Analysis of panoramio photo tags in order to extract land use information

In the recent past, hardly anyone could predict this course of GIS development. GIS is moving from desktop to cloud. Web 2.0 enabled people to input data into web. These data are becoming increasingly geolocated. Big amounts of data formed something that is called "Big Data". Scientists still don't know how to deal with it completely. Different Data Mining tools are used for trying to extract some useful information from this Big Data. In our study, we also deal with one part of these data - User Generated Geographic Content (UGGC). The Panoramio initiative allows people to upload photos and describe them with tags. These photos are geolocated, which means that they have exact location on the Earth's surface according to a certain spatial reference system. By using Data Mining tools, we are trying to answer if it is possible to extract land use information from Panoramio photo tags. Also, we tried to answer to what extent this information could be accurate. At the end, we compared different Data Mining methods in order to distinguish which one has the most suited performances for this kind of data, which is text. Our answers are quite encouraging. With more than 70% of accuracy, we proved that extracting land use information is possible to some extent. Also, we found Memory Based Reasoning (MBR) method the most suitable method for this kind of data in all cases.

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.

Fair value measurement: Is the debate around level II and level III assets and liabilities relevant

Double Degree. A Work Project presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA- School of Business and Economics and a Masters Degree in Business Engineering from Louvain school of Management

Modeling and forecasting value-at-risk for the Portuguese stock market

The aim of this work project is to find a model that is able to accurately forecast the daily Value-at-Risk for PSI-20 Index, independently of the market conditions, in order to expand empirical literature for the Portuguese stock market. Hence, two subsamples, representing more and less volatile periods, were modeled through unconditional and conditional volatility models (because it is what drives returns). All models were evaluated through Kupiec’s and Christoffersen’s tests, by comparing forecasts with actual results. Using an out-of-sample of 204 observations, it was found that a GARCH(1,1) is an accurate model for our purposes.

Market value of corruption

This paper studies the impact of the Brazilian anticorruption legislation, PL 6826/2010, on stock returns. I show that, around the law approval date, the greater the link between the corporate and political worlds, the worse is the companies’ performance. Companies awarded with public contracts in 2012 suffer more with the new legislation approval. Firms with above median contract values have 2.9% lower returns than its peers. The negative effect is more pronounced for bigger and more complex entities, associated with higher levels of Corporate Responsibility and Governance and not subject to the US FCPA.

The accuracy of the escrowed dividend model on the value of European options on a stock paying discrete dividend

This project characterizes the accuracy of the escrowed dividend model on the value of European options on a stock paying discrete dividend. A description of the escrowed dividend model is provided, and a comparison between this model and the benchmark model is realized. It is concluded that options on stocks with either low volatility, low dividend yield, low ex-dividend to maturity ratio or that are deep in or out of the money are reasonably priced with the escrowed dividend model.

Evaluating the potential of yeast strains to produce added value products for the food and/or pharmaceutical industries

This study focus in the valorization of the apple pomace with the main goal of obtaining added value products. For that, hot compressed water technology was used for the extraction of phenolic compounds and hydrolysis of polysaccharides presents in the lignocellulosic structure of apple pomace to obtain simple sugars. The sugars have been utilized as alternative carbon source for growth, lipid accumulation and carotenoids production by five different yeast Yarrowia lipolytica, Rhodotorula mucilaginosa, Rhodotorula glutinis, Rhodosporidium babjevae and Rhodosporidium toruloides. Hydrolysis experiments were carried out with constant pressure of 100 bar, flow rate of 2mL/min and temperatures between 50°C and 250°C. The amount of total sugars present in apple pomace hydrolysates showed maximum values for the hydrolysis temperatures of 110°C and 190°C. In fact, these temperatures revealed the best results regarding the monosaccharides quantities. The amount of 5-HMF and furfural in each hydrolysate varied through the different temperatures. Maximum values for 5-HMF were obtained with 170°C, while furfural showed to be maximum at 210°C. Extraction of phenolic compounds were performed in simultaneously with hydrolysis reactions. Total phenolic compounds (TPC) increased along the temperature, however with small variations between 170°C and 250°C. Hydrolysates were then used as alternative carbon source to yeast growth. R. mucilaginosa shows the highest optical density, with the hydrolysate obtained at 130°C. Carotenoids produced by these yeast scored a total of 7.02μg carotenoids/g cell dry weight, while for the control assay, the same yeast scored 9.31μg caratonoides/g cell dry weight. β-carotene was quantified by HPLC, were 33% of the carotenoid production by R. mucilaginosa with hydrolysate as carbon source, corresponded to β-caroteno.

Comprehensive income: evidence from Portuguese listed companies on reporting choices and value relevance

This work project aims at analysing choices related to Comprehensive income (CI) of Portuguese listed firms and understanding the reasons behind them. Additionally, it studies the relevance of CI versus Net Income (NI). It was found that firm’s size and volume of Other comprehensive income (OCI) are positively related with the choice for separate statements while smaller firms with positive NI and negative OCI tend to disclose less information about taxes. The value relevance of CI proved to be superior to that of NI but OCI seems to have no incremental value relevance.

Lifetime-value creation through a customized offer adressed to diferent lifestyles

This paper purposes a method for marketing segmentation based on customers‟ lifestyle. A quantitative and qualitative segmentation established by the Whitaker Lifestyle™ Method was created in order to define a concrete and clear identification of the customer, by understanding the behavior, style and preferences of each segment. After conducting 18 in-depth interviews, it was concluded that four main personas characterize the customer base of the company. These four personas will be the support for the creation of „quick-wins‟ that address to the expectations of each lifestyle, projecting a significant impact on the lifetime-value of the company‟s customer base

Developing a gamification system to increase customer lifetime value at MyGon

There have never been so many touch points between companies and consumers as there are today, which paradoxically makes it very challenging for companies to be able to retain and engage customers. Gamification is a strategy used by a large number of companies to increase customer engagement and customer lifetime value. This work aims at developing a gamification system for MyGon, a Portuguese startup working in the market of discounts and experiences. In addition to examining the literature concerning gamification, its elements and characteristics, recommendations were developed for addressing MyGon’s business goals of increasing conversion and customer engagement. The gamification mechanisms suggested include badges, missions, points, leaderboards and levels.

Value creation in the nursing home real estate investment market - A case study

The underlying thesis examines the value drivers of direct investments in nursing home real estate in Germany. A survey among investors and operators is conducted in order to identify significant value drivers. Moreover, based on survey results, a framework for assessing German nursing home real estate is developed. This is applied in a case-study about the set-up of a nursing home value-add fund which will demonstrate the value creation process of redeveloping an existing nursing home real estate portfolio. Through a concluding analysis the sources of value creation, sensitivities and future prospects of direct investing into German nursing home real estate are concluded.

Future lending income and security value

I test the Duffie, Gârleanu, and Pedersen hypothesis that security prices incorporate expected future securities lending income. To determine whether institutional investors anticipate gains from future lending of securities, I examine their trading behavior around loan-fee increases. The evidence suggests that institutions buy shares in response to an increase in lending fees, and that this could explain the premium associated with high- lending-fee stocks. Expected future lending income affects stock prices, although the effect seems to be attenuated by the negative information that arises from short selling.

Generalized Dasymetric Mapping Algorithm for Accessing Land-Use Change

Contém resumo

A type system for value-dependent information flow analysis

Information systems are widespread and used by anyone with computing devices as well as corporations and governments. It is often the case that security leaks are introduced during the development of an application. Reasons for these security bugs are multiple but among them one can easily identify that it is very hard to define and enforce relevant security policies in modern software. This is because modern applications often rely on container sharing and multi-tenancy where, for instance, data can be stored in the same physical space but is logically mapped into different security compartments or data structures. In turn, these security compartments, to which data is classified into in security policies, can also be dynamic and depend on runtime data. In this thesis we introduce and develop the novel notion of dependent information flow types, and focus on the problem of ensuring data confidentiality in data-centric software. Dependent information flow types fit within the standard framework of dependent type theory, but, unlike usual dependent types, crucially allow the security level of a type, rather than just the structural data type itself, to depend on runtime values. Our dependent function and dependent sum information flow types provide a direct, natural and elegant way to express and enforce fine grained security policies on programs. Namely programs that manipulate structured data types in which the security level of a structure field may depend on values dynamically stored in other fields The main contribution of this work is an efficient analysis that allows programmers to verify, during the development phase, whether programs have information leaks, that is, it verifies whether programs protect the confidentiality of the information they manipulate. As such, we also implemented a prototype typechecker that can be found at http://ctp.di.fct.unl.pt/DIFTprototype/.