Adeoluwa Akande

The growth of cities’ population increased the interest in the opportunities and challenges that Information and Communication Technology (ICT) have on carbon footprint reduction, which fosters their environmental sustainability. Using Principal Component Analysis (PCA), six ICT-related variables from European Union (EU) cities were combined into a single two- dimensional ICT index. Then, through cluster analysis, cities were clustered into four groups based on the ICT index and Carbon dioxide (CO2) emissions. Using ICT as an indicator of smartness and CO2 emissions as an indicator of sustainability, we show that it is possible for a city to be smart but not sustainable and vice versa. Results also indicate that there is a gap between cities in northern Europe, which are the top performers in both categories, and cities in south-eastern Europe, which do not perform as well. The need for a common strategy for achieving integrated smart, sustainable and inclusive growth at a European level is demonstrated.

There has recently been a conscious push for cities in Europe to be smarter and more sustainable, leading to the need to benchmark these cities' efforts using robust assessment frameworks. This paper ranks 28 European capital cities based on how smart and sustainable they are. Using hierarchical clustering and principal component analysis (PCA), we synthesized 32 indicators into 4 components and computed rank scores. The ranking of European capital cities was based on this rank score. Our results show that Berlin and other Nordic capital cities lead the ranking, while Sofia and Bucharest obtained the lowest rank scores, and are thus not yet on the path of being smart and sustainable. While our city rank scores show little correlation with city size and city population, there is a significant positive correlation with the cities' GDP per inhabitant, which is an indicator for wealth. Lastly, we detect a geographical divide: 12 of the top 14 cities are Western European; 11 of the bottom 14 cities are Eastern European. These results will help cities understand where they stand vis-à-vis other cities, giving policy makers an opportunity to identify areas for improvement while leveraging areas of strength.

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

Desertification, the process of land degradation in dry areas, is one of the most complex environmental and socio-economical threatening events. Although Nigeria is a party to the United Nations Convention to combat Desertification, the extent and severity of desertification in Nigeria has not been fully established, neither the rate of its progression properly documented. Nevertheless there is a general consensus that desertification is by far the most pressing environmental problem in the northern parts of the country. The desertification processes are identifiable from the gradual shift in vegetation of trees and bushes to more grasses, and bushes to desert-like sand along the past decades. Moreover, this agricultural area serves as the food basket for the entire country making the region highly vulnerable to the impact of these phenomena. Increasing aridity is an important cause of desertification, which is a major challenge experienced in northern Nigeria. It is therefore important to quantitatively determine the level of aridity being experienced in this region. This study assessed the spatio-temporal patterns of a measurement of aridity, named Aridity Intensity Index (AII), to determine the extent of this phenomenon over the period from 1998 to 2013, using daily precipitation data from the Tropical Rainfall Measuring Mission (TRMM) satellite. These data were grouped into two 5-year periods (1998–2002, and 2009–2013), and we produced interpolated surfaces of the AII using Inverse Distance Weighting and Ordinary Kriging for each year within the two groups, as well as for averaged values of the AII for both groups. Additionally, the corresponding error prediction maps were produced. The results showed that the most northern region of Nigeria is threatened by high aridity and dryness with a general small decline in dryness in the south most tip of the study area itself.