Artificial Intelligence

A sparklyr extension for analyzing geospatial data


sparklyr.sedona is now available
as the sparklyr-based R interface for Apache Sedona.

To install sparklyr.sedona from GitHub using
the remotes package
, run

remotes::install_github(repo = "apache/incubator-sedona", subdir = "R/sparklyr.sedona")

In this blog post, we will provide a quick introduction to sparklyr.sedona, outlining the motivation behind
this sparklyr extension, and presenting some example sparklyr.sedona use cases involving Spark spatial RDDs,
Spark dataframes, and visualizations.

Motivation for sparklyr.sedona

A suggestion from the
mlverse survey results earlier
this year mentioned the need for up-to-date R interfaces for Spark-based GIS frameworks.
While looking into this suggestion, we learned about
Apache Sedona, a geospatial data system powered by Spark
that is modern, efficient, and easy to use. We also realized that while our friends from the
Spark open-source community had developed a
sparklyr extension for GeoSpark, the
predecessor of Apache Sedona, there was no similar extension making more recent Sedona
functionalities easily accessible from R yet.
We therefore decided to work on sparklyr.sedona, which aims to bridge the gap between
Sedona and R.

The lay of the land

We hope you are ready for a quick tour through some of the RDD-based and
Spark-dataframe-based functionalities in sparklyr.sedona, and also, some bedazzling
visualizations derived from geospatial data in Spark.

In Apache Sedona,
Spatial Resilient Distributed Datasets(SRDDs)
are basic building blocks of distributed spatial data encapsulating
“vanilla” RDDs of
geometrical objects and indexes. SRDDs support low-level operations such as Coordinate Reference System (CRS)
transformations, spatial partitioning, and spatial indexing. For example, with sparklyr.sedona, SRDD-based operations we can perform include the following:

  • Importing some external data source into a SRDD:
library(sparklyr)
library(sparklyr.sedona)

sedona_git_repo <- normalizePath("~/incubator-sedona")
data_dir <- file.path(sedona_git_repo, "core", "src", "test", "resources")

sc <- spark_connect(master = "local")

pt_rdd <- sedona_read_dsv_to_typed_rdd(
  sc,
  location = file.path(data_dir, "arealm.csv"),
  type = "point"
)
  • Applying spatial partitioning to all data points:
sedona_apply_spatial_partitioner(pt_rdd, partitioner = "kdbtree")
  • Building spatial index on each partition:
sedona_build_index(pt_rdd, type = "quadtree")
  • Joining one spatial data set with another using “contain” or “overlap” as the join predicate:
polygon_rdd <- sedona_read_dsv_to_typed_rdd(
  sc,
  location = file.path(data_dir, "primaryroads-polygon.csv"),
  type = "polygon"
)

pts_per_region_rdd <- sedona_spatial_join_count_by_key(
  pt_rdd,
  polygon_rdd,
  join_type = "contain",
  partitioner = "kdbtree"
)

It is worth mentioning that sedona_spatial_join() will perform spatial partitioning
and indexing on the inputs using the partitioner and index_type only if the inputs
are not partitioned or indexed as specified already.

From the examples above, one can see that SRDDs are great for spatial operations requiring
fine-grained control, e.g., for ensuring a spatial join query is executed as efficiently
as possible with the right types of spatial partitioning and indexing.

Finally, we can try visualizing the join result above, using a choropleth map:

sedona_render_choropleth_map(
  pts_per_region_rdd,
  resolution_x = 1000,
  resolution_y = 600,
  output_location = tempfile("choropleth-map-"),
  boundary = c(-126.790180, -64.630926, 24.863836, 50.000),
  base_color = c(63, 127, 255)
)

which gives us the following:

Example choropleth map output

Wait, but something seems amiss. To make the visualization above look nicer, we can
overlay it with the contour of each polygonal region:

contours <- sedona_render_scatter_plot(
  polygon_rdd,
  resolution_x = 1000,
  resolution_y = 600,
  output_location = tempfile("scatter-plot-"),
  boundary = c(-126.790180, -64.630926, 24.863836, 50.000),
  base_color = c(255, 0, 0),
  browse = FALSE
)

sedona_render_choropleth_map(
  pts_per_region_rdd,
  resolution_x = 1000,
  resolution_y = 600,
  output_location = tempfile("choropleth-map-"),
  boundary = c(-126.790180, -64.630926, 24.863836, 50.000),
  base_color = c(63, 127, 255),
  overlay = contours
)

which gives us the following:

Choropleth map with overlay

With some low-level spatial operations taken care of using the SRDD API and
the right spatial partitioning and indexing data structures, we can then
import the results from SRDDs to Spark dataframes. When working with spatial
objects within Spark dataframes, we can write high-level, declarative queries
on these objects using dplyr verbs in conjunction with Sedona
spatial UDFs, e.g.

, the
following query tells us whether each of the 8 nearest polygons to the
query point contains that point, and also, the convex hull of each polygon.

tbl <- DBI::dbGetQuery(
  sc, "SELECT ST_GeomFromText(\"POINT(-66.3 18)\") AS `pt`"
)
pt <- tbl$pt[[1]]
knn_rdd <- sedona_knn_query(
  polygon_rdd, x = pt, k = 8, index_type = "rtree"
)

knn_sdf <- knn_rdd %>%
  sdf_register() %>%
  dplyr::mutate(
    contains_pt = ST_contains(geometry, ST_Point(-66.3, 18)),
    convex_hull = ST_ConvexHull(geometry)
  )

knn_sdf %>% print()
# Source: spark<?> [?? x 3]
  geometry                         contains_pt convex_hull
  <list>                           <lgl>       <list>
1 <POLYGON ((-66.335674 17.986328… TRUE        <POLYGON ((-66.335674 17.986328,…
2 <POLYGON ((-66.335432 17.986626… TRUE        <POLYGON ((-66.335432 17.986626,…
3 <POLYGON ((-66.335432 17.986626… TRUE        <POLYGON ((-66.335432 17.986626,…
4 <POLYGON ((-66.335674 17.986328… TRUE        <POLYGON ((-66.335674 17.986328,…
5 <POLYGON ((-66.242489 17.988637… FALSE       <POLYGON ((-66.242489 17.988637,…
6 <POLYGON ((-66.242489 17.988637… FALSE       <POLYGON ((-66.242489 17.988637,…
7 <POLYGON ((-66.24221 17.988799,… FALSE       <POLYGON ((-66.24221 17.988799, …
8 <POLYGON ((-66.24221 17.988799,… FALSE       <POLYGON ((-66.24221 17.988799, …

Acknowledgements

The author of this blog post would like to thank Jia Yu,
the creator of Apache Sedona, and Lorenz Walthert for
their suggestion to contribute sparklyr.sedona to the upstream
incubator-sedona repository. Jia has provided
extensive code-review feedback to ensure sparklyr.sedona complies with coding standards
and best practices of the Apache Sedona project, and has also been very helpful in the
instrumentation of CI workflows verifying sparklyr.sedona works as expected with snapshot
versions of Sedona libraries from development branches.

The author is also grateful for his colleague Sigrid Keydana
for valuable editorial suggestions on this blog post.

That’s all. Thank you for reading!

Photo by NASA on Unsplash