Home Artificial Intelligence A sparklyr extension for analyzing geospatial information

A sparklyr extension for analyzing geospatial information

A sparklyr extension for analyzing geospatial information


sparklyr.sedona is now obtainable
because the sparklyr-based R interface for Apache Sedona.

To put in sparklyr.sedona from GitHub utilizing
the remotes bundle
, run

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

On this weblog submit, we’ll present a fast introduction to sparklyr.sedona, outlining the motivation behind
this sparklyr extension, and presenting some instance sparklyr.sedona use circumstances involving Spark spatial RDDs,
Spark dataframes, and visualizations.

Motivation for sparklyr.sedona

A suggestion from the
mlverse survey outcomes earlier
this 12 months talked about the necessity for up-to-date R interfaces for Spark-based GIS frameworks.
Whereas wanting into this suggestion, we discovered about
Apache Sedona, a geospatial information system powered by Spark
that’s fashionable, environment friendly, and simple to make use of. We additionally realized that whereas our mates from the
Spark open-source neighborhood had developed a
sparklyr extension for GeoSpark, the
predecessor of Apache Sedona, there was no related extension making more moderen Sedona
functionalities simply accessible from R but.
We due to this fact determined to work on sparklyr.sedona, which goals to bridge the hole between
Sedona and R.

The lay of the land

We hope you’re prepared for a fast tour by means of among the RDD-based and
Spark-dataframe-based functionalities in sparklyr.sedona, and in addition, some bedazzling
visualizations derived from geospatial information in Spark.

In Apache Sedona,
Spatial Resilient Distributed Datasets(SRDDs)
are primary constructing blocks of distributed spatial information encapsulating
“vanilla” RDDs of
geometrical objects and indexes. SRDDs help low-level operations resembling Coordinate Reference System (CRS)
transformations, spatial partitioning, and spatial indexing. For instance, with sparklyr.sedona, SRDD-based operations we will carry out embody the next:

  • Importing some exterior information supply right into a SRDD:

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

sc <- spark_connect(grasp = "native")

pt_rdd <- sedona_read_dsv_to_typed_rdd(
  location = file.path(data_dir, "arealm.csv"),
  kind = "level"
  • Making use of spatial partitioning to all information factors:
sedona_apply_spatial_partitioner(pt_rdd, partitioner = "kdbtree")
  • Constructing spatial index on every partition:
sedona_build_index(pt_rdd, kind = "quadtree")
  • Becoming a member of one spatial information set with one other utilizing “comprise” or “overlap” because the be a part of predicate:
polygon_rdd <- sedona_read_dsv_to_typed_rdd(
  location = file.path(data_dir, "primaryroads-polygon.csv"),
  kind = "polygon"

pts_per_region_rdd <- sedona_spatial_join_count_by_key(
  join_type = "comprise",
  partitioner = "kdbtree"

It’s value mentioning that sedona_spatial_join() will carry out spatial partitioning
and indexing on the inputs utilizing the partitioner and index_type provided that the inputs
are usually not partitioned or listed as specified already.

From the examples above, one can see that SRDDs are nice for spatial operations requiring
fine-grained management, e.g., for making certain a spatial be a part of question is executed as effectively
as potential with the correct kinds of spatial partitioning and indexing.

Lastly, we will strive visualizing the be a part of consequence above, utilizing a choropleth map:

  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 supplies us the next:

Example choropleth map output

Wait, however one thing appears amiss. To make the visualization above look nicer, we will
overlay it with the contour of every polygonal area:

contours <- sedona_render_scatter_plot(
  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

  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 supplies us the next:

Choropleth map with overlay

With some low-level spatial operations taken care of utilizing the SRDD API and
the correct spatial partitioning and indexing information buildings, we will then
import the outcomes from SRDDs to Spark dataframes. When working with spatial
objects inside Spark dataframes, we will write high-level, declarative queries
on these objects utilizing dplyr verbs along with Sedona
spatial UDFs, e.g.

, the
following question tells us whether or not every of the 8 nearest polygons to the
question level incorporates that time, and in addition, the convex hull of every 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, okay = 8, index_type = "rtree"

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

knn_sdf %>% print()
# Supply: spark<?> [?? x 3]
  geometry                         contains_pt convex_hull
  <listing>                           <lgl>       <listing>
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, …


The creator of this weblog submit wish 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 supplied
intensive code-review suggestions to make sure sparklyr.sedona complies with coding requirements
and greatest practices of the Apache Sedona mission, and has additionally been very useful within the
instrumentation of CI workflows verifying sparklyr.sedona works as anticipated with snapshot
variations of Sedona libraries from improvement branches.

The creator can also be grateful for his colleague Sigrid Keydana
for worthwhile editorial strategies on this weblog submit.

That’s all. Thanks for studying!

Picture by NASA on Unsplash


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