My experience with the GSoC 2017 project “Simple Features for Protobuf and others” came to an end by presenting my results at the Geospatial Sensor Webs Conference 2017. It was really pleasing to see that a majority of the audience was really interested in the project outcome and eager to see it progress in the future.more >
The core idea of my GSoC 2017 project “Simple Features for protobuf and others” is to define and implement a serialization API for spatial vector data, which will transparently serialize geometries based on the Simple Feature specification using Protobuf into binary encoding. The Simple Feature Access Specification is a common standard that is widely used in geoinformatics for exchanging spatial features. This serialization API also supports decoding serialized binary data into prefered output models such as JTS and others. Protocol Buffers is used as the primary serialization framework. Other serialization frameworks, such as Avro, are being considered as well. I am currently implementing serializing support for raster data into the API, where it will utilize raster data formats, such as GeoTIFF, and modeling libraries, such as GeoTools.ommon standard that is widely used in geoinformatics for exchanging spatial features. This serialization API also supports decoding serialized binary data into prefered output models such as JTS and others. Protocol Buffers is used as the primary serialization framework. Other serialization frameworks, such as Avro, are being considered as well. I am currently implementing serializing support for raster data into the API, where it will utilize raster data formats, such as GeoTIFF, and modeling libraries, such as GeoTools.
ArcGIS is the central tool to handle and derive geoinformation in many applications. However, the standard kriging capabilities only include a few covariance functions and hide the estimation and fit quality of the semivariogram to a large degree. R is less appealing for working with maps, but features a variety of statistical, i.e. geostatistical, extensions. In this showcase, we exploit the semivariogram modeling and kriging capabilities of the gstat R package. In order to give the user visual control over the estimation procedure and the model selection, we use the Shiny framework to realize an interactive graphical user-interface for the semivariogram fitting step. This tool hides the entire R implementation from the user, but delivers a good deal of the geostatistical power to perform kriging.more >