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HomeArtificial IntelligenceA sparklyr extension for analyzing geospatial information

A sparklyr extension for analyzing geospatial information


sparklyr.sedona is now out there 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 publish, we are going to 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 yr talked about the necessity for up-to-date R interfaces for Spark-based GIS frameworks. Whereas wanting into this suggestion, we realized about Apache Sedona, a geospatial information system powered by Spark that’s fashionable, environment friendly, and straightforward to make use of. We additionally realized that whereas our buddies from the Spark open-source group 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 subsequently determined to work on sparklyr.sedona, which goals to bridge the hole between Sedona and R.

The lay of the land

We hope you might be prepared for a fast tour by way of a few of the RDD-based and Spark-dataframe-based functionalities in sparklyr.sedona, and likewise, 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 equivalent to Coordinate Reference System (CRS) transformations, spatial partitioning, and spatial indexing. For instance, with sparklyr.sedona, SRDD-based operations we are able to carry out embrace the next:

  • Importing some exterior information supply right into a SRDD:
library(sparklyr)
library(sparklyr.sedona)

sedona_git_repo <- normalizePath("~/incubator-sedona")
data_dir <- file.path(sedona_git_repo, "core", "src", "take a look at", "assets")

sc <- spark_connect(grasp = "native")

pt_rdd <- sedona_read_dsv_to_typed_rdd(
  sc,
  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(
  sc,
  location = file.path(data_dir, "primaryroads-polygon.csv"),
  kind = "polygon"
)

pts_per_region_rdd <- sedona_spatial_join_count_by_key(
  pt_rdd,
  polygon_rdd,
  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 should 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 best sorts of spatial partitioning and indexing.

Lastly, we are able to attempt visualizing the be a part of outcome above, utilizing 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 provides us the next:

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

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 provides us the next:

Choropleth map with overlay

With some low-level spatial operations taken care of utilizing the SRDD API and the best spatial partitioning and indexing information buildings, we are able to then import the outcomes from SRDDs to Spark dataframes. When working with spatial objects inside Spark dataframes, we are able to write high-level, declarative queries on these objects utilizing dplyr verbs along side Sedona spatial UDFs, e.g. , the next question tells us whether or not every of the 8 nearest polygons to the question level comprises that time, and likewise, 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() %>%
  dplyr::mutate(
    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
  <record>                           <lgl>       <record>
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 creator of this weblog publish want to thank Jia Yu, the creator of Apache Sedona, and Lorenz Walthert for his or her suggestion to contribute sparklyr.sedona to the upstream incubator-sedona repository. Jia has offered in depth code-review suggestions to make sure sparklyr.sedona complies with coding requirements and greatest practices of the Apache Sedona venture, 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 beneficial editorial options on this weblog publish.

That’s all. Thanks for studying!

Photograph by NASA on Unsplash

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