In what extent can toy premiums promote healthy eating habits?: A study with school-age children

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Land cover mapping analysis and urban growth modeling using remote sensing techniques

The rapid growth of big cities has been noticed since 1950s when the majority of world population turned to live in urban areas rather than villages, seeking better job opportunities and higher quality of services and lifestyle circumstances. This demographic transition from rural to urban is expected to have a continuous increase. Governments, especially in less developed countries, are going to face more challenges in different sectors, raising the essence of understanding the spatial pattern of the growth for an effective urban planning. The study aimed to detect, analyse and model the urban growth in Greater Cairo Region (GCR) as one of the fast growing mega cities in the world using remote sensing data. Knowing the current and estimated urbanization situation in GCR will help decision makers in Egypt to adjust their plans and develop new ones. These plans should focus on resources reallocation to overcome the problems arising in the future and to achieve a sustainable development of urban areas, especially after the high percentage of illegal settlements which took place in the last decades. The study focused on a period of 30 years; from 1984 to 2014, and the major transitions to urban were modelled to predict the future scenarios in 2025. Three satellite images of different time stamps (1984, 2003 and 2014) were classified using Support Vector Machines (SVM) classifier, then the land cover changes were detected by applying a high level mapping technique. Later the results were analyzed for higher accurate estimations of the urban growth in the future in 2025 using Land Change Modeler (LCM) embedded in IDRISI software. Moreover, the spatial and temporal urban growth patterns were analyzed using statistical metrics developed in FRAGSTATS software. The study resulted in an overall classification accuracy of 96%, 97.3% and 96.3% for 1984, 2003 and 2014’s map, respectively. Between 1984 and 2003, 19 179 hectares of vegetation and 21 417 hectares of desert changed to urban, while from 2003 to 2014, the transitions to urban from both land cover classes were found to be 16 486 and 31 045 hectares, respectively. The model results indicated that 14% of the vegetation and 4% of the desert in 2014 will turn into urban in 2025, representing 16 512 and 24 687 hectares, respectively.

Popular music exhibitions in Portugal: practices of representation from 2007 to 2013

This thesis focuses on the representation of Popular Music in museums by mapping, analyzing, and characterizing its practices in Portugal at the beginning of the 21st century. Now that museums' ability to shape public discourse is acknowledged, the examination of popular music's discourses in museums is of the utmost importance for Ethnomusicology and Popular Music Studies as well as for Museum Studies. The concept of 'heritage' is at the heart of this processes. The study was designed with the aim of moving the exhibiting of popular music in museums forward through a qualitative inquiry of case studies. Data collection involved surveying pop-rock music exhibitions as a qualitative sampling of popular music exhibitions in Portugal from 2007 to 2013. Two of these exhibitions were selected as case studies: No Tempo do Gira-Discos: Um Percurso pela Produção Fonográfica Portuguesa at the Museu da Música in Lisbon in 2007 (also Faculdade de Letras, 2009), and A Magia do Vinil, a Música que Mudou a Sociedade at the Oficina da Cultura in Almada in 2008 (and several other venues, from 2009 to 2013). Two specific domains were observed: popular music exhibitions as instances of museum practice and museum professionals. The first domain encompasses analyzing the types of objects selected for exhibition; the interactive museum practices fostered by the exhibitions; the concepts and narratives used to address popular music discursively, as well as the interpretative practices they allow. The second domain, focuses museum professionals and curators of popular music exhibitions as members of a group, namely their goals, motivations and perspectives. The theoretical frameworks adopted were drawn from the fields of ethnomusicology, popular music studies, and museum studies. The written materials of the exhibitions were subjected of methods of discourse analysis methods. Semi-structured interviews with curators and museum professional were also conducted and analysed. From the museum studies perspective, the study research suggests that the practice adopted by popular music museums largely matches that of conventional museums. From the ethnomusicological and popular music studies stand point, the two case studies reveal two distinct conceptual worlds: the first exhibition, curated by an academic and an independent researcher, points to a mental configuration where popular music is explained through a framework of genres supported by different musical practices. Moreover, it is industry actors such as decision makers and gatekeepers that govern popular music, which implies that the visitors' romantic conception of the musician is to some extent dismantled; the second exhibition, curated by a record collector and specialist, is based on a more conventional process of the everyday historical speech that encodes a mismatch between “good” and “bad music”. Data generated by a survey shows that only one curator, in fact that of my first case study, has an academic background. The backgrounds of all the others are in some way similar to the curator of the second case study. Therefore, I conclude that the second case study best conveys the current practice of exhibiting Popular Music in Portugal.

To what extent do aspects of sustainability impact established firms' resilience

An organizations´ level of sustainability has so far been primarily been analyzed within the context of economic performance. This study changes that dependent variable to “resilience”, namely a company’s ability to recover from potential lethal shocks or disruptive events. The research questions aims to investigate whether sustainability and resilience are related. This study utilizes the financial crisis from 2007/08 as disruptive event, as it encompassed market phase-out but also survival by established firms. Two Swiss luxury watchmaking companies have been chosen as industry sample and the study’s investigation is based on a comparative case study approach. The latter applies both quantitative data, in the form of the respective annual company reports, and qualitative data, in the form of semi-structured interviews with three stakeholder groups. Findings indicate that the investigated measures of sustainability are related the investigated companies’ level of resilience. These findings contribute to the building of new theory towards resilience as this study outlines specifically which measures have been proven to be of relevance for companies’ resilience. Moreover, the results are of high relevance for companies that are operating in constant evolving markets and struggling adapting to any disruptive environment as it is outlined why and how comparative companies have to be sustainable in order to become more resilient towards future shocks.

Flood resilient housing recovery models: a theoretical case study in Maldives

Natural disasters are events that cause general and widespread destruction of the built environment and are becoming increasingly recurrent. They are a product of vulnerability and community exposure to natural hazards, generating a multitude of social, economic and cultural issues of which the loss of housing and the subsequent need for shelter is one of its major consequences. Nowadays, numerous factors contribute to increased vulnerability and exposure to natural disasters such as climate change with its impacts felt across the globe and which is currently seen as a worldwide threat to the built environment. The abandonment of disaster-affected areas can also push populations to regions where natural hazards are felt more severely. Although several actors in the post-disaster scenario provide for shelter needs and recovery programs, housing is often inadequate and unable to resist the effects of future natural hazards. Resilient housing is commonly not addressed due to the urgency in sheltering affected populations. However, by neglecting risks of exposure in construction, houses become vulnerable and are likely to be damaged or destroyed in future natural hazard events. That being said it becomes fundamental to include resilience criteria, when it comes to housing, which in turn will allow new houses to better withstand the passage of time and natural disasters, in the safest way possible. This master thesis is intended to provide guiding principles to take towards housing recovery after natural disasters, particularly in the form of flood resilient construction, considering floods are responsible for the largest number of natural disasters. To this purpose, the main structures that house affected populations were identified and analyzed in depth. After assessing the risks and damages that flood events can cause in housing, a methodology was proposed for flood resilient housing models, in which there were identified key criteria that housing should meet. The same methodology is based in the US Federal Emergency Management Agency requirements and recommendations in accordance to specific flood zones. Finally, a case study in Maldives – one of the most vulnerable countries to sea level rise resulting from climate change – has been analyzed in light of housing recovery in a post-disaster induced scenario. This analysis was carried out by using the proposed methodology with the intent of assessing the resilience of the newly built housing to floods in the aftermath of the 2004 Indian Ocean Tsunami.

In what extent can a segmented packaging make children choose healthier breakfast cereals?

Field lab in marketing: Children consumer behaviour

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Many municipal activities require updated large-scale maps that include both topographic and thematic information. For this purpose, the efficient use of very high spatial resolution (VHR) satellite imagery suggests the development of approaches that enable a timely discrimination, counting and delineation of urban elements according to legal technical specifications and quality standards. Therefore, the nature of this data source and expanding range of applications calls for objective methods and quantitative metrics to assess the quality of the extracted information which go beyond traditional thematic accuracy alone. The present work concerns the development and testing of a new approach for using technical mapping standards in the quality assessment of buildings automatically extracted from VHR satellite imagery. Feature extraction software was employed to map buildings present in a pansharpened QuickBird image of Lisbon. Quality assessment was exhaustive and involved comparisons of extracted features against a reference data set, introducing cartographic constraints from scales 1:1000, 1:5000, and 1:10,000. The spatial data quality elements subject to evaluation were: thematic (attribute) accuracy, completeness, and geometric quality assessed based on planimetric deviation from the reference map. Tests were developed and metrics analyzed considering thresholds and standards for the large mapping scales most frequently used by municipalities. Results show that values for completeness varied with mapping scales and were only slightly superior for scale 1:10,000. Concerning the geometric quality, a large percentage of extracted features met the strict topographic standards of planimetric deviation for scale 1:10,000, while no buildings were compliant with the specification for scale 1:1000.

Generalized Dasymetric Mapping Algorithm for Accessing Land-Use Change

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Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.