Articles repérés par Hervé Le Crosnier

Je prend ici des notes sur mes lectures. Les citations proviennent des articles cités.

  • How much electricity does a country use? Just ask cell-phone users. - MIT Technology Review
    https://www.technologyreview.com/s/613987/how-much-electricity-does-a-country-use-just-ask-cell-phone-users

    Socioeconomic data is generally expensive and difficult to gather. The most important data generally comes from censuses and reveals the size of the population, its geographical distribution, its age and gender structure, and a host of other details.

    But a census requires significant, costly planning, carefully analysis, and a relatively stable society. That makes such studies hard to do in the developing world, where countries are often by afflicted by poverty, war, disease, and famine.

    So economists, sociologists, and policy experts would dearly love a cheaper and faster way to gather data. And in recent years, just such a method has emerged thanks to mobile phones.

    Mobile phones have spread widely in the developing world, more quickly than other services such as electrification. In Senegal, for example, only 24% of households are electrified, and yet 75% have mobile phones, with people presumably charging them from car engines, from neighbors, or wherever they can.

    Today, we get a partial answer thanks to the work of Hadrien Salat and colleagues at the Future Cities Laboratory in Singapore. These guys have analyzed mobile-phone data from Senegal and say it has the potential to help infrastructure planning for the entire country.

    It can also be used to estimate factors such as electricity usage even when it includes just a fraction of total inhabitants. “Our aim is to use the resulting data to reduce the logistic costs of gathering information for infrastructure planning in developing countries,” they say.

    The results offer some interesting surprises. For example, they find that mobile-phone activity is not strongly correlated with the population density found in the census. However, mobile-phone activity is strongly correlated with electricity consumption. Indeed, it is a significantly better indicator than population density.

    At first sight that is something of a puzzle. But Salat and co explain the result by suggesting that electricity consumption is the result of a range of interlinked activities that are better correlated with mobile-phone usage than with the density of people alone.

    #Géolocalisation #Recensement #Développement #Urban_planning #Infrastructure

    • Résumé de l’article source sur arxiv (le texte est accessible en pdf) :
      [1907.04812] Mobile phone data’s potential for informing infrastructure planning in developing countries
      https://arxiv.org/abs/1907.04812v2

      High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a trending proxy to evaluate population density, activity and social characteristics. They offer additional advantages for infrastructure planning such as being updated in real-time, including mobility information and recording visitors’ activity. We combine various data sets from Senegal to evaluate mobile phone data’s potential to replace insufficient census data for infrastructure planning in developing countries. As an applied case, we test their ability at predicting domestic electricity consumption. We show that, contrary to common belief, average mobile phone activity does not correlate well with population density. However, it can provide better electricity consumption estimates than basic census data. More importantly, we use curve and network clustering techniques to enhance the accuracy of the predictions, to recover good population mapping potential and to reduce the collection of required data to substantially smaller samples.


      Figure 2 Curve profiles and network structure. (a) Number of calls per hour aggregated at national level for each day of the year. (b) Yearly average of the number of texts per hour of the day sent from each tower. (c) Network structure limited to edges corresponding to at least 2000 text messages sent in January.