Big Data for Territorial Analysis and Housing Dynamics

Theme: Data development


The gathering and harmonisation of international statistical data in a multidisciplinary environment are key to international comparative analysis and policy work. The availability of timely, accurate statistical information enables policy-makers, practitioners, researchers and other stakeholders to address a wide range of issues in today’s rapidly-evolving global economic and social landscape.

The use of traditional data such as official administrative statistics however has some shortcomings. Traditional data in general takes long to be published and used because they are subject to a long technical and sometimes political process of harmonization and validation. Also, traditional data does not cover all topics of interest for territorial cohesion.

Increasingly, data and information from analysing internet activities or social media can be used for observing territorial development trends. New developments for the availability and use of big data may help to overcome the shortcomings and bring new and interesting opportunities to support policy with up-to-date information relevant for territorial analysis.

Currently, the interest from policy makers is growing as the sources for Big Data (Facebook, Google, Twitter, Instagram or blogs for example) contain valuable information, which can normally be hard to gather, and these data can be collected with very short notice.  This means that Big Data could provide a more regular, cost-effective and harmonised data collection and provide an opportunity to more easily address new issues of interest.

The aim of this ESPON activity is to further develop ways and methodologies for using existing big data sources and platforms to develop and measure indicators for territorial monitoring and analysis. In addition, these methodologies should be applied for indicators measuring the housing dynamics in European cities and the wellbeing of European citizens, in particular related to their housing and living situation. Finally, these methodologies should be made available and applicable to others for measuring these and other aspects in cities.

Main outcomes envisaged

The result of the activity will be an increased awareness about new and applicable methodologies for using Big Data on analysing spatial patterns and dynamics in urban areas and especially related to housing. Eventually, this will lead to wider utilisation of such methods, which will improve the evidence-base for planning and decision making in European urban areas.

In this perspective, the main outcome of this activity should be:

  • A description of a methodological framework and methodologies developed and applied for using Big Data to analyse spatial patterns and dynamics in urban areas.
  • A report describing the housing dynamics in European cities as well as the wellbeing of European citizens focussed on affordable housing and their living situation.
  • Data, maps, graphs and other resources used as main input for the report above.
  • A guidance document describing how the methodologies developed can be use.


  • Université Paris Diderot - UMS RIATE, FR (lead contractor)
  • Uniwersytet Lodzki Wydzial Nauk Geograficznych, PL
  • Haute Ecole de Gestion Arc, CH
  • Agencia Estatal Consejo Superior de Investigaciones Cientificas, ES


€ 75,000.00


September 2018 – June 2019


  • First delivery, 5 November 2018
  • Second delivery, 5 March 2019
  • Final delivery, 5 June 2019


Marjan van Herwijnen (Senior Project Expert) [email protected], Caroline Clause (Senior Financial Expert) [email protected]


ESPON Big Data-Guidance_Document.pdf

  • Acrobat Document | 2.30MB

ESPON Big Data-Housing_Affordability.pdf

  • Acrobat Document | 20.34MB