spaRtially: A small blog about spatial processing in ‘R’

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We are happy to report that our MODIStsp package for automatic preprocessing of MODIS time series has been recently approved for being included in the rOpenSci ecosystem of R packages for reproducible science! We wish to thank reviewers Leah Wasser and Jeffrey Hanson for providing really valuable insights during the onboarding review process. We think their contribution really helped in improving the package! Please also note that MODIStsp website was also migrated, and is now available at http://ropensci.

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We are happy to report that a new version of MODIStsp (1.3.4) is on CRAN as of today ! The new version introduces a strongly improved GUI (thanks mainly to @lwasser comments in her review for MODIStsp onboarding on ropensci). The new GUI facilitates the selection of layers to be processed, and allows interactive selection of the processing spatial extent over a map (thanks to @timsalabim and @timelyportfolio for implementing some changes on mapview to allow this!

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In the last months, I started increasingly using Rmd documents for preparing scientific reports, blog posts, etcetera. While I really like the flexibility offered by the system, one thing that I thought could be improved is the support for easily inserting tables. So, “inspired” also by the recent addition of the excellent insert image addin in blogdown, I decided to give it a go and try to implement some kind of addin to facilitate table insertion in Rmd documents.

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The problem I am in the process of migrating my (rather ugly) small blog from “Bloggers” to blogdown and, as several others, I choose to use the hugo-academic theme due to its good looks, simplicity, and “focus” towards researchers. One nice feature of hugo-academic is that it includes out-of-the-box a “Publications” section, allowing researchers to easily create a list of their publication as a section of the website. Unfortunately, in order to populate that list, users have to manually create one different .

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The problem Last week, I replied to this interesting question posted by @Tim_K over stackoverflow. He was seeking efficient solutions to identify all points falling within a maximum distance of xx meters with respect to each single point in a spatial points dataset. If you have a look at the thread, you will see that a simple solution based on creating a “buffered” polygon dataset beforehand and then intersecting it with the original points is quite fast for “reasonably sized” datasets, thanks to sf spatial indexing capabilities which reduce the number of the required comparisons to be done (See http://r-spatial.

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A new version of MODIStsp (1.3.3) is on CRAN as of today ! Below, you can find a short description of the main improvements. Processing speed improvements Processing of MODIS layers after download (i.e., scale and offset calibration, computation of Spectral Indexes and Quality Indicators) is now much faster. As you can see in the figure, processing time was almost halved on my (not so fast) laptop. This was achieved by modifying all computation functions so to use raster::calc() and raster::overlay() (more on this in a later post).

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As promised in my last post, here is a short guide with some tips and tricks for building a documentation website for an R package using pkgdown. In the end, this guide ended up way longer than I was expecting, but I hope you’ll find it useful, although it often replicates information already available in pkgdown documentation ! Prerequisites To build a website using pkgdown, all you need to have is an R package hosted on Git Hub, with a file structure “tweaked” with some functionality provided by devtools.

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The MODIStsp website, which lay abandoned since several months on github pages, recently underwent a major overhaul thanks to pkgdown. The new site is now available at http://ropensci.github.io/MODIStsp/ We hope that the revised website will allow to navigate MODIStsp-related material much more easily than either github or the standard CRAN documentation, and will therefore help users in better and more easily exploiting MODIStsp functionality. The restyling was possible thanks to the very nice “pkgdown” R package (http://hadley.

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We are glad to report that MODIStsp is now also available on CRAN ! From now on, you can therefore install it by simply using: install.packages("MODIStsp") In v 1.3.2 we also added the functionality to automatically apply scale and offset coefficients on MODIS original values according with the specifications of single MODIS products. Setting the new “Scale output values” option to “Yes”, scale factors and offsets are applied (if existing).

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MODIStsp is a R package allowing automatic download and preprocessing of MODIS Land Products time series, available at this https://github.com/ropensci/MODIStsp github page (See also here for additional information) v1.3.1 adds functionality for processing MODIS snow cover products, accelerated download, processing specified portions of years, plus various bug fixing and improvements. MODIStsp: the main processing GUI See here for a detailed description of introduced changes We hope you will find the new version useful and that we didn’t introduce too many bugs !

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