How Our Chapters Leaders Enable Powerful Collaborations


Guest blog: Heidi Hernandez Gatty, DataKind’s Senior Network Strategist

I’ve been here at DataKind for a little over a year now. But I’m not a data scientist. My background is in nonprofit infrastructure and networks of practice. For a little over 15 years, I’ve been looking at how nonprofits structure themselves from the inside out to maximize the good work they do in the world. It’s been a journey of helping nonprofit social entrepreneurs and infrastructure providers themselves learn from each other to increase the efficiencies and effectiveness of the nonprofits they serve.

At its heart, DataKind’s work depends on collaboration. For one of our projects to be successful, it takes input from experts of all walks – nonprofit leaders, subject matter experts, data scientists, coders, designers and more all come together on projects that apply data science to some of the toughest challenges out there. But these collaborations don’t just happen – they need space to take shape and encouragement to stick together.

From San Francisco to Singapore, our global network of Chapters exists to do just this. Primarily volunteer-led, our Chapters provide the space to mix and mingle and the great excuse for folks that don’t typically get to cross paths to come together, rub elbows and generate entirely new solutions together. Each DataKind Chapter has a small team of Chapter Leaders who are responsible for keeping the DataKind vision alive in their communities. They are supported by Core Volunteers who focus on finding new partners, bringing in new volunteers, and planning events. They in turn recruit and engage thousands of data scientists and other technology enthusiasts who come to those events, meet people, share ideas, and work together to make the world a better place. One data science project at a time.

I get the pleasure and honor of helping these collaborations grow and thrive across our Chapter Network. We sometimes call this role a “Network Weaver” as I have my eyes across the tapestry of DataKind’s work, pulling the right thread at the right time to make the pattern richer, more beautiful. While our volunteers already speak the same language of data science, they also have to learn the language of the nonprofit and charity sector, as well as the issue area they’re focused on to do their work effectively. In turn, our project partners – be they foundations, nonprofits, or social enterprises – become familiar with new lingo from Python to Pandas to p-values.

Deep collaboration like this takes time, trust, and engaged listening on all sides. Our Chapter Leaders enable this wonderful chemistry to take place by leading by example, representing some of the best servant leaders you could ever imagine. They exhibit compassion and caring – from helping two strangers strike up a conversation at a networking event, to demystifying the latest buzz words, to troubleshooting when a project has an obstacle to overcome. They are experts at bringing ideas to fruition and in following through to help seed the next stop on the journey.

What has been immensely exciting to see over the past year is the potential we have as a network to share learnings from our projects across the world. We can see patterns across cultures, over issue areas, and in the work itself. We have the opportunity to make work in homelessness in the Bay Area of California relevant and useful to homelessness in the UK. Our volunteer Chapter Leaders choose to spend their free time in communication with each other to build bridges and connections that would have been unthinkable 20 years ago.

At DataKind, we believe that data science, thoughtfully applied to humanity’s toughest issues, can make a real difference in the world. We’re so in awe of our Chapter Leaders that tirelessly dedicate their time to building relationships and bringing together talented, humble, awesome data science volunteers and social changemakers.

We’re Hiring!

Want to help even more of this data-driven collaboration goodness happen worldwide? We’re hiring a Community Engagement Manager to join our Network team and help inspire even more data science volunteers to give back. Apply >



Source: DataKind – How Our Chapters Leaders Enable Powerful Collaborations

The Power of Data and Collaboration to Improve Traffic Safety


Visualization of estimated “exposure” or traffic volume by street in Seattle.

According to the National Safety Council, traffic collisions cause more than 40,000 deaths and injure thousands of people every year across the United States. These are not traffic accidents, but entirely preventable tragedies.

Since cities in Sweden started the Vision Zero movement in the 1990s, many U.S. cities are now joining the effort as part of the Vision Zero Network, pledging to reduce traffic fatalities and injuries to zero in their communities.

With limited budgets and resources, these local city officials face a daunting question: what will it take to reach zero? Given the sheer number of factors that contribute to traffic collisions and the many potential interventions that might address them, where should a city focus its efforts? 

This is where a little bit of math, a few cross-sector friendships and a healthy dose of data can be a game changer. We recently completed our first Labs project, in partnership with Microsoft and its Tech & Civic Engagement Group, after over a year of work and close collaboration with the cities of New York, Seattle and New Orleans. This was the first and largest multi-city, data-driven collaboration of its kind to support Vision Zero efforts within the U.S.

Leveraging newly-available datasets including open data, internal city data and data from private companies, our Labs team – Erin Akred, Michael Dowd, Jackie Weiser and Sina Kashuk – as well as dozens of DataKind volunteers have built models to help cities identify where there is greater risk of traffic collisions, built tools to empower city officials to test what safety interventions will be most effective on what streets, and even helped cities estimate total vehicle traffic volumes citywide when the data didn’t exist. All these insights, tools and methodologies enable city officials to better allocate resources, select the best safety interventions and focus their efforts to keep all road users safe. Check out our case study  for more detail.

How Collaboration Made It All Possible

While we think the world of our Labs team, we also know they depend on a world of collaborators to get a job like this done. Applying data science for good requires that we bring together not only relevant data sets, but also relevant decision makers, technical and issue area experts, funders and advocates that can inform and help co-design solutions that will have an impact.

We like to think of it as an ecosystem. Tackling the complicated question of reducing traffic fatalities in three different cities requires more than just data and data scientists. You need a strong project focus and strong project partners. You need funding to fuel your journey and subject matter experts to guide your path. DataKind is the convener that connects the dots, bringing all these usually far-flung resources and people together.

Not only was Microsoft the funder that made our first ever Labs project possible, we also turned to them as subject matter experts in civic tech and as thought partners in organizing such a long-term, wide-reaching initiative. For more, check out this blog from Elizabeth Grossman, Director of Civic Projects for Microsoft’s Technology and Civic Engagement group.

We couldn’t have asked for stronger project partners than the amazing folks we worked with in New York, Seattle and New Orleans. Taking on a project like this shows not only how committed they are to making streets safer, but how forward-thinking they are in their approach. They are pioneering some of the most cutting-edge techniques available and we hope to inspire other cities to do the same. Special thanks to the many hours and wisdom each city contributed – we are so proud to have worked with each of you.

And a special thanks to all those that have supported and contributed to this initiative including the Vision Zero Network and the University of Washington for hosting our Vision Zero DataDive. 

More Resources Coming Soon

For more on our work in each city, read our case study and sign up to receive updates on several related resources coming in the next few weeks:

  • For those who like to get geeky, watch out for a technical report detailing some of the models and approaches from this project that may be applicable for your city.
  • For a look under the hood at the good, bad and the fascinating about what it takes to bring folks and data of all walks together for a collaboration of this scale, we’ll be publishing a blueprint with our favorite pro tips and pitfalls.
  • For those always asking “but how do we make it scalable?” – we knew there was a reason we liked you. This question also keeps us up at night so we’ll be sharing some research we’re doing with the Alfred P. Sloan Foundation on how other groups we greatly admire approach this.



Source: DataKind – The Power of Data and Collaboration to Improve Traffic Safety

Protecting Democratic Freedoms With Omidyar Network


In light of recent rhetoric and policy in the U.S. targeting immigrants, refugees, people of color and other vulnerable groups, we’re doing a call for proposals with Omidyar Network to bolster the efforts of organizations protecting these communities.

From helping organizations use data to better understand the impact of their programs, cut costs, better target resources or anticipate needs from their community, we can help with a variety of needs leveraging cutting edge technology and approaches.

If your organization is working to champion democratic freedoms and civil liberties in the U.S., we’d love to hear from you.

Learn more and apply by April 30th >

We’ll match selected organizations with a team of data scientists to work together on a long-term project starting in June.

Reach out to magdalen@datakind.org with any questions.



Source: DataKind – Protecting Democratic Freedoms With Omidyar Network

Celebrating Women’s Day: 33 Women in Data Science from around the World & AV Community


Introduction She Believed, she could. So, she did This Women’s Day we are celebrating the women power. We are celebrating all those women who …

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Introduction to Gradient Descent Algorithm (along with variants) in Machine Learning


Introduction Optimization is always the ultimate goal whether you are dealing with a real life problem or building a software product. I, as a …

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How to read most commonly used file formats in Data Science (using Python)?


Introduction If you have been part of data industry, you would know the challenge of working with different data types. Different formats, different compression, …

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#GivingTuesday DataDive Capacity


Thank you for your interest in joining us at the #GivingTuesday DataDive March 3-5 in partnership with 92Y and the Bill and Melinda Gates Foundation! Together, we’ll be using data to unravel tough questions and prototype new solutions to support social change through increased philanthropic giving. Because we may have a full house this weekend, please continue to check this blog for the latest updates on event capacity!

We’ll update the text below and the image above to let you know if we’re full or if we still have room for more DataDivers to attend.

 

RIGHT NOW WE ARE….

 

ANXIOUSLY AWAITING FRIDAY MARCH 3RD!

Doors open 6:00pm!

 

What’s this #GivingTuesday DataDive all about?

#GivingTuesday is a movement to celebrate giving of all kinds. Founded by 92Y in 2012 and celebrated on the Tuesday after Thanksgiving, #GivingTuesday inspires people around the world to take collaborative action to improve their local communities and contribute in countless ways to the causes they believe in. On #GivingTuesday 2016, individuals, corporations and civic coalitions raised over $170 million to benefit a tremendously broad range of causes, and gave much more in volunteer hours, nonmonetary donations, and acts of kindness.

While #GivingTuesday’s reach has grown significantly over the past five years, philanthropic giving in the U.S. still has not risen above 2% GDP. If we could increase it by even 1%, the impact would be massive – almost $4 billion of additional funding for causes addressing tough social issues from poverty to healthcare to education and more. To understand what might motivate more people to give, volunteers will dive into data from #GivingTuesday 2016 to generate insights for a report that will be shared publicly. Philanthropic giving is what fuels social change – lend your skills to help unleash even more of this critical resource.

Collaborate and engage with some of the brightest minds in data science, social change and technology as you work in teams to analyze, visualize, and mashup fascinating data sets to create real world change. We believe data has the power to change the world, but only when we all work together. Join us for a data adventure like you’ve never seen and get ready to make friends, build skills and help unleash the power of data to serve humanity!



Source: DataKind – #GivingTuesday DataDive Capacity

Introductory guide on Linear Programming for (aspiring) data scientists


Introduction Optimization is the way of life. We all have finite resources and time and we want to make the most of them. From …

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5 More Deep Learning Applications a beginner can build in minutes (using Python)


Introduction Deep Learning is fundamentally changing everything around us. A lot of people think that you need to be an expert to use power of …

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Source: Vidhya – 5 More Deep Learning Applications a beginner can build in minutes (using Python)

DataDiving to Support Youth with the Annie E. Casey Foundation


In early December, we packed our bags to host a DataDive with DataKind DC in partnership with the Annie E. Casey Foundation, an organization devoted to developing a brighter future for millions of children at risk of poor educational, economic, social and health outcomes. What made this DataDive special is that all the teams worked on challenges focused on protecting and improving the lives of at risk children and young adults, and in some cases they even used the same datasets. It was also unique in that teams were able to get input from youth experts and students from Code in the Schools, a nonprofit dedicated to teaching programming to students in Baltimore.

A huge thanks to the approximately 100 volunteers that came together ready to roll up their sleeves and dive in to the data, as well as our inspiring project champions that are doing such critical work to help support children at risk. We are also grateful to Allegheny County and the Philadelphia Youth Network for sharing their data and expertise throughout the whole process.

 

Helping Children in Foster Care in Allegheny County, Pennsylvania

This visual shows the impact of a child’s age on having a successful exit from the foster care system in Allegheny County, Pennsylvania. The visual was created by high school students from Code in the Schools that had never worked with data science before. With coaching from our DataKind DC Chapter Leaders, they learned onsite how to produce visualizations like this.

 

Children have more successful outcomes when they are in stable, loving families, but too often children in foster care move from home to home or are placed in group homes.  According to a report by the Annie E. Casey Foundation, children in group homes were more likely to test below or far below in basic English and mathematics, more likely to drop out of school and less likely to graduate from high school than children placed with families. Given these considerations, volunteers worked to optimize a child’s potential for a successful initial placement.

When volunteer data scientists at the DataDive began to look at the data regarding movement between placements, it was important to incorporate several contextual factors. Sometimes children move for a “positive” reason, such as moving to live with a relative. When the reason is “negative,” such as when a foster parent decides they can’t handle a child’s behavior and requests the child be moved, it’s called a disruption. There are some moves in a grey area that are not clearly good or bad, such as when a child is moved from a traditional foster home to a therapeutic foster home due to a need for health-related treatment. No matter the reason, the moves can be traumatic for children and typically have a negative effect on a child’s behavior. Minimizing such moves and increasing the likelihood that a child will be placed with a family rather than in a group home, is critical in providing children with a stable environment and increasing their chances for a successful exit from foster care.

Two teams set out to see how data on foster care placements in Allegheny County, which includes Pittsburgh, could help prevent mismatched foster placements and minimize moves overall for children in foster care.

 

Improving Foster Care Placements

Many children who enter into foster care are placed into homes based on immediacy of availability instead of fit, leading to a potential mismatch between children and their home environment. When a mismatch takes place, children may end up being moved repeatedly, with some placed in homes far away from their family, schools, community, courts and other support systems critical to their success.

Led by Data Ambassadors Janet Montgomery and Abhishek Sharma, a volunteer team of data scientists, and a small cohort of high school students studying coding, worked to uncover trends and insights about placements within the Allegheny County foster care system as a first step towards creating a matching placement algorithm and application for children entering the foster care system to improve the quality of initial placements.

The team discovered a number of insights including the impact a child’s age has on successfully exiting the foster system, as shown above. In addition, they mapped where children were being removed from homes in Allegheny County compared to where facilities were located and looked at which types of foster facilities might be leading to more mismatches. This was an exploratory analysis and further investigation is needed, but the team’s work provides a strong foundation for the future development of a matching algorithm. They successfully identified what characteristics could be used for predictive analytics to flag which children entering the system may be at greater risk for removal and therefore in need of extra support to succeed. Having better placements up front would mean more stability for children and hopefully a smoother return home, with their kin or legal guardians.

 

Reducing Foster Care Placement Moves

Each of these graphs shows the movement of a different child as they are given multiple placements with different families. The volunteer team identified four basic patterns that children follow represented above.

 

While some data is captured when a child gets moved from one placement to another, the reason for the move is not always documented, which makes it difficult to know when a child might be in need of extra support. For instance, disruptive moves might signal a mismatched placement, while positive moves might signal that a correct placement has been attained. In some cases, where an ideal placement isn’t available, placing a child near critical support systems might be a suitable, if imperfect, alternative. If move types were better classified, caseworkers would have greater insight into how best to support children in foster care and potentially predict when a move is likely so they could intervene.

Data Ambassadors Ravi Solter and Sharang Kulkarni led a team to understand and potentially discover some overarching reasons that might explain disruptions. They also wanted to understand what might influence the likelihood a child will have a “positive exit” from foster care overall. The team dove in, analyzing and visualizing over 14,000 cases of children switching placements. They confirmed Allegheny County’s hunch that a child’s race and age indeed have a significant impact on whether or not they successfully exit foster care. Gender also has a significant impact, as they found that boys have a higher percentage of good placement exits than girls. Only about 50% of girls have good exits, versus almost 70% for boys (as shown with the graph below).

 

 

This graph shows the percentage of good and bad exit outcomes by gender.

 

This analysis is an important first step for caseworkers and child service agencies to better understand what factors make a disruption likelier so they can make better initial placements.

 

The Philadelphia Youth Network – Helping Young People Find Early Employment for a Strong Long-term Career

Studies have shown that youth who do not have early work experiences are more susceptible to unemployment in the future and are less likely to achieve higher levels of career attainment. The Philadelphia Youth Network (PYN) aggregates outcomes of youth enrolled in a variety of employment programs across different service providers. They wanted to understand what types of employment, wages, sectors, earnings, hours and other factors help young people achieve success and stability in their careers and what kinds of employment programs are best suited for different kinds of backgrounds.

Led by Data Ambassadors Nick Becker and Helen Wang, the team set out to analyze PYN’s data to provide insight into which employment programs are successful overall, which are successful for some groups, and which factors are driving success. Jobs assigned to students in an employment program are typically designed to be 120 hours over the course of six weeks, with a student “passing” the program if they’ve worked a minimum 86 hours. The team found that the program was actually getting more successful over time.

 

This graph shows the percentage of students in employment programs that have worked over 86 hours in their job assignments has been increasing. The program is becoming more successful over time.

 

The team also explored how demographics of the students, the length of job placement, what month the job starts and more were affecting success rates. They suggested future analysis on the students who don’t repeat the program to understand if it’s because they were unsuccessful or because they are going to school or found long-term employment. With better information about their employment programs, the Philadelphia Youth Network will be able to offer more targeted programs to help even more children achieve positive outcomes in their adult lives.

 

Annie E. Casey Foundation – Connecting Public Data Systems to Better Understand System-Involved Youth 

Youth who become part of the child welfare system are more likely to run away or become homeless; youth who age out of foster care face high risks of homelessness, and mental health issues are higher in homeless youth. While these issues are interconnected, youth service programs and agencies often do not share data with each other, making it difficult to view all aspects of a young person’s risks for homelessness and other negative outcomes. Inspired by Allegheny County’s integrated data system, Annie E. Casey Foundation wondered how might other municipalities adopt a similar integrated data system to show a more comprehensive picture of youth and help agencies better support youth involved in multiple systems.

Led by Data Ambassadors Greg Matthews and Aimee Barciauskas, the volunteer team aimed to explore the benefits of using an integrated data system for Allegheny County’s Office of Child, Youth, and Families by describing the populations of children and youth who have received services from their Behavioral Health and Homelessness programs.

The team produced population profiles of young people that have used each service or both services and described the groups of individuals who reappear between and within the same services.

 

This diagram shows the overlap of services used by young people – “bhs” or Behavioral Health Services, “shelt” or Homeless Services and “cyf” or Child Welfare Services.

 

 

These two graphs show the demographics of youth clients who used all services vs. Child Welfare services only.

 

The team also created an interactive tool to describe the youth clients and mapped their pathways through the systems over time.

These are two screenshots from the interactive tool. The top presents demographic information on the youth clients. The bottom shows how youth clients move through the systems over time.

 

The team recommended that the Annie E. Casey Foundation leverage data visualization tools for deeper exploration and more consistently categorize behavioral health services to allow for more robust analysis in the future. The Foundation is hoping to ultimately persuade other jurisdictions to link their disparate data sources on youth in an integrated data system like Allegheny County’s, allowing better monitoring of the risks that young people face and potentially improving targeted services to prevent disconnection.

 

Thank You!

Big thanks to all the volunteers that joined us for an inspiring weekend using data to support America’s youth – especially those that drove down from New York to be there! We are also grateful to Allegheny County and the Philadelphia Youth Network for sharing their data and expertise throughout the whole process. And a special shout out to the youth experts and Code in the Schools’ students that shared their wisdom and donated their time to give the teams’ context and help inform their analysis. And, of course, a sincere thanks to the Annie E. Casey Foundation for their generous support to make the weekend possible and the expertise they offered from their many years dedicated to building a brighter future for young people. Collaborations like this that bring experts together across sectors, age and geography are exactly what make new solutions possible so we are grateful to everyone that joined us to make the weekend a success!

 



Source: DataKind – DataDiving to Support Youth with the Annie E. Casey Foundation