Web Scraping to Create Open Data

Open data is the idea that some data should be freely available to everyone to use and republish as they wish, without restrictions from
copyright, patents or other mechanisms of control.

My first experience with open data was in the year 2010. I wanted to create a better app for Bicing, the local bike sharing system in
Barcelona. Their website was a nightmare to use and I was tired of needing to walk to each station, trying to guess which ones had bicycles.
There was no app for Android, other than a couple of unofficial attempts that didn’t work at all.

I began as most would; I searched the internet and found a library named python-bicing that was somehow able to retrieve station and
bike information. This was my first time using Python and, after some investigation, I learned what the code was doing: accessing the
official website, parsing the JavaScript that generated their buggy map and giving back a nice chunk of Python objects that represented
bike share stations.

This I learned was called web scraping. It was like I had figured out a magic trick that would allow me to always be able to access the data I
needed without having to rely on faulty websites.

The rise of OpenBicing and CityBikes

Shortly after, I launched OpenBicing, an Android app for the local bike sharing system in Barcelona, together with a backend that used
python-bicing. I also shared a public API that provided this information so that nobody else had to do the dirty work ever again.

Since other cities were having the same problem, we expanded the scope of the project worldwide and renamed it CityBikes. That was 6
years ago.

To date, CityBikes is the most comprehensive and widely used open API for bike sharing information, with support for over 400 cities
worldwide. Our API processes around 10 requests per second and we scrape each of the 418 feeds about every three minutes. Making our
core library available for anyone to contribute has been crucial in maintaining and adding coverage for all of the supported systems.

The open data fallacy

We are usually regarded as “an open data project” even though less than 10% of our feeds come from properly licensed, documented and
machine-readable feeds. The remaining 90% is composed of 188 feeds that are machine-readable, but not licensed nor documented and
230 that are entirely maintained by scraping HTML pages.

North American BikeShare Association) recently published GBFS (General Bikeshare Feed Specification). This is clearly a step in the right
direction, but I can’t help but look at the almost 60% of services we currently support through scraping and wonder how long it will take the
remaining organizations to release their information, if ever. This is even more the case considering these numbers aren’t even taking into
account worldwide coverage.

Over the last few years there has been a progression by transportation companies and city councils toward providing their information as
“open data”. Directive 2003/98/EC encourages EU member states to release information regarding public services.

Yet, in most cases, there’s little action in enforcing Public Private Partnerships (PPP) to release their public information under a non-
restrictive license or even to transfer ownership of the data to city councils to be included in their open data portals.

Even with the increasing number of companies and institutions interested in participating in open data, by no means should we consider
open data a reality or something to be taken for granted. I firmly believe in the future and benefits of open data, I have seen them
happening all around CityBikes, but as technologists we need to stress the fact that the data is not out there yet.

The benefits of open data

When I started this project, I sought to make a difference in Barcelona. Now you can find tons of bike sharing apps that use our API on all
major platforms. It doesn’t matter that these are not our own apps. They are solving the same problem we were trying to fix, so their
success is our success.

Besides popular apps like Moovit or CityMapper, there are many neat projects out there, some of which are published under free software
licenses. Ideally, a city council could create a customization of any of these apps for their own use.

Most official applications for bike sharing systems have terrible ratings. The core business of transportation companies is running a service,

so they have no real motivation to create an engaging UI or innovate further. In some cases, the city council does not even own the rights to
the data, being completely at the mercy of the company providing the transportation service.

Open data over apps

When providing public services, city councils and companies often get lost in what they should offer as an aid to the service. They focus on
a nice map or a flashy application, rather than providing the data behind these service aids. Maps, apps, and websites have a limited focus
and usually serve a single purpose. On the other hand, data is malleable and the purest form of representation. While you can’t create
something new from looking and playing with a static map (except, of course, if you scrape it), data can be used to create countless
different iterations. It can even provide a bridge that will allow anyone to participate, improve and build on top of these public services.

Wrap Up

At this point, you might wonder why I care so much about bike sharing. To me it’s not about bike sharing anymore. CityBikes is just too
good of an open data metaphor, a simulation in which public information is freely accessible to everyone. It shows the benefits of open
data and the deficiencies that arise from the lack thereof.

We shouldn’t have to create open data by scraping websites. This information should be already available, easily accessed and provided in
a machine-readable format from the original providers, be they city councils or transportation companies. However, until there’s another
option, we’ll always have scraping.


Source : https://blog.scrapinghub.com/2016/03/30/web-scraping-to-create-open-data/

Web Scraping – Ethical Data Collection Activity or an Illegal Practice?

Abiding by the definition, web scrapping is a method to extract data from website. There can be different reasons to perform this task, such as for reporting, market research, to determine share indexes, know website updates, product rate updates, to monitor data, and so on. Besides these, data theft is another of the prominent motives behind web data extraction, which ultimately holds the use of a web scraper as unethical and at times, illegal.

Technical definition

In technical terms, data scraping is a method of collecting data from a website through specific software. These software programs or web scrapers give the website owners the impression of human web surfing and extract a big volume of data, which is usually difficult for any user visitor to access manually. The apps simulate human exploration of online data by embedding web browsers, or implementing HTTP to fulfill the cause of data extractors.

Relation with data mining

Usually, data mining refers to analyzing data from varied perspectives and transforming it to meaningful information that could help in boosting sales or mitigating financial risks in a business. As for web scraping, it involves extraction of analytical data from the web. At present, web scrapping comprises major source of data extraction carried out by data miners. This is because almost everything is now available online and for any data miner, this resource is no less than a gold mine.

The web scraping process

In this data scraping method, the experts look out for tricks to format the URLs into pages that include the usable information. The web scrapers then parse the DOM tree to extract data from the website. In simple language, the web scrapers process the semi-structured or unstructured data pages of the desired website and then convert the resulting data into a well structured form. The users can harvest or modify the structured data in a better manner.

Web scraping – legal or unethical?

It solely relies on your intentions, whether you are doing this activity in the interest of the masses or just wish to satisfy your personal interests. If it is for a goodwill, such as to research on share index to predict the market situation in the coming days, it is fine. Another positive example could be to identify the trend of market and suggest a client on viable business boosting methods accordingly.

However, if you are doing web scraping for personal gratification then it may well be termed as intrusion into one’s personal data. For example, if you are hacking into the database of a university to steal the academic articles and using them in your own project. Any such instance is definitely an act of stealth and may accompany relevant punishment. Concisely, to get hold of someone’s creative work for individual gains is unethical. Such people also deploy several bots to for data scraping or spinning, which in turn choke the search engine results and hardly useful to the internet.

Considerations that deem web scraping illegal

Generally, web scraping is illegal in two instances:

1. When you violate the terms and conditions of the service of the concerned website:

Most of the data-oriented websites disallow data scraping. Hence, if you are trying to extract data from that website, the owner has all the rights to sue you on the offense of breach of contract.

2. When you publish scraped content:

This is yet another condition that may delve you into violating the right of the copyright holders. If you are only scraping the content for fair use, it may be permissible. However, companies often hold all the publishing rights and may file suit against you if you publish their data without their permission.

Remedy to illegal web scraping

Despite running the apprehensions of getting identified, unethical web scrapers deter to steal data from websites. Hence, the web owners themselves need to be alert enough not to fall prey to such fraudulent activities. Indeed, it is your data and you won’t like it to get compromised at any cost. Just like there are many web scraping tools available online, you can also opt for applications that offer protection against web data extraction as a fruitful remedy. These software safeguard your website content from hacking attacks such as bots, denial of service, brute force, session opening and transaction anomalies, and more.

Summary: Technology has two facets – good and bad. It depends on us which one to adopt; the same holds in the case of web scraping as well. We should make sure to use this innovation for the benefit of society and not to steal away some one’s creativity, which is indeed unethical and at times, illegal

Source : http://www.web-parsing.com/blog/ethical-data-collection-activity-or-an-illegal-practice

Taking a cue from the Ryanair screen scraping judgment

Screen scraping is the best way to aggregate web data much faster than a human possibly can. However, is there any such thing known as ethical web scraping?

Well it’s not scraping in itself that is good or bad. What matters is how you use the scraped data. It would be extremely unethical to steal data, republish it or use it to cause harm to a business. This was clearly established when the EU passed a judgment in favour of Ryanair.

The EU’s highest court passed a judgment in favour of Ryan air, and this will positively prevent people from scraping and using others data in an unethical manner. Ryanair had claimed that PR Aviation scraped its data regarding the flight scheduled and then used it to allow people book Ryanair flights via the PR Aviation website and this is clearly an infringement of database rights.

Source:http://www.habiledata.com/blog/taking-cue-ryanair-screen-scraping-judgment/

Data Scraping - About Hand Scraped Flooring

Hand scraped hardwood flooring is one of the best floors that you can install in your house.

Advantages of Hand Scraped Hardwood Flooring

The product comes with a number of advantages which include:

Antique and modern technology: The floor professionally brings out the best elements of both antique and modern technology. The modern elements are in the quality of the product.

Unique patterns: Who doesn't want to be unique? These floors allow you to create your unique design. If you are going to use a machine, all you need to do is to set the machine such that it creates the pattern that you want. If the floor will be scraped by a craftsman, you should ask the craftsman to craft your desired pattern.

Character: The different depths in the floor provide you with character and color that you can't find in other types of floors. As the sun changes its angle during the day, the nooks and valleys on the board lit differently thus providing your board with an endless rich appearance.

Durability: Experts have been able to show that hand-scraped hardwood retains its look for a long time. If your kid or pet hits the floor, the dent just blends with the rest of the character making it hard for people to tell that there is a dent.

Making the floors shine again

Although, the scraped floors are designed to look worn and aged, they are made from modern wood which needs to be taken care of in order to retain its original look.

To make the floors shine again you need to remove all the dust and dirt that might be causing the wood to look dull.

After doing this you should mix 1 gallon of warm water with ½ teaspoon of dishwashing detergent and use it to clean the surface of the floor. The aim of doing this is to remove any stains that might be on the floor. When you complete doing this you should dampen the piece of cloth with club soda and then use another piece of cloth to buff the wood until it shines.

Conclusion

This is what you need to know about hand scraped hardwood flooring. When cleaning the floors you should avoid using oil based soaps as they dull the surface making your efforts worthless.

If the above method of shining the floor doesn't work, you should mix one part white vinegar and one part of cooking oil and use it to clean the floor.

Source: http://ezinearticles.com/?About-Hand-Scraped-Flooring&id=8990255

Rvest: easy web scraping with R

Rvest is new package that makes it easy to scrape (or harvest) data from html web pages, by libraries like beautiful soup. It is designed to work with magrittr so that you can express complex operations as elegant pipelines composed of simple, easily understood pieces. Install it with:

install.packages("rvest")

rvest in action

To see rvest in action, imagine we’d like to scrape some information about The Lego Movie from IMDB. We start by downloading and parsing the file with html():

library(rvest)

lego_movie <- html("http://www.imdb.com/title/tt1490017/")

To extract the rating, we start with selectorgadget to figure out which css selector matches the data we want: strong span. (If you haven’t heard of selectorgadget, make sure to read vignette("selectorgadget") – it’s the easiest way to determine which selector extracts the data that you’re interested in.) We use html_node() to find the first node that matches that selector, extract its contents with html_text(), and convert it to numeric with as.numeric():

lego_movie %>%

  html_node("strong span") %>%
  html_text() %>%
  as.numeric()

#> [1] 7.9

We use a similar process to extract the cast, using html_nodes() to find all nodes that match the selector:

lego_movie %>%

  html_nodes("#titleCast .itemprop span") %>%
  html_text()

#>  [1] "Will Arnett"     "Elizabeth Banks" "Craig Berry"   

#>  [4] "Alison Brie"     "David Burrows"   "Anthony Daniels"

#>  [7] "Charlie Day"     "Amanda Farinos"  "Keith Ferguson"

#> [10] "Will Ferrell"    "Will Forte"      "Dave Franco"   

#> [13] "Morgan Freeman"  "Todd Hansen"     "Jonah Hill"

The titles and authors of recent message board postings are stored in a the third table on the page. We can use html_node() and [[ to find it, then coerce it to a data frame with html_table():

lego_movie %>%

  html_nodes("table") %>%
  .[[3]] %>%
  html_table()

#>                                              X 1            NA

#> 1 this movie is very very deep and philosophical   mrdoctor524

#> 2 This got an 8.0 and Wizard of Oz got an 8.1...  marr-justinm

#> 3                         Discouraging Building?       Laestig

#> 4                              LEGO - the plural      neil-476

#> 5                                 Academy Awards   browncoatjw

#> 6                    what was the funniest part? actionjacksin

Other important functions

    If you prefer, you can use xpath selectors instead of css: html_nodes(doc, xpath = "//table//td")).

    Extract the tag names with html_tag(), text with html_text(), a single attribute with html_attr() or all attributes with html_attrs().

    Detect and repair text encoding problems with guess_encoding() and repair_encoding().
    Navigate around a website as if you’re in a browser with html_session(), jump_to(), follow_link(), back(), and forward(). Extract, modify and submit forms with html_form(), set_values() and submit_form(). (This is still a work in progress, so I’d love your feedback.)

To see these functions in action, check out package demos with demo(package = "rvest").

Source: http://www.r-bloggers.com/rvest-easy-web-scraping-with-r/

Web Scraping : Data Mining vs Screen-Scraping

Data mining isn't screen-scraping. I know that some people in the room may disagree with that statement, but they're actually two almost completely different concepts.

In a nutshell, you might state it this way: screen-scraping allows you to get information, where data mining allows you to analyze information. That's a pretty big simplification, so I'll elaborate a bit.

The term "screen-scraping" comes from the old mainframe terminal days where people worked on computers with green and black screens containing only text. Screen-scraping was used to extract characters from the screens so that they could be analyzed. Fast-forwarding to the web world of today, screen-scraping now most commonly refers to extracting information from web sites. That is, computer programs can "crawl" or "spider" through web sites, pulling out data. People often do this to build things like comparison shopping engines, archive web pages, or simply download text to a spreadsheet so that it can be filtered and analyzed.

Data mining, on the other hand, is defined by Wikipedia as the "practice of automatically searching large stores of data for patterns." In other words, you already have the data, and you're now analyzing it to learn useful things about it. Data mining often involves lots of complex algorithms based on statistical methods. It has nothing to do with how you got the data in the first place. In data mining you only care about analyzing what's already there.

The difficulty is that people who don't know the term "screen-scraping" will try Googling for anything that resembles it. We include a number of these terms on our web site to help such folks; for example, we created pages entitled Text Data Mining, Automated Data Collection, Web Site Data Extraction, and even Web Site Ripper (I suppose "scraping" is sort of like "ripping"). So it presents a bit of a problem-we don't necessarily want to perpetuate a misconception (i.e., screen-scraping = data mining), but we also have to use terminology that people will actually use.

Source: http://ezinearticles.com/?Data-Mining-vs-Screen-Scraping&id=146813

Scraping Services - Assuring Scraping Success with Proxy Data Scraping

Have you ever heard of "Data Scraping?" Data Scraping is the process of collecting useful data that has been placed in the public domain of the internet (private areas too if conditions are met) and storing it in databases or spreadsheets for later use in various applications. Data Scraping technology is not new and many a successful businessman has made his fortune by taking advantage of data scraping technology.

Sometimes website owners may not derive much pleasure from automated harvesting of their data. Webmasters have learned to disallow web scrapers access to their websites by using tools or methods that block certain ip addresses from retrieving website content. Data scrapers are left with the choice to either target a different website, or to move the harvesting script from computer to computer using a different IP address each time and extract as much data as possible until all of the scraper's computers are eventually blocked.

Thankfully there is a modern solution to this problem. Proxy Data Scraping technology solves the problem by using proxy IP addresses. Every time your data scraping program executes an extraction from a website, the website thinks it is coming from a different IP address. To the website owner, proxy data scraping simply looks like a short period of increased traffic from all around the world. They have very limited and tedious ways of blocking such a script but more importantly -- most of the time, they simply won't know they are being scraped.

You may now be asking yourself, "Where can I get Proxy Data Scraping Technology for my project?" The "do-it-yourself" solution is, rather unfortunately, not simple at all. Setting up a proxy data scraping network takes a lot of time and requires that you either own a bunch of IP addresses and suitable servers to be used as proxies, not to mention the IT guru you need to get everything configured properly. You could consider renting proxy servers from select hosting providers, but that option tends to be quite pricey but arguably better than the alternative: dangerous and unreliable (but free) public proxy servers.

There are literally thousands of free proxy servers located around the globe that are simple enough to use. The trick however is finding them. Many sites list hundreds of servers, but locating one that is working, open, and supports the type of protocols you need can be a lesson in persistence, trial, and error. However if you do succeed in discovering a pool of working public proxies, there are still inherent dangers of using them. First off, you don't know who the server belongs to or what activities are going on elsewhere on the server. Sending sensitive requests or data through a public proxy is a bad idea. It is fairly easy for a proxy server to capture any information you send through it or that it sends back to you. If you choose the public proxy method, make sure you never send any transaction through that might compromise you or anyone else in case disreputable people are made aware of the data.

A less risky scenario for proxy data scraping is to rent a rotating proxy connection that cycles through a large number of private IP addresses. There are several of these companies available that claim to delete all web traffic logs which allows you to anonymously harvest the web with minimal threat of reprisal. Companies such as offer large scale anonymous proxy solutions, but often carry a fairly hefty setup fee to get you going.

The other advantage is that companies who own such networks can often help you design and implementation of a custom proxy data scraping program instead of trying to work with a generic scraping bot. After performing a simple Google search, I quickly found one company (www.ScrapeGoat.com) that provides anonymous proxy server access for data scraping purposes. Or, according to their website, if you want to make your life even easier, ScrapeGoat can extract the data for you and deliver it in a variety of different formats often before you could even finish configuring your off the shelf data scraping program.

Whichever path you choose for your proxy data scraping needs, don't let a few simple tricks thwart you from accessing all the wonderful information stored on the world wide web!

Source: http://ezinearticles.com/?Assuring-Scraping-Success-with-Proxy-Data-Scraping&id=248993