Share spatial data on web.

A lot of data produced in analysis is still being shown as pictures with almost zero interactivity. Wouldn’t it be nice to visualize (if not shared) data on an interactive detailed background base map. I think it would be but then the complexities of setting up a web GIS application scares people. Well, now a days there are many ways one can share spatial data using web GIS platforms without going into the complexities of installation/configuration. Following are some candidates for data sharing.


Uploading and creating maps is very easy.

Features in free packages includes

  • views per month  = 50,000
  • cloud stroage  = 100MB

Once done, one can share maps through a URL or embed map object onto a web page, provided that <iframe> element is allowed, on wordpress it is not  :). Results are good (see below)Once


Mapbox has tool for cartographic operations know as Mapbox Studio.

Mapbox also has an option for students know as Mapbox Eduction. Its a good resource to learn about web GIS.


Is an open source platform for hosting spatial data on cloud. It has advance options, for example

  • Many layers to select from which will be part of map.
  • Data can be uploaded from file, Google Drive, Dropbox and twitter.
  • Styling layer is very easy

Features in free packages includes

  • No limit on views
  • cloud stroage  = 50MB
  • If your data is from online resource e.g. URL, Google Drive or DropBox, updates to your data updates your maps (in real time)
  • Customizeable CSS 


But if you want quick/simple way then gist is the simplest (except no rasters). You need to register with github, once done login and click on gist option. Just drag drop your geojson file and that is it.

Now share your map’s URL or you can embed your map in web page like I did (below).


There are options like mangomap ( etc. but they do not have a free package thus I skipped these products. However, there are some spatial data portal like Landscape portal ( that allow free data upload. Landscape portal is a geonode implementation with customization.

Principal Component Analysis 4 Dummies: Eigenvectors, Eigenvalues and Dimension Reduction

Nice one, simple explanation

George Dallas

Having been in the social sciences for a couple of weeks it seems like a large amount of quantitative analysis relies on Principal Component Analysis (PCA). This is usually referred to in tandem with eigenvalues, eigenvectors and lots of numbers. So what’s going on? Is this just mathematical jargon to get the non-maths scholars to stop asking questions? Maybe, but it’s also a useful tool to use when you have to look at data. This post will give a very broad overview of PCA, describing eigenvectors and eigenvalues (which you need to know about to understand it) and showing how you can reduce the dimensions of data using PCA. As I said it’s a neat tool to use in information theory, and even though the maths is a bit complicated, you only need to get a broad idea of what’s going on to be able to use it effectively.

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Brown-bag seminars: Learn R by coding examples and hands-on.


Some people listens lectures/talks (“Ted talks” or “Talks at Google”) while eating lunch, its an effective way of learning new things. This habit is similar to Brown-bag seminars (I did not realize it until a friend mentioned the similarities).

Brown-bag seminars are held around lunch time to learn new things (read more here), they are short (30 to 45 minutes) thus utilizing only lunch time. Brown-bags are excellent opportunities especially for students/interns or for some one who wants to learn new things in a relaxed environment while enjoying lunch.

Geo Science Lab (ICRAF) holds weekly Brown-bags seminars related to R. These are code examples (with exercise dataset) and hands-on. Good thing about code examples is that they are easy to learn and replicate (fast track learning), I mean look at the following example (taken from seminar 6), any one can replicate these commands for one’s own use within minutes.

#load ggplot package
#Let's plot Clay by Carbon
#First Graphic
#Second Graphic- add the color of Site
#Third Graphic- add faceting of VegStructure
#Fourth Graphic- add xaxis and yaxis labels and move legend title
ggplot(data=tree)+geom_point(aes(x=Carbon,y=Clay,col=Site))+facet_wrap(~VegStructure,ncol=1)+theme(legend.position="top")+ xlab("Carbon (%)")+ylab("Clay (%)")

Below is a list of 7 seminars

1) Welcome to Brown-bags seminars 

2) Introduction to R: part 2 (data import, data frames)

3) Introduction to R: part 3 and 4  (Basic data analysis, basic stats)

4) Introduction to R: part 5 (graphs)

5) Introduction to ggplot

6) ggplot and dplyer packages

Next few sessions will be on ggplot, dplyr and raster packages.