Digital Geography

Some ways to produce topographic swath profiles

For a geomorphological study that I am working on I want to produce topographic swath profiles across a mountain range, that is, I want the average elevation along a profile plus the min and max values within a certain distance of said profile. I have used three different methods to achieve that and found some nice resources that I’d like to share with you: GMT – Generic Mapping Tools GMT is a powerful suite of command-line small programs to manipulate all kinds of geographic data (Wessel and Smith, 1998; Wessel et al., 2013). A walk through on how to produce…

Webmaps with R: the leaflet package for R

Some months ago I published qgis2leaf which enables a QGIS user to publish a webmap the easy way. It was integrated into qgis2web which offers a leaflet and a openlayers based output for qgis users. But what about R users? Jean-Francois recently published a longer post about GPX tracks and to publish them using some heavy coding. So let’s welcome leaflet for R: an easy leaflet webmap exporter.

GPX overview: An R function to create an overview of your .gpx files (using leaflet and RgoogleMaps)

Why GPX? For what? It's convenient to record tracks of your hiking/field trips with the GPS of your smartphone, tablet or just GPS as .gpx files. You can use them to georeference your pictures (for example with the great georefencer of Digikam) or use them for any kind of mapping purpose. I'm mainly using Maverick (and sometimes the Offline Logger ) to do that, Maverick creates files named with the form "2015-08-26 @ 11-31-59.gpx", therefore I'm quickly collecting a large amount of such files.

Using Jupyter for data analysis

Data analysis in the modern-day computing industry is of great essence as the world tries to understand the data that has been accumulated in many systems across the globe. Extraction of useful information is a task being focused so much in most organizations as this is marking the lifetime for existence in the business world.

Overview: R Cheat Sheets

Recently we had a discussion about where to find a nice cheat sheet (No! We are not talking about Simon the Sorcerer walk-throughs). Especially for scripting languages cheat sheets are an excellent way to support your learning attempts and are a handy tool for your every work in data visualisation, automation of tasks and analysis steps. So let’s have a look on R cheat sheets.

Create custom markers with R for your webmap

When it comes to webmapping there are thousands of possible markers you can choose from but when it comes to markers depending on the data, which is inside the shapefile, possibilities are more limited. In leaflet you can define different icons according to the attributes of your data by defining the icon url in an attribute. Let me show you, how to use the data in each feature to create a custom icon like a piechart marker using R.

short announcement: new R learning material

Coursera, hail to Coursera. Despite the uprising criticism on MOOCs and their footprint in the educational landscape at universities Coursera created an interesting R learning course. It is divided and scheduled for 4 weeks and has video-tutorials as well as written material. The guys over at RevolutionAnalytics packed it all together: Content: Setting working directory and getting help How to get help Data Types Subsetting Vectorized Operations Reading/Writing Data Control Structures in R Writing Functions Avoiding loops using xapply Plotting Regular expressions Regular expressions in R Classes and methods in R It is a free course and is very userfriendly. The…

Shiny @RStudio: an analytical webapp

As i was preparing myself for getting funding for the trip to the R user conference this year in Albacete, Spain I was coming across a highlightning talk by Josh Paulson about an interactive way of using the power of R without real struggling with R as a programming language: Shiny is a cool webapp which lets the user control the application via some drop-down menus and buttons and R computes the result in the background and displays them as well on the webpage:

unsupervised classification of a Landsat image in R: the whole story or part two

The main question when using remote sensed raster data, as we do, is the question of NaN-treatment. Many R functions are able to use an option like rm.NaN=TRUE to treat these missing values. In our case the kmeans function in R is not capable to use such a parameter. After reading the tif-files and creating of a layer stack we will go on with a work-around to solve the missing values problem of the non-covered areas of a Landsat picture.