Introducing Our New Labs Blueprint


Thanks to support from the Rita Allen Foundation, we are pleased to share our Labs Blueprint, a new resource documenting our learnings and approach from our first Labs project using data science to improve traffic safety in three U.S. cities.

Labs projects differ from other DataKind projects in that they are designed to address sector-wide challenges instead of a specific organization’s. As these projects look to move the needle on sector-wide issues, our hope is this document can help others learn from and hopefully be able to launch similar projects to drive change. 

Download the Labs Blueprint >

How To Use This Guide

Just as the data science process isn’t linear, neither is this guide. This is actually a series of learning modules that you can move between depending on your interests.

Dive into the Labs Blueprint by clicking on a module below.

 

 

 

Modules one to three provide background about DataKind Labs, data science and our Vision Zero Labs project, while modules four to six are designed for project managers and intermediary organizations and offer in-depth information on how to replicate DataKind’s Labs process and more details on our approach.

This resource is intended to support those interested in replicating or building upon our work to help advance Vision Zero efforts in communities or those looking to create similar scalable data-driven projects to help move the needle on sector-wide social issues. For example:

  • Data scientists or civic tech advocates interested in seeing examples of how you can apply data science for sector-wide impact
  • Social change organizations or local governments  interested in learning about data science and how it can move the needle on sector-wide issues
  • Intermediary organizations that, like DataKind, convene stakeholders on similar tech for good projects and are interested in an “under the hood” look at our work

At the end of every module is a feedback form where you can share your thoughts and questions. We’d love to hear from you. 

Learn More

Check out our full case study for more detail on our Vision Zero Labs project and for additional resources.



Source: DataKind – Introducing Our New Labs Blueprint

DataDiving with DataKind Singapore


DataKind Singapore hosted its second DataDive this past April, gathering more than 70 volunteers for a weekend of analyzing data to help three phenomenal organizations advance their missions. Learn more about the work achieved in support of the Singapore Children’s Society, Singapore Red Cross, and O’Joy Care Services.

Improving Understanding of Behaviors and Attitudes Around Child Abuse and Neglect

“The DataDive was a truly wonderful experience for us, with the atmosphere full of excitement as we uncovered insights from the data.”
— Denise Liu, Principal Research Officer, Singapore Children’s Society

Singapore Children’s Society
To better serve Singaporean youth, the Singapore Children’s Society (SCS) has always been interested in conducting, stimulating and supporting research on issues related to the well-being of children. The SCS wanted to better understand perceptions about behaviors that suggest child abuse and neglect and the seriousness of these behaviors to help inform advocacy efforts, identify areas of improvement for educating the public, and enrich availability of data on child abuse and neglect in Singapore.

Analyzing survey responses, from both professionals and the general public, about the perceptions surrounding child abuse and neglect, the SCS and DataDive team looked to compare the differences in views between professionals and the public, and gain insight on abuse and neglect case characteristics. The team was able to establish characteristics associated with particular abuses such as sexual abuse as well as correlations between abusive behaviors (e.g. criticizing a child and calling a child ‘useless’). In addition, a text analysis toolkit was produced, providing the SCS team with an informative, visual and fresh perspective, that will help them analyze open-ended survey questions and identify underlying topics. The team was also able to provide insights and recommendations to help advance future research efforts for SCS.

Maximizing Impact of Blood Donation Drives Across Singapore

“The collaboration highlights the tangible benefits that can result when a group of committed volunteers lend their skills and expertise to benefit our community. We are very thankful for the enthusiasm and dedication shown by DataKind and all the volunteers involved in the project, and the interactive dashboards and projection model will definitely enable us to better plan our community blood drives.”

 — Robert Teo, Head of Blood Donor Recruitment Programme, Singapore Red Cross

Singapore Red Cross
Since 2001, the Singapore Red Cross’ (SRC) Blood Donor Recruitment Programme (BDRP) has led the recruitment, retention and education of blood donors in Singapore. A key component of their efforts are its community blood drives. Together with bloodmobile organizers (BMOs) including hospitals, schools, private companies, religious groups and other community organizations, the SRC organizes nearly 500 drives across the country each year.

Only about 1.87% of Singapore’s resident population donates blood, according to the Health Sciences Authority (HSA). The HSA estimates that 118,750 units of blood will be required to meet patients’ needs in 2017, greater than the 115,976 units collected in 2016. To meet this anticipated increase in need, the SRC wished to  develop predictive models that could identify key factors influencing blood drive donation levels and project the amount of blood given at a drive within 20% of actual collection numbers. Were donation drives being held too frequently and in locations too close together? The SRC sought to answer this question and also explore possible trends surrounding blood donation levels and the types of BMOs organizing the drive.

The team set to work and created an interactive dashboard and predictive models to identify key factors that impacted blood drive donation levels. They unearthed several interesting insights on how different days of the week affected different types of organizations’ blood collection performance and found that factors such as the duration of the blood drive, distance from nearby blood drives that recently took place, and timing around public holidays, were most significant in influencing donation levels at drives. Although the team was unable to develop a model to project blood donation amounts within 20% of actual units of blood collected, they came close to hitting the goal and were able to provide recommendations about other data that can be collected to improve the model’s projection power.

The analytical models created and insights gained from the DataDive will help inform the SRC’s day-to-day operations and determine better allocation of resources for drives, all supporting their ultimate goal to maximize blood collection at donation drives and ensure an adequate supply of safe blood for patients’ in need.

Supporting Mental Well-Being and Services for the Elderly

“This event has scientifically confirmed our suspicion that the current client clinical assessment tools we are using is not indicative of resources needed. We have started using additional tools towards this purposes. DataKind is indeed an enabling partner for social service organisations to have such scientific understanding.”

— Jin Kiat, Executive Director, O’Joy Care Services

O’Joy Care Services
O’Joy Care is a social service organization dedicated to promoting the psycho-emotional health of the elderly. To help improve care for their clients, O’Joy looked to analyze data to answer several questions including:

  • What factors contribute towards clients attending the number of sessions that they do?
  • Is there a way to indicate what the performance of individual counselors might be?
  • What is the profile of the clients that are being referred?
  • What factors may contribute to caregiver stress?

Tasked with finding these answers, the team first familiarized themselves with the data, exploring over 600 client records from COMIT, a community mental health intervention programme for persons at risk of or diagnosed with depression, anxiety and dementia. Using various analytic techniques they were able to extract a number of insights pertaining to O’Joy Care’s initial questions.

From the data, which included medical details and demographic information about clients, the team discovered that the primary drivers for the number of sessions clients participated in included education level, type of housing and age of the client. Gaining further understanding about client characteristics, they found that professionals and administrative staff, as well as men, tend to make up the majority of clients when anxiety is the issue. When it comes to psychosis, the make up tends to be largely the unemployed. When the issue is caregiver stress, it was revealed that homemakers and females may be overrepresented.

 

Example of the dashboard created to analyze referral sources for clients. Apart from hospitals, the community was found to be the greatest source for referral of clients to O’Joy Care.

With the dashboard and the insights gained, O’Joy Care will be able to better determine the needed resources and tools to support their team in delivering quality service to improve the mental well-being and psychosocial health of the elderly and their families, who may be dealing with with aging-related issues such as chronic disease, isolation and bereavement.

Thank You and Get Involved with DataKind Singapore

Thank you to all the volunteers that came out to help support these organizations and the tremendous work they do. Special thanks to Expedia for hosting us!

If you’re local, we’d love to see you at the next DataDive or Meetup. Sign up to get involved!



Source: DataKind – DataDiving with DataKind Singapore

Summer of 2017 at DataKind Bangalore

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Summer arrives early in India, stays long, and leaves many souls parched, longing for a splash of rain. While monsoon is far way off, our passionate volunteers at DataKind Bangalore have been keeping cool with mission-driven DataCorps projects, a successful Project Accelerator event, and a lively DataLearn session. Read on as we share the exciting highlights of an eventful season at our chapter.

Data for Good Accelerated

On April 16, at our fourth Project Accelerator event, we worked with  three mission-driven nonprofits to help brainstorm new ideas and potential solutions to their data challenges.  Each with a groundbreaking agenda for social good, these nonprofit partners will collaborate with DataKind Bangalore to put data science to work.

A precursor to the Project Accelerator was a sneak peek event, held in February 2017, to show and tell how we do data for good at DataKind Bangalore, which was summed up by our in-house design expert Rasagy Sharma in the following sketch:

About Our Nonprofit Partners at Project Accelerator

  • Commonwealth Human Rights Initiative (CHRI) champions the cause of right to information and justice. In its journey to improve access to information and justice for all, CHRI will now partner with DataKind Bangalore to harness the power of data to enhance their data pipeline for  research and reporting.
  • Karnataka Learning Partnership has developed a public platform to contribute to the cause of building better schools in the state of Karnataka. At DataKind, we are looking to assist this nonprofit in a variety of projects—from detecting anomalies in data to building prescriptive and predictive reports.
  • Pollinate Energy—a social business with operations in Bangalore, Hyderabad, Kolkata, and Lucknow—works with people living in urban slums in India and helps them transition to affordable, solar-powered lanterns, cooktops, fans, and water filters. Its representatives will partner with DataKind Bangalore to address the challenge of detecting various urban poor communities via satellite images and other data proxies.

An Afternoon of Incubation
More than 80 data-savvy minds shared their enthusiasm and creativity to submit ideas to unique data challenges experienced by our partners. Participants formed three focus groups, one for each nonprofit, and pored over the sample data. Each focus group, led by a DataKind Bangalore volunteer, identified skill requirements to solve the problems at hand. 

Hosted by our longtime supporter Sahaj Software Solutions, this event marks the beginning of a new Sprint—a series of DataJam and DataDive events to build ingenious solutions to solve the big data problems of our partners.

Learning for Social Good


In our pursuit of building a community of learners and expanding our network of data do-gooders, we kicked off 2017 with an interactive DataLearn session. Led by our in-house expert Jayant Pahuja, this learning workshop in January provided an overview of the Bayesian modeling.

In this introductory session, participants played with dices and coins to learn about the Bayesian framework and discussed the pros and cons of Bayesian. Attended by more than 100 energetic participants, this session surely charged us up for an exciting year and whetted our appetite for data for good.

Volunteerism with Values

Our data for good mission is charged by the passion of our talented volunteers, and the spirit of our volunteerism is fueled by the core values of DataKind. The DataKind Bangalore Values award is a recognition program to express our gratitude to our extraordinary volunteers for demonstrating DataKind core values. We’re excited to announce the recipients of this award from this quarter.

Jay Kumar
Mindfulness

Jay’s commitment to engage with our nonprofit partners has been exemplary. In the past year, he has gone the extra mile to lead a successful collaboration with Daksh India. Jay’s passion and commitment have resulted in Daksh investing more resources in opening up their data and plan more data-driven reports.

Jayan Pahuja
Fun & Approachable

Jayant has expertise in a variety of areas ranging from NLP to text detection in images to metric forecasting on a medium scale. He has been a spirited volunteer since 2015, juggling multiple projects and responsibilities. A noteworthy milestone of his volunteering journey at DataKind was his DataLearn session on Bayesian modeling in January 2017. In his partnership with Daksh India, Jayant has championed the task of analyzing the performance of district-level courts in India.

His zen-like perspective to crucial challenges and down-to-earth disposition are admired by our volunteers and nonprofit partners alike.

What’s Next

While the temperature in Bangalore keeps soaring with no monsoon in sight, we are excited about our upcoming DataJam and DataDive sessions. We are incredibly proud to see our nonprofit engagement grow, and we would love to see you at our events!

Join our Meetup to get involved.
Follow us on Facebook and Twitter for updates and announcements.

 



Source: DataKind – Summer of 2017 at DataKind Bangalore

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

#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

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

DataKind Named One of Fast Company’s Top 10 Most Innovative Nonprofits


Big news – DataKind has been named one of Fast Company’s Top 10 Most Innovative Nonprofits for 2017 and we have you to thank.

Part of the magazine’s annual ranking of the World’s 50 Most Innovative Companies, this list honors leading enterprises and rising newcomers that exemplify the best in nimble business and impactful innovation. We were humbled to be recognized in the nonprofit category, alongside truly inspiring organizations like our friend and past project partner GiveDirectly, fellow New York-based The Fund for Public Housing and The Movement for Black Lives just to name a few.

While our stylish orange hoodies almost certainly swayed the judges, we know the real reason we were selected is because of all of you.

Even more than data, our work is about people. As we tell our incredible volunteers and project partners at our community events or before they kick off a project – YOU are DataKind. Our work depends on our global community of over 14,000 socially conscious data scientists, social innovators, subject matter experts, funders and data for good enthusiasts of all stripes.

Thank you for donating your time and talent to apply data science, AI and machine learning in the service of humanity. This honor goes to all of you, dear DataKinders – congratulations!

 

 

 



Source: DataKind – DataKind Named One of Fast Company’s Top 10 Most Innovative Nonprofits

Data4Good Job Alert Roundup


If you’re looking for a job that lets you use your data and technology skills for social good, check out the selection of opportunities below we’ve heard about through the grapevine or stumbled upon online. Know of a great opportunity we missed? Tweet at us or email us at contact@datakind.org and we’ll share it.

DataKind is Hiring!

  • Our Executive Associate (New York) will be the right-hand person to our Director of Operations.

Other Great Organizations Are Hiring Too…

*STILL OPEN FROM LAST MONTH*



Source: DataKind – Data4Good Job Alert Roundup