Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator—SDG 11.3.1

TLDR

The Global Human Settlement Layer (GHSL) delivers global, time‑resolved spatial data on built‑up density and resident population, enabling trend analysis for SDG 2030 monitoring. This study estimates the Land Use Efficiency (LUE) indicator SDG 11.3.1 for roughly 10,000 urban centers by computing the ratio of land consumption to population growth from 1990 to 2015, and shows how GHSL data and tools can fill data gaps for SDG 11. GHSL derives its estimates from global, multi‑temporal high‑resolution satellite imagery, census statistics, and volunteered geographic information, which are processed through the GHSL tools suite to calculate the LUE ratio. The analysis demonstrates that GHSL can elevate SDG 11.3.1 from Tier II to Tier I by providing a global baseline for the required variables.

Abstract

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics describing the human presence on the planet that is based mainly on two quantitative factors: (i) the spatial distribution (density) of built-up structures and (ii) the spatial distribution (density) of resident people. Both of the factors are observed in the long-term temporal domain and per unit area, in order to support the analysis of the trends and indicators for monitoring the implementation of the 2030 Development Agenda and the related thematic agreements. The GHSL uses various input data, including global, multi-temporal archives of high-resolution satellite imagery, census data, and volunteered geographic information. In this paper, we present a global estimate for the Land Use Efficiency (LUE) indicator—SDG 11.3.1, for circa 10,000 urban centers, calculating the ratio of land consumption rate to population growth rate between 1990 and 2015. In addition, we analyze the characteristics of the GHSL information to demonstrate how the original frameworks of data (gridded GHSL data) and tools (GHSL tools suite), developed from Earth Observation and integrated with census information, could support Sustainable Development Goals monitoring. In particular, we demonstrate the potential of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for Sustainable Development Goal 11. The results of our research demonstrate that there is potential to raise SDG 11.3.1 from a Tier II classification (manifesting unavailability of data) to a Tier I, as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.