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Refugee Migration: How Predictive Analytics Coupled with Big Data is Developing Urgent Solutions for Countless Refugees?

Refugee Migration: How Predictive Analytics Coupled with Big Data is Developing Urgent Solutions for Countless Refugees?

In total, 65 million people are currently displaced or live refugees – owing to the Syrian Civil War. Each day, thousands of refugees are fleeing their homes and seeking asylums in foreign countries. Many countries have opened their borders, countless UN agencies have come forward to help and handle the ongoing global crisis – but how bad is the current situation? What are the chances of working out a satisfactory solution?

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Predictive Analytics is the key. It’s a raw form of statistical science that mines through available data for future prediction of outcomes. Though we agree to the potentials of predictive analytics, we can’t turn a blind eye to the political and financial roadblocks it poses in front of us, which keeps us from addressing the current crisis with same gusto.

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The Power of Prediction

Past data helps! They help the algorithms to anticipate the challenges even before they arise. Also migration data… gathered from a plethora of sources, including World Bank data, population censuses, sample surveys, population registers, and other administrative sources. Such treasure troves of data could be groundbreaking, especially for representatives working on forefront of the ongoing crisis. Armed with meaningful data, officials using advanced analytics could chart out most likely locations, where the refugees are about to head next. Spotting the possible signs of influx, government and respective policymakers might reroute the refugees to different locations, where better assistance is possible and expected. This kind of real-time data helps respectable authorities to transfer money and goods to locales that need them the most.

Nevertheless, predictions are not always on-point or don’t lead to the best guesses, all the time, yet in many cases, refugees could benefit – remember refugee crisis is not only a serious humanitarian crisis but also a development issue for countries that accept the asylum seekers. Thus, the authorities should refrain from bottling up hundreds and thousands of refugees from bottling them up in overcrowded camps, without food, water and other basic amenities. And for that, they need adequate data, which could help them make the best possible decision in such situations of distress.

A Hope in Sight

Technical challenges are soaring; if the world is resilient to solve the ongoing international crisis, predictive analytics has to be embraced, but make sure you give adequate importance to data security. Accidental data breaches and releases are happening all around, which could result in triggering targeted violence in specific, highly-populated, vulnerable areas.

Addressing the growing concern, hefty financial investment is the best play. Several private players and multinational organizations, including UN till now have given undue attention but devoted limited resources to tackle the challenge. That needs to be changed now. And fortunately, change is in motion; recently two key players in the humanitarian aid and development area of work signed a partnership to formulate innovative solutions for refugee crisis using far-reaching claws of big data and technology. The striking partnership between the World Bank and United Nations Refugee Agency is the first stepping stone towards improving the quality of data about refugees, prompting an improved smarter assistance for refugees across the globe.

No longer are such initiatives a distant concept; the phenomenal rise of big data hadoop and predictive analytics technology has stepped up the quality and speed of data resulting in tailor-made sophisticated assistance, perfect for refugee crisis. In a nutshell, the new dimension is going to make a lot of difference, and technology is going to be a game-changer in this.

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The blog has been sourced from 

sisense.com/blog/refugee-migration-where-are-people-fleeing-from-and-where-are-they-going

mashable.com/2018/04/24/big-data-refugees/#8md_gh7p2iqr

theconversation.com/millions-of-refugees-could-benefit-from-big-data-but-were-not-using-it-86286

 

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How Many Category 5 Hurricanes Have We Had in the Atlantic?

With Hurricane Irma battering the pretty state of Florida while ripping through the Caribbean like a mammoth buzzsaw blade, we start wondering how often such rare Category 5 hurricanes occur. We know hurricanes of such magnitude are rare, but how much rare?

According to Wikipedia page information, hurricanes having wind speed greater than or equal to 157 mph are termed as category 5 hurricanes, enough to wreak havoc around. Using SAS Analytics, let’s start digging some data to unravel how many hurricanes of such great magnitude have hit the Atlantic coastal towns and cities with such dangerous wind speeds..

After indulging in a bit of research work, we came across weather.unisys.com website that contains exhaustive data about all the past hurricanes formed out of Atlantic. It turned out to be a good repository of data – we jotted down a bit of code and parsed the data into SAS data set. Next, we marked all the Atlantic hurricane paths on a map, and highlighted the line segments in bright red, where the wind speed fell under the Category 5 tab. So, come know how often they have taken place, along with their accurate position.

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Hit the above image to view the full-size interactive version of the map with HTML mouse-over text displaying the hurricane names in red for category 5 descriptions.

Take a look at the technical details of the code we used to draw the map:

  1. The map is created using SAS/Graph Proc GMap.
  2. We projected the map with the help of Proc GProject. Followed to that, we saved the projection parameters using the brand new Parmout=option. It was only then that we can project the hurricane paths individually, using GProject’s parmin=option. (Before the innovation of parmout/parmin parameters, we used to combine the map and hurricane paths, compile and project them together, and then divide the results into two separate datasets – of course the new functionality eases the things out).
  3. The paths of hurricane were plotted using regular ‘move’ and ‘draw’ Annotate functions.
  4. We first plotted the land areas (choropleth map), then covered (annotated) the hurricane tracks (while doing so, make sure the red lines lie on top for better visibility), and finally overlaid the country border contours on top again so as to make them prominent.
  5. As lines are incompatible with mouse-over text, we annotated circles using mouse-over text along the red hurricane paths. We outlined these circles at the very beginning (using when=’b’), hence they would become invisible later.

Have a look at the table we presented below. The table comprises of 34 Category 5 Atlantic hurricanes, derived from 150 years of data. You can also run your eyes through a snapshot image – click on the image to see the entire interactive table. And if you are interested in knowing more, hit each hurricane name and ask Google to give you information.

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Nota Bene: There might be some hurricanes under Category 5 domain we missed out. Kindly excuse us there, but we think we have nicely hit our main point of discussion. If you have anything to say us, scroll down and comment!

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How Predictive Analysis Could Have Saved the World from Ransomware

How Predictive Analysis Could Have Saved the World from Ransomware
 

Kudos to you, if you have stayed offline for the last couple of days, so you could actually spend the weekend well with your family and loved ones. The world is reeling under the shattering news surrounding WannaCry Ransomware this weekend. The situation was worse on Monday, after the offices opened. Going by the figures, revealed out on Monday evening by Elliptic, a Bitcoin forensics firm, which is keeping a watch overall – $57,282.23 in ransom has been shelled out to the hackers of Ransomware malware attack, who took over hundreds and thousands of computers worldwide on Friday and through the weekend.

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How to Code Colour Values Within SAS Enterprise Guide

Colours are amazing, they are the subject of many romantic poems and songs, they are what can alter our moods drastically, they are these magical wavelengths that transform into incredible visions for our eyes.

 
How to Code Colour Values Within SAS Enterprise Guide
 

Some feel warm, while others feel cool, some make us happy while others make us sad… but as colours are so important, how to add these values within the SAS Enterprise Guide?

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How Predictive Analysis Works With Data Mining

We know that you have probably heard many times that predictive analysis will further optimize and accentuate your marketing campaigns. But it is hard to envision that in more concrete terms what it will achieve. This makes it harder to choose and direct analytics technology.

 

How Predictive Analysis Works With Data Mining

 

Wondering how you can get a functional value for marketing, sales and product directions without being an expert? The solution to all your problems lies in how predictive analytics may offer with benefits for the current marketing operations. But to use it you must learn a few specifics about how it works.

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Data Preparation using SAS

Data Preparation using SAS

Before doing any data analysis, there are tasks which are critical to the success of the data analysis project. That critical task is known as data preparation. You may have heard that in the last years the data production is expanding at an astonishing pace. Experts now point to a 4300% increase in annual data generation by 2020. This can be due to the switch from analog to digital technologies and the rapid increase in data generation by individuals and corporations alike. The most of the data generated in the last few years are unstructured.

sass

In the above context, it is highly important to prepare your data from the unstructured dataset to a structured dataset to do a meaningful analysis.

“Data preparation means manipulation of data into a form suitable for further analysis and processing”

“Data Preparation techniques consists of Cleaning, Integration, Selection and Transformation”

We will discuss some of the data preparation techniques in SAS using SAS. INFORMAT is used to read the data with special characters. FORMAT is used to display the data with special characters.

 

Data DP.Practice;

length City $10.;
 input City $ ID $ Age Salary DOJ Profit;
 informat Salary dollar6. DOJ ddmmyy10. Profit dollar7.2;
 format Salary dollar6. DOJ ddmmyy10. Profit dollar7.2;
 label DOJ = "Date of Joining";
 rename Salary = Salary_of_Employee;
 datalines;
 Bangalore T101 24 $2,000 12/12/2010 $300.50
 Pune T102 29 $3,000 11/10/2006 $400.50
 Hyderabad T103 $5,000 12/10/2008 $500.70
 Delhi T104 $6,000 12/12/2009 $450.00
 Pune T105 $7,000 12/12/2009 $450.00
 ;
 run;

 

On the above SAS code, we have used both the INFORMAT and FORMAT to read and display the data with special characters. The SAS INFORMAT statement read the salary as numeric variable and in a specific format i.e. $5,000 which is of 6 characters including $. The FORMAT statement displays the same in your input data. Rename and label statements helps modify the variables metadata for further understanding of the dataset.

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We will apply some transformations techniques in a dataset which helps us to apply some advanced analytical techniques in the data. We have a dataset that has various attributes of a customer who has subscribed or not subscribed an edition. In our dataset we have a categorical variable status which holds the observation either “Subscribed” or “Not Subscribed”.  We can transform the categorical variable into a dichotomous variable to run a logistic regression on our dataset.

 

Data media01;
 set DP.media;
 length status $15;
 If status =”subscribed” then status = “0”;
 else status = “1”;
 run;

 

On the above SAS code, we have applied simple If Else statements to transform our dataset called media. Transforming a categorical variable into a dichotomous variable helps us to apply the analytical techniques that we want to run in our dataset. Once after the transformation is done, the dataset is good to go for the next stage i.e. data analysis.

The more you torture your data i.e. Data Preparation, the more the success on the outcome of the data analysis.

 

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