Data Analysis

Data cleaning and preparation is mostly necessary before doing any data analysis. Incorrect or critical data entries, such as missing values, duplicates or misspellings, need to be detected and corrected. Experience shows, this can be quite time-consuming and can’t be handled manually for big data but must be done automatically with software like R.

There are a variety of methods for data analysis in order to get the most out of large data sets:

Geodata Processing

  • Coordinate Transformation
  • Spatial polygon aggregation
  • Simplification of geodata


Descriptive Statistics

  • Extreme values: maximum, minimum, quantiles, percentiles
  • Mean values, median and mode
  • Scattering parameters

Uni- and Multivariate Statistics

Digital Image Processing

  • Gradientoperator
  • Averaging Operator