BUSINESS INTELLIGENCE Tools and Techniques Essay

BUSINESS INTELLIGENCE Tools and Techniques - Essay Example This is a change from the 30 responses required last year. III. There are 13 capabilities described by the author that must be delivered by BI platform. These 13 capabilities can be classified into 3 categories of functionality Integration Information delivery Analysis 1. INTEGRATION BI Infrastructure All tools, interfaces and applications in the platform should have same look and feel. Metadata Management The platform should have the ability to store, search, and capture and reuse the formats, measures, dimensions and report layouts. Development Tools It should provide programmatic development tools and visual development environment to facilitate scheduling, delivering, administering and managing. Collaboration It deals with sharing and discussing information throughout the organization. 2. INFORMATION DELIVERY Reporting It facilitates the reporting procedure by developing formatted and interactive reports in various dimensions (financial, operational, managerial, etc) Dashboards T his is a subset of reporting having the ability to publish web-based reports with interactive tools for display. Ad hoc Query This enables the user to ask their own questions and data queries rather than IT created reports. Microsoft Office Integration Integration with Microsoft tools, formats and formulas is necessary item to be provided. Search-Based BI Application of search index to both structured and unstructured data sources and their mapping enable user to search from (Google-like) interface. 3. ANALYSIS OLAP This enables user to analyze data with extremely fast query and calculation performance making analysis style of ‘slicing and dicing’ possible. Interactive Visualization It includes display of data in a more effective way using charts, tables and other formats. Predictive modeling and Data Mining It helps to classify categorical variables and continuous variables using advance mathematical techniques. Scorecards It implies the use of performance management m ethodology like six sigma and it involves analysis and comparisons. PART B 1. IBM (Cognos) solution has a broad functional footprint and is reporting-centric. It follows ‘information versus an applications agenda’. Information Builders’ WebFOCUS product has a very consumer-centric approach and is found to be as one of the industry's easiest-to-use solutions. It offers integrated search, mobile, use of rich Internet applications and mashups, predictive analytics, data discovery, and visualization but they lack self-service support, ad-hoc analysis, and OLAP capabilities. Microsoft offers low price but they do not provide a road map. MicroStrategy specializes in running deployments on top of large enterprise data warehouses tackling large volumes of data. Oracle offers domain-specific and prepackaged solutions. SAP offers data warehousing, text analytics, on-demand BI, search coupled with BI, metadata, data lineage and impact analysis, and data quality. SAS focuses on forecasting, predictive modeling, and optimization, as well as its investments in data discovery and visualization. QlikTech offers low-cost deployments. Tibco products have unique architecture, combining analytics and interactive

The Transforming of Women in Medieval Literature Essay Example for Free

The Transforming of Women in Medieval Literature Essay Over the countless years of history man and woman have realized that they must come together in order to survive. Whether it was solely for the continuation of our race through procreation, or by uniting one with another in matrimony; the two genders have found it impediment to spend their lives in each other’s midst. Over the span of several millennia we not only see the evolution of these relationships, but we can also witness the transformation of the roles each gender plays in everyday life. One such period where we see many of these roles evolving occur is chronicled in Medieval Literature. Writings such including Chaucer’s â€Å"The Canterbury Tales† and many Arthurian Legends present women and their treatment by their male counterparts in a ways uncommon to earlier writings. One of the best representations of such thinking is found in â€Å"Sir Gawain and the Green Knight. † The text includes women of varying types and gives an excellent paradigm to the changing culture of the Medieval Era. At the genesis of the tale we are presented with the ideal medieval lady. The narrator describes Queen Guenevere’s immense beauty and states that â€Å"fair queen, without a flaw†¦ A seemlier that once he saw, / In truth no man could say† (81-84). Guenevere serves as an example of the prior period’s typical woman. She is quiet, obedient to her husband, and the attractive object of the male gaze. Previously this was the norm for woman, to be confined to a set of restrictions that kept her inferior to all other men. Compared to Chaucer’s Wife of Bath who is loud, assertive, and extremely sexually open, Guenevere knows her roles and offers little complaint of her place in the castle. The lack of her contention exemplifies the base portrayal of a woman’s traditional position. The next female we come across in the journey of Gawain is Bertilak’s wife. At the first moment of meeting the lovely lady, we are presented with the fact that she is of a different breed than Guenevere. As she enters the room, Gawain’s mind wanders, â€Å"her body and her bearing were beyond praise, / And excelled the queen herself† (944-945). Here a knight admits the greatness of a lady beyond his own queen. This reveals the higher complexity found in the lady of the castle. Where we see the deepening contrasts is in the lady’s actual description. Whereas Guenevere was praised for solely her beauty and carriage, we see depth beyond this in the description of Bertilak’s wife. In lines 1204-1207, we read, â€Å"sweetly she does speak / And kindling glances dart, / Blent white and red on cheek / And laughing lips apart,† a noticeably more sexualized description than the one offered for our former lady. Delving even deeper into the story we read her actions as exceedingly daring for the wife. She wanders into the room of the night herself to seduce him for a kiss. In this time period women made no attempt at such provocation of a man’s lustful desires. Now one may point out that the lady was under orders from Bertilak. What I see is the man counting his wife as equal and including her in his plan to trick Gawain. Either case we see a woman who enjoys the confines of being a lady yet at the same time the freedoms of equality. In this we see more of a modern woman. She is developed more complexly in that she is neither completely virtuous yet neither is she corrupt. Finally we have one last woman; one whom tears down all the conceptions of the conventional feminine roles of the time. Gawain’s Aunt Morgan la Faye is the magical temptress who devises the plan to test her worthy nephew. La Faye is the ultimate foil of our first character and an extreme version of the second. She has no husband and nor any other male too hold her to the constraints of society. She is able to use her powerful skills to do as she pleases and cause any amount of mayhem she sees fit. For example we find out at the end of the tale the old lady accompanying Bertialk’s wife is indeed Morgan la Faye in disguise. Morgan though she is extremely beautiful and young in her true form, stands for the free unconfined woman. Women across time have continually had to deal with confining gender roles. Yet in ever period there have been women who redefined the roles and pushed to break the trends stressed upon them. This condition is reflected by the writers of the time. From their efforts we are able to see the transformation and how the human condition has been affected. The poet who penned †Sir Gawain and the Green Knight† was able to cleverly weave this into the tale. From the examples of Guenevere’s demure attitude, to Lady Bertilak’s seductive ways, and finally ending with Morgan la Faye’s free and chaotic spirit; this paradigm is clearer in the middle ages than many others. Woman made great strides in the era of chivalry and began to break free of the bonds that contained them.

A Hierarchical Regression Analysis Psychology Essay

A Hierarchical Regression Analysis Psychology Essay This study was conducted to determine what the predictors of Body Mass Index are. There were two research questions of this study. First research question was How well the type of chocolate and frequency of chocolate consumption predict body mass index, after controlling for gender physical activity? Second research question was How well do fat percentage and cacao percentage in chocolate explain body mass index, after controlling the results of the first research question? In order to reveal the predictors hierarchical regression analysis was used. In this study BMI was outcome variable; gender, type of chocolate, fat rate in chocolate, cocoa rate in chocolate, frequency of chocolate consumption and frequency of physical activity in a week were predictor variables. The study was conducted with 600 university students. Method Participants and the Variables The sample of the study was consisted of 600 Middle East Technical University students; 46.3% (n=278) were male and 53.7% (n=322) were female. Convenience sampling method was used to determine the participants. The most crowded places of the university, such as library, market area, dormitory area, were selected as data collection areas. Requisite sample size for multiple regression could be calculated with the formula of number of predictors * 8 + 50. According to formula required sample size is 106 (7*8+50). While there are 600 students, sample size is quite enough to conduct multiple regression. The questionnaire used in this study was consisted of seven items which are presented in Table 1. Moreover, there is an id number for each participant. Totally, there were six continuous and two categorical variables on data file. Table 1 List of variables and brief descriptions in the data file Variable Name Description of the variable Id Identity number of each participant BMI Body Mass Index Gender Gender (1: Male; 2: Female) Type Type of chocolate ( 1: Milk; 2: Berry; 3: Peanut) Fat Fat rate (%) in chocolate Cacao Cacao rate (%)in chocolate Frequency Frequency of chocolate consumption (number of chocolates eaten in the last week) Activity Frequency of physical activity in a week Data Analysis Plan In this study hierarchical regression will be held to find out how much the predictors can explain the dependent variable, BMI. In hierarchical regression different models are tested sequentially. In contrast to stepwise regression, researcher decides the sequence of the predictors that included the model. Three different models will be used to determine how much these independent variables predict the dependent variable. In the first model gender and frequency of physical activity in a week will be included into analysis. In the second model, gender and frequency of physical activity in a week will be controlled; type of chocolate and frequency of chocolate consumption will be included into analysis. In the third model, gender, frequency of physical activity in a week, type of chocolate and frequency of chocolate consumption will be controlled, fat percentage and cacao percentage in chocolate will be included into analysis. To conduct the regression analysis, categorical data should be recoded. There are three different ways to do this; dummy coding, effects coding and contrast coding. In this study, dummy coding will be used to recode categorical data. In dummy coding, one categorical variable recode into different variables that the number of new variables are one less than the number of categories. Nevertheless, a categorical variable should have at least three levels to be recoded. A categorical variable with two levels such as gender neednt to be recoded. In this study there were two categorical data; gender and type of chocolate. As it mentioned before, gender neednt to be recoded. The other categorical variable, type of chocolate, should be recoded. Milk chocolate will be selected as reference variable; and, two other variables will be coded as milkvsberry and milkvspeanut. Likewise all other multivariate statistical methods, Multiple Regression has various assumptions; and, all these assumptions should be checked before conducting the analysis. First assumption of multiple regression is normality. Unlike other multivariate analysis, regression analysis checks whether the error distributes normally or not. Secondly, multicollinearity, which is high level of intercorrelation among predictor variables, should be checked. Thirdly, assumption of homoscedasticity should be checked. Homoscedasticity assumes that the variance of the error term is constant across each value of the predictor. This means that there should not be seen a pattern on scatter plot. Fourth assumption is independence, that the error term is independent of the predictors in the model and of the values of the error term for other cases. The fifth assumption of multiple regression is linearity. Lastly, outliers should be check whether they affect the results or not. Partial plots, leverage statistics, Cooks D, DFBeta and Mahalonobis distance could be used to determine outliers. Results Descriptive Statistics Table 2 shows the descriptive statistics of the study. Table 2 shows that there is no missing data; mean of dependent variable, BMI, is 24.65 and the standard deviation is 4.48. Table 2 Descriptive Statistics Mean Std. Deviation N body mass index 24.65 4.48 600 Gender 1.54 .50 600 physical activity in a week 2.62 .74 600 milk chocolate vs berry chocolate .25 .44 600 milk chocolate vs peanut chocolate .27 .45 600 frequency of chocolate consumption 4.66 .73 600 fat rate (%) in chocolate 51.70 9.69 600 cacao rate (%) in chocolate 51.95 9.96 600 Table 3 shows the correlations between the variables. If the table is examine it is seen that the best predictor of BMI is fat rate in chocolate. There is a positive and high correlation between the BMI and fat rate in chocolate. On the other hand, there is no correlation between BMI and gender, physical activity in a week, milk chocolate vs berry chocolate. Moreover, there is no correlation higher than .90 between the independent variables. Table 3 Correlation Matrix 1 2 3 4 5 6 7 8 Pearson Correlation body mass index (1) 1.00 Gender (2) -.03 1.00 physical activity in a week (3) .04 -.13 1.00 milk chocolate vs berry chocolate (4) -.03 .03 -.11 1.00 milk chocolate vs peanut chocolate (5) .23 -.02 .12 -.36 1.00 frequency of chocolate (6) consumption .31 .12 .15 -.05 .19 1.00 fat rate (%) in chocolate (7) .64 -.12 .08 .02 .21 .30 1.00 cacao rate (%) in chocolate (8) .52 .08 .03 -.04 .22 .28 .51 1.00 Assumptions The first assumption of multiple regression to be checked is normality. Unlike other analysis, normality of residuals is checked whether errors normally distributed or not. Normality of residuals could be checked via two different ways; histogram and P-P plot. Figure 1 shows the histogram of regression standardized residuals. The histogram shows that there is a normal distribution of residuals. The frequency distribution of residuals is close to normal distribution line. Moreover, figure 2 shows the P-P plot of regression standardized residuals and it shows that distribution of errors is normal. It can be said that first assumption of multiple regression, normality, is not violated. Figure 1 Histogram of Regression Standardized Residual Figure 2 P-P Plot of Regression Standardized Residual The second assumption of multiple regression to be checked is multicollinearity. Multicollinearity could be checked with correlation matrix, VIF or tolerance values. There should not be any correlation that is higher than .90 between two independent variables. When the correlation matrix (Table 3) is examined there is no correlation higher than .90 between two independent variables. Table 4 shows the collinearity statistics of all three models. VIF values more than four or tolerance values higher than .20 are indicators of multicollinearity. Table 4 shows that there is no VIF value higher than four or tolerance value higher than .20. So, assumption of multicollinearity is not violated. Table 4 Collinearity Statistics Model Collinearity Statistics Tolerance VIF 1 (Constant) Gender .98 1.02 physical activity in a week .98 1.02 2 (Constant) Gender .96 1.04 physical activity in a week .94 1.06 milk chocolate vs berry chocolate .87 1.15 milk chocolate vs peanut chocolate .84 1.19 frequency of chocolate consumption .93 1.08 3 (Constant) Gender .92 1.08 physical activity in a week .94 1.06 milk chocolate vs berry chocolate .86 1.17 milk chocolate vs peanut chocolate .80 1.24 frequency of chocolate consumption .84 1.19 fat rate (%) in chocolate .67 1.49 cacao rate (%) in chocolate .70 1.43 The third assumption of multiple regression to be checked is homoscedasticity. Scatter plot of predicted value and residual is used to control homoscedasticity. Any pattern should not be seen on the scatter plot. Figure 4 shows that there is no pattern on the scatter plot; so, there is not homoscedasticity. Figure 4 Scatter plot of predicted value and residual The fourth assumption of multiple regression to be checked is independence. Independence is affected by the order of the independent variables and can be ignored if the order of independent variables is not important. Order of the independent variables is important in this study; so, independence should be checked in this study. Independence is checked with Durbin-Watson value that should be between 1.5 and 2.5. Durbin-Watson value of the model is 1.88; so, independence assumption is not violated. The last assumption of multiple regression is linearity. We assume that linearity is not violated in this study. Influential Observations Data should be checked whether there are outliers or not. Outliers could cause misleading results. There are different ways of checking outliers in multiple regression such as Partial plots, leverage statistics, Cooks D, DFBeta and Mahalonobis distance. Each method uses a different calculation method; so, multiple methods should be used and then make a decision whether a data is outlier or not. At first, partial plots of the dependent variable with each of the independent variable is examined (see on figure 5,6,7,8 and 9). Some cases that could be outliers are seen on each partial plot; but, this should not be forgotten, making decision over partial plots is a subjective way and other ways of controlling outliers should be used. A decision could be made even after all methods were conducted. Figure 5 Partial Plot of BMI and physical activity in a week Figure 6 Partial Plot of BMI and milk chocolate vs peanut chocolate Figure 7 Partial Plot of BMI and frequency of chocolate consumption Figure 8 Partial Plot of BMI and fat rate in chocolate Figure 9 Partial Plot of BMI and cacao rate in chocolate After controlling partial plots, leverage value could be controlled to identify the outliers. It is seen that there is no case, leverage value of which is higher than .50. According to leverage test results there is no outlier. Table 5 Extreme Values of Leverage Test Case Number Value Centered Leverage Value Highest 1 448 .04 2 384 .04 3 141 .03 4 324 .03 5 592 .03 Lowest 1 196 .00 2 103 .00 3 535 .05 4 160 .05 5 8 .05 After controlling leverage values, Cooks distance could be controlled. In Cooks Distance, a value greater than the value, calculated with the formula of mean + 2 * standard deviation, can be admitted as outlier. In this study critical value is .008 (.002+2*(.003)). Maximum value of Cooks distance is .03; so, it is expected that there will be outliers. Boxplot of Cooks distance (figure 10) shows that the cases 499, 438, 449, 236, 284, 484, 37, 354, 137, 97, 324 and 165 could be outliers. On the other hand, according to Cook and Weisberg (1982) values greater than 1 could be admitted as outlier. So, it can be assumed that there is no outlier. Figure 10 Boxplot of Cooks distance After controlling Cooks Distance, DF Beta values of each independent variable could be checked. DF Beta value shows the change in regression coefficient due to deletion of that row with outlier. According to Field (2009) a case can be outlier if absolute value of DF Beta is higher than one. According to Stevens (2002) a case can be outlier if absolute value of DF Beta is higher than two. In this study there is no case that has DF Beta value higher than one (see figure 11). According to DF Beta test values there is no outlier in this study. Figure 11 Boxplots of DF Beta values of Independent Variables Lastly, Mahalanobis Distance could be controlled to identify the outliers. If there is any case that is greater than the value of chi square at ÃŽÂ ±=.001 that could be admitted as outlier. The critical value at ÃŽÂ ±=.001 with seven predictors is 24.32. Table 6 shows the extreme values for this study and there is no value greater than 24.32. According to Mahalanobis distance test there is no outlier. Table 6 Extreme Values of Mahalanobis Distance Case Number Value Mahalanobis Distance Highest 1 448 23.72 2 384 20.90 3 141 20.50 4 324 19.15 5 592 17.99 Lowest 1 196 2.62 2 103 2.62 3 535 2.78 4 160 2.78 5 8 2.78 If the results of each test is summarized; Partial plots shows that there could be outliers, Leverage values show that there is no outliers, Cooks distance values show that there is no outlier, DF Beta values show that there is no outlier. According to results of the tests, it could be assumed that there is no outlier. Regression Results A hierarchical regression analysis was conducted to identify the predictors of BMI. Three different models were examined to understand which predictor explains has how much variance. Table 7 shows the summary of three models. Among three models, the first model is not statistically significant; the second and third models are significant. In the first model; gender and physical activity in a week were the predictors. This model explains the .2% of total variance, but insignificant; F (2, 597) = .67; p > .05. In the second model, milk chocolate vs berry chocolate, milk chocolate vs peanut chocolate and frequency of chocolate consumption are the predictors after controlling for the effect of gender and physical activity in a week. This model explains 13% of total variance explained significantly, F (3, 594) = 28.901; p In the third model, cacao rate (%) in chocolate, fat rate (%) in chocolate are the predictors of BMI after controlling for the effect of gender, physical activity in a week, milk chocolate vs berry chocolate, milk chocolate vs peanut chocolate and frequency of chocolate consumption. This model explains 34% of total variance explained significantly, F (2, 592) = 189.154, p Table 7 Regression Analysis Model Summary Model R R2 Change Statistics Durbin-Watson ΆR2 ΆF df1 df2 Ά Sig. F 1 .05a .00 .00 .69 2 597 .50 2 .36b .13 .13 28.90 3 594 .00 3 .69c .47 .34 189.15 2 592 .00 1.879 a. Predictors: (Constant), physical activity in a week, gender b. Predictors: (Constant), physical activity in a week, gender, milk chocolate vs berry chocolate, frequency of chocolate consumption, milk chocolate vs peanut chocolate c. Predictors: (Constant), physical activity in a week, gender, milk chocolate vs berry chocolate, frequency of chocolate consumption, milk chocolate vs peanut chocolate, cacao rate (%) in chocolate, fat rate (%) in chocolate d. Dependent Variable: body mass index Table 8 shows the Coefficients of Hierarchical Regression Analysis that shows the significance and total variance explained by each predictor. In the first model any of the predictors significantly predicts the dependent variable, BMI. It can be said that neither the model, nor the predictors are statistically significant and do not predict the outcome variable, F (2, 597) = .67; p > .05. In the second model, overall model is significant, F (3, 594) = 28.901; p In the third model, overall model is significant, F (2, 592) = 189.154, p Table 8 Coefficients of Hierarchical Regression Analysis Model Unstandardized Coefficients Standardized Coefficients t p Correlations B Std. Error Beta Part 1 (Constant) 24.419 .941 25.938 .000 Gender -.232 .370 -.026 -.628 .530 -.026 physical activity in a week .226 .251 .037 .900 .369 .037 2 (Constant) 17.165 1.309 13.110 .000 milk chocolate vs berry chocolate .539 .423 .052 1.273 .204 .049 milk chocolate vs peanut chocolate 1.943 .420 .193 4.629 .000 .177 frequency of chocolate consumption 1.751 .245 .283 7.135 .000 .273 3 (Constant) 5.426 1.191 4.557 .000 fat rate (%) in chocolate .221 .017 .477 13.033 .000 .390 cacao rate (%) in chocolate .109 .016 .242 6.766 .000 .203 a. Dependent Variable: body mass index Discussion Two different research questions were tried to be answered in this study. First research question was How well the type of chocolate and frequency of chocolate consumption predict body mass index, after controlling for gender physical activity?. Second research question was How well do fat percentage and cacao percentage in chocolate explain body mass index, after controlling the results of the first research question?. A hierarchical regression analysis was conducted to answer the research questions. Three models were examined to find the predictors and their contribution to these models. The first model that examines that how well gender and physical activity in a week predict the dependent variable. Result of the first model shows that neither model nor predictors significantly predict the BMI. The second model examined to answer the first research question. This model predicts 13% of total variance explained. Milk chocolate vs berry chocolate does not significantly explain the BMI. Milk chocolate vs peanut chocolate explains 3%, frequency of chocolate consumption explains 7% of total variance explained. The third model examined to answer the second research question. This model predicts 47% of total variance explained and 34% of total variance explained uniquely. Fat rate in chocolate explains 15% and cacao rate in chocolate explains 4% of total variance uniquely. When all models were examined it is seen that fat rate in chocolate is the best predictor of BMI by explaining 15% of total variance explained. Frequency of chocolate consumption is the second by explaining 7% of total variance explained. Cacao rate is the third predictor by explaining 4% of total variance explained.

Catcher In The Rye :: essays research papers

Catcher in the Rye... J.D. Salinger’s, The Catcher in the Rye, is one of the most well-known novels of the past fifty years. It’s a story about a kid named Hold Caulfield who experiences some interesting things and people. From having breakfast with a couple of nuns, to hooking up with a prostitute, to getting kicked out of school, Holden handles each situation the best way he can. Some of the people Holden meets, he likes, but the type of people Holden can’s stand are the ‘phonies.’ Holden met a lot of phonies in his lifetime. Holden lived in a dorm that was named after a phony, he heard a phony playing a piano, and he met his date’s phony friend. Holden went to a boarding school named Pencey Prep. There, Holden lived in the Ossenburger Memorial Wing which is the name for the new dorms. The hall was only for juniors and seniors. The dorms were named after this guy named Ossenburger who also went to Pencey a long time ago. After Ossenburger got out of Pencey, he made a lot of money in the undertaking business. After making a bundle of dough, Ossenburger gave some of it to Pencey and that’s why the new wing of the dorms are named after him. Then the next morning, Ossenburger gave a speech to the students of Pencey Prep about how he was never ashamed when he was in some kind of trouble or something that Ossenburger would get right down on his knees and pray to God. Ossenburger kept on rambling bout how you should always pray to God and to talk to God wherever you were. Ossenburger said think of him as your buddy. Holden got a kick out of his speech. Holden could â€Å"just see the big phony bastard... asking Jesus to send him a few more stiffs.† Holden next went to this night club called Ernie’s. Holden was going there for a few drinks. Even though it was so late, the club was jam-packed. Ernie, the piano player, was playing some tune that Holden couldn’t recognize. Ernie was putting all these high notes, show-offy ripples in the high notes, and a lot of other tricky stuff that Holden thought was dumb. The crowd was going crazy for Ernie though, clapping and all that. â€Å"Old Ernie turned around on his stool and gave this very phony, humble bow.† Holden thought Ernie’s snobbish attitude was so phony but Holden felt kind of sorry for Ernie. Holden doesn’t even think that Ernie knows when he’s playing the tunes right or not. The last phony Holden met was while

Roskill and Howard Davies Airport Commissions and the Third London Airport

Introduction London’s airports are operating close to capacity and there are challenges associated with the location particularly of Heathrow airport, such as noise pollution and safety of London’s populace (DOT, 2003; Helsey and Codd, 2012). Capacity expansion pursuits have been long drawn over half a century involving two airport commissions and political intrigues (FT, 2014). The Third London Airport commission popularly known as the ‘Roskill Commission’ anticipated growth in air transport and speculated that by the end of the century London might have to accommodate 100 million passengers (Abelson and Flowerdew, 1972). It was an appropriate estimate as the actual number was 115 million (CAPA, 2013). This ceiling has been surpassed and London airports are operating under strenuous volumes. The pursuit of an alternative airport, additional runways to expand capacity, among other options continue to feature in public discourse almost half a century later with myriad ar guments and counterarguments (FT, 2014; The Independent, 2014). This report explores the works of the airports commissions (Roskill and Howard Davies commissions), as well as the consideration of the controversial Boris Island alternative. It focuses on the demand and supply of airports among other considerations significant for such ventures as the development of new airports or aviation facilities. History of commissions and development of arguments A 1964 interdepartmental committee on the Third London Airport forecast that the capacity of Heathrow and Gatwick airports combined, even with the addition of a second runway at Gatwick, would be insufficient for London’s air traffic by 1972 (Mishan, 1970). After the consideration of options, the commission on the Third London Airport (Roskill Commission) was set up in 1968. With their evaluation of the timing of need, expansion capacity requirement, and after a careful study of a total of 80 proposed project sites, the commission finally chose four sites, among them a new airport at Cublington (Abelson and Flowerdew, 1972). It was the first time that a full range of environmental and economic arguments were brought to bear on a major investment decision, providing substantial and significant systemic evidence on which to base decisions (HC, 1971). Its excellence in approach and output was however to not much good as government, with a variant perception and opinion immediatel y rejected its findings choosing instead a scheme to build an airport at Foulness, in the Thames Estuary (Mishan, 1970). Interestingly, this option had been considered and had been decisively rejected by the Roskill Commission on the basis of cost, distance and convenience to prospective passengers (FT, 2014). Neither of the two propositions (Cublington and Foulness) was built and a subsequent change in government and complexion led to the devise of a different scheme – a limited expansion of an existing airport at Stansted which was accomplished a decade after proposition. This option had also been considered by the Roskill Commission and never made its shortlist of key options (Helsey and Codd, 2012). It was a predictable failure and is still challenged by the lack of success in supporting long-haul operations by airlines, only benefitting from low-cost carriers (principally Ryan air) drawn by attractive landing charges which offset consequent inconvenience to their passeng ers (AOA, 2013). A proposal which has re-emerged and gained prominence is the new airport at the Thames Estuary. The ‘Boris Island’ alternative Dubbed ‘Boris Island’ as a consequence of its support by London Mayor Boris Johnson, the London Britannia Airport (a name adopted for the latest iteration of the idea in 2013) is a proposed airport to be built on an artificial island in the River Thames estuary to serve London. Plans for this airport go several years back but the idea was revived by the Mayor in 2008 (CAPA, 2013; Mayor of London, 2013). Proponents of the project cite the significant advantage it portends in the avoidance of flights over densely populated areas with consideration of noise pollution and attendant safety challenges. However, its critics who include some local councils, nature conservation charity – RSPB, as well as current London airports, oppose the scheme, suggesting that it is impractical and expensive (AC, 2013b). It is still under consideration of the Howard Davies Airports Commission, which estimates the entire undertaking including feeder roads and rail to cost ?112 billion, a bout five times the presently shortlisted short-term options (AC, 2013c). The overall balance of economic impacts of the project would be uncertain given the requirement for the closure of Heathrow and by extension London city for airspace reasons (CAPA, 2013). Renewed pursuit – Howard Davies Airports Commission In spite of the myriad arguments and criticisms of the various alternatives, not much has changed and the Howard Davies Airports Commission set up in 2012 still wades in the long running controversy (CAPA, 2013; AOA, 2013). There has evidently been little learnt in the several decades of bad policy making given the hedging, stonewalling, and political posturing that still characterizes the endeavour, a readiness to oppose policies espoused by those of different complexions or the persistent complication of issues when there is requirement for bold action. This characterizes policy today as it did half a century earlier with elaborate models being grossly misused and deliberately disregarded. Minor challenges and disadvantages are greatly amplified overshadowing potentially more substantial benefits (FT, 2014). The Airports Commission was set up to examine the need for additional UK airport capacity and to recommend to government how this can be met in the short, medium and long term. The commission is tasked with creating economic, sustainable and socially responsible growth through competitive airlines and airports. (AC, 2013a). The findings of the Howard Davies Airports Commission contained in their interim report released in December 2013 (preceding a final report expected in 2015) are mainly focused on the continued growth of air travel, mainly in the South East of England. The Commission considers that the region needs an extra runway by 2030, and another possibly by 2050. On the shortlist for the expansion of airport capacity are three options comprising a third runway at Heathrow 3,500m long; lengthening of the existing northern runway to at least 6,000m enabling it to be used for both landing and take-off; as well as a new 3,000m runway at Gatwick (CAPA, 2013; AOA, 2013). Not included is the brand new hub airport in the Thames Estuary, which is side-lined citing uncertainties and challenges surrounding the proposal at this stage (AC, 2013d). However, th e Commission promises an evaluation of its feasibility and a decision on its viability later in 2004 (The Independent, 2014). The Stansted and Birmingham options, however, failed to make the shortlist, although the decision remains open for their qualification in the long term (CAPA, 2013). In the Commission’s view, the capacity challenge is yet to become critical although there is potential if no action is taken soon. However, capacity challenges and the jostling and vying for a slice of anticipated extra capacity by airports signals need (AC, 2013d). Arguments on the expansion of airport capacity The Howard Davies Commission acknowledge the ‘over-optimism’ in recent forecasts of growth in demand for the aviation sector, but consider the level of growing demand as prominent requiring focus on the earliest practicable relief (AC, 2013c). This is in response to contentions by opponents that the current capacity is adequate basing their primary argument on earlier inaccurate demand forecasts. These opponents posit operational changes including quieter and bigger planes could serve to accommodate more passengers negating the need for ambitious and expensive ventures. Some also argue that constraining growth in the aviation industry would be the best option for emissions reduction and that government should utilise available capacity, pushing traffic from London’s crowded airports to others around the country, (AC, 2013b; c; d; AOA, 2013 DOT, 2013). The Commission accepts the changes in aviation practice and aircraft design could deliver modest improvements in c apacity but argue that none of these submissions suggested significant transformational gains (AC, 2013c). It also stresses that deliberations were alive to the issue of climate change and were focused on the delivery of the best solution for the UK, which entails the achievement of carbon targets and delivery of required connections for the economy and society(AC, 2013c; d). The Commission notes that doing nothing to address capacity constraints could have unintended economic and environmental consequences with the possibility of some flights and emissions being displaced to other countries (AC, 2013d; CAPA, 2013; Mayor of London, 2013). Reliance on runways currently in operation would likely produce a clearly less ideal solution for passengers, global and regional connectivity, and would be sub-optimal in the endeavour to minimize the overall carbon impact of aviation (AC, 2013a; AOA, 2013). To achieve statutory mechanisms aimed at operational efficiency and emission reduction are critical. Conservationists, such as the Friends of Earth, decry growth arguing that the building of more airports and runways will have a major impact on local communities and the environment (Mayor of London, 2013; AC, 2013b). The argument for sustainable growth is welcomed by industry players in light of calls for constraint (AOA, 2013; The Independent, 2014). Through time, the argument has significantly centred on the timing of need for expansion of capacity with the uncertainty over growth and demand estimates. The drive for more intensive use of existing capacity is most appropriate in the short-term given that operational and aircraft design improvements have enabled the handling of more volumes than anticipated. Though limited, there is still capacity for improvement benefitting environmental conformity and overall efficiency. Several tactical improvements are proposed by the Davies commission to enable full and efficient use of available resource and capacity (DOT, 2013; AC , 2013d). The Davies Commission proposes the encouragement of greater adherence to schedules by airlines through stricter enforcement of aircraft arrival time. This would enhance efficient sequencing of arrivals ending the practice of ‘stacking’ especially at Heathrow (Europe’s busiest airport), which is expensive in fuel costs and time and has adverse environmental impact. They also propose ‘smoothing’ of timetables and the tackling of surges in traffic and bottlenecks, such as restrictions of arrivals before 6am and the designation procedures of runways which impede efficiency (AC, 2013d). Also considered are ‘mixed-mode’ operations which entail simultaneous use of runways for take-offs and landings. Through this mode, Heathrow expects to gain 15% in airport capacity without extra building (AOA, 2013). The Airports Commission rules out proposed mixed-mode operations suggesting its use when arrival delays arise and eventually to allow e nvisaged gradual traffic build up and increase in operations towards the opening of additional runways rather than a flood-gate of activity. In their consideration of noise pollution and impact on residents, the Commission recommends ending of simultaneous landings at both runways with an exception of times of disruption (AC, 2013d). Presently, Heathrow designates different runways for landings and departure which are switched daily at 3 pm to allow for respite for communities near the airport (AOA, 2013; FT, 2014). The Howard Davies Commission suggests that there might not be need for one huge hub airport as growth in recent years has come from low-cost carriers (AC, 2013a). This view makes the case for expansion of Gatwick Airport. In anticipation of confirmation of expansion priorities and solutions, airport bosses are at loggerheads with Gatwick bosses suggesting that it would not make business sense for their second runway if Heathrow is also given a green light for simultaneou s expansion (AOA, 2013). This is in consideration of an extension of time to achieve return on investment from the expected 15-20 years to 30-40 years. Gatwick’s case is compelling given that it is cheaper, quicker, has significantly lower environmental impact and is the most deliverable solution in the short term (CAPA, 2013). Heathrow rejects this argument insisting there is a clear business case for a third runway regardless of development at Gatwick. With the airport operating at 98% of its capacity, they highlight potential for parallel growth delivering choice for passengers (AOA, 2013). Mayor Johnson is, however, opposed to Heathrow’s expansion citing the misery inflicted on a million people or more living in west London. He notes that there has been significantly more concern for the needs of passengers superseding the concerns of those on the ground. Johnson proposes focus on the new hub airport (Boris Island) to relieve impact on residents as well as to enhan ce UK’s competitiveness (Mayor of London, 2013). Supporters of Heathrow’s expansion say it will be quicker and will help to maintain the UK as an international aviation hub increasing global connections. Paris, Amsterdam and Frankfurt are closely competing for this business (DOT, 2013). Conclusion The examination of need for additional airport capacity and recommendation of solutions for the short, medium and long term, has taken the UK half a century and two commissions and still there is no confirmed venture despite the raft of proposals. The earlier Roskill Commission reached conclusions on four promising sites-including a new ‘Boris Island’ airport, which are still under consideration in the later commission the Howard Davies Airports Commission. Considering several arguments with regard to their mandate, the latter commission has proposed additional runways one at Gatwick and possibly two at Heathrow despite potential adverse effects to London residents. They are still to deliver a verdict on the new Thames Estuary project, promising a decision later in 2014 after evaluation. References Abelson, P. and A., Flowerdew, 1972. Roskill’s successful recommendation.† In: Journal of the Royal Statistical Society. Vol. 135. No. 4, pp.467 Airports Committee, 2013a. Emerging thinking: Aviation Capacity in the UK. 7th October. Viewed from: https://www.gov.uk/government/news/aviation-capacity-in-the-uk-emerging-thinking Airports Commission, 2013b. Stakeholder responses to Airports Commission discussion papers. 25th October. Viewed from: https://www.gov.uk/government/publications/stakeholder-responses-to-airports-commission-discussion-papers Airports Commission, 2013c. Airports Commission discussion papers. 29th July. Viewed from: https://www.gov.uk/government/collections/airports-commission-discussion-papers–2 Airports Commission, 2013d. Short and medium term options: proposals for making the best use of existing airport capacity. 7th August. Viewed from: https://www.gov.uk/government/publications/short-and-medium-term-options-proposals-for-making-the-best-use-of-existing-airport-capacity CAPA, 2013. The Davies Commission’s Interim Report on UK airports: the big loser remains UK competitiveness. Centre for Aviation. Department of Transport, 2003. The Future of Air Transport – White Paper and the Civil Aviation Bill. [online] viewed on 14/1/2014 from: http://webarchive.nationalarchives.gov.uk/+/http:/www.dft.gov.uk/about/strategy/whitepapers Financial Times, 2014. London’s new airport held to ransom by folly. December, 2013 Helsey, M., and F., Codd, 2012. Aviation: proposals for an airport in the Thames estuary, 1945-2012. House of Commons Library. Viewed from: http://cambridgemba.files.wordpress.com/2012/02/sn4920-1946-2012-review.pdf House of Commons Hansard, 1971. Thhird London Airport (Roskill Commission Report). 4th March. Vol. 812. cc1912-2078. HC Mayor of London, 2013. Why London needs a new hub airport. Transport for London. Viewed from: http://www.tfl.gov.uk/corporate/projectsandschemes/26576.aspx Mishan, E., 1970. What is wrong with RoskillLondon: London School of Economics Airports Operators Association, 2013. The Airport Operator, Autumn 2013. The Independent, 2014. Sir Howard Davies’ Airports Commission: Air travel could be transformed within a few years – with no more ‘stacking’. 17th December, 2013

The Prohibiton Movement essays

The Prohibiton Movement essays The article that I have chosen to review discusses and explains the entire prohibition movement of the 1920s. It explains that the temperance movements were began when there was an idea that the consumption of alcohol was hazardous to peoples virtue. The early efforts of people to ban alcohol were only partially effective. They were able to help 23 of the 48 states at the time to adopt antisaloon laws, which closed saloons and prohibited the manufacture of any alcoholic beverage in the state. These events all led up to the growth of the idea of a national prohibition law. By 1919, the dry members (prohibition supporters) outnumbered the wet members (against prohibition) by more than two to one. Due to this, on Dec. 22, 1917, Congress submitted the 18th Amendment to the Constitution. By January 1919 ratification was complete, and the 18th Amendment was in place. It officially banned the manufacture, sale, or transportation of intoxicating liquors. By the time the law was in place it had a large following of support both popularly and in Congress. Congress passed the National Prohibition Act in order to enforce the 18th Amendment. It defined what an intoxicating liquor was, and also made concessions for certain personal uses of lighter liquors. However, Congress was never really willing to give much money towards enforcing the movement, and people blatantly disregarded the unstable law. Because of the inability for the law to uphold itself it remained more of an ideal than an actuality. Almost as soon as prohibition had set in, it was challenged by many groups of people. Some claimed that it led to a social disorder and decay which was exemplified by the raids, seizures, and searchings of the police. People claimed that this style of law enforcement was an encroachment upon the private lives of civilians. Some of ...