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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. [ABSTRACT FROM AUTHOR]

The application of last-generation spatial data modelling, integrating Earth Observation, population, economic and other spatially explicit data, enables insights into the sustainability of the global urbanisation processes with unprecedented detail, consistency, and international comparability. In this study, the land use efficiency indicator, as developed in the Sustainable Development Goals, is assessed globally for the first time at the level of Functional Urban Areas (FUAs). Each FUA includes the city and its commuting zone as inferred from statistical modelling of available spatial data. FUAs represent the economic area of influence of each urban centre. Hence, the analysis of land consumption within their boundary has significance in the fields of spatial planning and policy analyses as well as many other research areas. We utilize the boundaries of more than 9,000 FUAs to estimate the land use efficiency between 1990 and 2015, by using population and built-up area data extracted from the Global Human Settlement Layer. This analysis shows how, in the observed period, FUAs in low-income countries of the Global South evolved with rates of population growth surpassing the ones of land consumption. However, in almost all regions of the globe, more than half of the FUAs improved their land use efficiency in recent years (2000–2015) with respect to the previous decade (1990–2000). Our study concludes that the spatial expansion of urban areas within FUA boundaries is reducing compactness of settlements, and that settlements located within FUAs do not display higher land use efficiency than those outside FUAs. • Land Use Efficiency (SDG 11.3.1) is assessed in all FUAs of the world. • FUAs in the Global South show population growing faster than land consumption. • Development trajectories in FUAs show an improvement of efficiency in recent years. • Settlements within FUA commuting areas are not more efficient than those outside. • The proportion of urban centre population does not imply higher efficiency of the FUA. [ABSTRACT FROM AUTHOR]

To mitigate the impact of greenhouse gas (GHG) and air pollutant emissions, it is of utmost importance to understand where emissions occur. In the real world, atmospheric pollutants are produced by various human activities from point sources (e.g. power plants and industrial facilities) but also from diffuse sources (e.g. residential activities and agriculture). However, as tracking all these single sources of emissions is practically impossible, emission inventories are typically compiled using national-level statistics by sector, which are then downscaled at the grid-cell level using spatial information. In this work, we develop high-spatial-resolution proxies for use in downscaling the national emission totals for all world countries provided by the Emissions Database for Global Atmospheric Research (EDGAR). In particular, in this paper, we present the latest EDGAR v8.0 GHG, which provides readily available emission data at different levels of spatial granularity, obtained from a consistently developed GHG emission database. This has been achieved through the improvement and development of high-resolution spatial proxies that allow for a more precise allocation of emissions over the globe. A key novelty of this work is the potential to analyse subnational GHG emissions over the European territory and also over the United States, China, India, and other high-emitting countries. These data not only meet the needs of atmospheric modellers but can also inform policymakers working in the field of climate change mitigation. For example, the EDGAR GHG emissions at the NUTS 2 level (Nomenclature of Territorial Units for Statistics level 2) over Europe contribute to the development of EU cohesion policies, identifying the progress of each region towards achieving the carbon neutrality target and providing insights into the highest-emitting sectors. The data can be accessed at 10.2905/b54d8149-2864-4fb9-96b9-5fd3a020c224 specifically for EDGAR v8.0 (Crippa et al., 2023a) and 10.2905/D67EEDA8-C03E-4421-95D0-0ADC460B9658 for the subnational dataset (Crippa et al., 2023b). [ABSTRACT FROM AUTHOR]

Measuring rates of coverage and spatial access to healthcare services is essential to inform policies for development. These rates tend to reflect the urban-rural divide, typically with urban areas experiencing higher accessibility than rural ones. Especially in Sub-Saharan Africa (SSA), a region experiencing high disease burden amid fast urbanisation and population growth. However, such assessment has been hindered by a lack of updated and comparable geospatial data on urbanisation and health facilities. In this study, we apply the UN-endorsed Degree of Urbanisation (DoU or DEGURBA) method to investigate how geographic access to healthcare facilities varies across the urban-rural continuum in SSA as a whole and in each country, for circa 2020. Results show that geographic access is overall highest in cities and peri-urban areas , where more than 95% of inhabitants live within 30 min from the nearest HCF, with this share decreasing to 80–90% in towns. This share is lowest in villages and dispersed rural areas (65%), with about 10–15% of population more than 3 h away from any health post. Challenges in geographic access seem mostly determined by high travel impedance, since overall spatial densities of HCF are comparable to European levels. • The Degree of Urbanisation enables monitoring the urban-rural access to resources and services across boundaries worldwide. • This work characterizes distribution and accessibility of Health Care Facilities (HCF) by Degree of Urbanisation (DoU). • The accessibility to HCF at given travel time bins is shown by HCF tier of specialisation and share of covered population. • Access to HCF varies significantly across countries, and within countries by DoU territorial class (Fig. 7). • HCF are widespread in Sub-Saharan Africa but the inadequate infrastructure makes them inaccessible, notably in rural areas. [ABSTRACT FROM AUTHOR]

Knowing where emissions occur is essential for planning effective emission reduction measures and for atmospheric modelling. Emission inventories are typically compiled at national level and provide sector-specific emission estimates. Disaggregating national emissions on high-resolution grids requires spatial proxies that contain information on the location of different emission sources (e.g. point sources, linear and area sources). Knowing the correct allocation of emissions from point sources is essential to avoid the misallocating high emission levels. However, gathering information on point sources covering the entire globe and a wide temporal domain (1970 to present) is challenging due to limited data availability, accuracy of the reporting and completeness of data. The latest spatial proxies developed as part of the Emissions Database for Global Atmospheric Research (EDGARv8.0) provide the user with the possibility to work with different geographical details using a consistently developed GHG emissions database. A key novelty of EDGARv8.0 is the possibility to analyse sub-national GHG emissions over the European domain, but also over the US, China, India and main world countries. The relevance of using updated spatial information is assessed on the basis of regional case studies. [ABSTRACT FROM AUTHOR]