Big data, big challenge? Together with Harald Sterly of the University of Cologne I presented a little piece of research in the Extended Spatial Analytics session of the German Geography Congress (Deutscher Kongress für Geographie) in Berlin. The project “Calling Abidjan” that we worked on with Kouassi Dongo of Université de Cocody-Abidjan was started after we successfully applied for participation of the D4D Challenge. According to the initiator Orange telecommunications ‘Data for Development’ is “an innovation challenge open on ICT Big Data for the purposes of societal development”. The project allowed us to work with anonymised mobile phone data from individual call records by Orange in the country of Côte d’Ivoire (Ivory Coast).
We were interested in investigating, what non-computer scientists with a social science and urban planning background can do with such data in a more contextual rather that technically driven way and therefore explored how mobile phone call records can be used to better estimate population distribution.
For our analysis we used anonymised call data records consisting of information about the base station, timestamp, and caller ID produced by the approximately 500.000 Orange Télecom users in the country. There were 1079 base stations at the time the data was generated and we were able to work with data covering 183 days. The dataset consisted of 13GB of raw data which some would perhaps call ‘Big Data’ (though I personally do not like this term for many reasons).
The following two (draft) maps give an insight into the results. The purple circles show the distribution and density of population estimates that we derived using only mobile phone call records dataset. To better see the correlation with what other population data tells us about where people live, we did not only produce a normal land area map (on the left, also displaying some basic idea of the topography in the country) but also showed the data on a gridded population cartogram which we generated from the LandScan population grid, the perhaps most detailed population dataset currently available on a globally consistent high-resolution basis:
Category Archives: GIS
Hyperspectral remote sensing and analysis of intertidal zones
The growing amount of remotely sensed data and the ongoing developments in the improvement of spatial and spectral resolutions lead to high expectations. These often inflated expectations are usually not fulfilled. I explored these expectations and aimed to make a contribution to bring them to a more accurate level in research in the field of hyperspectral image analysis of small scale and heterogeneous biotopes in the intertidal zones of coastal areas which I undertook back at my time at the University of Cologne and the Alfred Wegener Institute for Polar and Marine Research Bremerhaven. Here are some insights from my work.
Overview of the study area on the islands of Helgoland and Sylt, Germany
(click for larger version)
Rediscovering the World
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A population-centric map projection
Here is some material from a presentation at this year’s AAG Annual Conference in Washington DC. The presentation People powered maps: A population-centric map projection was given in the session on Topics in GIS, Remote Sensing, and Spatial Analysis and showed some new works on our grid-based cartograms (as presented at GISRUK 2009 and ESRI UC 2009).
The following animation shows the transformation of a topographic map of the United States, ending in a grid-based population cartogram (and then reversing). Please notice that loading the animation takes a while on slower internet connections:
This is the full presentation given at the AAG Meeting. Please note that the animated parts such as the above animation are not shown in this Slideshare version:
The content on this page has been created by Benjamin Hennig. Please contact me for further details on the terms of use.
Gridded cartogram tutorial
This is a short slideshow showing the basic steps that are needed to do your own gridded population cartograms (with a quite rough 1 degree grid – good for starting with this whole thing). Software needed for this simple click-through tutorial are ArcGIS and ScapeToad. If you want to go one step further, I’d recommend using the ArcScript Cartogram Geoprocessing Tool by Tom Gross, even though this is not featured in this demo:
The tutorial was given in October 2009 to students of the Module GEO6016 Data, Visualisation and GIS in the MSc in Social and Spatial Inequalities at the University of Sheffield.
The content on this page has been created by Benjamin Hennig. Please contact me for further details on the terms of use.