|Figure 2: The Guatemalan government relies on expensive, time-consuming surveys to gather poverty distribution data.|
|Figure 3: More accurate, updated data means poverty aid can be better targeted.|
The study’s results are encouraging. Researchers concluded that CDRs can predict poverty distribution fairly accurately when measured against the collected data from recent surveys. Accuracy was higher in urban areas, where there are higher concentrations of poverty, due to the penetration of mobile phone usage.
|Figure 4: Actual Poverty Rates for Municipalities of Study.|
|Figure 5: Modelled Poverty Rates differ in the Municipalities of Study.|
These conclusions mean that CDR-based data collection can be used as a supplement to the more costly surveys to provide the Guatemalan government with updated, real-time poverty information. Currently, the CDR analysis does not fully match the accuracy of census data, but it is a strong complement. Additionally, researchers theorize that using a larger data set than the initial five regions used for this study will allow for greater accuracy and new possible applications of the data.
Enrique Frías, who works as one of our lead researchers in Telefónica and LUCA’s R&D department said:
“This study will help to implement and measure public policies in a very effective way, it has the potential of changing how to tackle and advance in the fight against poverty.”
|Figure 4: Big Data can be a valuable tool in the fight against poverty.|