NEWS

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spacescans 0.3.4

• Tests only; nothing in the package changed. One test expected /bin/sh back unchanged from the interpreter lookup, which canonicalises the directory: on a Linux with a merged /usr (current Ubuntu) that is /usr/bin/sh. It now uses a file of its own. The tests that need a POSIX shell, 0700 permissions or POSIX paths are skipped on Windows, which is not a supported platform. (Seen in the R-universe checks of 0.3.3.)

spacescans 0.3.3

Bug fixes

• With Python 3.13 or later, interrupting ss_link() could, rarely, leave the Python bridge hung: R stopped it only after spacescans.interrupt_grace (10 s), and a bridge whose R session had died stayed behind. A cancelled job could also be reported as failed instead of interrupted. Fixed in the Python package.

spacescans 0.3.2

New features

• workers now also covers the raster weights (NPS Soundscape Noise, VIIRS Nighttime Lights, TEMIS UV Exposure, ACAG), the distances of NHD Bluespace and TIGER Road Distance, and the HDF4 path of the TEMIS reader: Step 1 of every catalog source uses the workers. The result still does not depend on their number.

spacescans 0.3.1

New features

• ss_link(workers =) and ss_run(workers =): worker processes per pipeline step for the polygon-overlap weights (block groups, tracts, ZCTAs) and ACAG's NetCDF files. The default NULL is one process, as in 0.3.0. The result does not depend on the number of workers; each worker adds memory.

spacescans 0.3.0

Breaking change

• ss_link() returns one row per participant by default. The new argument output_level = c("participant", "episode") chooses the level; use output_level = "episode" for the behaviour of 0.2 (your data frame with the exposure columns added, one row per location episode).
• output_level is the third argument of ss_link(): a call that passed data_dir by position must name it (data_dir = ).

New features

• Participant level: pid (in the type of your id column) and one column per exposure, one row per participant in order of first appearance, including participants without any value. Each value is the mean of the participant's episode values weighted by contributing days, counted per variable (see ?ss_link). The result carries attr(, "output_level").
• Linkage rates (attr(res, "coverage")) and the warning for a variable with a value for fewer than 90% of the rows count participants at participant level and episodes at episode level. ss_coverage() still estimates per location episode.
• Registered datasets: ss_register_dataset() checks and prepares your own data once (polygon tables keyed by a Census geography, static, yearly or daily; GeoTIFF rasters, static, yearly, biweekly or daily; point, line or polygon features, linked as the distance to the nearest feature) and stores it with its metadata in a registry shared with the Python package. ss_datasets(), ss_dataset(), ss_update_dataset() and ss_remove_dataset() list, show, change and remove datasets; the option spacescans.datasets_dir (or the environment variable SPACESCANS_DATASETS_DIR) chooses the registry.
• ss_link(custom = ) and ss_coverage(custom = ) take registered dataset names, ids, variable keys or records, alone or together with ss_custom_polygon() and ss_custom_raster() definitions, in the order given; variables may then be empty.
• Jobs (ss_link(wait = FALSE), ss_job_attach()) work at both levels; job.json records the level and counts, never participant ids. A job directory written by 0.2 is read as episode level.
• ss_link() refuses, with an ss_install() hint, a Python package that returns episodes when participants were asked for.
• ss_install() works after an install from r-universe: it reads the repository from RemoteUrl and installs the Python package from the recorded commit (RemoteSha), or from the tag of the package version when no commit is recorded.

Package

• Released under the MIT License (LICENSE).
• Title: "Link Time-Referenced Geolocation Data to the Spatial and Contextual Exposome".

Documentation

• The vignette and the README use the terms location episode, participant and time-referenced geolocation data, explain both output levels, and show two example scripts installed with the package: inst/examples/minimal-example.R (three synthetic participants and the NPS noise model, the catalog source whose files are all on the project mirror) and inst/examples/registered-dataset.R (a registered features dataset, no catalog data needed). The live tests run both.
• ?ss_link and the vignette state two rules of the participant level: overlapping location episodes each contribute their own days, and variables derived after aggregation (the FARA flags, the ACAG _nbm columns) are derived from the participant's means.