, first executes the Predicate as a filter, then executes the join query.
 
 Spark SQL Example:

docs/api/sql/Overview.md

@@ -1,6 +1,7 @@
 # Introduction
 
 ## Function list
+
 SedonaSQL supports SQL/MM Part3 Spatial SQL Standard. It includes four kinds of SQL operators as follows. All these operators can be directly called through:
 ```scala
 var myDataFrame = sedona.sql("YOUR_SQL")

docs/api/sql/Parameter.md

@@ -1,4 +1,5 @@
 ## Usage
+
 SedonaSQL supports many parameters. To change their values,
 
 1. Set it through SparkConf:
@@ -18,6 +19,7 @@ println(sedonaConf)
 ```scala
 sparkSession.conf.set("sedona.global.index","false")
 ```
+
 ## Explanation
 
 * sedona.global.index

docs/api/sql/Predicate.md

@@ -119,6 +119,7 @@ true
 ```
 
 ## ST_OrderingEquals
+
 Introduction: Returns true if the geometries are equal and the coordinates are in the same order
 
 Format: `ST_OrderingEquals(A: geometry, B: geometry)`

docs/api/sql/Raster-operators.md

@@ -23,6 +23,7 @@ POINT (156.5 -75.5)
 ```
 
 ### RS_PixelAsCentroids
+
 Introduction: Returns a list of the centroid point geometry, the pixel value and its raster X and Y coordinates for each pixel in the raster at the specified band.
 Each centroid represents the geometric center of the corresponding pixel's area.
 
@@ -104,6 +105,7 @@ IndexOutOfBoundsException: Specified pixel coordinates (6, 2) do not lie in the
 ```
 
 ### RS_PixelAsPoints
+
 Introduction: Returns a list of the pixel's upper-left corner point geometry, the pixel value and its raster X and Y coordinates for each pixel in the raster at the specified band.
 
 Format: `RS_PixelAsPoints(raster: Raster, band: Integer)`
@@ -173,6 +175,7 @@ POLYGON ((131 -246, 139 -246, 139 -254, 131 -254, 131 -246))
 ```
 
 ### RS_PixelAsPolygons
+
 Introduction: Returns a list of the polygon geometry, the pixel value and its raster X and Y coordinates for each pixel in the raster at the specified band.
 
 Format: `RS_PixelAsPolygons(raster: Raster, band: Integer)`
@@ -1450,6 +1453,7 @@ Output:
 ```
 4
 ```
+
 ### RS_Resample
 
 Introduction:

docs/api/sql/Raster-visualizer.md

@@ -1,9 +1,11 @@
 Sedona offers some APIs to aid in easy visualization of a raster object.
 
 ## Image-based visualization
+
 Sedona offers APIs to visualize a raster in an image form. This API only works for rasters with byte data, and bands <= 4 (Grayscale - RGBA). You can check the data type of an existing raster by using [RS_BandPixelType](../Raster-operators/#rs_bandpixeltype) or create your own raster by passing 'B' while using [RS_MakeEmptyRaster](../Raster-loader/#rs_makeemptyraster).
 
 ### RS_AsBase64
+
 Introduction: Returns a base64 encoded string of the given raster. If the datatype is integral then this function internally takes the first 4 bands as RGBA, and converts them to the PNG format, finally produces a base64 string. When the datatype is not integral, the function converts the raster to TIFF format, and then generates a base64 string. To visualize other bands, please use it together with `RS_Band`. You can take the resulting base64 string in [an online viewer](https://base64-viewer.onrender.com/) to check how the image looks like.
 
 !!!Warning
@@ -26,6 +28,7 @@ iVBORw0KGgoAAAA...
 ```
 
 ### RS_AsImage
+
 Introduction: Returns a HTML that when rendered using an HTML viewer or via a Jupyter Notebook, displays the raster as a square image of side length `imageWidth`. Optionally, an imageWidth parameter can be passed to RS_AsImage in order to increase the size of the rendered image (default: 200).
 
 Format: `RS_AsImage(raster: Raster, imageWidth: Integer = 200)`

docs/api/sql/Visualization_SedonaKepler.md

@@ -38,6 +38,7 @@ 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:
 
 ```python
@@ -60,6 +61,7 @@ The parameters name has the same conditions as 'create_map'
     ```
 
 ### **Setting a config via the map**
+
 A map rendered by accessing the map object created by SedonaKepler includes a config panel which can be used to customize the map
 
 ### **Saving and setting config**

docs/community/develop.md

@@ -85,11 +85,13 @@ Re-run the test case. Do NOT right click the test case to re-run. Instead, click
 To run all Python test cases, follow steps mentioned [here](../../setup/compile/#run-python-test).
 
 #### Run all python tests in a single test file
+
 To run a particular python test file, specify the path of the .py file to pipenv.
 
 For example, to run all tests in `test_function.py` located in `python/tests/sql/`, use: `pipenv run pytest tests/sql/test_function.py`.
 
 #### Run a single test
+
 To run a particular test in a particular .py test file, specify `file_name::class_name::test_name` to the pytest command.
 
 For example, to run the test on ST_Contains function located in sql/test_predicate.py, use: `pipenv run pytest tests/sql/test_predicate.py::TestPredicate::test_st_contains`

docs/community/publication.md

@@ -3,6 +3,7 @@
 Apache Sedona was formerly called GeoSpark, initiated by Arizona State University [Data Systems Lab](https://www.datasyslab.net/).
 
 ## Key publications
+
 **"Spatial Data Management in Apache Spark: The
 GeoSpark Perspective and Beyond"** is the full research paper that talks about the entire GeoSpark ecosystem. Please cite this paper if your work mentions GeoSpark core system.
 
@@ -20,6 +21,7 @@ GeoSpark were evaluated by papers published on database top venues. It is worth
 > GeoSpark comes close to a complete spatial analytics system. It also exhibits the best performance in most cases.
 
 ## Full publications
+
 ### GeoSpark Ecosystem
 
 ["Spatial Data Management in Apache Spark: The

docs/index.md

@@ -1,5 +1,9 @@
 ### 10/12/2023: Sedona 1.5.0 released. It adds comprehensive raster data ETL and analytics, native support of Uber H3 functions, and SedonaKepler / SedonaPyDeck for interactive map visualization
+
 ### 06/25/2023: Sedona 1.4.1 released. It adds geodesic / geography functions, more raster functions and support Spark 3.4.
+
 ### 03/19/2023: Sedona 1.4.0 released. It provides GeoParquet filter pushdown (10X less memory footprint), faster serialization (3X speed), S2-based fast approximate join and enhanced R language support
+
 ### 01/2023: Apache Sedona graduated to an Apache Top Level Project!
+
 ### 12/23/2022: Sedona 1.3.1-incubating is released. It adds native support of GeoParquet, DataFrame style API, Scala 2.13, Python 3.10, spatial aggregation on Flink. Please check Sedona release notes.

docs/setup/cluster.md

@@ -3,6 +3,7 @@
 Download a Spark distribution from [Spark download page](http://spark.apache.org/downloads.html).
 
 ## Preliminary
+
 1. Set up password-less SSH on your cluster. Each master-worker pair should have bi-directional password-less SSH.
 2. Make sure you have installed JRE 1.8 or later.
 3. Add the list of your workers' IP address in ./conf/slaves
@@ -23,6 +24,7 @@ spark.driver.maxResultSize 5g
 For more details of Spark parameters, please visit [Spark Website](https://spark.apache.org/docs/latest/configuration.html).
 
 ## Start your cluster
+
 Go the root folder of the uncompressed Apache Spark folder. Start your spark cluster via a terminal
 
 ```

docs/setup/compile.md

@@ -3,6 +3,7 @@
 [![Scala and Java build](https://github.com/apache/sedona/actions/workflows/java.yml/badge.svg)](https://github.com/apache/sedona/actions/workflows/java.yml) [![Python build](https://github.com/apache/sedona/actions/workflows/python.yml/badge.svg)](https://github.com/apache/sedona/actions/workflows/python.yml) [![R build](https://github.com/apache/sedona/actions/workflows/r.yml/badge.svg)](https://github.com/apache/sedona/actions/workflows/r.yml) [![Example project build](https://github.com/apache/sedona/actions/workflows/example.yml/badge.svg)](https://github.com/apache/sedona/actions/workflows/example.yml) [![Docs build](https://github.com/apache/sedona/actions/workflows/docs.yml/badge.svg)](https://github.com/apache/sedona/actions/workflows/docs.yml) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/apache/sedona/HEAD?filepath=binder)
 
 ## Compile Scala / Java source code
+
 Sedona Scala/Java code is a project with multiple modules. Each module is a Scala/Java mixed project which is managed by Apache Maven 3.
 
 * Make sure your Linux/Mac machine has Java 1.8, Apache Maven 3.3.1+, and Python3.7+. The compilation of Sedona is not tested on Windows machines.
@@ -64,6 +65,7 @@ User can specify `-Dspark` and `-Dscala` command line options to compile with di
 Sedona uses GitHub Actions to automatically generate jars per commit. You can go [here](https://github.com/apache/sedona/actions/workflows/java.yml) and download the jars by clicking the commits ==Artifacts== tag.
 
 ## Run Python test
+
 1. Set up the environment variable SPARK_HOME and PYTHONPATH
 
 For example,
@@ -103,6 +105,7 @@ cd python
 pipenv run python setup.py build_ext --inplace
 pipenv run pytest tests
 ```
+
 ## Compile the documentation
 
 The website is automatically built after each commit. The built website can be downloaded here:

docs/setup/docker.md

@@ -104,12 +104,14 @@ This docker image can only be built against Sedona 1.4.1+ and Spark 3.0+
 ## Cluster Configuration
 
 ### Software
+
 * OS: Ubuntu 22.02
 * JDK: openjdk-19
 * Python: 3.10
 * Spark 3.4.1
 
 ### Web UI
+
 * JupyterLab: http://localhost:8888/
 * Spark master URL: spark://localhost:7077
 * Spark job UI: http://localhost:4040

docs/setup/install-scala.md

@@ -28,6 +28,7 @@ Please refer to [Sedona Maven Central coordinates](maven-coordinates.md) to sele
     ```
 
 ### Download Sedona jar manually
+
 1. Have your Spark cluster ready.
 
 2. Download Sedona jars:

docs/setup/maven-coordinates.md

@@ -282,12 +282,16 @@ Under BSD 3-clause (compatible with Apache 2.0 license)
 		```
 
 ## SNAPSHOT versions
+
 Sometimes Sedona has a SNAPSHOT version for the upcoming release. It follows the same naming conversion but has "SNAPSHOT" as suffix in the version. For example, `{{ sedona_create_release.current_snapshot }}`
 
 In order to download SNAPSHOTs, you need to add the following repositories in your pom.xml or build.sbt
+
 ### build.sbt
+
 resolvers +=
   "Apache Software Foundation Snapshots" at "https://repository.apache.org/content/groups/snapshots"
+
 ### pom.xml
 
 ```xml