She is currently working on a project calledRevising Ekphrasis, which uses advanced computational tools to explore connections between 4,500 English-language poems. You can find her online at LisaRhody.com and follow her on Twitter at @lmrhody.]
In last week’s post about social network analysis, I introduced NodeXL and its potential use for understanding online social networks. In this post, I want to focus on what we can learn from conference Twitter backchannel conversations, and how we can use software like NodeXL to improve the way we use social media to build computer-mediated scholarly networks.
During this year’s annual meeting of the Modern Language Association, I worked withMarc Smith, co-founder of the Social Media Research Foundation and chief social scientist for Connected Action, to upload several sample datasets that mapped Twitter networks at the conference. On Friday, January 4, 2013, Marc uploaded a social media network graph of tweets that included the hashtag #mla13from this year’s MLA convention to the NodeXL Gallery.
Figure 1 (click to enlarge): A NodeXL network visualization of tweets from January 4, 2013 that include the hashtag #mla13.
The graph uses the Twitter images associated with each user id to represent an account (called an “actor” or “vertex” in the network) that sent a tweet, retweeted, replied to, or was mentioned in another tweet, and each “follows” relationship, tweet, retweet, reply, or mention creates a connection (represented by a green or blue line and called a “tie” or “edge”) between each Twitter user.
What can NodeXL show us about our conference Tweeting habits?
What we learn from the NodeXL graph is that the MLA Twitter network represents a tightly-bound community. Since the graph includes very few “isolates” (ie. tweets that come from people less closely connected to the group), the MLA network does not expand beyond those members already connected to the network. Marc mentioned this to me when he uploaded the graph. He explained that the MLA Twitter network represents an “in-group bounded community,” meaning that those who tweeted about MLA generally were read and retweeted by others with close ties to the MLA conference themselves. Few “outsiders” were visible in this network, in contrast to the many “isolates” found in more widely-discussed topics like brands or news events.
While it’s perfectly reasonable to assume that the Twitter backchannel during a scholarly convention may serve to form a more tightly-knit social network, the NodeXL graphs of the Twitter conversation at MLA 2013 also suggest that participation in the Twitter exchange did not broaden the community’s outreach. Sociologists distinguish between “bridging” versus “binding forms of social connections. The MLA Twitter network suggests it is used for bonding existing groups more than bridging to new connections. If the purpose of the backchannel conversation had been to strengthen existing ties, then the next step might be reaching out to connect to less-well-connected people, thereby extending the conversation to a larger community.
How can Twitter backchannel networks help us understand our community better?
Measures of betweenness centrality, for example, can show who creates the most connections in the network. In the case of the MLA graph from January 4th, the Twitter handles @MLAConvention, @rgfeal, @HASTAC, and @kfitz are central parts of the network because they act as hubs between disparate Twitter user groups. This is relatively unsurprising because the accounts are either the personal or professional Twitter presences of representatives from larger, pre-existing face-to-face networks, such as MLA and HASTAC. More interesting, though are the betweenness centralities of individuals, such as @briancroxall,@adelinekoh, and @mkirschenbaum. These are individual accounts not associated with the larger conference organizers whose tweeting was central to the interconnectedness of the MLA network as a whole.
Lists of words frequently used in the network of those who mention #mla13 are also detected in many of the MLA graphs. For example, when using the grouping algorithms to cluster Twitter users by their closest affiliations and most commonly used words, we can trace the communities within the network formed by the recurring use of particular vocabularies. In the case of MLA, session tags and workshop titles created smaller communities within the larger network.
By listing the top URLs used in Tweets from the entire #mla13network as well as from each of the smaller groups within the network, we can trace a pattern of interests, values, and sites of engagement that drew the most attention across the network.
What does social network analysis miss?
There are caveats to looking at network graphs. The graphs produced in NodeXL are not direct representations of whole conference networks, as they cannot capture real-time, face-to-face conversations that are not recorded online. Social media analysis is skewed toward those participants who are the most prolific, and more work needs to be done to understand the influence of single actors within the larger community. For example, the graph from January 4 includes the hashtag #wcw as one of the most frequently discussed terms for the day. In fact, what we know from experience of the conference is that there was a large amount of Tweeting going on in William Carlos Williams Society session, which made the #wcw hashtag appear so prominently. Even with more than 400 Twitter network participants, the sampling is still relatively small and so more easily influenced.
Still, social network analysis and visualizations produce much more than hairball graphs and can become a valuable activity to undertake — especially if you are interested in learning how to change, improve, and expand your own online scholarly communities. Future posts will help get you started on using NodeXL yourself: installing NodeXL, importing data from social media applications, and creating visualizations to share.
What networks would you like to better understand? Have you used social network analysis to understand your relationship to your peers online?
In the mid-2000s that lab was, however, one of the only places on earth to do the kind of science Couzin wanted. He didn’t care about locusts, per se—Couzin studies collective behavior. That’s swarms, flocks, schools, colonies … anywhere the actions of individuals turn into the behaviors of a group. Biologists had already teased apart the anatomy of locusts in detail, describing their transition from wingless green loners at birth to flying black-and-yellow adults. But you could dissect one after another and still never figure out why they blacken the sky in mile-wide plagues. Few people had looked at how locusts swarm since the 1960s—it was, frankly, too hard. So no one knew how a small, chaotic group of stupid insects turned into a cloud of millions, united in one purpose.
Couzin would put groups of up to 120 juveniles into a sombrero-shaped arena he called the locust accelerator, letting them walk in circles around the rim for eight hours a day while an overhead camera filmed their movements and software mapped their positions and orientations. He eventually saw what he was looking for: At a certain density, the bugs would shift to cohesive, aligned clusters. And at a second critical point, the clusters would become a single marching army. Haphazard milling became rank-and-file—a prelude to their transformation into black-and-yellow adults.
That’s what happens in nature, but no one had ever induced these shifts in the lab—at least not in animals. In 1995 a Hungarian physicist named Tamás Vicsek and his colleagues devised a model to explain group behavior with a simple—almost rudimentary—condition: Every individual moving at a constant velocity matches its direction to that of its neighbors within a certain radius. As this hypothetical collective becomes bigger, it flips from a disordered throng to an organized swarm, just like Couzin’s locusts. It’s a phase transition, like water turning to ice. The individuals have no plan. They obey no instructions. But with the right if-then rules, order emerges.
Couzin wanted to know what if-then rules produced similar behaviors in living things. “We thought that maybe by being close to each other, they could transfer information,” Couzin says. But they weren’t communicating in a recognizable way. Some other dynamic had to be at work.
Rules that produce majestic cohesion out of local jostling turn up everywhere.
The answer turned out to be quite grisly. Every morning, Couzin would count the number of locusts he placed in the accelerator. In the evening, his colleague Jerome Buhl would count them as he took them out. But Buhl was finding fewer individuals than Couzin said he had started with. “I thought I was going mad,” Couzin says. “My credibility was at stake if I couldn’t even count the right number of locusts.”
When he replayed the video footage and zoomed in, he saw that the locusts were biting each other if they got too close. Some unlucky individuals were completely devoured. That was the key. Cannibalism, not cooperation, was aligning the swarm. Couzin figured out an elegant proof for the theory: “You can cut the nerve in their abdomen that lets them feel bites from behind, and you completely remove their capacity to swarm,” he says.
Couzin’s findings are an example of a phenomenon that has captured the imagination of researchers around the world. For more than a century people have tried to understand how individuals become unified groups. The hints were tantalizing—animals spontaneously generate the same formations that physicists observe in statistical models. There had to be underlying commonalities. The secrets of the swarm hinted at a whole new way of looking at the world.
But those secrets were hidden for decades. Science, in general, is a lot better at breaking complex things into tiny parts than it is at figuring out how tiny parts turn into complex things. When it came to figuring out collectives, nobody had the methods or the math.
A flock of red-winged blackbirds forms and re-forms over California’s Sacramento Valley.
Photo: Lukas Felzmann
The first thing to hit Iain Couzin when he walked into the Oxford lab where he kept his locusts was the smell, like a stale barn full of old hay. The second, third, and fourth things to hit him were locusts. The insects frequently escaped their cages and careened into the faces of scientists and lab techs. The room was hot and humid, and the constant commotion of 20,000 bugs produced a miasma of aerosolized insect exoskeleton. Many of the staff had to wear respirators to avoid developing severe allergies. “It wasn’t the easiest place to do science,” Couzin says.
Now, thanks to new observation technologies, powerful software, and statistical methods, the mechanics of collectives are being revealed. Indeed, enough physicists, biologists, and engineers have gotten involved that the science itself seems to be hitting a density-dependent shift. Without obvious leaders or an overarching plan, this collective of the collective-obsessed is finding that the rules that produce majestic cohesion out of local jostling turn up in everything from neurons to human beings. Behavior that seems impossibly complex can have disarmingly simple foundations. And the rules may explain everything from how cancer spreads to how the brain works and how armadas of robot-driven cars might someday navigate highways. The way individuals work together may actually be more important than the way they work alone.
Blue jack mackerel merge into a bait ball, a torus that confuses predators.
Photo: Christopher Swann/Science Photo Library
Aristotle first posited that the whole could be more than the sum of its parts. Ever since, philosophers, physicists, chemists, and biologists have periodically rediscovered the idea. But it was only in the computer age—with the ability to iterate simple rule sets millions of times over—that this hazy concept came into sharp focus.
HOW SWARMS EMERGE
Individuals in groups from neurons and cancer cells to birds and fish organize themselves into collectives, and those collectives move in predictable ways. But the ways those swarms, schools, flocks, and herds flip from chaos to order differ. Here’s a look at some of the behaviorial triggers. —Katie M. Palmer
GOLDEN SHINERS
Behavior: Seek darkness
Presumably for protection, shiners search out dark waters. But they can’t actually perceive changes in light levels that might guide their way. Instead, they follow one simple directive: When light disappears, slow down. As a result, the fish in a school pile up in dark pools and stay put.
ANTS
Behavior: Work in rhythm
When ants of a certain species get crowded enough to bump into each other, coordinated waves of activity pulse through every 20 minutes.
HUMANS
Behavior: Be a follower
Absent normal communication, humans can be as impressionable as a flock of sheep. If one member of a walking group is instructed to move toward a target, though other members may not know the target—or even that there is a target—the whole group will eventually be shepherded in its direction.
LOCUSTS
Behavior: Cannibalism
When enough locusts squeeze together, bites from behind send individuals fleeing to safety. Eventually they organize into conga-line-like clusters to avoid being eaten. They also emit pheromones to attract even more locusts, resulting in a swarm.
STARLINGS
Behavior: Do what the neighbors do
These birds coordinate their speed and direction with just a half dozen of their closest murmuration-mates, regardless of how packed the flock gets. Those interactions are enough to steer the entire group in the same direction.
HONEYBEES
Behavior: Head-butting
When honeybees return from searching for a new nest, they waggle in a dance that identifies the location. But if multiple sites exist, a bee can advocate for its choice by ramming its head into other waggling bees. A bee that gets butted enough times stops dancing, ultimately leaving the hive with one option.
For most of the 20th century, biologists and physicists pursued the concept along parallel but separate tracks. Biologists knew that living things exhibited collective behavior—it was hard to miss—but how they pulled it off was an open question. The problem was, before anyone could figure out how swarms formed, someone had to figure out how to do the observations. In a herd, all the wildebeests/bacteria/starlings/whatevers look pretty much alike. Plus, they’re moving fast through three-dimensional spaces. “It was just incredibly difficult to get the right data,” says Nigel Franks, a University of Bristol biologist and Couzin’s thesis adviser. “You were trying to look at all the parts and the complete parcel at the same time.”
Physicists, on the other hand, had a different problem. Typically biologists were working with collectives ranging in number from a few to a few thousand; physicists count groups of a few gazillion. The kinds of collectives that undergo phase transitions, like liquids, contain individual units counted in double-digit powers of 10. From a statistical perspective, physics and math basically pretend those collectives are infinitely large. So again, you can’t observe the individuals directly in any meaningful way. But you can model them.
A great leap forward came in 1970, when a mathematician named John Conway invented what he called the Game of Life. Conway imagined an Othello board, with game pieces flipping between black and white. The state of the markers—called cells—changed depending on the status of neighboring cells. A black cell with one or no black neighbors “died” of loneliness, turning white. Two black neighbors: no change. Three, and the cell “resurrected,” flipping from white to black. Four, and it died of overcrowding—back to white. The board turned into a constantly shifting mosaic.
Conway could play out these rules with an actual board, but when he and other programmers simulated the game digitally, Life got very complicated. At high speed, with larger game boards, they were able to coax an astonishing array of patterns to evolve across their screens. Depending on the starting conditions, they got trains of cells that trailed puffs of smoke, or guns that shot out small gliders. At a time when most software needed complex rules to produce even simple behaviors, the Game of Life did the opposite. Conway had built a model of emergence—the ability of his little black and white critters to self-organize into something new.
Sixteen years later, a computer animator named Craig Reynolds set out to find a way to automate the animated movements of large groups—a more efficient algorithm would save processing time and money. Reynolds’ software, Boids, created virtual agents that mimicked a flock of birds. It included behaviors like obstacle avoidance and the physics of flight, but at the heart of Boids were three simple rules: Move toward the average position of your neighbors, keep some distance from them, and align with their average heading (alignment is a measure of how close an individual’s direction of movement is to that of other individuals). That’s it.
Boids and its ilk revolutionized Hollywood in the early ’90s. It animated the penguins and bats of Batman Returns. Its descendants include software like Massive, the program that choreographed the titanic battles in the Lord of the Rings trilogy. That would all be miraculous enough, but the flocks created by Boids also suggested that real-world animal swarms might arise the same way—not from top-down orders, mental templates of orderly flocks, or telepathic communication (as some biologists had seriously proposed). Complexity, as Aristotle suggested, could come from the bottom up.
The field was starting to take off. Vicsek, the Hungarian physicist, simulated his flock in 1995, and in the late 1990s a German physicist named Dirk Helbing programmed sims in which digital people spontaneously formed lanes on a crowded street and crushed themselves into fatal jams when fleeing from a threat like a fire—just as real humans do. Helbing did it with simple “social forces.” All he had to do was tell his virtual humans to walk at a preferred speed toward a destination, keep their distance from walls and one another, and align with the direction of their neighbors. Presto: instant mob.
By the early 2000s, the research in biology and physics was starting to intersect. Cameras and computer-vision technologies could show the action of individuals in animal swarms, and simulations were producing more and more lifelike results. Researchers were starting to be able to ask the key questions: Were living collectives following rules as simple as those in the Game of Life or Vicsek’s models? And if they were … how?
TAKING SHAPE
Changing simple parameters has profound effects on a swarm. By controlling only attraction, repulsion, and alignment (how similar a critter’s direction is to that of its neighbors), researcher Iain Couzin induced three different behaviors in a virtual collective, all akin to ones in nature.—Katie M. Palmer
DISORDER Alignment with only the closest neighbors produces … nothing but a disordered swarm.
TORUS Raise the alignment and the chaotic swarm swirls into a doughnut shape called a torus.
FLOCK Maximize alignment across the flock and the torus shifts; everyone travels in the same direction.
Before studying collectives, Couzin collected them. Growing up in Scotland, he wanted pets, but his brothers’ various allergies allowed only the most unorthodox ones. “I had snails at the back of my bed, aphids in my cupboard, and stick insects in my school locker,” he says. And anything that formed swarms fascinated him. “I remember seeing these fluidlike fish schools on TV, watching them again and again, and being mesmerized. I thought fish were boring, but these patterns—” Couzin pauses, and you can almost see the whorls of schooling fish looping behind his eyes; then he’s back. “I’ve always been interested in patterns,” he says simply.
When Couzin became a graduate student in Franks’ lab in 1996, he finally got his chance to work on them. Franks was trying to figure out how ant colonies organize themselves, and Couzin joined in. He would dab each bug with paint and watch them on video, replaying the recording over and over to follow different individuals. “It was very laborious,” he says. Worse, Couzin doubted it worked. He didn’t believe the naked eye could follow the multitude of parallel interactions in a colony. So he turned to artificial ones. He learned to program a computer to track the ants—and eventually to simulate entire animal groups. He was learning to study not the ants but the swarm.
For a biologist, the field was a lonely one. “I thought there must be whole labs focused on this,” Couzin says. “I was astonished to find that there weren’t.” What he found instead was Boids. In 2002 Couzin cracked open the software and focused on its essential trinity of attraction, repulsion, and alignment. Then he messed with it. With attraction and repulsion turned up and alignment turned off, his virtual swarm stayed loose and disordered. When Couzin upped the alignment, the swarm coalesced into a whirling doughnut, like a school of mackerel. When he increased the range over which alignment occurred even more, the doughnut disintegrated and all the elements pointed themselves in one direction and started moving together, like a flock of migrating birds. In other words, all these different shapes come from the same algorithms. “I began to view the simulations as an extension of my brain,” Couzin says. “By allowing the computer to help me think, I could develop my intuition of how these systems worked.”
By 2003, Couzin had a grant to work with locusts at Oxford. Labs around the world were quietly putting other swarms through their paces. Bacterial colonies, slime molds, fish, birds … a broader literature was starting to emerge. Work from Couzin’s group, though, was among the first to show physicists and biologists how their disciplines could fuse together. Studying animal behavior “used to involve taking a notepad and writing, ‘The big gorilla hit the little gorilla,’ ” Vicsek says. “Now there’s a new era where you can collect data at millions of bits per second and then go to your computer and analyze it.”
A swarm of locusts.
Photo: Mitsuhiko Imamori/Minden
Today Couzin, 39, heads a lab at Princeton University. He has a broad face and cropped hair, and the gaze coming from behind his black-rimmed glasses is intense. The 19-person team he leads is ostensibly part of the Department of Ecology and Evolutionary Biology but includes physicists and mathematicians. They share an office with eight high-end workstations—all named Hyron, the Cretan word for beehive, and powered by videogame graphics cards.
Locusts are verboten in US research because of fears they’ll escape and destroy crops. So when Couzin came to Princeton in 2007, he knew he needed a new animal. He had done some work with fish, so he headed to a nearby lake with nets, waders, and a willing team. After hours of slapstick failure, and very few fish, he approached some fishermen on a nearby bridge. “I thought they’d know where the shoals would be, but then I went over and saw tiny minnow-sized fish in their buckets, schooling like crazy.” They were golden shiners—unremarkable 2- to 3-inch-long creatures that are “dumber than I could possibly have imagined,” Couzin says. They are also extremely cheap. To get started he bought 1,000 of them for 70 bucks.
When Couzin enters the room where the shiners are kept, they press up against the front of their tanks in their expectation of food, losing any semblance of a collective. But as soon as he nets them out and drops them into a wide nearby pool, they school together, racing around like cars on a track. His team has injected colored liquid and a jelling agent into their tiny backs; the two materials congeal into a piece of gaudy plastic, making them highly visible from above. As they navigate courses in the pool, lights illuminate the plastic and cameras film their movements. Couzin is using these stupid fish to move beyond just looking at how collectives form and begin to study what they can accomplish. What abilities do they gain?
For example, when Couzin flashes light over the shiners, they move, as one, to shadier patches, presumably because darkness equals relative safety for a fish whose main defensive weapon is “run away.” Behavior like this is typically explained with the “many wrongs principle,” first proposed in 1964. Each shiner, the theory goes, makes an imperfect estimate about where to go, and the school, by interacting and staying together, averages these many slightly wrong estimations to get the best direction. You might recognize this concept by the term journalist James Surowiecki popularized: “the wisdom of crowds.”
But in the case of shiners, Couzin’s observations in the lab have shown that the theory is wrong. The school could not be pooling imperfect estimates, because the individuals don’t make estimates of where things are darker at all. Instead they obey a simple rule: Swim slower in shade. When a disorganized group of shiners hits a dark patch, fish on the edge decelerate and the entire group swivels into darkness. Once out of the light, all of them slow down and cluster together, like cars jamming on a highway. “That’s purely an emergent property,” Couzin says. “The sensing ability really happens only at the level of the collective.” In other words, none of the shiners are purposefully swimming toward anything. The crowd has no wisdom to cobble together.
Other students of collectives have found similar feats of swarm intelligence, including some that happen in actual swarms. Every spring, honeybees leave their old colonies to build new nests. Scouts return to the hive to convey the locations of prime real estate by waggling their bottoms and dancing in figure eights. The intricate steps of the dances encode distance and direction, but more important, these dances excite other scouts.
Thomas Seeley, a behavioral biologist at Cornell, used colored paint to mark bees that visited different sites and found that those advocating one location ram their heads against colony-mates that waggle for another. If a dancer gets rammed often enough, it stops dancing. The head-butt is the bee version of a downvote. Once one party builds past a certain threshold of support, the entire colony flies off as one.
House-hunting bees turn out to be a literal hive mind, composed of bodies. This is no cheap metaphor. In the 1980s cognitive scientists began to posit that human cognition itself is an emergent process. In your brain, this thinking goes, different sets of neurons fire in favor of different options, exciting some neighbors into firing like the waggling bees, and inhibiting others into silence, like the head-butting ones. The competition builds until a decision emerges. The brain as a whole says, “Go right” or “Eat that cookie.”
If a falcon attacks, all the starlings dodge almost instantly—even those on the far side of the flock that haven’t seen the threat.
The same dynamics can be seen in starlings: On clear winter evenings, murmurations of the tiny blackish birds gather in Rome’s sunset skies, wheeling about like rustling cloth. If a falcon attacks, all the starlings dodge almost instantaneously, even those on the far side of the flock that haven’t seen the threat. How can this be? Italian physicist Andrea Cavagna discovered their secret by filming thousands of starlings from a chilly museum rooftop with three cameras and using a computer to reconstruct the birds’ movements in three dimensions. In most systems where information gets transferred from individual to individual, the quality of that information degrades, gets corrupted—like in a game of telephone. But Cavagna found that the starlings’ movements are united in a “scale-free” way. If one turns, they all turn. If one speeds up, they all speed up. The rules are simple—do what your half-dozen closest neighbors do without hitting them, essentially. But because the quality of the information the birds perceive about one another decays far more slowly than expected, the perceptions of any individual starling extend to the edges of the murmuration and the entire flock moves.
All these similarities seem to point to a grand unified theory of the swarm—a fundamental ultra-calculus that unites the various strands of group behavior. In one paper, Vicsek and a colleague wondered whether there might be “some simple underlying laws of nature (such as, e.g., the principles of thermodynamics) that produce the whole variety of the observed phenomena.”
Couzin has considered the same thing. “Why are we seeing this again and again?” he says. “There’s got to be something deeper and more fundamental.” Biologists are used to convergent evolution, like the streamlining of dolphins and sharks or echolocation in bats and whales—animals from separate lineages have similar adaptations. But convergent evolution of algorithms? Either all these collectives came up with different behaviors that produce the same outcomes—head-butting bees, neighbor-watching starlings, light-dodging golden shiners—or some basic rules underlie everything and the behaviors are the bridge from the rules to the collective.
Stephen Wolfram would probably say it’s the underlying rules. The British mathematician and inventor of the indispensable software Mathematica published a backbreaking 1,200-page book in 2002, A New Kind of Science, positing that emergent properties embodied by collectives came from simple programs that drove the complexity of snowflakes, shells, the brain, even the universe itself. Wolfram promised that his book would lead the way to uncovering those algorithms, but he never quite got there.
Couzin, on the other hand, is wary of claims that his field has hit upon the secret to life, the universe, and everything. “I’m very cautious about suggesting that there’ll be an underlying theory that’ll explain the stock market and neural systems and fish schools,” he says. “That’s relatively naive. There’s a danger in thinking that one equation fits all.” Physics predicts the interactions of his locusts, but the mechanism manifests through cannibalism. Math didn’t produce the biology; biology generated the math.
Still, just about any system of individual units pumped with energy—kinetic, thermal, whatever—produces patterns. Metal rods organize into vortices when bounced around on a vibrating platform. In a petri dish, muscle proteins migrate unidirectionally when pushed by molecular motors. Tumors spawn populations of rogue, mobile cells that align with and migrate into surrounding tissues, following a subset of trailblazing leader cells. That looks like a migrating swarm; figure out its algorithms and maybe you could divert it from vital organs or stop its progress.
The same kind of rules apply when you step up the complexity. The retina, that sheet of light-sensing tissue at the back of the eye, connects to the optic nerve and brain. Michael Berry, a Princeton neuroscientist, mounts patches of retinas on electrodes and shows them videos, watching their electrophysiological responses. In this context, the videos are like the moving spotlights Couzin uses with his shiners—and just as with the fish, Berry finds emergent behaviors with the addition of more neurons. “Whether the variable is direction, heading, or how you vote, you can map the mathematics from system to system,” Couzin says.
A crowd of humans.
Photo: Amanda Mustard/Corbis
In a lab that looks like an aircraft hangar, several miles from Princeton’s main campus, an assortment of submersibles are suspended from the ceiling. The cool air has a tang of chlorine, thanks to a 20,000-gallon water tank, 20 feet across and 8 feet deep, home to four sleek, cat-sized robots with dorsal and rear propellers that let them swim in three dimensions.
The robots are called Belugas, and they’re designed to test models of collective behavior. “We’re learning about mechanisms in nature that I wouldn’t have dreamed of designing,” says engineer Naomi Leonard. She plans to release pods of underwater robots to collect data on temperature, currents, pollution, and more. Her robots can also track moving gradients, avoid each other, and keep far enough apart to avoid collecting redundant data—just enough programming to unlock more complex abilities. Theoretically.
Today it’s not working. Three Belugas are out of the tank so Leonard’s team can tinker. The one in the water is on manual, driven by a thick gaming joystick. The controls are responsive, if leisurely, and daredevil maneuvers are out of the question.
Leonard has a video of the robots working together, though, and it’s much more convincing. The bots carry out missions with a feedback-controlled algorithm programmed into them, like finding the highest concentrations of oil in a simulated spill or collecting “targets” separately and then reuniting.
Building a successful robot swarm would show that the researchers have figured out something basic. Robot groups already exist, but most have sophisticated artificial intelligence or rely on orders from human operators or central computers. To Tamás Vicsek—the physicist who created those early flock simulations—that’s cheating. He’s trying to build quadcopters that flock like real birds, relying only on knowledge of their neighbors’ position, direction, and speed. Vicsek wants his quadcopters to chase down another drone, but so far he’s had little success. “If we just apply the simple rules developed by us and Iain, it doesn’t work,” Vicsek says. “They tend to overshoot their mark, because they do not slow down enough.”
Another group of researchers is trying to pilot a flock of unmanned aerial vehicles using fancy network theories—the same kind of rules that govern relationships on Facebook—to communicate, while governing the flocking behavior of the drones with a modified version of Boids, the computer animation software that helped spark the field in the first place. Yet another team is working on applying flocking behaviors to autonomous cars—one of the fundamental emergent properties of a flock is collision avoidance, and one of the most important things self-driving cars will have to be able to do is not run into people or one another.
So far, the Belugas’ biggest obstacle has been engineering. The robots’ responses to commands are delayed. Small asymmetries in their hulls change the way each one moves. Ultimately, dealing with that messiness might be the key to taking the study of collectives to the next level. Ever since the days of Boids, scientists have made big assumptions about how animals interact. But animals are more than models. They sense the world. They communicate. They make decisions. These are the abilities that Couzin wants to channel. “I started off with these simple units interacting to form complex patterns, and that’s fine, but real animals aren’t that simple,” Couzin says. He picks up a plastic model of a crow from his bookshelf. “Here we have a pretty complex creature. It’s getting to the point where we’ll be able to analyze the behavior of these animals in natural, three-dimensional environments.” Step one might be to put a cheap Microsoft Kinect game system into an aviary, bathing the room in infrared and mapping the space.
Step two would be to take the same measurements in the real world. Every crow in a murder would carry miniature sensors that record its movements, along with the chemicals in its body, the activity in its brain, and the images on its retina. Couzin could marry the behavior of the cells and neurons inside each bird with the movements of the flock. It’s a souped-up version of the locust accelerator—combine real-world models with tech to get an unprecedented look at creatures that have been studied intensively as individuals but ignored as groups. “We could then really understand how these animals gain information from each other, communicate, and make decisions,” Couzin says. He doesn’t know what he’ll find, but that’s the beauty of being part of the swarm: Even if you don’t know where you’re going, you still get there.
Ed Yong (edyong209@gmail.com) writes the blog Not Exactly Rocket Science for National Geographic.
Every day, millions of people check in on Foursquare. We took a year's worth of check-ins in New York City and Tokyo and plotted them on a map. Each dot represents a single check-in, while the straight lines link sequential check-ins.
What you can see here represents the power of check-in data -- on Foursquare, every city around the world pulses with activity around places every hour of every day.
Related: Also see our data visualization of four days worth of Foursquare check-ins in New York CIty during Hurricane Sandy (and the subsequent power outage) during October 2012:
BAD NEWS SELLS. If it bleeds, it leads. No news is good news, and good news is no news.
Those are the classic rules for the evening broadcasts and the morning papers, based partly on data (ratings and circulation) and partly on the gut instincts of producers and editors. Wars, earthquakes, plagues, floods, fires, sick children, murdered spouses — the more suffering and mayhem, the more coverage.
But now that information is being spread and monitored in different ways, researchers are discovering new rules. By scanning people’s brains and tracking their e-mails and online posts, neuroscientists and psychologists have found that good news can spread faster and farther than disasters and sob stories.
“The ‘if it bleeds’ rule works for mass media that just want you to tune in,” says Jonah Berger, a social psychologist at the University of Pennsylvania. “They want your eyeballs and don’t care how you’re feeling. But when you share a story with your friends and peers, you care a lot more how they react. You don’t want them to think of you as a Debbie Downer.”
Researchers analyzing word-of-mouth communication — e-mails, Web posts and reviews, face-to-face conversations — found that it tended to be more positive than negative, but that didn’t necessarily mean people preferred positive news. Was positive news shared more often simply because people experienced more good things than bad things?
To test for that possibility, Dr. Berger looked at how people spread a particular set of news stories: thousands of articles on The New York Times’s Web site. He and Katherine Milkman, a Penn colleague, analyzed the “most e-mailed” list for six months, controlling for factors like how much display an article received in different parts of the home page.
One of his first findings to be reported — which I still consider the most important social-science discovery of the past century — was that articles and columns in the Science section were much more likely to make the list than nonscience articles. He found that science aroused feelings of awe and made Times readers want to share this positive emotion with others.
Readers also tended to share articles that were exciting or funny, or that inspired negative emotions like anger or anxiety, but not articles that left them merely sad. They needed to be aroused one way or the other, and they preferred good news to bad. The more positive an article, the more likely it was to be shared, as Dr. Berger explains in his new book, “Contagious: Why Things Catch On.”
“Stories about newcomers falling in love with New York City,” he writes, were more likely to be e-mailed than “pieces that detailed things like the death of a popular zookeeper.” Debbie Downer is apparently no match for Polly Positive, at least among Times readers.
In another attempt to understand what’s buzzworthy, neuroscientists have scanned the brains of people while they hear about new ideas. Then, as these people told others about what they had heard, the scientists observed which ideas spread and which didn’t.
You might predict that people would pass along the most memorable ideas — the ones that lighted up the brain regions associated with encoding and retrieving memories. But that’s not what happened in the experiments, which were conducted by Emily Falk along with colleagues at the University of Michigan and researchers at the University of California, Los Angeles.
The best predictors of buzz were elsewhere, in the brain regions associated with social cognition — thoughts about other people. If those regions lighted up when something was heard, people were more likely to talk about the idea enthusiastically, and the idea would keep spreading.
“You’d expect people to be most enthusiastic and opinionated and successful in spreading ideas that they themselves are excited about,” says Dr. Falk. “But our research suggests that’s not the whole story. Thinking about what appeals to others may be even more important.”
This social consciousness comes into play when people are sharing information about their favorite subject of all: themselves. This is intrinsically pleasurable and activates the brain regions associated with rewards like food, as demonstrated in a study by Diana Tamir and Jason Mitchell of Harvard. In fact, the study showed, it’s so pleasurable that people will pass up monetary rewards for the chance to talk about themselves.
Past research into everyday conversation showed that a third of it is devoted to oneself, but today that topic has become an obsession thanks to social media. Rutgers researchers classify 80 percent of Twitter users as "meformers" who tweet mainly about themselves.
The result is even more Polly Positivity, and not just because people are so adept at what psychologists call self-presentation: pointing out one’s own wonderfulness. While people have always said nice things about themselves in traditional conversations and saved the nastier comments for others, today they’re more diligent in spreading the word through written media like e-mail, Facebook and Twitter.
“In most oral conversations, we don’t have time to think about exactly the right thing to say,” Dr. Berger explains. “We fill conversational spaces by saying what’s top of mind. But when you write something, you have the time to construct and refine what you say, so it involves more self-presentation.”
Dr. Berger’s experiments have shown that people say more positive things when they’re talking to a bigger audience, rather than just one person — a result that helps explain the relentlessly perfect vacations that keep showing up on Facebook.
But does all this positivity actually make the audience feel any better? Not necessarily. A study in Utah showed that the longer people spend on Facebook, the more they think that life is unfair and that they’re less happy than their “friends.”
Similar results were observed in Germany by a team led by Hanna Krasnova, which recently reported a “rampant nature of envy” and other “invidious emotions” among heavy users of Facebook.
“The spread and ubiquitous presence of envy on social networking sites is shown to undermine users’ life satisfaction,” the German researchers conclude, describing this phenomenon as “the self-promotion-envy spiral.”
That spiral hardly sounds like a positive trend, but there’s probably a quick way to reverse it: turn on the television. Mass-media producers and editors have always known a reliable way to assuage envy. Once they’ve scoured the globe to bring calamity and chaos into the living room, even the most miserably unhappy couch potato knows that there is someone, somewhere, doing worse.
A version of this article appeared in print on March 19, 2013, on page D3 of the New York edition with the headline: Good News Beats Bad on Social Networks.
An incredible map of which countries e-mail each other, and why
Posted by Caitlin Dewey on March 7, 2013 at 7:47 am
A normalized map of e-mail density between countries, where closer proximity indicates more e-mail. The colors correspond to Huntington’s “civilizations.” (Bogdan State et al)
The Internet was supposed to let us bridge continents and cultures like never before. But after analyzing more than 10 million e-mails from Yahoo! mail, a team of computer researchers noticed an interesting phenomenon: E-mails tend to flow much more frequently between countries with certain economic and cultural similarities.
Among the factors that matter are GDP, trade, language, non-Commonwealth colonial relations, and a couple of academic-sounding cultural metrics, like power-distance, individualism, masculinity and uncertainty. (More on those later.)
The findings were released in a paper titled “The Mesh of Civilizations and International Email Flows,” written by researchers at Stanford, Cornell, Yahoo! and Qatar’s Computational Research Institute.
Predictably, countries with measurable real-life ties — like a border, a number of international flights or a serious trade relationship — tend to e-mail more. But there are discrepancies, as well: Countries in the European Economic Area, for instance, e-mail far less than the research model predicted, and countries with colonial ties to the U.K. don’t e-mail any more as a result.
Some of those anomalies could be attributed to cultural differences. The researchers analyzed culture using the “Hofstede measures,” a set of attributes devised during a study of international IBM employees in the 1980s. Countries with similar levels of masculinity (distinct gender roles) and uncertainty avoidance (society-wide intolerance to uncertain situations) e-mailed more, the study found. Oddly, countries with similar levels of individualism e-mailed less.
If you zoom in, you’ll notice the United States, for instance, falls closest to Israel, Switzerland and Italy. China logically falls closer to Japan and Thailand.
To this point, of course, the study amounts to little more than very interesting trivia. The real conclusion comes toward the end, when the researchers posit it as possible evidence for Samuel Huntington’s controversial “Clash of Civilizations” theory. From the paper:
In this respect we cautiously assign a level of validity to Huntington’s contentions, with a few caveats. The first issue was already mentioned – overlap between civilizations and other factors contributing to countries’ level of association. Huntington’s thesis is clearly reflected in the graph presented in Figure 3, but some of these civilizational clusters are found to be explained by other factors in Table 5. The second limitation concerns the fact that we investigated a communication network. There is no necessary “clash” between countries that do not communicate, and Huntington’s thesis was concerned primarily with ethnic conflict.
Indeed, the validity of Huntington’s ideas with respect to ethnic conflict has come into controversy, and we limit ourselves to showing the validity – at least partial – of this division for communication networks.
“Come into controversy” seems like an understatement for Huntington’s thesis, which argued that future global conflicts would be fought along cultural and religious lines between a set of eight civilizations he defined. A Post writer once called it “the most dangerous idea of our time“; elsewhere, scholars like Edward Said and Noam Chomskyhave gone to lengths to shoot it down.
Don’t jump to any conclusions, though — even the authors aren’t willing to assign their findings more significance quite yet.
“We consider these findings interesting puzzles,” the paper says, for which “the advancement of an explanation is premature.”
Social Network Analysis: A Systematic Approach for Investigating By Jennifer A. Johnson, Ph.D., John David Reitzel, Ph.D., Bryan F. Norwood, David M. McCoy, D. Brian Cummings, and Renee R. Tate
Social network analysis (SNA) is often confused with social networking sites, such as Facebook, when in fact, SNA is an analytical tool that can be used to map and measure social relations. Through quantitative metrics and robust visual displays, police can use SNA to discover, analyze, and visualize the social networks of criminal suspects.
SNA, a social science methodology, serves as a valuable tool for law enforcement. While technologically sophisticated, SNA proves easy to employ. Using available data, police departments structure the examination of an offender’s social network in ways not previously possible.
Social network analysis provides a systematic approach for investigating large amounts of data on people and relationships.
Manual examination of social networks tends to be difficult, time consuming, and arbitrary, making it more prone to error. SNA provides a systematic approach for investigating large amounts of data on people and relationships. It improves law enforcement effectiveness and efficiency by using complex information regarding individuals socially related to suspects. This often leads to improved clearance rates for many crimes and development of better crime prevention strategies.
SNA derives its value from human organization and social interaction for criminal and noncriminal purposes. Social networks sometimes promote illegal behavior (e.g., juvenile delinquency and gang-related crime) among related offenders across criminal domains. They can provide a source for illicit drug and pornography distribution and international terrorism.1 The networks may supply an essential first condition for many serious criminal behaviors.
Social networks that enable crime are not mutually exclusive from the networks of law- abiding citizens. They are interspersed within these communities, drawing support from residents and extracting significant costs from host neighborhoods.2 The influence of social networks in producing criminal behavior indicates that effective crime-fighting strategies are contingent upon law enforcement’s ability to identify and respond appropriately to the networks where the behavior is embedded.
Theory and Method
SNA is a theory about how humans organize and a method to examine such organization. The approach indicates that actors are positioned in and influenced by a larger social network. Methodologically, it provides a precise, quantitative tool through which agencies can identify, map, and measure relationship patterns.
Three points of data—two actors and the tie or link between them—comprise the basic unit of analysis. Actors “nodes” are people, organizations, computers, or any other entity that processes or exchanges information or resources. Relationships “ties, connections, or edges” between nodes represent types of exchange, such as drug transactions between a seller and buyer, phone calls between two terrorists, or contacts between victims and offenders. SNA focuses on both positive and negative relationships between sets of individuals.
This analysis produces two forms of output, one visual and the other mathematical. The visual consists of a map or rendering of the network, called a social network diagram, which displays the nodes and relationships between them. In larger networks, key nodes are more difficult to identify; therefore, the analysis turns to the quantitative output of SNA.
The centrality of nodes, such as those representing offenders, identifies the prominence of persons to the overall functioning of the network. It indicates their importance to the criminal system, role, level of activity, control over the flow of information, and relationships. Basic centrality metrics provide further details. “Degree” gauges how many connections a particular node possesses, “betweenness” measures how important it is to the flow, and “closeness” indicates how quickly the node accesses information from the network. Nodes are rank ordered according to their centrality, with those at the top playing the most prominent role. These measures cannot tell an analyst what the structure should be, but they can elaborate on the actual makeup of the network. The value and actionable intelligence of each of these metrics is determined by the information the analyst wants.
Case Study
Social networks that enable crime are not mutually exclusive from the networks of law-abiding citizens.
In January 2008 a collaborative pilot project was launched to explore the viability of incorporating SNA into the precinct-level crime analysis methodologies of the Richmond, Virginia, Police Department (RPD). Participants included representatives of RPD, a university sociologist, and a software designer. The goal was for the research team, comprised of the sociologist and the software designer, to use crime data to assess how constructive SNA would be in solving the most prevalent crimes in the area and to determine the feasibility of training the precinct-level analysts to incorporate it into their workflow.
Researchers needed to determine what initiated violence between two groups of previously friendly young males. Several persons of interest, at one time on good terms, began to argue and assault one another. The source of the violence was not clear, and police were looking for ways to respond. They wanted to know if SNA could help them understand what sparked the violence and which strategies could be developed using a network approach.
The research team received access to RPD’s records management system to obtain information on criminal occurrences, arrests, criminal associates, demographics, and victim/offender relationships. The police provided no other background information on the individuals. The research team did not meet or discuss the ongoing investigation with the detectives. Analysis was done off-site, and the only recurring contact was with the police manager to extract the data in relational form.
Using 24 persons of interest labeled by a gang unit detective as “seeds”—starting points, or initially identified persons—the records management system extracted all connections among the seeds from 2007 through October 2008, proceeding four layers out and including any interconnections among the seed and nodes in each step. The connections were categorized by incident type—common incident participation, victim/offender, gang memberships, field contacts, involved others, common locations, and positive or negative connections.
Positive ties included a cooperative relationship between individuals, such as having family connections, robbing a store together, or hanging out. Negative ties indicated hostile relationships, such as those between a victim and offender. Individuals could have multiple and varying connections. Four networks resulted from the sampling, one for each layer out from the seeds. The networks included the seeds, the relationships among them, people directly connected to them, and those related to their associates. This involved 434 individuals and 1,711 ties. Several weak spots existed where a single node connected regions of the network and indicated dense areas of heavy interconnectivity.
Using SNA software, an analyst quickly produced a visual representation, including names, to assess the structure of the group or reference to whom a person of interest was connected. Through visual analysis and examination of the metric of betweenness, analysts located the source of the disagreement. The metric pointed to critical junctures in the network that revealed interpersonal tensions among males revolving around their relationships with females.
Two powerful male gang members reportedly had a positive relationship in October of 2007; however, in April 2008, one victimized a female friend of the other. During the same incident, this male also victimized the female friend of another male. Throughout the episode, a pattern emerged involving situations where a dominant male engaged with a female associate of another strong male. In other words, boys were fighting over girls.
Quantitative metrics provided additional information identifying the powerful players in this network. By rank ordering the individuals according to their centrality measures, the analysis confirmed that the gang unit was watching the right people and using community resources effectively. The metrics also helped unit members further analyze the importance of the seed nodes. Many of the nodes targeted by the unit ranked as powerful in the network based on an SNA metric. The quantitative metrics indicated six other vital players, including one critical to the flow of the network.
Results
Using social network analysis (SNA) software, an analyst can produce a visual representation to assess the structure of a group or reference to whom a person of interest is connected.
Unanticipated administrative processes delayed the timeline of the pilot project, making these results and recommendations too late to be actionable. The police already had solved the conflict. The knowledge of the detectives, which the research team was not privy to, validated the results. Officers confirmed that the answer the research team had discerned from the data—boys fighting over girls—was the cause of the conflict.
Detectives acknowledged that they would have solved the case more quickly and easily if they had this analysis available to guide their strategies. This feedback validated the worth of the approach and the usefulness of SNA and moved the project into the next phase. Precinct-level crime analysts received training in SNA through a 36-hour, in-house seminar. Through lectures and hands-on training, crime analysts from police and federal agencies used data from their own projects to learn to incorporate SNA to meet their needs. Within 2 weeks of completing the course, the analysts used SNA in several cases, including an aggravated assault/ shooting and several convenience store robberies.
In the shooting incident, the analyst used SNA to provide data on an associate of the suspect who previously was not noticed by the detective working the case. The analyst provided that information to the detective who used it to locate and interview that individual, which put additional pressure on the suspect, who was attempting to elude capture. This, combined with other social and financial pressures, caused the suspect to surrender.
Another case involved a string of convenience store robberies. Using an SNA map of a separate case, an analyst noticed a connection between a person of interest in the robberies in one precinct and a member of the network under investigation. Using the two names as seeds, the analyst extracted another previously unknown network. The analyst and a colleague identified one of the seed names as a person of interest in robberies involving multiple juveniles. Through cooperation and an SNA social diagram, they pieced together robberies not previously thought to be connected and identified a suspect involved in other robberies. The chart provided a source where they quickly, easily, and effectively could share observations with investigative personnel.
Social Network Diagrams
Social network diagrams have become a method for RPD to use social relationships among offenders and their associates. Renowned for its technological innovation and policing strategies, RPD has found SNA effective in facilitating better communication between crime analysts and investigators. SNA enabled the department to significantly increase crime clearance rates and reduce violence.
Prior to the SNA training, analysts conceptualized a series of “star” networks with an “ego” at the center and immediate connections radiating out. To understand the network, analysts identified the immediate connections of a person of interest. To identify one of the people connected to the original person of interest, a second ego network was constructed. In the end, the analyst faced a series of networks, leaving out the interconnections between them. Through training, analysts began to interpret the effectiveness of the larger network environment using diagrams as social maps to orient themselves and officers.
In the shooting case, the detective used the network analysis to apply pressure to the suspect by interviewing an associate whose relationship with the offender previously was unknown. Mounting social and financial pressures, ultimately, led the individual to surrender. In the convenience store robberies, an SNA diagram provided vital clues that allowed multiple analysts to share information and identify previously unknown connections between individuals, which led to a possible suspect. If SNA had been available to analysts in other jurisdictions, a connection may have been discovered earlier.
Analysis
The cases described illustrate the success of SNA in developing law enforcement strategies and interdiction techniques. The pilot project demonstrated how SNA can help answer sophisticated questions regarding motivations for a crime—an area previously underdeveloped in crime analysis processes.3 The research team was asked to determine why violence occurred among groups who previously were amicable. Using visual analysis and without any subject matter knowledge, investigators used SNA to reveal behavioral motivation rooted in complex interpersonal relationships. The project provided confirmation of the effectiveness of the current resource allocation of the gang unit and indicated new avenues of policing, which have the potential to produce a high return on investment.
These two cases produced actionable results, illustrating how SNA can facilitate a productive working relationship between crime analysts and detectives. The academic research on policing indicated that one of the biggest hurdles in establishing effective communication is finding a common language between the analytics of numbers and the immediate pressures of reality.4 Each case described illustrates how SNA and social network diagrams function as a common ground. Analysts used the charts visually to depict their analysis, which resonated with detectives because it reflected their reality. The analysts provided something new to the detectives, thus, aiding each investigation.
The visual and quantitative output of SNA helps solve institutional memory issues associated with analysts’ longevity and attrition, as well as new hires. By producing a current overview, SNA allows new analysts to grasp the present status of the network. It assists experienced analysts in maintaining an understanding of the network by chronicling growth and development as members and connections appear and disappear.
Law enforcement agencies, such as RPD, benefit from having access to structured, relational, and temporal data. Analysts reliably map changes in the network using an automated extraction process. Through this dynamic procedure, experienced analysts appear less likely to develop data analysis blind spots.
Conclusion
Law enforcement agencies have come a long way from pinpoint mapping. The technological advancements in recent years can provide personnel more confidence to handle complex crime problems confronting departments around the country. Social network analysis demonstrated its utility and effectiveness as a means of solving crimes or determining persons of interest and bridging the gap between crime analysts and police officers in the field. With the support of robust technology, SNA becomes reliable across time, data, analysts, and networks and quickly produces actionable results inside any operational law enforcement environment.
ENDTEXT
The authors commend and recognize the Richmond, Virginia, Police Department’s Crime Analysis Unit for its critical role and ongoing cooperation in the research and writing of this article.
Dr. Johnson is an associate professor of sociology at Virginia Commonwealth University in Richmond.
Dr. Reitzel is an assistant professor of criminal justice at Virginia Commonwealth University in Richmond.
Mr. Norwood retired as chief of the Richmond, Virginia, Police Department.
Chief McCoy serves as the associate vice president of public safety and chief of police with the University of Richmond Police Department, Richmond, Virginia.
Mr. Cummings manages the planning division of the Richmond, Virginia, Police Department.
Ms. Tate is the crime analysis supervisor for the Richmond, Virginia, Police Department.
Endnotes
1 E. Patacchini and Y. Zenou, “The Strength of Weak Ties in Crime,” European Economic Review 52, no. 2 (2008); D.L. Haynie, “Delinquent Peers Revisited: Does Network Structure Matter?” American Journal of Sociology 106, no.4 (2001): 1013-1057; K. Murji, “Markets, Hierarchies, and Networks: Race/Ethnicity and Drug Distribution,” Journal of Drug Issues 37, no. 4(2007): 781-804; V. Krebs, “Mapping Networks of Terrorist Cells,” Connections 24, no. 3 (2004): 43-52; and J.A. Johnson, “To Catch a Curious Clicker: A Social Network Analysis of the Online Commercial Pornography Network” in Everyday Pornographies, Karen Boyle, ed. (Routledge Press, 2012).
2 C. Kadushin, “Who Benefits from Network Analysis: Ethics of Social Network Research” Social Networks 27, no. 2 (2005): 139-153.
3 T.C. O’Shea and K. Nicholls, “Police Crime Analysis: A Survey of U.S. Police Departments with 100 or More Sworn Personnel,” Police Practice and Research 4, no. 3 (2003): 233-250.
4 N. Cope, “Intelligence Led Policing or Policing Led Intelligence?” British Journal of Criminology 44 (2004): 188-203; and S. Belledin and K. Paletta, “Finding Out What You Don’t Know: Tips on Using Crime Analysis,” The Police Chief 75, no. 9 (2008).
[This is a guest post by Lisa Rhody, who works for the Roy Rosenzweig Center for History and New Media at George Mason University as the project manager forWebWise 2013.
