November 24, 2014
I worked for a while in broadcasting during the 1960s. It was an interesting time, since I was introduced to personality types that aren't found in an academic or scientific environment. Those were the days of "top forty, news, weather, and sports," and the on-air personalities were interesting characters for the simple reason that they needed to be or they wouldn't have been hired.
At that time, the advertising manager had an interesting idea on how to do a survey to determine the relative popularity of the AM radio station in the local market. His idea, which he likely read in one of his trade publications, was to see what stations were tuned-in on the radios of parked cars around the city. This was easy, since there were just four radio stations at the time. Today, that type of survey couldn't be done for two principal reasons. First, people would wonder about people peering into every parked automobile on a street; and, second, modern radios show no indication of what station was tuned when the automobile is off.
In those days, however, radios were mechanically-tuned with a knob that also moved a frequency indicator along a slide display (see photo). Since the FCC assigns station frequencies at intervals to prevent interference between stations, it was easy to see from this indicator which of the four stations (950, 1250, 1310, and 1550 kHz) was last heard. Later, car radios were designed to receive both AM and FM radio stations, the tuning dial was used for both, and it would have been hard to determine whether an AM or an FM station was last tuned; but, this survey method was workable at that time. Our station ranked first in this survey.
Wireless technologies, such as RFID, have enabled very efficient inventory control. This is, in effect, a survey of goods, but other wireless devices enable surveys when that's not their intended purpose. One controversial method of traffic survey is through use of the now ubiquitous E-Zpass system that's intended for electronic toll collection. Since E-Zpass RFID tags can be detected at non-toll locations, they're also used to provide estimates of travel time between points. While it's claimed that these data are scanned in an encrypted form and deleted as soon as the travel time estimate has been completed, people are still concerned about later "enhancements" to these systems.
Nearly everyone has a cellphone, and this includes residents of Africa, who use cellphones because there are few landline telephone. The technology exists to track cellphones, at least at the granularity of the cell tower grid, since cellphones transmit an unique identification number along with their voice and data signals. Such tracking has been used to assemble data on population movements in Africa that might predict how the Ebola virus would spread.
Orange Telecom, a West African mobile network operator, has provided cellphone location data for Senegal, and it previously provided such data for the Ivory Coast. The Senegal data was from 150,000 phones in 2013, it was anonymized and aggregated, and then analyzed for population movement by Flowminder, a Swedish nonprofit organization. Although not yet used in the Ebola campaign, the data do show where people go after leaving an Ebola hot spot, thus suggesting where the disease will appear next. As in the E-Zpass example above, such tracking could reveal social and business connections of individuals, so privacy problems do exist.
Scientists from the Université catholique de Louvain (Louvain-la-Neuve, Belgium), Northeastern University (Boston, Massachusetts), the Fonds National de la Recherche Scientifique (Brussels, Belgium), the Université Libre de Bruxelles (Brussels, Belgium), the Université de Lorraine (Vandoeuvre-lès-Nancy, France), the University of Louisville (Louisville, Kentucky), the University of Southampton (Southampton, United Kingdom), the National Institutes of Health (Bethesda, Maryland), and the Flowminder Foundation (Stockholm, Sweden), have recently used cellphone data as a means for census-taking. Their study showed that such estimates compare favorably to data from traditional techniques.
The usual census-taking method, at least in the US, involves mailed questionnaires, and home visits to those who don't respond. All this could be done more quickly, although with less accuracy at first, using cellphone data. However, in the extended period between traditional census-taking, it would offer better accuracy. The international research team, led by geographer, Catherine Linard, of the Université Libre de Bruxelles and data scientist, Pierre Deville, of the Université Catholique de Louvain used cellphone data as a way to estimate the population density of France and Portugal. They used a dataset of more than a billion call records from these countries.
In the case of Portugal, the call records were for two million users, which is about 20% of the population. For France, the records were for seventeen million users, which is about 30% of the population. Call records for Portugal included the cellphone identifier code, the locations of the originating and receiving cellphone towers, and the start and stop times of the call. The data for France were limited to just the day of the call and the tower locations. Corrections were made to allow for the fact that cell towers are not uniformly distributed.
Not unexpectedly, the population distribution varied during time of day, time of week, and season of the year; e.g., holiday and vacation time away from work is generally sacrosanct in France. More people were in cities during the work week, and there were more people in rural areas on weekends. The authors remark that texting might be more popular in some countries than voice communication, and that would modify the data. Politicians are not about to relinquish their fortunes to electoral districts apportioned by cellphone usage. However, these data would be useful in countries where traditional methods of census-taking are unreliable. One example cited in a Science article is that the last census of the Democratic Republic of the Congo took place in 1984.
Possibly the most important application of such cellphone data is the ability to track population flow in emergencies to allow for adequate impact assessments and intervention planning. As the authors write in their article,
"...The prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography."
- David Talbot, "Cell-Phone Data Might Help Predict Ebola's Spread," Technology Review, August 22, 2014.
- Pierre Deville, Catherine Linard, Samuel Martin, Marius Gilbert, Forrest R. Stevens, Andrea E. Gaughan, Vincent D. Blondel, and Andrew J. Tatem, "Dynamic population mapping using mobile phone data," Proc. Natl. Acad. Sci., published ahead of print, October 27, 2014, doi:10.1073/pnas.1408439111. This is an open access article with a PDF file available here.
- Jia You, "Taking the census, with cellphones," Science, October 27, 2014.
- Pierre Deville, "Dynamic Population Mapping Using Mobile Phone Data," YouTube Video, October 27, 2014.
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