Demography can be defined as the statistical study of the size and characteristics of human population, and of its history and evolution across time and space. Through demography we can learn how many people inhabit different territories (e.g. a country, a region, or a city), what are their characteristics (age and sex structure, origin, ethnicity, educational levels) as well as anticipate the population’s future evolution in terms of size, characteristics and geographical distribution.

The demographic characteristics of a given territory in a given time are the cumulative result of a series of ‘demographic events’ that have occurred in the past and until that moment in time. Those events, typically births, deaths, emigration and immigration, are constantly affected by historical events of different nature – anthropological, political, economic, social, environmental, etc.

Detailed demographic information is crucial for many reasons. Demography has always influenced political power and strength of regions and countries, economic performance, resource consumption and hence, environmental conditions. In addition, the geographical patterns and the degree of population concentration determine urban-rural structures, infrastructure systems and landscapes.

Demographic knowledge requires detailed data about past and present demographic events and characteristics, as well as insights regarding possible future demographic evolutions. Detailed demographic data are collected by most European countries using sound scientific methods and on a regular basis since mid-XIX century. Demographic projections use observed data to infer future demographic developments.

Sources of demographic data and projections

Eurostat is the main source for demographic data and information for Europe. It collects demographic data from EU Member States and other European countries, and it regularly produces long-term European Population Projections (EUROPOP). Other international organizations are also committed to collecting demographic information, and projecting future population. Two prominent examples are the United Nations Population Division with its World Population Prospects and the International Institute for Applied Systems Analysis’ World Population research program.

Regional population scenarios compatible with reference projections

The LUISA territorial modelling platform encompasses methods to regionalise national-level reference demographic projections. The regionalisation methods, while being compatible with the projected trends at a high aggregation level (e.g. EU or national level), can generate different scenarios of regional population depending on a range of regional development assumptions. Work is being done to improve the migration module in order to incorporate population flows determined by empirically tested incentives (e.g. economic, amenities, etc.).

Gridded population maps and projections

Regional population projections – in addition to regional economic projections and assumptions in other policy domains – can be used within the LUISA territorial modelling platform to further assess local impacts translated as urbanisation rates and patterns, changes in accessibility levels, and environmental and quality of life-related indicators. A key output of LUISA is the projected gridded population, produced at high spatial resolution (100m x 100m), covering the pan-European area for a long time frame.

These gridded population projections rely primarily on a detailed population grid map for the base year, currently set at 2011. This map is produced in-house, combining detailed census information from Member States and Eurostat, and a state-of-the-art remote sensing of built-up areas by means of geographical information systems.

In parallel, an on-going research project (ENACT – ENhancing ACTivity and population mapping) is attempting to map spatiotemporal population distribution, in order to reflect both daily and seasonal population variations. This objective requires taking into account location of activities, commuting patterns and different population sub-groups, (e.g. employed persons per sector, tourists, students, etc.). Many different sources of data, from conventional ones (e.g. land use maps, statistical data) to unconventional (e.g. mobile phone operator data, voluntary geographical information) are being combined for the purpose of this project.

RHOMOLO’s migration module

RHOMOLO is a spatial Computable General Equilibrium model comprising a migration module. This module dynamically predicts migration flows between NUTS2 regions in response to local labour market conditions (wages and unemployment) and hurdles such as geographical distance and international borders. Within the general equilibrium model, migration flows in turn affect the labour market in every region, and the regional economies as a whole. The parameters driving the migration behaviour have been estimated using an econometric framework based on discrete choice theory. The framework reflects the reality that even in the long run, labour is imperfectly mobile and does not become infinitely responsive to local labour market conditions.