# Category Archives: Spatial Analysis

## Geospatial Data and Mapping in R

I share slides presented at a recent meeting of  Doha R users on geospatial data and mapping in R .

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## 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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## Aerosol Animation

Aerosol Optical Depth (AOD) defines the degree to which aerosols prevent the transmission of sunlight by absorption or scattering.  AOD is measured using an integrated extinction coefficient over a vertical column of air.  The extinction coefficient can be used to analyze solar extinction and the performance of solar power systems as a function of location and time.

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## Crop Raster Images in R

The 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: 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;
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## Solar Data Animation

A solar data animation is supplied to assess quality, spatial and time series dimensions:

• The quality dimension is a function of the availability of different sunlight components: Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI).
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