Sergio Freire

The European Commission’s Joint Research Centre (JRC) produces open and free gridded data on human settlements and population at the European and global level. These datasets provide robust sources for decision making, planning, disaster risk management and scientific research. In this talk, we will provide an overview of recent developments and advances with this regard. Specifically, we will highlight ongoing work, novel datasets and underlying methods, including global, gridded future projections (GHS-WUP-POP; 1-km population estimates from 1980 to 2100), historical gridded population data for Europe since the 1960s using spatially-explicit backcasting models and innovative, chain-linking based dasymetric population downscaling, including age-sex disaggregations, as well as global historical gridded population data from 1900 onwards produced by integrating historical, long-term land-use models with data from the Global Human Settlement Layer.For robust and transparent gridded population data production, uncertainty awareness and -quantification is key. Hence, at the JRC, we explore novel ways to conduct accuracy assessments of gridded population data. For example, we benchmark our datasets against increasingly available authoritative gridded population and other official data reported by national census agencies, and develop new metrics tailored to estimate the accuracy of gridded population data and similar datasets in meaningful and intuitive ways. In our talk, we will highlight recent methodological advances on gridded population data quality assessments and showcase exemplary results of benchmarking and cross-comparing different gridded population datasets. Moreover, we will reflect on pitfalls and caveats that may occur when gridded population data accuracy assessments involve unsuitable data processing or sampling design and highlight the importance of reflected considerations of the fitness-for-use of these datasets.

Many geospatial analyses require flexible aggregation of adjacent units to meet a minimum target area or attribute value. This is usually accomplished using several non-automated and complex GIS tasks. We developed an integrated user-friendly approach and algorithm implemented in the ‘GHS-SmartDissolve’ tool, which addresses minimum mapping unit or attribute value requirements, layers resolution mismatch, spatial uncertainty or modifiable areal unit problem in GIScience. This method automatically dissolves adjacent features updating fields’ values to reach a minimum target area or attribute value, using a flexible settings framework to meet user requirements. Also provided as a toolbox for ArcGIS (Esri), the approach is demonstrated by (i) estimating the mean particulate matter concentrations for all municipalities in Italy in 2011 by combining a coarse grid of global PM2.5 concentrations with fine administrative units; (ii) estimating boundaries of Metropolitan areas in Portugal as aggregation of municipalities, by matching their total population.

Human settlements typically expand to accommodate additional housing demand from a growing population and their socio-economic activities. This implies consumption of land, a limited resource necessary for many other services. The efficiency of this exploitation in relation to demographic trends is key to preserve land and natural capital that could otherwise be degraded. Here, we assess patterns of population and built-up area growth over the period 2000–2015, using demographic statistics and remote-sensing data. We find that on average, in the EU27, built-up areas grew at a faster pace than population and that they expanded even in regions where population has declined. We quantify the impact of future population growth under different assumptions on future built-up efficiency. Keeping current built-up per capita fixed could preserve up to 9,000 km² of land until 2030, especially outside predominantly urban regions, where land use efficiency is generally low and has been declining.

This review analyses peer-reviewed scientific publications and policy documents that use built-up density, population density and settlement typology spatial grids from the Global Human Settlement Layer (GHSL) project to quantify human presence and processes for sustainability. Such open and free grids provide detailed time series spanning 1975–2015 developed with consistent approaches. Improving our knowledge of cities and settlements by measuring their size extent, as well as the societal processes occurring within settlements, is key to understanding their impact on the local, regional and global environment for addressing global sustainability and the integrity of planet Earth. The reviewed papers are grouped around five main topics: Quantifying human presence; assessing settlement growth over time; estimating societal impact, assessing natural hazard risk and impact, and generating indicators for international framework agreements and policy documents. This review calls for continuing to refine and expand the work on societal variables that, when combined with essential variables including those for climate, biodiversity and ocean, can improve our understanding of the societal impact on the biosphere and help to monitor progress towards local, regional and planetary sustainability.

The knowledge of the spatial and temporal distribution of human population is vital for the study of cities, disaster risk management or planning of infrastructure. However, information on the distribution of population is often based on place-of-residence statistics from official sources, thus ignoring the changing population densities resulting from human mobility. Existing assessments of spatio-temporal population are limited in their detail and geographical coverage, and the promising mobile-phone records are hindered by issues concerning availability and consistency. Here, we present a multi-layered dasymetric approach that combines official statistics with geospatial data from emerging sources to produce and validate a European Union-wide dataset of population grids taking into account intraday and monthly population variations at 1 km²resolution. The results reproduce and systematically quantify known insights concerning the spatio-temporal population density structure of large European cities, whose daytime population we estimate to be, on average, 1.9 times higher than night time in city centers.

Sustainable Development Goal (SDG) 11 aspires to “Make cities and human settlements inclusive, safe, resilient and sustainable”, and the introduction of an explicit urban goal testifies to the importance of urbanisation. The understanding of the process of urbanisation and the capacity to monitor the SDGs require a wealth of open, reliable, locally yet globally comparable data, and a fully-fledged data revolution. In this framework, the European Commission–Joint Research Centre has developed a suite of (open and free) data and tools named Global Human Settlement Layer (GHSL) which maps the human presence on Earth (built-up areas, population distribution and settlement typologies) between 1975 and 2015. The GHSL supplies information on the progressive expansion of built-up areas on Earth and population dynamics in human settlements, with both sources of information serving as baseline data to quantify land use efficiency (LUE), listed as a Tier II indicator for SDG 11 (11.3.1). In this paper, we present the profile of the LUE across several territorial scales between 1990 and 2015, highlighting diverse development trajectories and the land take efficiency of different human settlements. Our results show that (i) the GHSL framework allows us to estimate LUE for the entire planet at several territorial scales, opening the opportunity of lifting the LUE indicator from its Tier II classification; (ii) the current formulation of the LUE is substantially subject to path dependency; and (iii) it requires additional spatially-explicit metrics for its interpretation. We propose the Achieved Population Density in Expansion Areas and the Marginal Land Consumption per New Inhabitant metrics for this purpose. The study is planetary and multi-temporal in coverage, demonstrating the value of well-designed, open and free, fine-scale geospatial information on human settlements in supporting policy and monitoring progress made towards meeting the SDGs.

Population data represent an essential component in studies focusing on human–nature interrelationships, disaster risk assessment and environmental health. Several recent efforts have produced global- and continental-extent gridded population data which are becoming increasingly popular among various research communities. However, these data products, which are of very different characteristics and based on different modeling assumptions, have never been systematically reviewed and compared, which may impede their appropriate use. This article fills this gap and presents, compares and discusses a set of large-scale (global and continental) gridded datasets representing population counts or densities. It focuses on data properties, methodological approaches and relative quality aspects that are important to fully understand the characteristics of the data with regard to the intended uses. Written by the data producers and members of the user community, through the lens of the “fitness for use” concept, the aim of this paper is to provide potential data users with the knowledge base needed to make informed decisions about the appropriateness of the data products available in relation to the target application and for critical analysis.