“Calling Abidjan” – estimating population distribution through analysis of mobile phone call data records

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:

Population maps of Ivory Coast / Côte d'Ivoire created using Mobile Phone Call Records
(click for larger version)

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Urbane Veränderungsprozesse in Stadtregionen Deutschlands

This is a German-language poster contribution looking at processes of change in the major urban agglomerations in Germany and novel ways of visualising these using cartogram visualisation techniques. Continue reading

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.

Map overview of the Study Areas on Helgoland and Sylt
Overview of the study area on the islands of Helgoland and Sylt, Germany
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Visualising wilderness


(click for larger version – high resolution PDF)

Following an article on IFL Science (which itself followed a recent online and print article in Popular Science) here comes some further background and material from my work on visualising travel times to the nearest large cities Continue reading

Inequality and Sustainability

“We should … dethrone the idea that maximising the growth in measured prosperity, GDP per capita, should be an explicit objective of economic and social policy.”
Adair Turner, Chair of the UK Financial Services Authority, 2007

Today I gave a talk at the meeting of the Sustainability Knowledge Alliance and the Environment Audit Committee (EAC) of the UK Parliament at the British Academy in London. The event aimed at discussing the relationship between growing inequality and sustainability. As the meeting’s announcement explains, “in so many ways inequality is a backdrop to many features of modern political, economic and social arrangements where structures of self-reinforcing power and influence combine to buttress non-sustainability. We see this in the lobbying for the perpetuation of a carbon economy, in the promotion of the “war on terror”, and in the huge biases built into the interweaving connections between business, politics, regulation and consumerism.”
In my talk I explained how inequality and a consumption correlate. I looked at the issue mainly from a global perspective, using evidence that Danny Dorling and I compiled to find out to what extent inequality and (un)sustainability correlate. The following series of charts give in insight into how the level of inequality and a range of indicators related to consumerism and consumption compare:

Inequality and the ecological footprint
Inequality and the ecological footprint Continue reading

Visualization of Satellite Data Availability

The currency of geodata is an important factor for many advanced geospatial applications. Examples for this are security questions in the control of international borders and coastal areas, or up-to-date information following natural hazards. Here a near real time availability of geoinformation is of high value. A wide range of commercial satellites providing near real time information are available. Satellites with active sensors, such as Synthetic Aperture Radar (SAR) systems, can deliver such information even at night and in areas with cloud coverage.

Cartogram visualisation of the TerraSAR-X and TanDEM-X latency
(click for larger version)

The German SAR satellite mission with the TerraSAR-X and TanDEM-X satellites provides coverage of every location on earth within 1-3 days. The acquired data can be made available for processing within hours or in certain cases even minutes. Continue reading