Category Archives: GIS

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.

Posted in Data, GDAL, GIS, Qatar RE, Solar, Spatial Analysis | Leave a comment

Glare from Solar PV Modules

PV_array_glareA common question concerning the safety of photovoltaic (PV) power systems is the impact of reflected sunlight.  PV modules have the potential to impact neighboring structures or activities, notably aviation.  It is important to know where the reflected light will go and what the intensity of the light will be at any point in time.

Posted in Dust, Engineering, GIS, Irradiance, O&M, Projects, Qatar RE, Solar | Leave a comment

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.

Posted in GDAL, GIS, R Data Import, R Graphics, Spatial Analysis | Leave a comment

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