To characterize the size distributions, concentrations and sources of heavy metal associated with suspended particles, a total of 12 months of sampling periods were taken by Micro-Orifice Uniform Deposit Impactor (MOUDI) in the Nantzu Export Processing Zone from January to December 2011, The concentrations of suspended particles ranged from 54.7 to 203 £gg/m3. Both autumn and winter had significantly higher levels of suspended particles than in spring and summer. The mass concentrations of fine particles accounted for ~50% of the mass concentrations of suspended particles. The mass concentrations of PM2.5 accounted for 50.2-70% of the mass concentrations of PM10. The mass concentrations of PM1 accounted for 24-38.3% of the mass concentrations of PM10. These results indicated that fine particles dominated in atmospheric particulates in Nanzih Export Processing Zone. In addition, among the PM10, PM2.5 and PM1, significant correlations were found. The crustal elements (Al, Fe, Ca, Mg, K and Na) and sulfate are dominant during the sampling periods, which accounting for ~95% of the total concentrations. The crustal elements were observed mainly in coarse particles, while sulfate was found mainly in fine particles. The concentrations of all crustal elements decreased in summer could be attributed to the meteorological conditions and chemical mechanism. By using the enrichment factor (EF) to distinguish the sources of heavy metals in PM10, PM2.5 and the results showed that EF values of crustal elements in PM10 ranged from 1 to 10, suggesting PM10 might come from the resuspension of soil and road dust. In addition, Pb, Zn, As, Se, Mo, Sb and sulfate were observed at higher EF values in both PM2.5 and PM1, indicating the influence of anthropogenic emissions in fine particles. The results from Pearson¡¦s correlations indicated that PM10 in the Nantzu Processing Zone were mainly from the resuspension of soil and road dust, while fine particles (PM2.5 and PM1) may be from the traffic emissions and petrochemical industry in Nanzih and Renwu.

With the shift toward online shopping, it has become necessary to customize customers' needs and give them more choices. Before making a purchase, buyers research the products' features. The recommender systems facilitate the search task for customers by narrowing down the search space within specific products that align with the customer's needs. A recommender system uses clustering to filter information, calculating the similarity between members of a cluster to determine the factors that will lead to more accurate predictions. We propose a new method for predicting scores in machine learning recommender systems using the Gaussian mixture model clustering and the Pearson correlation coefficient. The proposed method is applied to MovieLens data. The results are then compared to three commonly used methods: Pearson correlation coefficients, K-means, and fuzzy C-means algorithms. As a result of increasing the number of neighbors, our method shows a lower error than others. Additionally, the results depict that accuracy will increase as the number of users increases. Our model, for instance, is 5% more accurate than existing methods when the neighbor size is 30. Gaussian mixture clustering chooses similar users and takes into account the scores distance when choosing nearby users that are similar to the active user.

In this thesis, an analysis of Bitcoin, Monero price and volatility is conducted with respect to S&P500 and the VIX index. Moreover using Python, we computed correlation coefficients of nine cryptocurrencies with two different approaches: Pearson and Spearman from July 2016 -July 2018. Moreover the Pearson correlation coefficient was computed for each year from July2016 - July 2017 - July 2018. It has been concluded that in 2016 the correlation between the selected cryptocurrencies was very weak - almost none, but in 2017 the correlation increased and became moderate positive. In 2018, almost all of the cryptocurrencies were highly correlated. For example, from January until July of 2018, the Bitcoin - Monero correlation was 0.86 and Bitcoin - Ethereum was 0.82.

Nos últimos anos, o estudo de propriedades de redes espaciais urbanas conhecidas como centralidades, tem sido utilizado com frequência para descrever fenômenos de ordem sócio-econômica associados à forma da cidade. Autores têm sugerido que centralidades são capazes de descrever a estrutura espacial urbana (KRAFTA, 1994; ANAS et al., 1998) e, portanto através do estudo de centralidades, é possível reconhecer os espaços que mais concentram fluxos, os que possuem os maiores valores de renda da terra, os mais seguros, entre outros aspectos que parecem estar diretamente relacionados com o fenômeno urbano. A hipótese dessa pesquisa admite que centralidades em redes espaciais urbanas desempenham um papel fundamental na formação da estrutura espacial urbana e na maneira como são organizados os usos do solo da cidade. Assim, essa pesquisa investiga qual modelo de centralidade, processado sobre diversas formas de se descrever o espaço urbano na forma de uma rede, é capaz de apresentar resultados mais fortemente correlacionados com a distribuição espacial de atividades econômicas urbanas. Nessa pesquisa são avaliados cinco modelos de centralidade, aplicados sobre diferentes redes espaciais urbanas com a intenção de se verificar qual deles apresenta maior correlação com a ocorrência de atividades econômicas. Para realizar tal exercício, esses modelos são aplicados sobre três tipos de redes espaciais urbanas (axial, nodal e trechos de rua) – oriundas da configuração espacial de três cidades brasileiras – processados de forma geométrica e topológica. Os modelos de centralidade aplicados são conhecidos como centralidade por Alcance (SEVTSUK; 2010), centralidade por Excentricidade (PORTA et al.; 2009, 2011), centralidade por Intermediação (FREEMAN, 1977), centralidade por Intermediação Planar (KRAFTA, 1994) e centralidade por Proximidade (INGRAM, 1971). O coeficiente de correlação Pearson (r) é utilizado como ferramenta capaz de descrever qual modelo de centralidade, associado a qual tipo de representação espacial e qual modo de processamento de distâncias melhor se correlaciona com a distribuição de atividades econômicas urbanas nessas cidades. As evidências encontradas nessa pesquisa sugerem que os modelos de centralidade por Alcance, centralidade por Excentricidade e centralidade por Intermediação Planar destacam-se em comparação com os demais modelos processados. Além disso, os valores de correlação Pearson (r) mais relevantes foram obtidos quando os modelos de centralidade foram processados considerando-se a geometria da rede formada por trechos de rua, indicando que um tipo de representação espacial mais desagregada e processada de forma geométrica seria mais capaz de apresentar os melhores valores de correlação para a compreensão do fenômeno urbano estudado. / In recent years, the study of urban spatial networks has been often used to describe urban phenomena associated with the shape of the city. Researches suggested that centralities are able to describe the urban spatial structure (KRAFTA, 1994; ANAS et al., 1998) and then it is possible to recognize the spaces with more flows, which have the highest values of land revenue, the safest, among other aspects related to urban phenomenon. The hypothesis of this research accepts that centrality in urban spatial networks play a key role for the urban spatial structure and the way of land uses is organized. Thus, there would be some measures of centrality in urban spatial networks that would be more associated with economic activities occurring in the city. The research will evaluate five measures of centrality applied on three types of urban spatial networks (axial map, node map and segment map). Therefore we will use five models of centrality in urban spatial networks known as reach (SEVTSUK, MEKONNEN, 2012), straightness (PORTA et al., 2006b), betweenness (FREEMAN, 1977), planar betweenness (KRAFTA, 1994) and closeness (INGRAM, 1971) in order to determine which this most highly correlated with the occurrence of economic activities. The relationships between these measures of centrality and locations of economic activities are examined in three Brazilian cities, using as methodology the Pearson correlation coefficient (r). The highest correlation between the results of centrality in urban spatial networks and the location of economic activities will suggest which centrality measure, way of to describe urban space like a network and distance processing method (euclidian or topologic) is more associated with the occurrence of these activities in the city. The results indicate that Reach, Straightness and Planar Betweenness are most outstanding models of centrality. In addition, Pearson correlation coefficients (r) most relevant were obtained when models of centrality are processed considering euclidian paths in the street segments network, suggesting that this type of spatial network and distances processing method generates centralities with more significant correlation values within the urban phenomenon studied.

Nos últimos anos, o estudo de propriedades de redes espaciais urbanas conhecidas como centralidades, tem sido utilizado com frequência para descrever fenômenos de ordem sócio-econômica associados à forma da cidade. Autores têm sugerido que centralidades são capazes de descrever a estrutura espacial urbana (KRAFTA, 1994; ANAS et al., 1998) e, portanto através do estudo de centralidades, é possível reconhecer os espaços que mais concentram fluxos, os que possuem os maiores valores de renda da terra, os mais seguros, entre outros aspectos que parecem estar diretamente relacionados com o fenômeno urbano. A hipótese dessa pesquisa admite que centralidades em redes espaciais urbanas desempenham um papel fundamental na formação da estrutura espacial urbana e na maneira como são organizados os usos do solo da cidade. Assim, essa pesquisa investiga qual modelo de centralidade, processado sobre diversas formas de se descrever o espaço urbano na forma de uma rede, é capaz de apresentar resultados mais fortemente correlacionados com a distribuição espacial de atividades econômicas urbanas. Nessa pesquisa são avaliados cinco modelos de centralidade, aplicados sobre diferentes redes espaciais urbanas com a intenção de se verificar qual deles apresenta maior correlação com a ocorrência de atividades econômicas. Para realizar tal exercício, esses modelos são aplicados sobre três tipos de redes espaciais urbanas (axial, nodal e trechos de rua) – oriundas da configuração espacial de três cidades brasileiras – processados de forma geométrica e topológica. Os modelos de centralidade aplicados são conhecidos como centralidade por Alcance (SEVTSUK; 2010), centralidade por Excentricidade (PORTA et al.; 2009, 2011), centralidade por Intermediação (FREEMAN, 1977), centralidade por Intermediação Planar (KRAFTA, 1994) e centralidade por Proximidade (INGRAM, 1971). O coeficiente de correlação Pearson (r) é utilizado como ferramenta capaz de descrever qual modelo de centralidade, associado a qual tipo de representação espacial e qual modo de processamento de distâncias melhor se correlaciona com a distribuição de atividades econômicas urbanas nessas cidades. As evidências encontradas nessa pesquisa sugerem que os modelos de centralidade por Alcance, centralidade por Excentricidade e centralidade por Intermediação Planar destacam-se em comparação com os demais modelos processados. Além disso, os valores de correlação Pearson (r) mais relevantes foram obtidos quando os modelos de centralidade foram processados considerando-se a geometria da rede formada por trechos de rua, indicando que um tipo de representação espacial mais desagregada e processada de forma geométrica seria mais capaz de apresentar os melhores valores de correlação para a compreensão do fenômeno urbano estudado. / In recent years, the study of urban spatial networks has been often used to describe urban phenomena associated with the shape of the city. Researches suggested that centralities are able to describe the urban spatial structure (KRAFTA, 1994; ANAS et al., 1998) and then it is possible to recognize the spaces with more flows, which have the highest values of land revenue, the safest, among other aspects related to urban phenomenon. The hypothesis of this research accepts that centrality in urban spatial networks play a key role for the urban spatial structure and the way of land uses is organized. Thus, there would be some measures of centrality in urban spatial networks that would be more associated with economic activities occurring in the city. The research will evaluate five measures of centrality applied on three types of urban spatial networks (axial map, node map and segment map). Therefore we will use five models of centrality in urban spatial networks known as reach (SEVTSUK, MEKONNEN, 2012), straightness (PORTA et al., 2006b), betweenness (FREEMAN, 1977), planar betweenness (KRAFTA, 1994) and closeness (INGRAM, 1971) in order to determine which this most highly correlated with the occurrence of economic activities. The relationships between these measures of centrality and locations of economic activities are examined in three Brazilian cities, using as methodology the Pearson correlation coefficient (r). The highest correlation between the results of centrality in urban spatial networks and the location of economic activities will suggest which centrality measure, way of to describe urban space like a network and distance processing method (euclidian or topologic) is more associated with the occurrence of these activities in the city. The results indicate that Reach, Straightness and Planar Betweenness are most outstanding models of centrality. In addition, Pearson correlation coefficients (r) most relevant were obtained when models of centrality are processed considering euclidian paths in the street segments network, suggesting that this type of spatial network and distances processing method generates centralities with more significant correlation values within the urban phenomenon studied.

Nos últimos anos, o estudo de propriedades de redes espaciais urbanas conhecidas como centralidades, tem sido utilizado com frequência para descrever fenômenos de ordem sócio-econômica associados à forma da cidade. Autores têm sugerido que centralidades são capazes de descrever a estrutura espacial urbana (KRAFTA, 1994; ANAS et al., 1998) e, portanto através do estudo de centralidades, é possível reconhecer os espaços que mais concentram fluxos, os que possuem os maiores valores de renda da terra, os mais seguros, entre outros aspectos que parecem estar diretamente relacionados com o fenômeno urbano. A hipótese dessa pesquisa admite que centralidades em redes espaciais urbanas desempenham um papel fundamental na formação da estrutura espacial urbana e na maneira como são organizados os usos do solo da cidade. Assim, essa pesquisa investiga qual modelo de centralidade, processado sobre diversas formas de se descrever o espaço urbano na forma de uma rede, é capaz de apresentar resultados mais fortemente correlacionados com a distribuição espacial de atividades econômicas urbanas. Nessa pesquisa são avaliados cinco modelos de centralidade, aplicados sobre diferentes redes espaciais urbanas com a intenção de se verificar qual deles apresenta maior correlação com a ocorrência de atividades econômicas. Para realizar tal exercício, esses modelos são aplicados sobre três tipos de redes espaciais urbanas (axial, nodal e trechos de rua) – oriundas da configuração espacial de três cidades brasileiras – processados de forma geométrica e topológica. Os modelos de centralidade aplicados são conhecidos como centralidade por Alcance (SEVTSUK; 2010), centralidade por Excentricidade (PORTA et al.; 2009, 2011), centralidade por Intermediação (FREEMAN, 1977), centralidade por Intermediação Planar (KRAFTA, 1994) e centralidade por Proximidade (INGRAM, 1971). O coeficiente de correlação Pearson (r) é utilizado como ferramenta capaz de descrever qual modelo de centralidade, associado a qual tipo de representação espacial e qual modo de processamento de distâncias melhor se correlaciona com a distribuição de atividades econômicas urbanas nessas cidades. As evidências encontradas nessa pesquisa sugerem que os modelos de centralidade por Alcance, centralidade por Excentricidade e centralidade por Intermediação Planar destacam-se em comparação com os demais modelos processados. Além disso, os valores de correlação Pearson (r) mais relevantes foram obtidos quando os modelos de centralidade foram processados considerando-se a geometria da rede formada por trechos de rua, indicando que um tipo de representação espacial mais desagregada e processada de forma geométrica seria mais capaz de apresentar os melhores valores de correlação para a compreensão do fenômeno urbano estudado. / In recent years, the study of urban spatial networks has been often used to describe urban phenomena associated with the shape of the city. Researches suggested that centralities are able to describe the urban spatial structure (KRAFTA, 1994; ANAS et al., 1998) and then it is possible to recognize the spaces with more flows, which have the highest values of land revenue, the safest, among other aspects related to urban phenomenon. The hypothesis of this research accepts that centrality in urban spatial networks play a key role for the urban spatial structure and the way of land uses is organized. Thus, there would be some measures of centrality in urban spatial networks that would be more associated with economic activities occurring in the city. The research will evaluate five measures of centrality applied on three types of urban spatial networks (axial map, node map and segment map). Therefore we will use five models of centrality in urban spatial networks known as reach (SEVTSUK, MEKONNEN, 2012), straightness (PORTA et al., 2006b), betweenness (FREEMAN, 1977), planar betweenness (KRAFTA, 1994) and closeness (INGRAM, 1971) in order to determine which this most highly correlated with the occurrence of economic activities. The relationships between these measures of centrality and locations of economic activities are examined in three Brazilian cities, using as methodology the Pearson correlation coefficient (r). The highest correlation between the results of centrality in urban spatial networks and the location of economic activities will suggest which centrality measure, way of to describe urban space like a network and distance processing method (euclidian or topologic) is more associated with the occurrence of these activities in the city. The results indicate that Reach, Straightness and Planar Betweenness are most outstanding models of centrality. In addition, Pearson correlation coefficients (r) most relevant were obtained when models of centrality are processed considering euclidian paths in the street segments network, suggesting that this type of spatial network and distances processing method generates centralities with more significant correlation values within the urban phenomenon studied.

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摘要 皮爾森相關係數通常作為描述區間尺度變數間相關性的參考指標，然而在社會科學領域中，由於資料多數以順序尺度的形式呈現，因此藉由傳統的皮爾森相關係數來描述順序尺度資料間的相關性通常會導致某種程度的誤差。儘管如此，以往的文獻多數傾向支持以等距離分數來取代順序尺度資料，並直接計算皮爾森相關係數。藉由模擬實驗的結果，我們發現這樣的作法並非在所有情況下都合理。 此外本研究中也對多序類相關係數進行探討。就表示順序變數間相關性的準確程度而言，多序類相關係數明顯優於利用等距離分數來計算皮爾森相關係數的方法；但若以操作上的便利程度而言，後者仍具有其優勢。 關鍵字：順序尺度、皮爾森相關係數、多序類相關係數。 / Abstract Pearson correlation coefficient is typically used to describe the correlation between two interval-scaled variables. In social science, however, most of the data are represented in ordinal-scale, and hence describing the correlation between two ordinal-scaled variables in terms of Pearson correlation coefficient would inevitably result in certain errors. Though the practice is deemed acceptable and generally supported in literatures, we found, through intensive simulations, that it should be executed with care. Polychoric correlation coefficient was also investigated. In order to describe the correlation between two ordinal-scaled variables, we found, in terms of the degree of accuracy, that Polychoric correlation coefficient is definitely better than Pearson correlation coefficient with equal-distance scores. Pearson correlation coefficient, on the other hands, is much easier to calculate, and should not be totally ignored. Key words：Ordinal-scale、Pearson correlation coefficient、Polychoric correlation coefficient。

The objective of this thesis is to proof possibilities to use data from the electronic revenue records to measure the visit rate and process recommendations for the use in tourism. The thesis focuses on the tourism sector. Concepts and related terminology are explained. Described in this thesis are sources of the information about visitors, profiles of visitors, decision-making process about visits, and selected technologies used to measure the visit rate. Reasons, problems and classification related to measurements of the visit rate are included in the thesis as well. The practical part examines the use of information from electronic revenue records for the purpose of measuring the number of visitors based on the calculation of Pearson's correlation coefficients. The principal how EET functions is explained in the thesis. Significant part of the work is the analysis of daily and monthly revenues of electronic records in the sector of lodging in regions of the Czech Republic. Based on this analysis, 6 groups are determined in which the development of daily seasonality takes place in a specific way. An important part is also the calculation of average cost of accommodation in regions, which identifies certain economic impacts of tourism. Part of the thesis are summarized recommendations for the use of data from EET.

Recently, it has become more and more difficult for the existing web based systems to locate or retrieve any kind of relevant information, due to the rapid growth of the World Wide Web (WWW) in terms of the information space and the amount of the users in that space. However, in today&#039 / s world, many systems and approaches make it possible for the users to be guided by the recommendations that they provide about new items such as articles, news, books, music, and movies. However, a lot of traditional recommender systems result in failure when the data to be used throughout the recommendation process is sparse. In another sense, when there exists an inadequate number of items or users in the system, unsuccessful recommendations are produced. Within this thesis work, ReMovender, a web based movie recommendation system, which uses a content boosted collaborative filtering approach, will be presented. ReMovender combines the local/global similarity and missing data prediction v techniques in order to handle the previously mentioned sparseness problem effectively. Besides, by putting the content information of the movies into consideration during the item similarity calculations, the goal of making more successful and realistic predictions is achieved.