Category Archives: GDAL

SpatialPoints in R: Large Data Case Study

A common task in spatial data analysis is  extracting SpatialPoints inside a set of polygons or buffer zones.  Analysts can use standard GIS or map tools to extract a set of points within an area of interest using manual “point-and-click” routines. This method is easy, but will probably prove impractical, especially in cases involving big data.  The alternative is to train a machine to automatically extract the points in a polygon or buffer zone.  This post achieves that task and presents a case-study with R code.

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Crop Raster Images in R

E020N40The maptools package has a pruneMap() function t0 crop map objects in R.  In practice, the function extracts data from SpatialPolygon or SpatialLine objects given a boundary box or specific area of interest.  Unfortunately, there is no equivalent function for high resolution, large data, raster images, which are common in many Earth Science applications.  The following post defines a custom function to crop raster images in R and to extract data from SpatialGridDataFrames.  The function is tested using a raster image from the Shuttle Radar Topography Mission (SRTM; shown at left).  The resulting data is then mapped using the image() function in R.

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Qatar National Grid (QND95)

surveyingQND95 is a two dimensional coordinate reference system that is the standard for geographic mapping in Qatar.  QND95 is intended for onshore activities only. QND95 provides up-to-date specs to calibrate surveying tools, GPS devices, GIS tools, and analysis activity.  The coordinate reference system facilitates standardization and consistency across activities. 

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Qatar: Meteosat Solar Data

This is the second article in a series on Meteosat solar data from EUMETSAT.  The intent is to define the basic parameters of meteorological data coverage for the State of Qatar.  Specifically:

  • Simple trigonometry is defined to assess the resolution of the satellite coverage area; 
  • A land surface analysis is conducted to visualize the geographic coordinates of the satellite pixels across the State of Qatar; 
Posted in Data, GDAL, GIS, Qatar RE, Resource Assessment, Solar, Spatial Analysis | Leave a comment

Binary Data In R

There are many reasons to work with binary data in R.  Solar resource data, solar PV performance data, and real-time grid monitoring data are typically stored and transmitted in binary data formats.  

In practice, the ability to access binary data in R is impossible in the absence of a vender or format specific “can opener” and a properly configured scientific programming environment.  As a result, many business applications often bypass binary data use altogether or, instead, rely on secondary sources and summary statistics with no ability to validate data integrity and accuracy.  

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