Tag: data-driven solution

Welcome to the Future of Urban Living

Posted on by robbie

What might a city look like in the future if a tech company had a say in it? How can cities harness all the data at their disposal — on things like traffic, crime, health and income — and use it to eliminate the most common woes of urban living? At its core, the goal of a smart city is to improve the quality of life for its residents, by providing them good jobs, a clean environment and safe, sustainable infrastructure.
But as cities race to implement technology that can respond to the needs of its citizens, concerns over things like privacy, ownership and the energy needed to power millions of data-collecting sensors have increased right along with it.

Everything Is Connected

Though the term “smart city” is relatively new, the concept of cities using data to inform policies isn’t. More than a decade ago, for example, Seattle passed an ordinance that instructed its department of transportation to conduct a data analysis of city streets, taking into account traffic patterns, speed limits and collision history with the goal of encouraging residents to walk, bike and ride public transit more often.
More recently in 2015, the U.S. Department of Transportation launched the Smart City Challenge. A total of 78 mid-size cities responded, presenting ideas for revolutionizing their highways, roads and public-transit systems through the use of data, applications and other technology. (The winner: Columbus, Ohio.)
Besides easing the burdens of city life for residents, technology plays a major role in keeping them safe, too, especially as America’s infrastructure rating continues its steady decline across the board.
In September 2016, a team of engineers from Michigan State University and Washington University in St. Louis put self-monitoring stress sensors on the Mackinac Bridge — one of the longest suspension bridges in the U.S. — to log information on wear and tear, and send alerts when maintenance or repairs are needed.
“These sensors are going to continuously monitor the health of the structure, and if something goes wrong, then it’s going to report that to the cloud,” said Shantanu Chakrabartty, one of the sensors’ developers. “If something happens, you can go back and see that a certain part of the structure experienced abnormal levels of strain, and then according to that, you can schedule your emergency response and your maintenance.”

Michigan’s Mackinac Bridge features self-monitoring sensors that measure wear and tear and increase safety.

A New Model Emerges

Half the fun of envisioning a smart city isn’t just the nifty gadgets that make roads more stable, water cleaner or traffic lighter. What’s most exciting for many engineers and developers is the idea of making a city responsive to the people who live there.
An example is New York’s LinkNYC program, which is replacing thousands of pay phones around the city with kiosks that provide free Wi-Fi, phone calls, device-charging stations and touchscreen tablets that connect residents and tourists to city services and maps.
A more futuristic example is Sidewalk Toronto — a partnership between Sidewalk Labs, an Alphabet company, and Waterfront Toronto, a local organization charged with the revitalization of the city’s waterfront. The goal: Design an entirely new neighborhood on the city’s east side that will include sustainably built homes, roads designed for self-driving cars and green spaces that can adapt to how people act within them. (“Nobody’s using that bench? Let’s try moving it to a sunnier area then.”) If a tech company took over urban planning, this is what it might look like.

Private-Public Partnerships Put to the Test

But despite the competing interests of the private sector, which looks for ways to monetize its efforts, and the public sector, whose goal is to provide free services, there is the sense that tech companies will eventually be viewed as trustworthy gatekeepers of data — so long as they provide benefits to the city.
“This is a change in outlook,” Roman Serdar Mendle, smart cities program manager at the International Council for Local Environmental Initiatives, said a brief. “In the past, those concerned with sustainable cities saw the private sector as bad, and governments and NGOs as the ones that were fighting the good fight. Now companies are seen as the solutions providers.”
And then there is the confusion about who actually owns the data being harvested: the outside companies hired to collect it through sensors and other means, or the local officials who rely on it to make their cities smarter.
“We’re in the learning business, that is wholly true,” says Tracey Cook, executive director for  Municipal Licensing & Standards for the City of Toronto. By regulating Uber, for example, the city was able to gather data on every single trip that occured, down to where people were getting picked up and dropped off, “within inches.”
That information could inform the city’s future endeavors with Sidewalk Toronto, for example.
As cities become more reliant on the private sector to fill gaps that government can’t, the way forward, it seems, is two-fold: Cities will continue to use the data it collects on its citizens with the goal of improving their lives, and then partner with for-profit companies to ease concerns on privacy.

A New York City resident uses a LinkNYC kiosk to access free Wi-Fi, calling and other digital services.

The Future Is (Almost) Imminent

At first blush, smart cities sound like the ultimate solution for bridging the opportunity divide by giving people in all neighborhoods equal access to technology, creating a sort of digital utopia. But a backlash is brewing, not only over reasons of privacy but also over resources. The more a city relies on the Internet of Things — an interconnected network of devices that communicate with each other — the more energy is needed to power said “things.”
Currently, 7 percent of the world’s internet is used by the information technology sector, with that percentage expected to triple within the next two years. Annual Bitcoin transactions, for example, consume as much energy as the entire country of Iraq, according to the Bitcoin Energy Index.
As more and more people flock to urban areas, a partnership between tech companies and the services people use every day in a city — be it public buses or green spaces — is warranted. But given the privacy and environmental concerns that have yet to be addressed, the full-on smart city still has its obstacles.

Can Big Data Prevent Unnecessary Police Shootings?

Posted on by robbie

In September 2016, Keith Lamont Scott sat in a parked SUV outside an apartment complex in Charlotte, N.C. As he rolled a joint with a handgun at his side, police officers arrived to serve someone else a warrant. What happened next — a confused and unplanned altercation with the police…multiple warnings to drop his gun…the screams of Scott’s wife who filmed it all…and shots that killed him — is the kind of policing incident data scientists are now trying stop with so-called early intervention systems.  
Their aim: to identify which officers might be at risk of unnecessarily pulling the trigger in a high-adrenaline situation as a result of prior events they might have experienced.
“We don’t want officers to feel like they’re being tagged because they’ve been bad,” says Crystal Cody, technology solutions manager for Charlotte-Mecklenburg Police Department. “It really is an early intervention system.”
To be clear, early intervention systems are not new. For years, police chiefs have paged through documentation of officers’ personal and professional histories to help identify cops who might need to be pulled off their beat and brought back to headquarters. Charlotte, where mass demonstrations raged in the city’s central business district after Lamont’s death, will be among the first cities to use a version that utilizes machine learning to look for patterns in officer behavior. If its approach of data collection proves to be successful, other police departments will be able to feed their stats into the model and procure predictions for their city.
Responding to a number of stressful calls is highly correlated with leading to an adverse event, says University of Chicago data scientist Joe Walsh. He points to the widely seen video of the North Texas cop tackling a black girl at a pool party as an example. Earlier that shift, the officer had responded to two suicide calls.
When used as intended, experts say these intervention systems should reduce instances such as this. Police departments are advised to have them, but they aren’t required by law. Historically burdened by poor design and false positives, agencies nationwide have largely discredited theirs and let them languish. According to a Washington Post story last year, Newark, N.J., supervisors gave up on their system after just one year. In Harvey, a Chicago suburb, management tracked only minor offenses (like grooming violations) without notching the number of lawsuits alleging misconduct. And in New Orleans, cops ridiculed the ineffective system, considering it a “badge of honor” to be flagged.
“I think a lot of [police departments] give lip service to it because it’s important to have one, but they don’t really use it,” says criminologist Geoffrey Alpert.
In Charlotte, where the force is reputed to be technologically savvy, the internal affairs division built an early intervention system around 2004. It flagged potentially problematic cops by noting the number of use-of-force incidents, citizen complaints and sick days in a row. Analyzing those data points, 45 percent of the force was marked for review. “It was clear that [the warning system] over-flagged people,” Cody says.
At the same time, the simplistic method failed to identify the cop working a day shift with three use-of-force incidents as more at risk than an officer with the same record walking the streets of a tough neighborhood at night.
Charlotte is now giving the system a second try via a partnership with young data scientists affiliated with the University of Chicago’s Center for Data Science and Public Policy. The new version assigns each officer a score that’s generated by analyzing their performance on the beat — data that most police departments are reticent to hand over to researchers.

Data scientists from the University of Chicago’s Center for Data Science and Public Policy are using machine learning to predict which police officers are at-risk of unnecessarily pulling the trigger.

After crunching the numbers (more than 20 million records, to be exact), the officers that are more likely to fire their weapon are, not surprisingly, those who have breached department protocol or recently faced particularly intense situations on their beats, says Walsh, the data science team’s technical mentor. So far, this 2.0 version has improved the identification of at-risk cops by 15 percent and has reduced incorrect misclassification by half.
It’s important to note that the databases are not meant to be used as rap sheet of an officer’s performance — nor are they to be used as a disciplinary tool. Conceptually, if the system is effective, it will flag a potential crisis before it occurs and help keep officers safe. NationSwell reached out to the Fraternal Order of Police and the Police Benevolent Association in North Carolina, but neither responded to requests for comment.
“We look at the results in context of the history of that officer, where they work and what behaviors they’ve had in the past before we say, yes, this looks like a valid alert. We’re still giving humans the ability to look at it, instead of giving all the power to the computer,” says Cody.
Charlotte residents, for their part, expressed optimism about the system. “We think it’s important to have some type of outside audit,” says Robert Dawkins, state organizer for the SAFE Coalition NC, a group focused on police accountability.
The department isn’t promising the system will be a perfect solution, and it’s well aware it has plenty of jaded officers it needs to persuade. But as the system continues to gather new data — finding out which cops it overlooked or overreacted to — the model’s accuracy should improve, Walsh says. With man and machine taking a more rigorous look at the data, both law enforcement and citizen will be better protected.
MORE: 5 Ways to Strengthen Ties Between Cops and Citizens

Building a Better City Through Big Data

Posted on by robbie

In the nation’s capital, 28 percent of children live in a household that’s below the federal poverty line, and another 20 percent grow up barely above it. As executive director of DC Action for Children, NationSwell Council member HyeSook Chung studied exactly where this deprivation could be found and, more importantly, why. “What are we doing that’s not working, and why are we investing in it?” she asks repeatedly. Unlike the ideological think tanks that populate D.C.’s corridors, she’s a relentless empiricist who searches for answers in data. At DC Action, she partnered with DataKind and joined the Annie E. Casey Foundation’s Kids Count community to publicly post a number of visuals about the city online, graphically comparing, say, youth unemployment, Medicaid enrollment or the number of parks in every D.C. neighborhood. Last month, Chung accepted a new role as D.C.’s deputy mayor for health and human services. As she makes the transition, NationSwell caught up with her to discuss the data-driven accomplishments at her last job and reflect on what her new role means for the city.
How does better data guide decision-making in Washington, D.C.?
At DC Action, we were the first ones to really look at the neighborhood level. Looking at wards — the equivalent of a county level — was too broad. As a parent, I live in D.C. and my kids go to DCPS, and I wanted to know why parents in certain areas were able to move the needle, despite the lack of support from the city’s administrative offices. With neighborhood data, we could question why a cluster of a few elementary schools were doing better than all the others in that ward. It could be race or income, but I wanted to know exactly why.
That led to visual analysis and asset-mapping that we can show a council member. “Look at grocery stores and the lack of fresh produce in Wards 7 and 8. Look at the poverty in Wards 1, 4 and 5 that’s starting to kick up.” We were able to have a different conversation with city leaders. Some of the big fights in the city are about state representation and all the things happening on the Hill, so I don’t think they were ready for an organization to show up with data on the neighborhood level. Because then, the solutions are really localized solutions, not these macro, citywide policies. That’s a different way of thinking: One solution is not going to meet the needs of all 108,000 kids under 18.
There’s been a lot of debate about how data can be misused. How do you avoid trusting misleading figures or building biased algorithms?
Data is not so black and white, especially in human resources. People dealing with people is very subjective. How can you have an automated evaluation for hiring or firing? In public education, there’s this drive for outcomes in test scores that need to be improved if the teacher is to be effective. I heard from one teacher who scored 6 percent [in his evaluations] one year, then 97 percent the next. The educator said that nothing changed; the calculations were just different those two times. Their salaries, pensions, even their jobs are determined by these equations some person is putting together. That is one thing about open data about which we have to be conscientious.
As the repository for Kids Count at DC Action, we focused on making sure we had the most up-to-date, reliable, unbiased data out there, but we also kept track of how that data is used. We all have biases that data can further or can debunk. We took our role very seriously to be as unbiased as we could, to give as much context as we could, then let the data speak for itself.
How can service providers change their operations to keep better track of their data?
I was training a few of the intake coordinators at one community-based organization, and I walked them through why everything they do is so important to track. I referenced Amazon: As a user, every movement, every click is tracked to give me popups based on what I might like. For nonprofits, the only difference is you meet families and children every day, and you have all these interactions and conversations. But none of that is being recorded or tracked. One of the pitfalls of social finance data is that we’re very great about tracking quantities and caseloads, like how many families you served or how many kids graduated, but we’re not so good about tracking progress or the quality of services. That’s been something I’ve been pushing recently: It shouldn’t be about how many preschool slots we have, because we have to narrow down how many of those are quality. They’re not all equal. We’re trying to set a new bar. Caseloads are not enough information to show progress.

HyeSook Chung speaks in 2015 on the Books From Birth Bill, which provides a free book to D.C. children each month from birth to age 5.

DC Action, in making public data widely available, is really just scratching the surface on the reams of information agencies could collect. What does the future look like if the public sector fully embraces this tool?
Can you imagine what the impact would be on the social-service sector if we had real-time data? It’s profound: Netflix and Amazon are able to adjust, in a matter of seconds, based on consumer knowledge. At nonprofits, we have a long way to go to embrace that and redefine accountability. Of course, it’s not truly transferrable from the private sector, but our decisions about service delivery could be much more engaged and responsive to live information from a family. We have to be careful; we don’t want to profile. But how do we translate, with these ethical and business questions in mind, those insights to the social sector to be more effective for families? That’s my interest. I want to get to a place where we can say, “Because of this investment here, we had this result.” It’s not about money; it’s about how we use the resources we have. If a program is not improving outcomes, have the courage and the data to adapt it. We’re not quick enough, and that’s frustrating to me. I just don’t know why we are in this rut of not giving our kids what they deserve.
How do you define leadership?
Two words come to mind: integrity and resiliency. Being an executive director is really hard work. I’ve made decisions, I’ve dealt with funding changes, I’ve let go of friends and fired people. At the end of the day, if my integrity is intact, I can go to bed, knowing I did the best I could. There were plenty of times I cried a lot and had to make hard decisions. But the work continues, because the bottom line is kids need us. The mission keeps us moving.
Why did you decided to take a new job in city administration?
At DC Action, we were called upon by the mayor’s executive offices to help make data-informed decisions. In many ways, we were partners in an advisory capacity helping departments achieve results and made decisions based on outcomes, not simply compliance. After meeting with Mayor Muriel Bowser, I knew [this job] was another wonderful opportunity to push our starting principles to a much larger scale. The mayor invited me into the administration to help highlight the critical importance of data-driven work for some of the toughest challenges we have before us as a city: homelessness and reform of the Temporary Assistance for Needy Families benefits.  As a public servant, I am thrilled to be asked to think more strategically and systematically about how we can truly make a difference in the lives of our residents in need.
To learn more about the NationSwell Council, click here.

Can This Data-Driven Organization Help Those Most Desperate Escape Life on the Streets?

Posted on by robbie

Rosanne Haggerty grew up going to church in downtown Hartford, Conn. Her parents, both schoolteachers, never outright explained why they took their kids to church in a poor neighborhood full of single-room occupancy hotels and boarding houses. Haggerty, however, learned the lesson her folks were trying to instill. “My parents were both very devout Catholics in the social justice wing of the church,” Haggerty says, describing how the family visited fellow church members when they were sick and invited them over for holiday meals. Haggerty grew up with a sense that “we all can be doing more to provide that kind of support system for others.”
Today, Haggerty is a social change agent in her community, serving as the president of Community Solutions, a national organization that aims to end homelessness. Taking an entrepreneurial approach to address the problem, Community Solutions uses technology to capture data and tailor interventions to meet the needs of a region in the most effective way possible. At its heart, Community Solutions’s mission is the same as Haggerty’s parents’: helping people, one person at a time.
Community Solutions works in neighborhoods around the country to provide practical, data-driven solutions to the complicated problems involved in homelessness. The organization has already achieved great success: its 100,000 Homes campaign, which ran from 2010 to 2014, helped 186 participating communities house more than 105,000 homeless Americans across the country.” (Chronically homeless individuals make up 15 percent of the total homeless population, yet they utilize the majority of social services devoted towards helping them, including drop-in shelters.) To do this, it challenged the traditional approach of ending homelessness: requiring those living on the streets to demonstrate sobriety, steady income or mental health treatment, for example. Instead, it housed people first, an approach that has demonstrated overwhelming success: research finds that more than 85 percent of chronically homeless people housed through “Housing First” programs are still in homes two years later and unlikely to become homeless again.
“Technology played a critical role in the success of the 100,000 Homes campaign because it enabled multiple agencies to share and use the same data,” says Erin Connor, portfolio manager with the Cisco Foundation, which has supported Community Solutions’ technology-based initiatives. “By rigorously tracking, reporting and making decisions based on shared data, participating communities could track and monitor their progress against targets and contribute to achieving the collective goal.” As a result of this campaign, the estimated taxpayer savings was an astonishing $1.3 billion. Building on this achievement, its current Zero 2016 campaign works in 75 communities to sustainably end chronic and veteran homelessness altogether.
Technology and data gathering is critical for local and nationwide campaigns since homelessness is intimately connected to other social problems, like unemployment and poverty. One example of the local impact Community Solutions has had is in Brownsville (a neighborhood in Brooklyn, N.Y., that’s dominated by multiple public housing projects) via the Brownsville Partnership, which is demonstrating that these problems can be solved — to create “the endgame of homelessness,” as Haggerty puts it.
In Brownsville, the official unemployment rate is 16 percent, “about double that of Brooklyn” as a whole, Haggerty says, noting that the statistic excludes those not currently looking for work. In response, the organization works with existing job training programs, digging into their data and analyzing it to improve effectiveness and achieve success.
“Data is at the heart of everything we do, as far as understanding where to focus our efforts and how to improve our collective performance,” Haggerty explains. Analyzing usage data, Community Solutions works with health care providers, nonprofits, and city and state governments to figure out where the most vulnerable populations live, what systems they interact with and what help they need.
Because of this emphasis on data, Community Solutions increasingly thinks of itself as a tech company, Haggerty says. Since 2010, it’s partnered with Cisco to help bring practical, data-driven solutions to communities around the country, opening doors to innovation and progress. When the collaboration began, Community Solutions was a local New York City-based organization. Today, it works with communities throughout the United States. By looking at the problem more nationally and taking an entrepreneurial approach when it comes to applying technology, Community Solutions is now solving homelessness on a much larger scale and having greater impact — producing real social change.
One person benefitting from this tech-driven approach is Toni Diaz. In and out of homeless shelters since the age of 17, Diaz had three children and a fourth on the way by the time she was 23 years old. Escaping from an abusive partner, Diaz took her kids to a homeless shelter. “I didn’t have anywhere to go,” she says. Right when Diaz realized that she needed to make a change in her life, opportunity arrived in the form of a caseworker from the Brownsville Partnership.
Diaz’s journey out of homelessness took years, but Brownsville Partnership walked with her every step of the way. Today, she’s part of an innovative solution that helps people like her connect to the services and training programs that will help them break that same cycle. Stories like Diaz’s are one of the things Haggerty loves most about her work. “It’s especially satisfying when people we initially encountered in a time of crisis end up in a position where they are paying it forward,” she says. Diaz, Haggerty says, shows “what kind of resilience exists in people in this neighborhood” and communities like Brownsville around the country.
This was produced in partnership with Cisco, which believes everyone has the potential to become a global problem solver – to innovate as a technologist, think as an entrepreneur, and act as a social change agent.
Editors’ note: The original version of this story misspelled Rosanne Haggerty’s name. It also erroneously stated that Community Solutions’s 100,000 Homes campaign housed more than 105,000 chronically homeless people in 186 communities across the country. NationSwell apologizes for these errors.