Category Archives: fme

Wilderness 3d: The Canary Islands wilderness

Wilderness on Lanzarote, Canary Islands, Spain.

Wilderness on Lanzarote, Canary Islands, Spain.

Like many other Europeans I have had my share of visits to the Canary Islands (three and counting). The climate is decent from February until November. Some even like it in December and January. Most of us go to the Canary Islands for sunbathing, for long walks, to swim, spend time with friends or just get away from it all.

Few, if any, go to the Canary Islands for the wilderness. In this article I will be looking at your options if you were interested in getting away from people in the Canary Islands. It is not easy, but it is possible. If you are looking for wilderness in the Canary Islands I would suggest going to Lanzarote. Parque National de Timanfaya is the easiest accessible wilderness area.

The above map is based on publicly available vector data from the OpenStreetMap-project covering the Canary Islands. A wilderness analysis based on insufficient data will only represent a map of more or less mapped areas. In this case the basis for the analysis is decent. There will still be many errors.

The analysis has not in any way been sanctioned by Spanish authorities. It will not be used by the authorities and the audience (GIS geeks) of this posting is anyway quite limited.

The criterias used to map the wilderness areas based on OpenStreetMap in the Canary Islands are as follows:

  • Roads: primary, secondary, tertiary, unclassified, motorway, trunk
  • Land use: industrial, reservoir, military, farmland, residential, orchard, commercial, quarry, farmland and salt_pond
  • Railways
  • Waterways: canal

Like it or not – the presented map is what you get.

Wilderness 4: Wilderness degradation analysis

Area with wilderness and encroachment. Visualization of change of wilderness status.

Area with wilderness and encroachment. Visualization of change of wilderness status.

Wilderness degradation happens when new encroachments are made changing the wilderness status of an area. It is a complex issue which does not easily lend it self to a GIS based analysis. I will refer to my posting on wilderness for a peek into the complex world of wilderness philosophy.

It is possible to set up a system like FME to do an analysis of changes in wilderness due to new encroachments. The procedure I made generates a wilderness degradation data set based on wilderness and (new) encroachment data. It is based on procedures used by the Norwegian government in their analysis of wilderness and encroachment. The system should however be easy to accommodate for different preconditions by manipulating the number of wilderness zones and/or their buffer distances.

To visualize the degradation of wilderness it is necessary to make a categorization and furthermore establish a cartography to carry the information to the reader. In my view this can not be done unless the author/mapmaker to some extent takes side in what is good or not good related to wilderness degradation. This will be the focus on a forthcoming posting, but I will touch into the issues here as well.

FME is but one of the potential solutions for producing the results. QGISGeoKettle and even PostGIS could be good alternatives. The GitHub project has room for alternative implementations.

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Wilderness 3: About the examples

A philosophical approach towards wilderness analysis has been written. A FME-script has been designed and appropriately discussed. Now time has come to run the script on national level data to prove the concept and also open up for a discussion on challenges with the method and its results.

I have done this for several countries and will deliver the results and discuss them appropriately.

All examples are delivered as-is. There is no governmental context to the analysis. The data used for the analysis is OpenStreetMap data.

At the time of writing this article the number of examples has yet to be decided. The following are available as of writing this posting:

General challenges with the presented data are:

  • OSM data coverage
  • Encroachment criteria selection
  • Lack of context

In later postings I will look at the following:

  • Change analysis of wilderness
  • Cartography and wilderness mapping
  • Conclusions and discussion

Wilderness 3b: The Guinea analysis

The map in this posting is the results of a calculation of wilderness based on methods discussed earlier in this series using OpenStreetMap data for Guinea.

One of the reasons why I choose Guinea for a wilderness analysis is that I do not know the country. I have not worked with anyone in the conservation scene in Guinea. I barely know the geography of the country. Guinea did however seem to have a decent OSM coverage. It has also had a lot of focus lately due to the ebola virus.  Continue reading

Wilderness 2: A practical approach to wilderness analysis using FME

Psalter World Map ca 1265

Psalter World Map ca 1265

In a former posting I discussed how wilderness is not only about politics, religion, philosophy and legal instruments. Unless we force it into a practical context, the term “wilderness” remains an intangible size. Geographers have a long history for making representations of the intangible – be it disease (John Snow), social justice and injustice, demography and more. To my knowledge one of the first impressions of wilderness or “the wild” is what we can find in some older maps. “Hic sunt dracones” (here be dragons) is an expression which can be found on the “Hunt-Lenox Globe” (c. 1503–07). Other maps bear similar indications of uncharted or remote areas. We, the geographers, have moved on. Today we paint our dragons in more sophisticated ways.

The use of technology to “find” or delimit wilderness has a long history in Norway and other countries. I will continue this tradition and do an analysis which in many ways is similar to those done in Norway. The encroachment types will be slightly different. So will the technology used to do the analysis.

In this posting I will look at how FME can be used to establish a wilderness areas data set. The results will be presented in a separate posting.

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QDGC-files updated

qdgc_logoThe Quarter Degree Grid Cells (QDGC) data set has been updated. It is well over a year since last time. Some errors in the current release has made this necessary.

One of the things which kept me from doing the update was the rather complicated python-scripts combined with using the arcpy-library. The whole process was in it self complex. I dreaded sticking my head down into the python/arcpy/manual processing soup.

Through the last year or so I have started using FME more and more. Once you know a tool well you start thinking – what if… So I thought – what  if I instead of going one more round with python and arcpy tried solving this challenge using FME? Continue reading