Hong Kong - 10 Facts About the Chinese Region of Hong Kong

Hong Kong - 10 Facts About the Chinese Region of Hong Kong Located along the southern coast of China, Hong Kong is one of the two special administrative regions in China. As a special administrative region, the former British territory of Hong Kong is a part of China but gets a high level of autonomy and it does not have to follow certain laws that Chinese provinces do. Hong Kong is known for its quality of life and high ranking on the Human Development Index. A List of 10 Facts About Hong Kong 1) 35,000-Year History Archaeological evidence has shown that humans have been present in the Hong Kong area for at least 35,000 years and there are several areas where researchers have found Paleolithic and Neolithic artifacts throughout the region. In 214 B.C.E. the region became a part of Imperial China after Qin Shi Huang conquered the area. The region then became a part of the Nanyue Kingdom in 206 B.C.E. after the Qin Dynasty collapsed. In 111 B.C.E the Nanyue Kingdom was conquered by Emperor Wu of the Han Dynasty. The region then eventually became a part of the Tang Dynasty and in 736 C.E. a military town was built to protect the region. In 1276 the Mongols invaded the region and many of the settlements were moved.2) A British Territory The first Europeans to arrive in Hong Kong were the Portuguese in 1513. They quickly set up trading settlements in the region and they were eventually forced out of the area due to clashes with the Chinese military. In 1699 the British East India Company first entered China and established trading posts in Canton. In the mid-1800s the first Opium War between China and Britain took place and Hong Kong was occupied by British forces in 1841. In 1842 the island was ceded to the United Kingdom under the Treaty of Nanking. In 1898 the UK also got Lantau Island and nearby lands, which later became known as the New Territories.3) Invaded During WWII During World War II in 1941, the Empire of Japan invaded Hong Kong and the UK eventually surrendered its control of the area to Japan after the Battle of Hong Kong. In 1945 the UK regained control of the colony. Throughout the 1950s Hong Kong rapidly industrialized and as such its economy quickly began to grow. In 1984 the UK and China signed the Sino-British Joint Declaration to transfer Hong Kong to China in 1997 with the understanding that it would get a high level of independence for at least 50 years.4) Transferred Back to China On July 1, 1997 Hong Kong was officially transferred from the UK to China and it became the first special administrative region of China. Since then its economy has continued to grow and it has become one of the most stable and highly populated areas in the region.5) Its Own Form of Government Today Hong Kong is still governed as a special administrative region of China and it has its own form of government with an executive branch made up of a chief of state (its president) and a head of government (the chief executive). It also has a legislative branch of government that is composed of a unicameral Legislative Council and its legal system is based on English laws as well as Chinese laws. Hong Kongs judicial branch consists of a Court of Final Appeal, a High Court as well as district courts, magistrates courts and other lower level courts. The only areas in which Hong Kong does not get autonomy from China is in its foreign affairs and defense issues.6) A World of Finance Hong Kong is one of the worlds largest international finance centers and as such it has a strong economy with low taxes and free trade.   economy is considered a free market one that is highly dependent on international trade. The main industries in Hong Kong, other than finance and banking, are textiles, clothing, tourism, shipping, electronics, plastics, toys, watches and clocks (CIA World Factbook). Agriculture is also practiced in some areas of Hong Kong and the main products of that industry are fresh vegetables, poultry, pork and fish (CIA World Factbook).7) Dense Population Hong Kong has a large population with 7,122,508 (July 2011 estimate) people. It also has one of the densest populations in the world because its total area is 426 square miles (1,104 sq km). The population density of Hong Kong is 16,719 people per square mile or 6,451 people per square kilometer. Because of its dense population, its public transit network is highly developed and about 90% of its population utilizes it.8) Located on Chinas Southern Coast Hong Kong is located on the south coast of China near the Pearl River Delta. It is about 37 miles (60 km) east of Macau and is surrounded by the South China Sea on the east, south and west. On the north it shares a border with Shenzhen in Chinas Guangdong province. Hong Kongs area of 426 square miles (1,104 sq km) consists of Hong Kong Island, as well as the Kowloon Peninsula and the New Territories.9) Mountainous The topography of Hong Kong varies but it is mostly hilly or mountainous throughout its area. The hills are also very steep. The northern part of the region consists of lowlands and the highest point in Hong Kong is Tai Mo Shan at 3,140 feet (957 m).10) Nice Weather Hong Kongs climate is considered subtropical monsoon and as such it is cool and humid in the winter, hot and rainy in spring and summer and warm in the fall. Because it is a subtropical climate, the average temperatures do not vary much throughout the year.To learn more about Hong Kong, visit its official government website.ReferencesCentral Intelligence Agency. (16 June 2011). CIA - The World Factbook - Hong Kong. Retrieved from: https://www.cia.gov/library/publications/the-world-factbook/geos/hk.htmlWikipedia.org. (29 June 2011). Hong Kong - Wikipedia, the Free Encyclopedia. Retrieved from: http://en.wikipedia.org/wiki/Hong_Kong

Eating Fast Food Essay Example | Topics and Well Written Essays - 750 words

Eating Fast Food - Essay Example He was a very hardworking boy who was determined to achieve his dream and transforming the society to a better place. As such, at the age of 24, he decided to relocate from Ohio, his hometown to Anaheim, California where he was offered an opportunity to work in his uncle’s factory. Driven by ambition of doing his personal business, he decided to purchase a hot dog cart in 1939 and started hawking his hot dogs in the streets to customers using the cart. His main customers were mainly those in cars who could not have time to go and eat at home. With the growing demand, the business flourished very fast. In a span of just five months, he bought another cart to enable him expand his business, after which he decided to open Carl’s Drive-In Barbeque, the first ever fast food restaurant in the world (Schlosser, 2002, p.20). Karcher’s business grew very fast and as eating in the car became a way of life, which later extended to the church. The Drive-In Church in California is notably the fast church to adopt the fast food service. Later on, the idea was adopted by Richard and Maurice McDonald, who opened up â€Å"McDonald’s Famous Hamburgers† in 1939. Tired of having to hire carhops, as was the case with Karcher, they decided to close up the business and fired all their employees (Jakle and Sculle 2002, p.56-59). They then opened up a â€Å"New McDonald’s, restaurant that only operated on plastics and paper, thereby abandoning the use of dishes, glasses, and silverwares. This system increased speed and efficiency and received a lot of appreciation and admiration from many people who later on began imitating the McDonald Brothers. This saw the establishment of restaurants such as Wendy’s Old-fashioned Hamburger, Burger, King, Durkin Donuts, Taco Bell, and Kentucky Frie nd Chicken, just to name but a few (Hall, 2011, p.34-38). The idea of fast food later spread to other nation around the globe. For example, in the United

Zoo Paper Analysis Essay Example | Topics and Well Written Essays - 750 words

Zoo Paper Analysis - Essay Example I think that you should have expounded it more instead of jumping right away to your thesis. As suggested in the Argument Lecture, the thesis sentence should not be placed in the beginning of the essay; instead a good argument should come at the end of the sentence. In your paper, you immediately stated your thesis statement at the second paragraph stating, â€Å"It is morally wrong to keep wild animals in captivity†. In fact, you already hinted at it already at the first paragraph when you said, â€Å"No amount of replication, no matter how convincing, is enough to make up for denying these creatures their freedom.† You also expressed, â€Å"The argument is simple: The fact that we are stronger or smarter than animals does not give us the right to ambush and exploit them solely for the purposes of our own entertainment.† Since your audience is adversarial, it is not right for you to impose your belief to them at the start of your essay. You should have establish ed your points of argument first before stating your thesis statement. You failed to take note of the point in the Argument Lecture which said that in a great argument, the thesis is never explicitly stated. A positive point of your paper is that you were able to use cause and affect language in your essay. An example of this is when you said that â€Å"most animals in captivity cannot go back in the wild†. The cause here is the captivity which resulted to an effect of not being able to go back in the wild. Another cause and effect statement which you presented is that because of inbreeding in zoos, the offspring is often weakened. Your essay also stated a very good Point of Agreement in the third paragraph when you stated, â€Å"We humans take our own freedom quite seriously.† I would commend this statement because indeed your audience will agree to it because it is a given fact. It does not even mention anything about your desire to abolish zoos. In the same paragrap h you also stated a Point of Disagreement, â€Å"But too many of us apparently feel no obligation to grant that same right to animals†¦Ã¢â‚¬  The third paragraph is a good example of AGREEMENT  --> DISAGREEMENT. Reading the whole paper though, I think that you lack statements of Agreements. You concentrated more on Points of Disagreement. Maybe you should add more Points of Agreement so as not to intimidate your audience especially at the start of the paper. Another point which I want to mention and remind you is that â€Å"an argument is not a debate†. You did not have to present both sides of the argument like what you did in discussing about the case Jackie, the dolphin. As I was reading through your paper, I got confused as to what argument you were really presenting. Especially in the last four paragraphs of your essay, I was beginning to think that what you were fighting for was about the preservation of endangered species. You deviated from your previous argum ent about zoos being abolished. You jumped to a new topic about the destruction of the natural habitats of animals. There are now so many issues being presented so much so that you are already confusing your audience on what you really want them to do. Is it to abolish zoos, protect the ecosystem or is it the preservation of endangered species? Maybe you should have written another argumentative essay on these topics and just concentrate on the abolition of zoos in your essay. It is hard to persuade your audience if you confuse them with too many issues. Based on the above comments, the grade that I will give you is F because you failed to follow some of the basic guidelines which was presented in the Argument Lecture. Because of that, I cannot give you a high grade since from the start of your essay you have been out

Geographically Weighted Regression to Model Housing Prices

Geographically Weighted Regression to Model Housing Prices Introduction In chapter 2, HPM has been used to model the relationships between characteristics of property and neighbourhood. However, HPM treats the whole housing market as a single homogenous market and assumes a stationary process, i.e the parameter estimates are assumed to apply equally over space. This presumes the influences of various factors on house prices in one location are the same as those in another location so that space, place and location do no matter (Foster refer). However, as shown in Chapter 2, the residuals derived using HPM are correlated. Additionally, Chapter 3 shows that when MLM approach is employed to account for spatial heterogeneity, the effects of those various factors in fact vary across neighbourhoods at different scales and there are great price differentials between neighbourhoods. The global approach, such as HPM, masks those local deviations from this average relationship. Disadvantages of MLM Although MLM approach takes into account spatial heterogeneity by specifying the spatial units as levels in the model, there are some weakness of this approach. Firstly, there is no agreement on the definition of neighbourhoods (Kearns and Parkinson 2001: 2103), so the specification of the macro level units (i.e. neighbourhoods) is fairly arbitrary. In the past, census boundaries (†¦),administrative boundaries (†¦.), or school catchment areas (goodman) have all been used to delimitate the whole house market into smaller submarkets, or local neighbourhood areas. Some researchers combined a series of dataset, such as travel-to-work, immigration and house price information and constructed a so-called housing market areas (HMAs)(†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦..) . HMAs neither match the census boundaries, or the administrative boundaries, but instead, they represents†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. . The existence of spatial dependency in geographical data means that the observations that are most spatially dependent in the locations that are close to each other should constitute a neighbourhood. A predefined hierarchy of spatial units based on administrate or census boundaries may not necessarily appropriate. Secondly, MLM[1] treats space and assumes that same spatial process applies within the neighbourhoods and discontinues at the boundaries of the neighbourhoods. (†¦). Additionally, the highest level of spatial units (for example, MSOAs in our analysis) are assumed to be spatially dependent. This assumption is unrealistic because the â€Å"effect† of a neighbourhood is more likely change gradually from one neighbourhood to its adjacent ones rather than completely stops, the so-called â€Å"spill-over† effects. Therefore, there might be presence of spatial dependency between MSOAs that MLM is unable to capture. In contract, GWR (Brunsdon et al, 1996†¦..) relaxes the assumptions of the effects of various variables being constant over space (Dark, 2004,Mitchell, 2005andShi etal., 2006) and treats space as continuous. It calibrates locally a spatially varying coefficient regression model for each location of the study area by weighting the attributes of it neighbouring locations based on distance-decay functions (†¦Ã¢â‚¬ ¦.). The attributes of neighbours of a fitted location are all considered so the spatial dependency and heterogeneity can be taken into account in this approach (Paez 2005). This chapter therefore introduced this type of modelling technique to explore the spatial variations that may exist in the relationships between house price and its predictors. Purpose and Structure of the Chapter The aim of this chapter is to identify whether the relationships of house prices and a range of characteristics of houses and neighbourhood attributes) are relatively stable, or they vary substantially over space? If there are spatial variations, how does the relationships vary within and between neighbourhoods and how does this variation differ from the results derived from MLM approach? In addition, how good is the GWR approach in terms of its predictive capability, compared with MLM.? In the next section, a brief description of this technique is introduced. Section 3 follows with a review of previous applications of GWR is presented. The proposed study in relation to the empirical implementation of the technique then follows in section 4. The final section summarise the comparison between GWR and MLM the results and discusses the appropriateness of both techniques. 4.2 Brief Description on GWR Models What is GWR? GWR technique is fully descried by Fotheringham etal., 2002[2] and just a brief description of the approach is presented here. GWR is a spatial analysis technique that takes into account spatial autocorrelations among the observations in surrounding locations by allowing for spatial nonstationarity in the linear regression coefficients for each location. In GWR literature, the â€Å"location† can be a point or an aggregated area. describe local geographical variations in the relationships between a response variable and its explanatory variables by a set of local estimates for all the predictors for each geographical location (Fotheringham et al. 2002). A set of estimates and standard errors for each local coefficients are produced by focusing each location in the study region and weighted matrix of its nearby observation. The basic GWR equation can be written as: (4.1) Where denotes the coordinates of the th point in a two-dimensional study area; is the dependent variable at point , is the estimated intercept at point , ( represents the estimated coefficient for variable at point , is the independent variable of the th parameter at location , and is the error term for the local model at point . The estimation of ( is derived using weighted least squares (WLS) regressions (Moore and Myers, 2010; Fotheringham et al., 2002) by weighting the observations near location in accordance with their distance to that fit point. It is given by: where is a diagonal matrix denoting the geographical weighting of the observations around the fit point . Weighting The weighting is based on the distance between the regression location and its nearest neighbours, defined as bandwidth. The points in closer proximity to location is given more weight and therefore has more influence on the estimation of than the observations that are further away to location . A number of weighting schemes are available, but they tend to be Gaussian or â€Å"Gaussian-like† function, which is the types of dependency generally found in spatial processes (Forthemham). Two Commonly used distance-decay functions in GWR are Gaussian and Bi-square function (Fotheringham et al. 2002), which are expressed as below: Gaussian Bi-square Where is the th element of the diagonal of the matrix of the geographical weights , is the bandwidth, a threshold distance that any observations beyond this distance will not be used for calibrating the local model, and represents the distance between observation and focus point . When and coincide, the weighting equals to 1. Source: Gollini et al (2014) GW model: an R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models Both functions are continuous up until the bandwidth, but the weights of Bi-square function decrease faster than that of Gaussian function and eventually become zero at the boundary of the bandwidth, while the weights of Gaussian function do not become zero. Both of the weighting functions will be tried in the planned research. Bandwidth Bandwidths can be specified either as fixed or adaptive (in terms of physical distance). The physical distance for adaptive bandwidth is changeable according to the spatial density so as to capture a fixed nearest neighbours for each local model: a shorter distance for areas where observations are dense and longer distance when data are sparse. The benefit of using adaptive bandwidth is that it can ensure sufficient local information be utilised for areas where observations are spatially scares and reduce the estimate variance for local coefficient and still reveal subtle local variations where observations are dense (Fotheringham et al. 2002). Therefore, adaptive bandwidth will be used in the planned research as the density of house price data vary geographically. The size of bandwidth affects gradient of the kernel and thus the rate of decay function. A small bandwidth have fewer observations included in the local model and rapid decay whereas a large bandwidth will have more observations in the local model and a smoother weighting scheme. The size of the bandwidth is important as if the bandwidth is too small, although the model would fits better for the local observations, but at the same time local noise may also be fitted thus the local estimates will have large variances. Conversely, if the bandwidth is too large, although the variances will become smaller, but the estimates of local coefficients are based on a much larger area and result in biased estimates which masks the true local relationships, especially if the relationships vary dramatically over small areas. This is the so-called bias-variance trade-off (Fotheringham et al., 2002)[3]. The effective number can be used to reflect bias-variance trade-off in GWR, which is a measure o f the number of observations that have been used effectively for calibrating the local model. Bias-Variance Trade-Off To find the best bias-variance trade-off, an appropriate weighting function and optimal bandwidth need to be selected. It has been argued that the selection of bandwidth selection is far more important than the weighting scheme as the weighting all decreases as distances increase by all weighting functions but the size of bandwidth decides the degree of decay (Fortherham†¦). The optimization process is generally exploratory and can be very compute-intensive process as it requires all the local regressions fitted at each step[4]. It can be achieved by either cross-validation method or use corrected Akaike information criterion (AICc) (Fotheringham et al. (2002). Leave-one-out cross-validation (LOOCV) is a commonly used cross-validation method in GWR, where for each local model, it is validated by using all the cases except for one observation and the model is tested on that single observation. The bandwidth which produce the smallest root mean square prediction errors for all the dependent variables of all the local models is deemed as the optimal bandwidth. AICc is an indicator of goodness-of-fit and can be used to compare competing models while taking into account the complexity of a model. A lower AIC score indicate a better fit of a model. As a rule of thumb, a decrease of 3 in AIC of two competing model score indicates an improvement in the model fit for the model with lower AIC (Fotheringham et al 2002; Zhang etal., 2011). It is common though to get different optimal bandwidth from the two methods as the criteria for optimal is different for AICc and for CV[5] and the AIC value is not based on prediction of the dependant variable (†¦[6]..). In addition, AIC score can be corrected for small sample size, while classical CV method tend to produce under-smoothed result for small sample size[7]. One thing is note is that AIC should be avoided when the sample size is large as it requires the creation of an n by n matrix [8]so the optimization can be very slow[9]. Both method will be tried out in the planned research. Why Use GWR and when? As mentioned earlier, when there is spatial dependency between variables and spatial non-stationarity, GWR can be used to disaggregate global relations to local levels to obtain a better understanding of spatial data in more details. As every local model is fitted to local observations, it fits better to data than a global model and residuals are generally lower and less spatially dependent. The outputs, the estimates of local coefficient are specific to each location. In Chapter 2, Moran’s I has been used and indicate that there is statistical significant spatial autocorrelation within both house prices and the residuals of HPM results. This means that the global fitted coefficient value of HPM does not represent detailed location variations adequately and GWR should be used in this instance to taken into account the spatial dependency and examine the heterogeneity in housing market. A review of GWR approach in house price estimation This section reviews the application of GWR technique with a focus on residential real estate, as well as the comparisons of GWR with a range of other methodologies. The section will conclude with the identification of the research gap and thus the contribution of the current chapter. Application in Real Estate Valuation GWR has been applied to a number of field, including land use (Geniaux et al. 2011†¦.), environment (Harris et al. 2010a), health (Comber et al. 2011, Helbich et al. 2012b, Yang and Matthews 2012; [10]) and crime studies (Leitner and Helbich 2011), economics ([11]), regional studies ([12]) and residential real estate studies (Kestens et al. 2006; Bitter et al. 2007†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦). In terms of the application to real estate, GWR has been used to investigate the effects of the locations and surrounding neighbourhood characteristics, such as †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦,the effects of accessibility, such as the new bus transitway in†¦..((Mulley, 2013), infrastructure availability in †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.(Cellmer, 2012), and the effects of open space amenities (Nilsson, 2014). GWR has also been used to identify housing sub-markets (Borst Mccluskey, 2007; Crespo Grà ªt-Regamey, 2013; Helbich, Brunauer, Hagenauer, Leitner, 2013). GWR compared with other modelling techniques GWR has also been compared with a few valuation tools in real estate, such as multiple regression analysis (MRA), simultaneous autoregressive model (SAR), Artificial neural networks (ANN), spatial expansion method (SEM) and Spatial lag model (e.g., Brunsdon et al., 1999[13]; LeSage 1999[14]; (Bitter, Mulligan, Dall’erba, 2006; Helbich, Brunauer, Vaz, Nijkamp, 2013; McCluskey, McCord, Davis, Haran, McIlhatton, 2013; Yu, Wei, Wu, 2007). More specifically Bitter, Mulligan, Dall’erba (2006) demonstrated in their study that GWR was superior to spatial expansion method ( define briefly †¦.)in terms of predictive accuracy and explanatory power when applied to examine the marginal price of key housing attributes in the Tucson, Arizona housing market. McCluskey, McCord, Davis, Haran, McIlhatton (2013) also showed that GWR outperform MRA, ANN and SAR in term of predictive accuracy, transparency, and cost-effectiveness and offered when applied to 2,694 residual properties in for real estate price estimation. In a case study of spatial heterogeneity in Austria, Helbich, Brunauer, Vaz, et al. (2013) extended GWR to a mixed-GWR(MGWR), which allows some coefficient to be stationary while others to be non-stationary. This approach is more flexible and parsimonious than standard GWR (Wei and Qi, 2012). Both MGWR and GWR has smaller prediction errors in comparison with a global approach, such as OLS, SAR and spatial t wo stage least square procedure (S2SLS)[15]. There are other extensions of GWR. To deal with cross-sectional time series data, GTWR (Huang, Wu, Barry, 2010) was developed to integrate both temporal and spatial information in the weighting matrices to capture spatial and temporal dependency and heterogeneity[16] . GTWR is able to model spatial and temporal nonstationarity simultaneously and therefore offers a better goodness-of-fit. LeSage (2003) incorporate a Bayesian treatment into GWR in order to improve the estimates of GWR parameters. Contextualized Geographically Weighted Regression (CGWR) was developed by adding contextual variables into standard GWR. The research applied this approach to model spatial heterogeneity in the land parcel prices of Beijing in China and demonstrated that the incorporation of contextual information improved the model fit. However, multicollinearity between explanatory variables may result in unstable results in GWR models and cause more problem for GWR than in a global regression model (Lloyd 2007). Therefore, extreme caution should be exercised when analysing the spatial patterns of local coefficients derived from GWR (Wheeler Tiefelsdorf, 2005). A range of diagnostic tools was proposed and usage of PCA to identify the most influential predictors or integrating ridge regression into the GWR framework (D. C. Wheeler, 2007) can help stabilize GWR regression coefficients. There is only limited comparison of GWR with MLM, or random coefficient model (RCM). These two approaches are very different in terms of its underlying assumptions of the spatial process and yielded completely different results in the study of long-term illness in the UK (Brunsdon, Aitkin, Fotheringham, Charlton, 1999). There has no published research that compares GWR with MLM in terms of their capability to model spatial heterogeneity of house price data and their predictive accuracy. In addition, although GWR can be applied at any geographic scale of measurement, in practice however, may applications and previous research applied it to an coarsely aggregated scale due to the availability of data or keep anonymized information. Unlike previous studies, we have geo-code the â€Å"location† of each house based on its unit postcode location, which only contains typically around 15 residential addresses[17]. We hope to offer further insight into the geographical variation of the relationships at this detailed level, which previously might be disguised in previous research when the level of analysis was carried out at a much coarser scale. Planned Research Standard GWR is applied to the same dataset in chapter two and three, the house price data of the Greater Bristol area. Two extended version of GWR, GTWR and CGWR, will be explored with the former to capture the temporal dependency and heterogeneity and the later to incorporate contextual information into the model. In GWR and CGWR, the whole dataset will be split into yearly data to avoid the potential temporal autocorrelation within the data. There is no need of doing so in GTWR, as the time of sale has been taken into account in the model. Individual house characteristics are all categorical variables as described in Chapter 2 and will be modelled first and then neighbourhood variables will be added in the subsequent models. The planned procedures and a few methodological issues are addressed as follows. Firstly, before carrying out actual modelling of GWR, whether there is significant spatial autocorrelation within the data, which can be between the response variables and its lagged values or between the explanatory variables and their lagged value. Two most commonly used weighting function, Gaussian and Bi-squares functions will be used, although it has been shown that the selection of the weighting function does not have as much an effect on the results as the selection of bandwidth (Fotheringham, Brunsdon, and Charlton 1998). If it is the case, just one weighting function will be used in the subsequent yearly models and the focus will be one the optimization of bandwidth. An adaptive bandwidth is proposed, as there is a good mixture of rural/urban of housing stock in Greater Bristol and the density of the house sales varies dramatically over space. Both CV and AIC will be used to obtain optimal bandw idth and measure model fit as it was shown in the past that the two methods resulted in different optimal bandwidth and regression coefficients ([18]). Once a weighting function and bandwidth has been selected, the weighting matrix can are defined and used to estimate the coefficient for every location based on equation (4.1) and calibrating local GWR. The standardised residuals and the parameters, and their estimated standard errors will be mapped to investigate whether they vary spatially[19]. This will also be compared with the map of the shrinkage estimates of the neighbourhoods (OAs, LSOAs and MSOAs) derived by MLM in previous chapters. It is expected that the mapped patterns of MLM coefficient exhibit more â€Å"noise† than that of GWR, since GWR is essentially a spatially smoothing calibration. All of the model caliberation will be conducted in R, using GWmodel package as this software is free and the process can be easily replicated. Lastly, the predictive accuracy of GWR will be measured and compare with MLM. R squared is used for goodness of fit of the model and it measures the proportion of variation in the data that is explained by the model. Adjusted Rsquared takes into account the complexity of the model in terms of the number of variable that are specified in the model. It is expected that extended version of GWR, GTWR and CGWR, may provide better model fit and more accurate predictions based on their previous applications. In the past, there has been criticism that GWR cannot produce confidence intervals (†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦..) and the significance of the estimates for parameters cannot be tested. However, Monte Carlo significance tests have been used to test whether there is significant variability (†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦..) so this test is also planned to test if the spatial variation of the coefficients are statistically significant. â€Å"Wild bootstrap† approach as suggested by by H ¨ardle (1990) and McMillen (2004) can also be used to produce a weighted average of the variance of the separate parameter estimates. Conclusion GWR generally give much better fits to the data and the residuals are less autocorrelated. Its advantages over MLM is that it no longer treats space as discrete, which more likely resemble the spatial process in reality, and it models both spatial dependency and heterogeneity. In addition, it is essentially a non-parametric approach that does not requiring any assumptions with respect to the predictors, which can be categorical or the underlying distributions of the predictors can be highly skewed. There is no need to specify a functional form to produce the estimates of spatially varying parameters (Brunsdon et al 1998). The underlining concept of â€Å"letting the data speak for themselves† make it a good exploratory tool [20] for spatial analysis. This concept is very much similar to another modelling technique, ANN, except that in ANN, there is no implication of nearer locations have more influences on the estimates of local coefficients than locations that are further awa y as in GWR. This although unlikely in reality, but it might happen. How does GWR compared with ANN will be discussed in the next chapter. Link GWR and ANN: a set of estimates of spatially varying parameters WITHOUT specifying a functional form – â€Å"let the data speak for themselves† (Chris et al 1998) [1] the parameter estimates are assumed to be randomly distributed with either a finite (Wedel and Kamakura 2000) or a continuous mixture distribution (Aitkin 1996). [2] And Legendre, 1993 [3] Check: Bias-variance trade-off: MLM (Goldstein 1987) and Ridge Regeression (Hoerl and Kennard 1970a, 1970b) [4] check reference Schabenberger and Gotway (2005 316-317) statistical methods for spatial data analysis Waller and Gotway (2004, p434) – applied spatial statistics and Lloyd (2007 pp 79-86): local models for spatial analysis [5] http://webhelp.esri.com/arcgisdesktop/9.3/body.cfm?tocVisable=1ID=-1TopicName=Interpreting GWR results [6] Housing Sub-markets and Hedonic Price Analysis: A Bayesian Approach by David C. Wheeler1*, Antonio Pà ¡ez2* , Lance A. Waller1 and Jamie Spinney3 Chapter 4 [7] Encyclopedia of Geographic Information Science edited by Karen Kemp (p183) [8] (gwr.sel {spgwr}) [9] NOTE AIC be applied in non-Gaussian GWR( Local Models for Spatial Analysis, Second Edition By Christopher D. Lloyd) [10] Modelling spatially varying impacts of socioeconomic predictors on mortality outcomes, J Geograph Syst (2003) 5:161–184, DOI: 10.1007/s10109-003-0099-7, proposed for modelling spatially varying, predictor effects on a disease or mortality count outcome The methodology is illustrated by suicide mortality in 32 London Boroughs over the period 1979–1993, in terms of area deprivation and a measure of social fragmentation disease mapping methods [11] SPATIAL HETEROGENEITY AND THE WAGE CURVE REVISITED*Simonetta Longhi, ISER, Peter Nijkamp [12] The Geographic Diversity of U.S. Nonmetropolitan Growth Dynamics: A Geographically Weighted Regression Approach Mark D. Partridgey Dan 5. Rickman, Kamar AU, and M, Rose Olfertte.st for geographic heterogeneity in ihe growth parameters ami compare iliem to global regression estimates. The results indicate significant heterogeneity in the regression coejjkients across the country, most notably for amenities and college graduate shares. V.sing GWR also exposes .signiftimt local variations that are masked by global estimates [13] A Comparison of Random-Coefficient modelling and Modeling and Geographically Weighted Regression for Spatial Non-Stationary Regression Problems, Geographical and Environmental Modeling, 3 (1), 47–62 [1

Death and Duck Season :: Broughton Duck Season Essays

Death and Duck Season In the short story "Duck Season," T. Alan Broughton introduces an everyday family from upstate New York, during the windy, fall season. The protagonist, Gracie, is dying of cancer, while her husband and children live in denial and try their best to carry on with their lives. Broughton uses the repeated structural device of flashback to depict a vivid image from the eyes of a lonely, bed-ridden Gracie. In looking at this story from a structural criticism, it can be broken down into seven parts that reinforce the theme: Cherish the time with a loved one because it can end in an untimely manner. To begin, Gracie is lying in bed one morning and she describes the scenery through her window: "This fall had been unusually mild, but all night the wind had shaken and battered the house, ripping away the warm rainy weather" (135). Sadly, Gracie's only outlet to the outside world is what she sees through her bedroom window and her memories of when she was well. Broughton then uses flashback to introduce Gracie's husband Len. He is a mechanic by trade and stubborn by nature. The author describes the euphoria of duck hunting season as a symbol for the world of denial Len lives in, because he cannot face the fact that Gracie was dying of cancer. "Once she had said to Len, I'm going to die soon, Stop trying to pretend, but he looked at her as if she had betrayed him" (136). Len's state of denial continues to be reinforced until the climax of the story. Â   In the second part of the story, Broughton presents Len and Gracie's three young children: Georgie, Betsey, and Adele. He also presents Father Rivard, who later makes Len address the reality of Gracie's dying. Broughton shows that the children are being taught to move on with their lives before Gracie even passes. They became uncomfortable in their mother's presence. "She noticed how relieved they were to turn and go" (137). Then, Broughton employs irony in his flashback to liken Gracie to her son Georgie, all-alone in the schoolyard. "Now all of them were that way, further and further away from her, and sometimes even the children seemed to look at her from a huge distance" (137).

My philosophy in nursing Essay

My philosophy in nursing is to uphold the dignity of my patients during their vulnerable time of need, care for the patients physically, emotionally, spiritually, and mentally; current beliefs and values that I try to uphold throughout my life. According to Chitty, â€Å"philosophy is defined as the study of the principles underlying conduct, thought, and the nature of the universe† (p. 318). To determine my philosophy of nursing care I had to reevaluate my beliefs and values. see more:my philosophy of life My values are learned from my personal growth, involvement with family and friends, and respecting others beliefs and opinions. My beliefs are that modern medicine is a great way of healing through research and technology. I believe patients mental and physical health needs should be evaluated and treated if necessary by a healthcare team and support groups. As part of the team, I will care for the patients and their families during distressed times. I will work as an advocate to carry out, to the best of my knowledge and skills, the essential care to provide the best experience in healthcare that each patient and family deserves. I believe nursing care needs to be holistic, â€Å"nourishing the whole person,  that is, the body, mind and spirit† (Chitty, 2007 p. 312); I think that is what defines us as nurses in the healthcare field. It is the nurse’s responsibility to incorporate our knowledge, skills and available resources, so we can improve our patient’s quality of life. The American Nurses Association, ANA, defines nursing as â€Å"the protection, promotion, optimization of health and abilities, prevention of illness and injury† (Lance, 2007, p. 11). I try to incorporate these elements into my nursing practice; practicing my profession with care, compassion, and humanity. Being able to relate to people of different cultures and backgrounds will allow me to connect with a greater variety of patients and families. I come from an area where there are people from a variety of cultural backgrounds, socioeconomic status, and different spiritual beliefs and we all get along. We spend time together and their families are accepting. These experiences were needed to give me the knowledge to provide heightened nursing care to patients and their families. I believe in the importance of continuing education in nursing. To continue giving proficient patient care, it is important to remain educated on current research and evidence based practice, keeping licensures and certifications current. According to Randell, Tate and Lougheed, â€Å"it is important to reflect what we do as nurses so we keep learning† (p. 62). This allows patients to receive high quality nursing care. I will strive to apply my nursing philosophy to my nursing practice by continuing to reevaluate my beliefs and values as I grow in my profession and as a person. I will continue my education aiming to advance in my nursing practice, but continuing to remember nursing’s holistic point of view. This will allow me to care for my patients and their families more effectively. I will maintain my own physical, mental and emotional health, so I can continue giving the best care I am capable of to people in need. REFERENCESTC â€Å"REFERENCES† Chitty, K. K. (2007). Professional Nursing Concepts & Challenges (5th ed.). St. Louis: Elsevier Inc. Retrieved February 16, 2008, from University of Phoenix Library. Lance, K. (2007). Nurse life care planning. _Virginia Nurses Today,_ 52 (2), 11. Retrieved February 16, 2008, from Ebscohost database. Randell, C. E., Tate B., & Lougheed M. (2007). Emancipatory teaching-learning philosophy and practice education in acute care: navigating tensions. _Journal of Nursing,_ 46 (2), 62. Retrieved February 18, 2008, from EBSCOhost database.

My Goals Upon Graduation, And Where I Plan Working Within...

My goals upon graduation, and where I plan working within the social work field. Growing up I dealt with many forms of abuse, and due to all the trauma I had a lot of built up aggression. Unfortunately, at the time I felt there was no other way to express myself except to act out. As a consequence of my bad behavior, I lost many close friends, and missed many good opportunities. As a result, I was required to go to intensive family therapy for three days a week, with a women named Julie. Fortunately, after therapy I saw a change in myself, and I began to learn new skills to communicate, and cope with my anger. Although, I’ve gone to see many therapists before, Julie was the one counselor that gave me hope, and after meeting her my life has positively changed. This experience of going to family therapy, and meeting Julie has influenced me to want to help other troubled teens who have dealt with abuse. 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