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I'm interested in doing data visualization projects about mountains.

We have data from http://www.peaklist.org/ultras.html complied by Aaron Maizlish (Who we tried to contact but not sure of his contact details these days?)

The data he was curating contains name, elevation, co-ordinates and Key Saddle. The key bit of data we're after is the distance from elevation to either key saddle or sea level to get a very rough outline of shape.

I'm not sure if such data exists or we would have to derive it from some topography database? Would anyone have any ideas on how we might go about getting said data or access to free topographic data that we may be able to somehow match in?

I've attached the below artwork that inspired this project - while we wouldn't necessarily look to get all the shape types here, it would be nice to have some different natural shapes.

The idea being somewhere in between pure Art & pure geography

Thanks, Damian.

enter image description here

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    $\begingroup$ Hi Damian. Welcome to Earth Science SE. I can help you with this question but first I would like to know if you are interested in data at a global scale or in a specific area. Also: why you want to use the key saddle to get an idea of the shape instead of the slope in the surroundings of each mountain? That approach is awkward, for instance the key saddle of Mt. Denali in Alaska (the highest in North America), is in Nicaragua, and that would give you a very wrong idea of the shape of Mt. Denali. $\endgroup$
    – Camilo Rada
    Commented Feb 20, 2019 at 17:38
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    $\begingroup$ Hi Camilo! thanks for the reply I was originally thinking that at a global scale - say top thousand or so peaks across the globe might be most engaging rather that a specific area. I was thinking key saddle as a proxy to get and idea of the basic shape but if gradient or some other measure was available this would be fantastic! $\endgroup$
    – Damo Frankcom
    Commented Feb 21, 2019 at 4:11

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After investigating a number of the options and data outlined I thought the STRM was the best bet. I was unable to find a place where I could download the data in a file that I could read into some software I'm familiar with the DEM file was in a binary format which I struggled with to ready into R so I started to work with the JPGs to read these in as raster image in R. I faced a couple of roadblocks around how to query a bringing that back to latitudes.

Eventually I came across the R Package: rgbif: Interface to the Global 'Biodiversity' Information Facility API With this package you can query a specific lat & long and return the elevation in meters! also as a bonus you can get some biodiversity information. It connects to the geonames database other software that has clients to connect to the data can be found here

#Here is some R code for the POC:
#install.packages("rgbif")
library(rgbif)


#You need to sign up to your own Geonames account you get 1000 queries per hour for free max 30k per day
user <- Sys.getenv("GEONAMES_USER")

#I take 15 points of latitude either side of the peak at 0.005 intervals
Latitude <-c(-17.3991666666667,-17.4041666666667,-17.4091666666667,-17.4141666666667,-17.4191666666667,-17.4241666666667,-17.4291666666667,-17.4341666666667,-17.4391666666667,-17.4441666666667,-17.4491666666667,-17.4541666666667,-17.4591666666667,-17.4641666666667,-17.4691666666667,-17.3941666666667,-17.3891666666667,-17.3841666666667,-17.3791666666667,-17.3741666666667,-17.3691666666667,-17.3641666666667,-17.3591666666667,-17.3541666666667,-17.3491666666667,-17.3441666666667,-17.3391666666667,-17.3341666666667,-17.3291666666667)
Longitude <-c(145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333,145.818333333333)

#Use the elevation function to return the elevations
Bartle <- elevation(latitude=Latitude, longitude=Longitude, username = user)

#Order my co-ordinates for the plot
Bartle <- Bartle[order(Bartle$latitude),]
plot(x=Bartle$latitude, y=Bartle$elevation_geonames, main = "BARTLE FRERE, SOUTH PEAK")
lines(x=Bartle$latitude, y=Bartle$elevation_geonames) 
polygon(cbind(c(min(Bartle$latitude), Bartle$latitude, max(Bartle$latitude)), c(min(Bartle$elevation_geonames), Bartle$elevation_geonames, min(Bartle$elevation_geonames))), col="#00CC66")

Bartel Frere Example

So I hope to work on this over the next couple of weeks and hope to update further - but here is the start just localised to Australia

Example of basic Tableau Viz

Thanks Again Camilo!

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It seem that for your application more than mountain height and key saddle you want topographical profiles (like the ones in the figures).

To create such topographical profiles you can download a Digital Elevation Model of the area of interest and analyze it with specialized GIS software like QGis, Global Mapper or ArcGis. A recommended global digital elevation dataset is SRTM, but as it doesn't cover the polar areas should be combined with ArcticDEM, and REMA. Alternatively ASTER GDEM is a good one with global coverage or ALOS W3D.

If you don't want to download data and use specialized software. You might be able to get what you want from a online topographic profiling service like ArcGIS Profile geoprocessing service. This service allows you to draw a line anywhere in the world and get its topographic profile. For example, a profile I just made there of mount Aconcagua looks like this:

enter image description here

If you still want to calculate key saddles, you have to download the Digital Elevation Model and use even more specialized software, like winprom.

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  • $\begingroup$ This is Awesome thanks Camilo - I will research these options. $\endgroup$
    – Damo Frankcom
    Commented Feb 27, 2019 at 5:44
  • $\begingroup$ @DamoFrankcom Great! if you find the answer satisfactory, remember to accept it. $\endgroup$
    – Camilo Rada
    Commented Feb 27, 2019 at 6:22

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