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COUNTRY-LEVEL DRIVERS OF SEVERE MATERIAL DEPRIVATION RATES IN THE EU

100%

Dudek H.

nr 1

33 – 51

The severe material deprivation rate indicates the proportion of the population that cannot fulfil at least four of the nine needs identified as basic ones in the European conditions. Due to being an absolute measure, it is very useful for cross-country comparison. This study attempts to identify country-level factors affecting severe material deprivation rate by the use of the GEE methodology which enables to analyse correlated fractional outcome data. It is found that severe material deprivation rate is affected by such factors as: median equalised disposable income, relative median at-risk-of-poverty gap, long-term unemployment rate, GDP per capita and share of social protection expenditure in GDP. Results reveal that GEE models with clog log link function exhibit the best goodness of fit. Due to these models imposing non-constant marginal effects, therefore, changes of the severe material deprivation rates depend on levels of country-level factors.

Towards Time Series Sensor Data to Accurately Map Flood Hazard and Assess Damages under Climate Change Using Google Earth Engine Cloud Platform and GIS – Case of the Cities of Tetouan and Casablanca (Morocco)

63%

El Yacoubi Y. , Wahbi M. , Maatouk M. , El Kharki O. , Boulaassal H. , Alaoui O. Y.

tom Vol. 24, iss. 5

238--253

Climate change poses a major challenge in terms of urban planning management for the sake of a sustainable future. It is affecting the hydrological cycle around the world, leading to extreme weather conditions. Floods rank as the most frequent and widespread disaster in the world, they adversely affect inhabitants in terms of property damage and threat to human safety (and lives, in the worst cases). Uncontrolled urban sprawl also exacerbates floods by expanding impervious surfaces and affecting flow paths. Other factors that trigger flooding (apart from the rainfall intensity) are human involvement in the main waterways, thereby significantly impacting the hydraulic flow characteristics, structural engineering breakdowns, compounded by potential deforestation. For the purpose of monitoring the aftermath of floods experienced by the cities of Casablanca and Tetouan (Morocco) respectively in January and March 2021 and estimating their damages, optical and radar satellite images derived from the Google Earth Engine (GEE) cloud platform were used along with the Geographic Information System (GIS). In this study, a novel technique for extracting flooded areas from high-resolution Synthetic Aperture Radar (SAR) time series images has been developed. A comparison was carried out subsequently between the time-series approach and other traditional approaches including radiometric thresholding method, spectral indices namely Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) as well as Flood Water Index (FWI). Based on the above approach, the water levels were estimated and the damages were assessed and mapped, notably the number of people exposed to flood hazard and the amount of built-up areas and cropland affected. The results demonstrated that Casablanca city has witnessed a higher flood level than Tetouan city, putting a large number of people at risk and affecting a significant area of land use. The findings can also provide local authorities with a comprehensive view of flooding and enable them to make decisions on preparedness, mitigation, and adaptation to flood-related disasters.