Nepal Aid Workers Helped by Drones, Crowdsourcing

Curated on Posted on by Stefaan Verhulst
Shirley Wang et al in the Wall Street Journal: “….It is too early to gauge the exact impact of the technology in Nepal relief efforts, which have just begun amid chaos on the ground. Aid organizations have reported hospitals are overstretched, a shortage of capacity at Katmandu’s airport is crippling aid distribution and damaged roads and the mountainous country’s difficult terrain make reaching villages difficult.

Still, technology is playing an increasing role in the global response to humanitarian crises. Within hours of Saturday’s 7.8-magnitude temblor, U.S. giants such as Google Inc. and Facebook Inc. were offering their networks for use in verifying survivors and helping worried friends and relatives locate their loved ones.

Advances in online mapping—long used to calculate distances and plot driving routes—and the ability of camera-equipped drones are playing an increasingly important role in coordinating emergency responses at ground zero of any disaster.

A community of nonprofit groups uses satellite images, private images and open-source mapping technology to remap areas affected by the earthquake. They mark damaged buildings and roads so rescuers can identify the worst-hit areas and assess how accessible different areas are. The technology complements more traditional intelligence from aircraft.

Such crowdsourced real-time mapping technologies were first used in the 2010 Haiti earthquake, according to Chris Grundy, a professor in Geographical Information Systems at the London School of Hygiene and Tropical Medicine. The technology “has been advancing a little bit every time [every situation where it is used] as we start to see what works,” said Prof. Grundy.

The American Red Cross supplied its relief team on the Wednesday night flight to Nepal from Washington, D.C. with 50 digital maps and an inch-thick pile of paper maps that help identify where the needs are. The charity has a mapping project with the British Red Cross, Doctors Without Borders and the Humanitarian OpenStreetMap Team, a crowdsourced data-sharing group.

Almost a week after the Nepal earthquake, two more people have been pulled from the rubble in Katmandu by teams of international rescuers. But hope for finding more survivors is waning. Photo: Sean McLain/The Wall Street Journal.

Mapping efforts have grown substantially since Haiti, according to Dale Kunce, head of the geographic information systems team at the American Red Cross. In the two months after the Haiti temblor, 600 mapping contributors made 1.5 million edits, while in the first 48 hours after the Nepal earthquake, 2,000 mappers had already made three million edits, Mr. Kunce said.

Some 3,400 volunteers from around the world are now inspecting images of Nepal online to identify road networks and conditions, to assess the extent of damage and pinpoint open spaces where displaced persons tend to congregate, according to Nama Budhathoki, executive director of a nonprofit technology company called Katmandu Living Labs.

His group is operating from a cramped but largely undamaged meeting room in a central-Katmandu office building to help coordinate the global effort of various mapping organizations with the needs of agencies like Doctors Without Borders and the international Red Cross community.

In recent days the Nepal Red Cross and Nepalese army have requested and been supplied with updated maps of severely damaged districts, said Dr. Budhathoki….(More)”

The Incredible Jun: A Town that Runs on Social Media

Curated on Posted on by Stefaan Verhulst

Deb Roy and William Powers in the Huffington Post: “For the last four years, a town in southern Spain has been conducting a remarkable experiment in civic life. Jun (pronounced “hoon”) has been using Twitter as its principal medium for citizen-government communication. Leading the effort is Jun’s Mayor, José Antonio Rodríguez Salas, a passionate believer in the power of technology to solve problems and move society forward.

Since launching the initiative in 2011, Rodríguez Salas has been recruiting his 3,500 townspeople to not only join the social network but have their Twitter accounts locally verified at town hall. This extra step isn’t necessary to participate in the conversation – Twitter is open to anyone – but it helps town employees know they’re dealing with actual residents.

In the most basic scenario, a citizen who has a question, request or complaint tweets it to the mayor or one of his staff, who work to resolve the matter. For instance, in the sequence of tweets shown below (which we pulled from the 2014 Twitter data and translated into English), at 10:48 pm a citizen tells the mayor that a street lamp is out on Maestro Antonio Linares Street. Nine minutes later, the mayor replies that he’ll have the town electrician fix it the next day. The mayor’s tweet includes the Twitter handle of the electrician, who is automatically notified that he’s been mentioned and sees the exchange. That tweet is a public promise that the town will indeed take action, and to underline this it ends with the hashtag #JunGetsMoving. The next day, the electrician tweets a photo of the repaired fixture, thanking the citizen for his help and repeating the hashtag.

A citizen alerts the mayor to a broken street lamp. Two tweets later, it’s fixed.

Governments have been responding to citizens for centuries. But digital networks have made it possible to build much faster, more efficient feedback loops. Each of the participants in the above transaction wrote a single text of less than 140 characters, and in less than 24 hours the problem was solved….(More)”

Data Fusion Heralds City Attractiveness Ranking

Curated on Posted on by Stefaan Verhulst

Emerging Technology From the arXiv: “The ability of any city to attract visitors is an important metric for town planners, businesses based on tourism, traffic planners, residents, and so on. And there are increasingly varied ways of measuring this thanks to the growing volumes of city-related data generated by with social media, and location-based data.

So it’s only natural that researchers would like to draw these data sets together to see what kind of insight they can get from this form of data fusion.

And so it has turned out thanks to the work of Stanislav Sobolevsky at MIT and a few buddies. These guys have fused three wildly different data sets related to the attractiveness of a city that allows them to rank these places and to understand why people visit them and what they do when they get there.

The work focuses exclusively on cities in Spain using data that is relatively straightforward to gather. The first data set consists of the number of credit and debit card transactions carried out by visitors to cities throughout Spain during 2011. This includes each card’s country of origin, which allows Sobolevsky and co to count only those transactions made by foreign visitors—a total of 17 million anonymized transactions from 8.6 million foreign visitors from 175 different countries.

The second data set consists of over 3.5 million photos and videos taken in Spain and posted to Flickr by people living in other countries. These pictures were taken between 2005 and 2014 by 16,000 visitors from 112 countries.

The last data set consists of around 700,000 geotagged tweets posted in Spain during 2012. These were posted by 16,000 foreign visitors from 112 countries.

Finally, the team defined a city’s attractiveness, at least for the purposes of this study, as the total number of pictures, tweets and card transactions that took place within it……

That’s interesting work that shows how the fusion of big data sets can provide insights into the way people use cities.   It has its limitations of course. The study does not address the reasons why people find cities attractive and what draws them there in the first place. For example, are they there for tourism, for business, or for some other reason. That would require more specialized data.

But it does provide a general picture of attractiveness that could be a start for more detailed analyses. As such, this work is just a small part of a new science of cities based on big data, but one that shows how much is becoming possible with just a little number crunching.

Ref: arxiv.org/abs/1504.06003 : Scaling of city attractiveness for foreign visitors through big data of human economic and social media activity”

How Not to Drown in Numbers

Curated on Posted on by Stefaan Verhulst

Seth Stephens-Davidowitz in the New York Times: “BIG data will save the world. How often have we heard that over the past couple of years? We’re pretty sure both of us have said something similar dozens of times in the past few months.

If you’re trying to build a self-driving car or detect whether a picture has a cat in it, big data is amazing. But here’s a secret: If you’re trying to make important decisions about your health, wealth or happiness, big data is not enough.

The problem is this: The things we can measure are never exactly what we care about. Just trying to get a single, easy-to-measure number higher and higher (or lower and lower) doesn’t actually help us make the right choice. For this reason, the key question isn’t “What did I measure?” but “What did I miss?”

…

So what can big data do to help us make big decisions? One of us, Alex, is a data scientist at Facebook. The other, Seth, is a former data scientist at Google. There is a special sauce necessary to making big data work: surveys and the judgment of humans — two seemingly old-fashioned approaches that we will call small data….(More)”

A sentiment analysis of U.S. local government tweets: The connection between tone and citizen involvement

Curated on Posted on by Stefaan Verhulst

Paper by Staci M. Zavattaro, P. Edward French, and Somya D. Mohanty: “As social media tools become more popular at all levels of government, more research is needed to determine how the platforms can be used to create meaningful citizen–government collaboration. Many entities use the tools in one-way, push manners. The aim of this research is to determine if sentiment (tone) can positively influence citizen participation with government via social media. Using a systematic random sample of 125 U.S. cities, we found that positive sentiment is more likely to engender digital participation but this was not a perfect one-to-one relationship. Some cities that had an overall positive sentiment score and displayed a participatory style of social media use did not have positive citizen sentiment scores. We argue that positive tone is only one part of a successful social media interaction plan, and encourage social media managers to actively manage platforms to use activities that spur participation….(More)”

How Data Mining could have prevented Tunisia’s Terror attack in Bardo Museum

Curated on Posted on by Stefaan Verhulst

Wassim Zoghlami at Medium: “…Data mining is the process of posing queries and extracting useful patterns or trends often previously unknown from large amounts of data using various techniques such as those from pattern recognition and machine learning. Latelely there has been a big interest on leveraging the use of data mining for counter-terrorism applications

Using the data on more than 50.000+ ISIS connected twitter accounts , I was able to establish an understanding of some factors determined how often ISIS attacks occur , what different types of terror strikes are used in which geopolitical situations, and many other criteria through graphs about the frequency of hashtags usages and the frequency of a particular group of the words used in the tweets.

A simple data mining project of some of the repetitive hashtags and sequences of words used typically by ISIS militants in their tweets yielded surprising results. The results show a rise of some keywords on the tweets that started from Marsh 15, three days before Bardo museum attacks.

Some of the common frequent keywords and hashtags that had a unusual peak since marsh 15 , three days before the attack :

#طواغيت تونس : Tyrants of Tunisia = a reference to the military

بشرى تونس : Good news for Tunisia.

قريبا تونس : Soon in Tunisia.

#إفريقية_للإعلام : The head of social media of Afriqiyah

#غزوة_تونس : The foray of Tunis…

Big Data and Data Mining should be used for national security intelligence

The Tunisian national security has to leverage big data to predict such attacks and to achieve objectives as the volume of digital data. Some of the challenges facing the Data mining techniques are that to carry out effective data mining and extract useful information for counterterrorism and national security, we need to gather all kinds of information about individuals. However, this information could be a threat to the individuals’ privacy and civil liberties…(More)”

Nowcasting Disaster Damage

Curated on Posted on by Stefaan Verhulst

Paper by Yury Kryvasheyeu et al: “Could social media data aid in disaster response and damage assessment? Countries face both an increasing frequency and intensity of natural disasters due to climate change. And during such events, citizens are turning to social media platforms for disaster-related communication and information. Social media improves situational awareness, facilitates dissemination of emergency information, enables early warning systems, and helps coordinate relief efforts. Additionally, spatiotemporal distribution of disaster-related messages helps with real-time monitoring and assessment of the disaster itself. Here we present a multiscale analysis of Twitter activity before, during, and after Hurricane Sandy. We examine the online response of 50 metropolitan areas of the United States and find a strong relationship between proximity to Sandy’s path and hurricane-related social media activity. We show that real and perceived threats — together with the physical disaster effects — are directly observable through the intensity and composition of Twitter’s message stream. We demonstrate that per-capita Twitter activity strongly correlates with the per-capita economic damage inflicted by the hurricane. Our findings suggest that massive online social networks can be used for rapid assessment (“nowcasting”) of damage caused by a large-scale disaster….(More)”

How Google and Facebook are finding victims of the Nepal earthquake

Curated on Posted on by Stefaan Verhulst

Caitlin Dewey in the Washington Post: “As the death toll from Saturday’s 7.8-magnitude Nepalese earthquake inches higher, help in finding and identifying missing persons has come from an unusual source: Silicon Valley tech giants.

Both Google and Facebook deployed collaborative, cellphone-based tools over the weekend to help track victims of the earthquake. In the midst of both company’s big push to bring Internet to the developing world, it’s an important illustration of exactly how powerful that connectivity could be. And yet, in a country like Nepal — where there are only 77 cellphone subscriptions per 100 people versus 96 in the U.S. and 125 in the U.K. — it’s also a reminder of how very far that effort still has to go.

Facebook Safety Check

Facebook’s Safety Check essentially lets users do two things, depending on where they are. Users in an area impacted by a natural disaster can log onto the site and mark themselves as “safe.” Meanwhile, users around the world can log into the site and check if any of their friends are in the impacted area. The tool was built by Japanese engineers in response to the 2011 earthquake and tsunami that devastated coastal Japan.

(Facebook, via Sarah Pullman Bailey)

(Facebook, via Sarah Pullman Bailey)

…

Facebook hasn’t publicized how many people have used the tool, though the network only has 4.4 million users in the country based on estimates by its ad platform. Notably, you must also a smartphone running the Facebook app to use this feature — and smartphone penetration in Nepal is quite low.

Google Person Finder

Like Safety Check, Google Person Finder is intended to connect people in a disaster area with friends and family around the world. Google’s five-year-old project also operates on a larger scale, however: It basically provides a massive, open platform to collaboratively track missing persons’ reports. Previously, Google’s deployed the tool to help victims in the wake of Typhoon Haiyan and the Boston bombing.


(Google)

 

Does Twitter Increase Perceived Police Legitimacy?

Curated on Posted on by Stefaan Verhulst

Paper by Stephan G. Grimmelikhuijsen and Albert J. Meijer in Public Administration Review: “Social media use has become increasingly popular among police forces. The literature suggests that social media use can increase perceived police legitimacy by enabling transparency and participation. Employing data from a large and representative survey of Dutch citizens (N = 4,492), this article tests whether and how social media use affects perceived legitimacy for a major social media platform, Twitter. A negligible number of citizens engage online with the police, and thus the findings reveal no positive relationship between participation and perceived legitimacy. The article shows that by enhancing transparency, Twitter does increase perceived police legitimacy, albeit to a limited extent. Subsequent analysis of the mechanism shows both an affective and a cognitive path from social media use to legitimacy. Overall, the findings suggest that establishing a direct channel with citizens and using it to communicate successes does help the police strengthen their legitimacy, but only slightly and for a small group of interested citizens….(More)”

A New Source of Data for Public Health Surveillance: Facebook Likes

Curated on Posted on by Stefaan Verhulst

Paper by Steven Gittelman et al in the Journal of Medical Internet Research: “The development of the Internet and the explosion of social media have provided many new opportunities for health surveillance. The use of the Internet for personal health and participatory health research has exploded, largely due to the availability of online resources and health care information technology applications [1–8]. These online developments, plus a demand for more timely, widely available, and cost-effective data, have led to new ways epidemiological data are collected, such as digital disease surveillance and Internet surveys [8–25]. Over the past 2 decades, Internet technology has been used to identify disease outbreaks, track the spread of infectious disease, monitor self-care practices among those with chronic conditions, and to assess, respond, and evaluate natural and artificial disasters at a population level [6,8,11,12,14,15,17,22,26–28]. Use of these modern communication tools for public health surveillance has proven to be less costly and more timely than traditional population surveillance modes (eg, mail surveys, telephone surveys, and face-to-face household surveys).

The Internet has spawned several sources of big data, such as Facebook [29], Twitter [30], Instagram [31], Tumblr [32], Google [33], and Amazon [34]. These online communication channels and market places provide a wealth of passively collected data that may be mined for purposes of public health, such as sociodemographic characteristics, lifestyle behaviors, and social and cultural constructs. Moreover, researchers have demonstrated that these digital data sources can be used to predict otherwise unavailable information, such as sociodemographic characteristics among anonymous Internet users [35–38]. For example, Goel et al [36] found no difference by demographic characteristics in the usage of social media and email. However, the frequency with which individuals accessed the Web for news, health care, and research was a predictor of gender, race/ethnicity, and educational attainment, potentially providing useful targeting information based on ethnicity and income [36]. Integrating these big data sources into the practice of public health surveillance is vital to move the field of epidemiology into the 21st century as called for in the 2012 US “Big Data Research and Development Initiative” [19,39].

Understanding how big data can be used to predict lifestyle behavior and health-related data is a step toward the use of these electronic data sources for epidemiologic needs…(More)”