Resumen de Learning to build statistical indicators from open data sources
Pilar Rey del Castillo 
One of the biggest challenges facing official statistics today is the use of the massive amount of data generated on the web or by sensors and other electronic devices for the production of statistical figures. This paper presents the building of several statistical indicators from different Open Data sources. All the indicators have been built using a common methodological approach to estimate changes across time. The purpose of the paper is to show the different problems that must be addressed when using these data sources and to learn about the different ways to cope with them. The first Open Data source is traffic sensors data, where the data about the geographical location of the sensors permits to compute traffic intensity indicators at detailed geographical level. Apart from being proxies or lead indicators for economic activity, the figures can be used to measure the impact of different traffic arrangements in specific areas. Before constructing the indicators for the following source, call records from a multichannel citizen attention service, the data have been analyzed using Natural Language Processing tools to identify several categories of topics for the requests received. Other Open Data sources, Twitter messages and scraped data from a digital newspapers’ library website, are studied using similar tools in both situations. A rough idea about the evolution for the general sentiments in Spain is obtained from Twitter messages. From scraped data, the evolution of the average opinions and sentiments in the country’s newspapers is similarly computed. Usually, it is accepted that the ideas expressed in the newspapers are relevant to conform public opinion. On the other hand, an interesting result obtained in our research is that individuals react stronger and more quickly than newspapers to some social, political or economic events.
