A wide array of methods, as well as several dedicated ‘spatial’ econometric procedures (see, for
example, Anselin et al. 2004), for the statistical analysis of georeferenced data are available in the
literature. These techniques are useful when analysing regional unemployment data, as in our case
study, and, particularly, when the final aim is to develop forecasting models for some regional scale.
Among conventional spatial econometric methods, spatial autoregression (see, among others, Anselin
1988; Griffith 1988) is a powerful method commonly employed. Spatial autoregressive techniques take
into account spatial effects by means of geographic weights matrices that provide measures of the
spatial linkages (dependence) between values of georeferenced variables. Because of bias, statistical

efficiency concerns and the normality assumption, OLS should not be carried out with such data.
Furthermore, maximum likelihood estimators of spatial regression models are based on restrictive
assumptions. An alternative approach to spatial autoregression is the use of spatial filtering techniques,
such as the ones described in Griffith (1981), Haining (1991), Getis and Griffith (2002), and
Tiefelsdorf and Griffith (2006). The advantage of these filtering procedures is that the variables studied
(which, initially, are spatially correlated) are split into spatial and non-spatial components, which can
be employed in an OLS modelling framework. Filtering out spatially autocorrelated patterns also
enables one to reduce the stochastic noise in the residuals of conventional statistical methods such as
OLS. This conversion procedure requires the computation of ‘spatial filters.’ The approach developed
by Griffith (1996; 2000) will be briefly described here. This approach is preferred in our case study to
the one by Getis (1990; 1995), which requires variables with a natural origin. This constraint would not
allow us to analyse patterns in employment growth rates, which will be studied in the future

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Spatial matters are of critical importance when considering socio-economic (and other) phenomena
(see, for example, Bockstael 1996; Weinhold 2002), as well as because of their implications for
policymaking (Lacombe 2004). To account for the presence of spatial structures that influence
(positively or negatively) observable economic entities, such as unemployment or trade, calls for a
rigorous and systematic assessment of their impact and extent. Spatial autocorrelation (SA) represents
the correlation, computed among the values of a single georeferenced variable, that is attributable to the
geographic proximity of the objects to which the values are attached. Introduction of the SA concept is,
of course, a departure from the classical assumption of independence of observations constituting a
single variable. SA also complements the concept of temporal autocorrelation, which has been
extensively studied and dealt with in time-series econometrics. SA measures are used to quantify the
nature and degree of the spatial correlation within a variable, or to test the assumption of independence
or randomness. From a statistical analysis viewpoint, spatial correlation patterns are problematic, since
they make standard statistics, such as correlation coefficients or ordinary least squares (OLS) estimates,
potentially inappropriate.
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This paper aims to provide an assessment of how important spatial effects are in explaining
unemployment levels in Germany, and, particularly, to show that these (or, more precisely, a subset of
these) patterns are consistent over time. The definition of stable and recognizable spatial patterns
enables one to observe systematic differences in regional unemployment. Such findings can have
implications for policy evaluation and strategic planning. This paper presents analyses carried out by
means of a semi-parametric ‘spatial filtering’ technique, described in Griffith (2003), which is based on
the decomposition of geographic weights matrices. In our analysis, these matrices are defined for 439
German districts, according to both topological and distance-based criteria – such as shared boundaries
or centroid distance – and economic flows. In this regard, journey-to-work flows are employed as a
proxy for economic linkages.
Kosfeld and Dreger (2004) investigate spatial patterns of German regional labour markets, for the
period 1992–2000. However, their approach involves computing spatial filters for each year within the
framework of a spatial seemingly unrelated regression (SUR) model. Our approach differs from theirs
in that we focus on the search for a set of spatial filters that are significant and consistent over time, and
therefore can be employed for the entire time period considered (that is, 1996–2002). Also, we employ
data at a finer level of disaggregation (439 districts versus 180 regions), which enables a more detailed
analysis of the underlying spatial patterns.
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Socio-economic interrelationships among regions can be measured in terms of economic flows,
migration, or physical geographically-based measures, such as distance or length of shared areal unit
boundaries. In general, proximity and openness tend to favour a similar economic performance among
adjacent regions. Therefore, proper forecasting of socio-economic variables, such as employment,
requires an understanding of spatial (or spatio-temporal) autocorrelation effects associated with a
particular geographic configuration of a system of regions. Several spatial econometric techniques have
been developed in recent years to identify spatial interaction effects within a parametric framework.
Alternatively, newly devised spatial filtering techniques aim to achieve this end as well through the use
of a semi-parametric approach. Experiments presented in this paper deal with the analysis of and
accounting for spatial autocorrelation by means of spatial filtering techniques for data pertaining to
regional unemployment in Germany. The available data set comprises information about the share of
unemployed workers in 439 German districts (the NUTS-III regional aggregation level). Results based
upon an eigenvector spatial filter model formulation (that is, the use of orthogonal map pattern
components), constructed for the 439 German districts, are presented, with an emphasis on their
consistency over several years. Insights obtained by applying spatial filtering to the database are also
discussed.

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Task 4. Spatial ltering for noise removal
Study the eect of dierent ltering on a noise image, by writing, for example:
>> im = imread('moon.tif');
>> noisy_im = imnoise(im,'gaussian');
>> my_filter = ...
>> filtered_im = imfilter(im,my_filter);
>> ...
Give a look at the noisy image to choose a suitable lter.
Task 5. (optional)
Play by ltering images using dierent lters: Sobel, Laplacian...
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The written lab report should contain:
 The results from task 1, i.e. images and histograms before and after
applying 'histogram stretching'.
 The function my_histeq and the results after histogram equalization
of the image tire.tif.
 The results from task 3, i.e. images before and after ltering (using at
least one smoothing lter and one sharpening lter). Which lters did
you use? How did you choose the parameters? Motivate your choices.
 The results from task 4, i.e. images with and without noise (using at
least one lter). Which lter did you choose (size, weights)? Why?
Any additional comments (for example: what did you like? Was the lab
interesting, boring, too easy, too dicult?) is very welcome!
The report should be given to me no later than two weeks after
the lab has been performed.
Please, do NOT send me word document by e-mail. Thank you!
4

>> im=imread('trees.tif');
>> im_new = my_histstretch(im);
>> figure(1);clf;colormap gray
>> subplot(221)
>> imshow(im)
>> title('Original image')
>> subplot(222)
>> imshow(im_new)
>> title('Result image')
>> subplot(223)
>> imhist(im)
>> title('Histogram')
>> subplot(224)
>> imhist(im_new)
>> title('Histogram')
Task 2. Histogram equalization
In this part of the lab the student is supposed to write a Matlab-function
that performs histogram equalization on an image. The function should have
the following initial lines:
function [im_out] = my_histeq(im_in,n);
% IM_OUT = MY_HISTEQ(IM_IN,N) transforms IM_IN to IM_OUT
% according to the histogram equalization method.
% N specifies the number of possible grayscale values in IM_IN,
% default value is 256.
% The values in IM_IN are assumed to be in the range of 0 255
...
Useful commands:
nargin to check if N is set to a value dierent to 256
hist creates an histogram of the image values. Please, note that this function
calculates an histogram of a vector. Therefore, use (:) to vectorize an image
matrix.
cumsum calculates the transformation function for a given histogram .
You are allowed to use other functions that you can nd in Matlab. Not
histeq :-)

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Test your function on the image tire.tif
Task 3. Spatial ltering
Study the eect of dierent ltering, by writing, for example, the following
lines for a smoothing lter:
>> im = imread('saturn.tif');
>> mean_filter = ones(9,9); % change the size as you like...
>> mean_filter = mean_filter/numel(mean_filter);
>> filtered_im = imfilter(im,mean_filter);
>> figure(1);clf;colormap gray
>> subplot(121)
>> imshow(im)
>> title('Original image')
>> subplot(122)
>> imshow(filtered_im)
>> title('Smoothed image')
Repeat the exercise using a sharpening lter to enhance edges.

The aim of this lab is to acquire a deeper knowledge regarding image enhencement
using dierent histogram operation methods and ltering in the spatial
domain. All tasks imply that the student has to implement the methods
using Matlab.
In each task a number of appropriate commands in Matlab will be suggested.
In order to get more specied information about each command type
help and the command you want more information about. The command
lookfor is also useful.
Task 1. 'Histogram stretching'
Here the student is supposed to write a Matlab-function that performs
'histogram stretching' on an image in order to cover the whole range of gray
level values. Write the following in a m-le and complete it.
function [im_out] = my_histstretch(im_in);
% IM_OUT = MY_HISTSTRECH(IM_IN) transforms IM_IN to IM_OUT
% according to the histogram stretching method.
% The values in IM_IN are assumed to be in the range: 0 255
% Make sure that im_in has double precision
im_in = double(im_in);
low = min(min(im_in));
high = max(max(im_in));
im_out = ...
% Convert the image values in the range: 0 255
im_out = uint8(im_out);
Write help my_histstretch. Do you recognize the information? Study
how your function 'histogram stretching' operates on an image, for example
trees.tif, by writing:
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Google adwords review

Marketing experts have researched on the most efficient way to be visible and that they resorted to Google AdWords in order to do that. This strategy is proven to reach the masses since the increasing statistics of internet use. More people get to be informed about what you have to offer in terms of products and services via the net.
To elaborate on how Google AdWords can work at your business advantage, strategies need to be understood. In fact, there are three things you need to know about search engine marketing. The first of the three is appropriate keywords use or on conventional terms, more known as taglines.
On advertising lingo, taglines are used to make your product get noticed. This is comprised of words that are unique and has a good connection about your product whereabouts. It should be a standout, catchy and easy to remember. The same principle applies with your AdWords. Whenever people see your online advertisement, it should be inviting and probably quite entertaining.
After selecting what you think are effective keywords for Google AdWords, you may want to go on with pay per click strategy. This is a paid kind of online marketing and should be used coinciding with the right keywords. Note that internet users will only get to click on your ad if it sounds interesting and relevant to such an individual.
This kind of strategy enables you to penetrate a lot more markets existing on the web. With just one click, people will get to see your product details, price, etc. This in effect will boost your online presence and thereby increase your potential clients. Though pay per click may incur you some expense, it sure to be worthwhile as you go along.
Last but not the least is visual strategy or video streaming on the net. Google AdWords may appear visually through videos on the internet. This is in far the most effective way in advertising online. You maximize this by making visually attractive graphics.
Paid TV commercials are comparable to video ads online. The only difference is that on TV, there are time slots and these have corresponding fees. Online video advertisements do not have time slots. The wonder of internet is that people have an access to your sort of commercial anytime at their convenience. This also can be cost efficient in the long run, since it requires less work and manpower.
In conclusion, there are three tips to which you can strategize your online advertisements and these are: keyword appropriation, pay per click and video streaming you can choose from. Any of the aforementioned may help you enhance your web presence. Note that Google AdWords can be dynamic and that it offers a lot of ways in helping you market your business.
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Google is respected company

It's a battle to get to the top of the search engines, especially on competitive or high traffic keyword expressions. If you're not doing absolutely everything in your power to play the game and to play the game right then you're going to get buried under the weight of your competitors' website. They'll be hoovering up the traffic, converting prospects that should be yours into customers you'll never have. Don't let it happen. One way of fighting back is to understand exactly what Google wants to see in your site. Keep Google happy and you stand a fighting chance.
Here are 3 more proven paths into Google's affection:
Video - Video may have killed the radio star but it can do wonders for your SEO. Did you know Google own YouTube? Your videos represent fresh and unique content, something Google's super keen on. Your video outposts also act as sources for in bound links, so break out the camcorder and get your director's hat on.
But, as Google can't interpret video data for SEO purposes, make sure your SEO creates text metadata for each media asset. The metadata should be published along with the Flash media player in plain HTML documents. There are also transcript opportunities. Transcribe the contents of your videos and publish them as standard crawalable web pages. Great for linkbuiding.
Clean up your act - Well, make sure you clean up your site code at the very least. Anything that might slow down your page load times, from Flash, to big images, to redundant code and broken links. It's not just onsite SEO that can help. Take a look at your hosting too. Cheap hosting using badly located servers stuffed with too many sites can also impact on your SEO. Google loves fast loading websites and you will be rewarded with a pagerank boost.
Google loves quality - Especially these days. Since the recent Farmer (aka Panda) updates have come in to weed out 'evil websites' it's all the more important that what you say on your site is said in style. How good is your content? Do visitors stick around to read it or do they bounce? Do they regularly come back for more? Remember the better the quality your websites copywriting and content, the better your online profile.
For more detailed information on how to sweet talk Google into helping you enjoy a happy, healthy life online, talk to your SEO Agency today.

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Google Media

Google Inc is one of the most respected companies in the world and, according to Fortune Magazine; it is also the most preferred employer in the world. It is a fact that "Google" has become a part of our vocabulary and is synonymous with online searches. Many young people use the term 'Google it' instead of saying 'search it' which shows the degree of impact it has been able to create on people. Many important and useful services are offered by this company apart from the search engine service, they are mainly YouTube for video hosting, Orkut for social networking and a recently launched service called Google Finance etc. Apart from the internet based services the company is also the developer of the popular mobile operating system called android. Thus, it's a giant corporation with billions of dollars in assets and turnover and a business module which is diverse, expanding and advanced.
Google Finance:
It is a service that was launched in 21st of March, 2006 and has been active since then and is available in most countries around the world. The first Non-US country to have it was Canada; it was designed to provide a specialized search experience for information about the financial sector like trends, live market updates, stock news, financial news from around the globe etc.
Surprisingly not too many people use it or are aware of the various facilities associated with it. There are many advantageous of this financial service, they are:
1) It is an absolutely free of cost market information guide which takes live feeds and provides real time market information.
2) It takes input from search results on a given day to predict the kind of stocks or FOREX that people can trade in to reap maximum profits. As it is the market leader in search engine, so it has a reliable database to get the required information for such kind of predictions.
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http://amberbooks.com/modules/profile/userinfo.php?uid=34283) It has a unique feature called 'get quotes' through which we can get real time information about stocks of a company listed with NASDAQ. For example, if we type in 'HP', it will give us the current stock value of 'HP' as per NASDAQ.
4) Google Finance contains vital statistics about the monthly, quarterly and annual financial performance of various MNCs which can help people in deciding about the kind of company they can invest their money on.
5) It is a very simple and user friendly service that can be used by everyone.

Google and twitter

In the May 2009, many digital marketing professionals thought that Twitter would end Google's reign. However, a year later in 2010, Google ended such speculation by announcing that it had partnered with Twitter to include real-time tweets in its search results. Initially, it was believed that unless Twitter developed a system for cleaning up spam on its network, it could not possibly threaten Google. This was because the main USP of the Google is its ability to deliver relevant, high quality results for different search terms. Though these results were not completely spam-free, they were much better than the results provided by other search engines.
According to a Internet marketing article published in 2010 by Technology Review, Google equates a user following another user on a social networking website, to one web page adding a link to another on the Internet. Just as the value of a page increases when a highly-ranked page links to it, the quality of a user goes up if a more established user begins following him. Does this mean that getting more Twitter followers is the same as the common digital marketing strategy of collecting more incoming links?
No, not quite. Collecting followers on Twitter is one of the easiest things to spam. There are many tools out there that will follow users, based on specified digital marketing keywords. Most users, who are followed on Twitter, will usually return to the favour by following the follower. Once a digital marketing brand gets a ton of followers using such a tool, it is quite simple to unfollow a majority of these users with applications like ManageTwitter. Additionally, the brands could also get profile names which include a targeted digital marketing keyword, and also create other profiles having that same keyword.
The only way for Google to beat this is to modify their search engine algorithm to weed out profiles that do reciprocal social media following, similar to reciprocal link building. Another related issue is the importance of social search. Digital marketing pundits are always saying that people are more likely to trust the advice given by their friends, rather than Google's search results. Theoretically, this may be true, but in the actual practice, it is not so. In fact, most users begin to follow others simply because of a single smart tweet. They do not really know the person they are following or value their opinion

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