Exploring Data Hierarchies to Discover Knowledge in Different Domains

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Data Mining Techniques for Complex User-Generated Data

Nowadays, the amount of collected information is continuously growing in a variety of different domains. Data mining techniques are powerful instruments to effectively analyze these large data collections and extract hidden and useful knowledge. Vast amount of User-Generated Data (UGD) is being created every day, such as user behavior, user-generated content, user exploitation of available services and user mobility in different domains. Some common critical issues arise for the UGD analysis process such as the large dataset cardinality and dimensionality, the variable data distribution and inherent sparseness, and the heterogeneous data to model the different facets of the targeted domain. Consequently, the extraction of useful knowledge from such data collections is a challenging task, and proper data mining solutions should be devised for the problem under analysis. In this thesis work, we focus on the design and development of innovative solutions to support data mining activities over User-Generated Data characterised by different critical issues, via the integration of different data mining techniques in a unified frame- work. Real datasets coming from three example domains characterized by the above critical issues are considered as reference cases, i.e., health care, social network, and ur- ban environment domains. Experimental results show the effectiveness of the proposed approaches to discover useful knowledge from different domains

“The use of Discrete Choice Exercises for for estimating socio-economic acceptability of air quality policies: investigation on the possibility of interaction between DCA and GAINS model”

none7Deliverable 4.1, Coordinated Action Sefira - Socio Economic Implication for Individual Responses to Air Pollution policies in EU +27 (FP7/2012-2016), October 2014. The research on which this paper is based, was financially supported by European Union under the 7th Framework Programme; Theme: ENV 2013.6.5-2[ENV.2013.6.5-2 Mobilising environmental knowledge for policy and society Grant agreement: 603941 (Project Title: SEFIRA). The views expressed in this paper are those of the authors and do not necessarily correspond to those of the European Commission or their home institutions. The usual disclaimer applies.openEva Valeri; Paolo Polidori; Vittorio Sergi; Yuri Kazepov; Michela Maione; Markus Amann; Martin WilliamsEva, Valeri; Polidori, Paolo; Vittorio, Sergi; Kazepov, IURI ALBERT KYRIL; Maione, Michela; Markus, Amann; Martin, William

Quality of Institutions, Technological Progress, and Pollution Havens in Latin America. An Analysis of the Environmental Kuznets Curve Hypothesis

A set of 17-year panel data (1996–2013) across a representative sample from eighteen Latin American countries is used to respond four research questions: Did Latin American Greenhouse Gas (GHG) emissions prove the Environmental Kuznets Curve (EKC) hypothesis? Did the quality of institutions play a compensating role for income on environmental stress? Did technological progress help decouple income from environmental stress? Has the Pollution Haven Hypothesis (PHH) been proven? In order to answer the research questions, the paper expands the traditional EKC approach by including an exclusive quality analysis of institutions, technological progress, and PHH as part of the model. This innovation is developed considering the most recent literature about EKC as a starting point. Major findings show that the relationship between income and GHG emissions is adjusted to the traditional EKC hypothesis for the analyzed period. They also show that the quality of institutions and technological progress improve environmental sustainability. However, the variables, Foreign Direct Investment and International Trade, provide a negative answer to the fourth question. The main methodological contribution of this paper is to use a threefold extended classic EKC model to conduct the feasible generalized least squares method. The paper also contributes to the growing body of PHH literature.Junta de Andalucía proyecto SEJ-132Cátedra de Economía de la Energía y del MedioUniversidad Autónoma de Chil

Spatial Dynamic Modeling and Urban Land Use Transformation:

Assessing the economic impacts of urban land use transformation has become complex and acrimonious. Although community planners are beginning to comprehend the economic trade-offs inherent in transforming the urban fringe, they find it increasingly difficult to analyze and assess the trade-offs expediently and in ways that can influence local decisionmaking. New and sophisticated spatial modeling techniques are now being applied to urban systems that can quickly assess the probable spatial outcomes of given communal policies. Applying an economic impact assessment to the probable spatial patterns can provide to planners the tools needed to quickly assess scenarios for policy formation that will ultimately help inform decision makers. This paper focuses on the theoretical underpinnings and practical application of an economic impact analysis submodel developed within the Land use Evolution and Impact Assessment Modeling (LEAM) environment. The conceptual framework of LEAM is described, followed by an application of the model to the assessment of the cost of urban sprawl in Kane County, Illinois. The results show the effectiveness of spatially explicit modeling from a theoretical and a practical point of view. The agent-based approach of spatial dynamic modeling with a high spatial resolution allows for discerning the macro-level implications of micro-level behaviors. These phenomena are highlighted in the economic submodel in the discussion of the implications of land use change decisions on individual and communal costs; low-density development patterns favoring individual behaviors at the expense of the broader community.

Association between PM 2.5 in Minnesota and Influencing Factors: Tree Space Area, Road Pollution, and Rainfall

The purpose of this paper is trying to use air monitoring data of Particulate Matter (PM 2.5) from 19 monitoring sites in Minnesota, to determine the correlations between PM 2.5 and the influencing factors, such as road traffic, tree space area, and rainfall. The study will be based on pollutant data which were from Environment Protection Agency (EPA) and Minnesota Pollution Control Agency (MPCA), then through regression analysis and Pearson correlation analysis to determine the correlations of all variables. The correlation analysis results between PM 2.5 concentration and three variables (tree space area, traffic volume, and rainfall) showed that tree space area ratio had a negative, traffic volume had a positive and rainfall had a negative, correlation with PM 2.5 in Minnesota urban. The air traffic volume had a positive correlation with PM 2.5 in airport areas. In this study, GIS system is a useful tool for geostatistical analysis. It can be used for Normalized Difference Vegetation Index (NDVI) analysis, raster data geoprocessing, and kriging spatial analysis