Gather around kiddies, it’s story time — data story time that is. This week: counting Africa’s elephants; predicting box office success; and the story of sewage.
Fifty researchers and a fleet of small aircrafts have teamed together to track the number of African savanna elephants. In recent years, poachers have killed about 100,000. Data is already helping to crack down on poachers; now it will help to see if how well those crack-down efforts are working.
This effort is a combination of human curation and technology: observers record and photograph the elephants they see from the air while a “data logger” automates the capture of this data, “easing burden on the surveyors, improving accuracy and reducing fatigue.”
Afterwards, the data software goes to work: using an algorithm, it “combines the animal observations made in the air along with factors such as flying altitude, which gives researchers a scientifically sound animal count.”
For additional accuracy, ground survey researchers also track the elephants and cross check their information with that of the aerial team.
As cities across the country do battle with another tough winter, people are using apps to see if snowplow efforts are really being made.
The apps use data that’s already available — GPS information already collected to direct plows — and either shows “skeptics that plow drivers are working hard” and “not just clearing the streets of the wealthy and well-connected,” or give evidence on “snow-cleanup shortfalls.”
Is there a link between social media buzz and box office performance? The short answer is yes — at least according to a company that has developed “a proprietary social-ranking tracker” that tries to predict box office performance by measuring “social-media conversations” pre-release.
Using data provided by Twitter, the company assigns on a scale of one to 100 a social media “buzziness” score. They found that a movie from last summer that scored a 98 was also a “big winner” at the box office, while movies that scored in the low 30s did poorly in terms of ticket sales.
While online clothes shopping is convenient, fit isn’t a guarantee. As a result, return rates are at much as 50% to 80%, while shopping cart abandonment rates are between 60% and 75%.
Tech startups have attempted to address this issue by using data to create “virtual dressing rooms.” One company create “reference garments” based on the measurements of consumers’ favorite clothes. Another offers a “data-driven recommendation engine” that leverages data from users as well as clothing manufacturers, while a third startup focuses their recommendations on preferences such as a tighter or baggier fit.
Sewage tells a story, at least according to MIT researchers.
The Underworlds project seeks to address an issue that public officials have long been facing: a lack of metrics. The project’s goal is to collect data on the sewage of Cambridge, and as a result, to learn and predict more about the health of Cambridge residents.
Through a software platform, the collected data — specifically, the presence of viruses, bacterial pathogens, and biomarkers — will be correlated with demographic information such as ethnicity and age.
Such data could give multiple insights: it “could predict epidemics or tell when they’re waning”; demonstrate “the impact of shifts in regulations, such as bans on using trans fat in restaurants”; and help public health officials gain information to fight disease and provide health care more effectively.