WPL Overview


The aim of WPL is to explore the extended use of the Internet of Things (IoT) in order to produce trusted smart statistics. As the range of topics regarding the subject IoT is huge, the goal of this WP is to provide an overview of relevant topics for official statistics, to show their variety and to highlight topics that are promising and could be analysed further. Therefore, the availability and accessibility of the different data sources will also be checked.

The goal within this WP will be to explore how the digital footprints of daily life created by human wearables, city and vehicles sensors and other smart systems could change the way to produce trusted smart statistics taking advantage of societies’ datafication. The development of trusted smart statistics aims for data that might be pre-processed by the data providers or data sources ready to use for official statistics, which usually means for the combination with already used survey and/or administrative data.

WPL will prepare the ground for future actions on trusted smart statistics by concentrating on the IoT data sources and devices with potential relevance to official statistics, resulting in an overview of the data landscape. Given the duration foreseen for this WP, no proof of concepts (in the sense of a demonstration in theory or in practice of a method) and experimental statistics are envisaged. However, the results of WPL should enable future actions to result in proof of concepts and experimental statistics.

All the results of this WP keep in mind that the overall goal is the development of generic solutions and harmonized as well as standardized approaches and recommendations for the European Statistical System (ESS).

Description of work

Task 1 – Smart farming

Performed by Statistics Austria, Destatis (Statistics Germany) and GUS (Statistics Poland)

The ESS is only at the beginning of understanding the potential use of smart statistics on agricultural inputs and production to complement the traditionally collected information. The aim of this task is to explore the possible use of data coming from precision farming in agricultural statistics and to identify potential data sources.

Task 1 will provide an overview concerning identification, access and analysis of data with regard to agricultural production such as production resources used in plant and animal production (equipment, fuel, fertilizers, fodders), health and crop protection valuation, animal welfare, water and soil resources.

Moreover, the task would help to explore the possibilities of using data from precision farming information e.g. to determine vegetation differences, crop water requirement and nutrient supply, estimation yields and occurrence of diseases and pests. Furthermore, the databases may also be examined regarding its use for the analysis of animal production, i.e.: milking of cows (quantity and quality of the milk), animal welfare, livestock feeding and livestock buildings.

This information is the basis for achieving the goals in the field of eco-development, animal welfare, food safety and, as a result, to increase the efficiency of agricultural farming.

The data will be evaluated based on modern tools in terms of: data sources, data structures and the possibilities and the method of obtaining them, including machine learning.

The subject of the task will be farms equipped with devices that meet the requirements of intelligent farming, consulting companies offering services for precision farming and companies providing smart technology.

Thus, this task aims at stocktaking and answering the following questions:

  • What kind of data-producing technology is used in the farming sector that might serve as data source?
  • Who owns this technology, and who is the data-owner of the produced information?
  • What forms of cooperation might be possible between the data-owner and the NSI?
  • In which ways might information from farm machinery sensors, satellites, drones, wireless smart sensors in the field, etc. potentially complement or even substitute traditionally collected data? Which statistical products would be affected?
  • Which possibilities does information on smart farming offer?

Task 2 – The use of IoT for Smart Cities

Performed by BBB (Office for Statistics Berlin-Brandenburg, DE), NSI (Statistics Bulgaria), Destatis (Statistics Germany), INSEE (Statistics France), ISTAT (Statistics Italy) and ONS (Statistics UK)

This task aims at investigating the potential of IoT tools (such as smart sensors) in order to produce smart statistics related to the topic smart cities.

Case study 1

Performed by NSI (Statistics Bulgaria)

  • NSI-BG will explore existing communication between IoT devices, such as sensors and block chain networks in the city of Varna. The infrastructure allows for gathering a lot of information relevant for a city management.
  • Having a part of technical infrastructure in place some methodological and IT issues will be investigated concerning the processing of a variety of data accumulated to produce statistics such as people daily movements within the city, quality of air, road accident location, fires location, quality of roads, mobile devices etc.
  • At the next stage, the methodology developed for the city of Varna could be applied to other cities and statistics produced will cover more and more municipalities. This will enrich substantially statistics on regional level. Moreover, this information could be very useful when evaluating quality of life across munis.
  • The partner for this case study will be the private regional firm Mimirium Ltd., experienced with Software Development of Big Data, Blockchain, IoT and Statistical products.

Case study 2

Performed by BBB (Office for Statistics Berlin-Brandenburg, DE), Destatis (Statistics Germany) and ISTAT (Statistics Italy)

The European Commission has funded a total of 12 projects through the Horizon 2020 research and innovation programme, with the aim of bringing together cities, industry and citizens to demonstrate solutions and business models that can be scaled up and replicated, and that lead to measurable benefits in energy and resource efficiency, new markets and new jobs. These projects are called “Smart Cities and Communities lighthouse projects” and are driven by the same challenges that EU cities are facing in regard to the expectations of citizens and enterprises of cities in the era of IoT.

These projects will be analysed regarding their potential for official statistics. The questions that should be answered in this task are:

  • Which information needs do cities and regions have in order to transform to smart cities and regions?
  • Which small-area data could be offered to cities and regions, if necessary in cooperation with private institutions?
  • Which data are generated by smart cities and regions and how could these data be used for the production of official statistics?

A partner for this case study could be T-Systems, which is a subsidiary of Deutsche Telekom, and which has a strong knowledge and practical experience of the topic IoT.

As examples, the German cities Darmstadt and Berlin could be explored. Darmstadt has won the German competition „Digital City“ and is supposed to be transformed to a digital model city. Berlin has several initiatives regarding the topic “smart city” and “smart mobility”. Furthermore, the Statistical Insititute of Berlin-Brandenburg is connected with some of the players in this field.

Case study 3

Performed by INSEE (Statistics France) and ISTAT (Statistics Italy)

  • Study of the socioeconomic characteristics of people exposed to pollution with the aid of smart sensors (both noise and low air quality) and official data about dwellings.
  • The data of dwellings is geolocated and includes information about its physical characteristics (size, number of rooms, date of construction), as well as the characteristics of its inhabitants (number, age, income, type of income, occupation status).
  • If access to the geolocated pollution data collected by the Nice metropolis sensors is granted, the characteristics of the surrounding population could be analyzed.
  • It is planned to initiate a pilot project in the Nice metropolis which, as a possible follow-up to WPL, could act as proof of concept in order to get access to the data of other municipalities equipped with sensors and to reproduce this study.
  • The long term objective is to be able to give local public actors insights into the urban quality of life in different cities.

Task 3 – Smart devices

Performed by ISTAT (Statistics Italy), CBS (Statistics Netherlands) and INE (Statistics Portugal)

The use of smart devices producing digital footprints is growing and the data they generate is therefore a promising input for both traditional as well as future experimental official statistics. Examples are intelligent sensors, human wearables, medical devices, tracking apps / devices, smart home devices, security equipment and industrial facilities. These devices are very different in their nature, not only in terms of the functionality and decision making, but also in terms of communication and physical implementation. All these aspects have an implication on their potential use for official statistics.

The aim of this task is to conduct a first study on smart devices / citizen science from the viewpoint of official statistics and to explore some practical use cases. It is meant to be preparatory for future projects on trusted smart statistics that will be able to go deeper into the most promising use cases identified in this task. Generally speaking, data from any smart device or citizen science project could be of interest. However, one key precondition is that the data is accessible. Therefore, in this preparatory task we concentrate on smart devices that deliver data to some central point(s) where official statistics can retrieve it either as microdata or aggregated data.

In many cases this data has been made accessible voluntary by citizens and therefore this task also touches upon the concept of citizen science. Examples are:

  • (Real time) data on the production of solar energy from solar panel equipment reported to a citizen-driven central collection site. This data can be used to make more detailed estimates on solar energy production.
  • Aggregated data on sports activities or health monitors reported by mobile apps or wearables / activity trackers. This data is input to studies on human behaviour and use of public space.
  • Flight data collected by tracking radar signals and published publicly in a central collection point. This type of data, possibly combined with other data sources, yields information on transport of people and goods and air quality aspects.

Other cases where data can be easily accessed by the Statistical Offices are the results of the adoption and use of smart devices by public administration, like schools, hospitals and in some cases public transportation and public buildings. Examples of this are:

  • Sensors tracking students activity like attendance to classes, use of cafeteria and canteen spaces or shopping habits inside the school space. This kind of data will make possible the study of students behaviour as well as detect patterns in their way of acting.
  • Data on weather or atmospheric conditions collected by citizen sensors or equipment scattered throughout the parks and green areas of cities and transmitted to a central collection point. This data can complement the information available to study the environment and air quality statistics.
  • Sensor data from public transportation and traffic lights responsive to track intensity and delays of the public transports on route. This data may be combined to other data sources to provide information on the transport of people, but also on their use of the city spaces and the pressure they exert on some of the areas.

It is obvious that only the combination of citizen data with public administration data is a challenge in itself but one that can produce very comprehensive data sets on a specific phenomenon. With all these paraphernalia of smart devices, it’s important to categorize them not only through the theme of their data like weather or atmospheric conditions but also through the way of obtaining it: data streaming from sensors, data collected from an API, and so on, and of course by the data characteristics, or dimensions for analysis, like geographic and time variables. Treatment and storage synergies exist between data sets that are acquired in the same fashion albeit the data nature and use may vary greatly. In the same way, although it may have different nature, sharing some of the same characteristics can benefit of the same data analytics and processing methods.

In this task a long list will be put together of smart sensors used in the participating countries that comply with the definition above. Their primary use, the data being generated and other characteristics will be listed. Also, their applicability for official statistics will be explored. From this list two use cases will be examined in more depth. The experiences will be described in use case reports with the goal to be inspirational for the definition of future projects on trusted smart statistics. The use case reports will describe IT, methodology and quality aspects and the possible replication of the process in other countries. The results of this task will be described in a final report.

Task 4 – Smart traffic

Performed by Statistics Finland, SSB (Statistics Norway) and ONS (Statistics UK)

This task examines new data sources that can be used to analyse traffic and to use it for official statistics. With regards to ‘connected cars’, millions of SIM cards are already integrated in cars and are permanently sending and producing data, which could be used for subjects such as population mobility or traffic accidents. Besides these smart devices integrated in cars, in many countries there are different traffic monitoring systems used which also produce data which could be valuable for official statistics.

Subtask 4.1 – Use of Traffic loops for economic estimates

Performed by Statistics Finland and ONS (Statistics UK)

Traffic loops are already used as a big data source, but the aim of this subtask is to explore the potential of data obtained from an automatic traffic monitoring system, in order to form indicators of economic activity. The main contribution is to produce an automated procedure to first extract and cleanse the data, and subsequently model it in order to see its explanatory power in relation to various economic and social indicators of interest. Based on the encouraging results in the previous ESSnet, where one of the most important contributions was the quite accurate nowcasts of monthly and quarterly GDP figures using traffic loops, the work will be expanded in several ways. For example, regarding the frequency estimates of economic activity, which potentially could be produced daily. Thereby, traffic loops (and possibly other traffic information, such as ship movements in the AIS data) can be used in order to provide nowcasts of real economic activity in real time, before the month or quarter of reference is over, in addition to the production of flash estimates. As a result from this subtask, there a range of indicators could be extracted from traffic loops data, with an understanding of how they are related to economic activity, e.g. in the ability to forecasts sudden shifts, or long term trends. Also, this subtask aims at having a set of processes and requirements in terms of methodology and IT systems for continued production of these indicators.

Subtask 4.2 – Truck transport

Performed by SSB (Statistics Norway)

This subtask investigates how widespread the use of smart devices attached to transport vehicles are in Norway. There will be an inventory and an assessment of if and how to proceed to use such data sources to improve the quality of the Norwegian truck transport survey. Contact will be taken with other Norwegian transport authorities, branch organisations, and companies who are significant in the transport sector. The main aim of this subtask is to obtain an overview of measurement instruments that are mobile and moving with the vehicle. This may include factory installed devices, monitoring devices from transport companies and prototypes which can be tailored for statistical purposes. Interaction and usability of road sensors are not likely to be covered in this phase.

The output/deliverable will be a report assessing the technology available with respect to automatic tracking of trucks and/or goods and how to incorporate it in statistics production. This involves:

  1. Partnership discussion with relevant authorities and branch organizations.
  2. Screening and interview contact with the important players on road transport logistics and potential data owners such as manufacturers.
  3. Evaluation and analysis of the collected information on available data sources and technology with respect to their likely fit for a statistical production process. The latter will look at data sources from registers such as vehicles and identified automated data from onboard systems and logistics systems.
  4. Report writing and conclusion.

Obviously, since this project is international, the work will strive to look for as generic solutions and conclusions as possible.

Milestones and deliverables

See here for an overview of available milestones and deliverables.

WPL milestones

  LM1   Report on the WPL kick-off meeting   Month 3
  LM2   Report on the final WPL meeting   Month 10
  LM3   Final report   Month 12

WPL deliverables

  L1   Description of the findings regarding Task 1: Smart farming   Month 12
  L2   Description of the findings regarding Task 2: The use of IoT for smart cities   Month 12
  L3   Description of the findings regarding Task 3: Smart devices   Month 12
  L4   Description of the findings regarding Task 4: Smart traffic   Month 12