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Data for Social Good

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掌握資料原力,並非企業的獨享權利。在國際上,不論是公共服務或是公益計畫,有越來越多的案例顯示,成功的關鍵在於「善用資料、跨域合作」。從分析現況、提升工作效率、服務創新到政策研究,資料的價值和應用層面相當廣泛。本演講將介紹基本的資料分析流程,並針對多種類型的國際公益計畫,進行個案分享。

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Data for Social Good

  1. 1. Data for Social Good ! 由資料驅動的公益新浪潮 2014.11.11 劉嘉凱 (CK)! ck@dsp.im
  2. 2. DSP 智庫驅動 dsp.im https://www.facebook.com/dspim http://google.com/+dspIm
  3. 3. 2013 5 8 12 動念 第⼀一次公開 籌備會議 2014 團訓班 1 1 團訓班結業暨 資料狂歡節 3 團訓班 2 5 6 Code for Healthcare Workshop 資料爬理析班 2 資料爬理析班 1 8 9 10 看資料找故 事⼯工作坊 PIXNET Hackathon SmartGov Conference 7 激 發 資 料 價 值 資 料 科 學 教 育 Code for Tomorrow & DSP Timeline 外傷醫學會 DataWeekend #08 城市、資料、桌遊、社造 Open Data Day 2014 DataWeekend #07 衛星資料、LiDAR、災難防救 10 11 DataWeekend #03 司法判決、交通事故分析 7 2 http://www.slideshare.net/ckliu/z-b-38495724
  4. 4. DSP 智庫驅動 資料科學 教育訓練顧問諮詢改造社會 http://dsp.im/2014/10/going-where-no-man-has-gone-before/
  5. 5. D4SG 發展趨勢
  6. 6. Social Good A good or service that benefits the largest number of people in the largest possible way. Classic Examples Clean air, clean water, education, social welfare, human rights Modern Examples Healthcare, internet Related Concepts Common good, public service, corporate social responsibility
  7. 7. Data ⼀一個概念 Big Data for Social Good Data Science “Good people using data to do good things.”
  8. 8. ⼯工作⺫⽬目標 資源有限,需求無窮 辨先後、抓重點 找出成功模式 找對象、改⽅方向複製成功經驗
  9. 9. 最佳實務 公益⼈人 + 資料⼈人 BYOD* 配對 對應⾏行動初步成果 * BYOD: Bring your own data. 深⼊入研究 爭取對的資源 改變現在作法 問對問題 初步發現 系統雛形
  10. 10. 國際案例
  11. 11. http://goo.gl/4AEEEI Predicting Homelessness
  12. 12. Background • 53,000 people in living in homeless shelters in New York during November 2013, including over 12,000 families with over 22,000 children. • Eviction is one of the top reasons families lose their homes and transition into to the city’s shelter system. Question • What if we could predict which families are at heightened risk of homelessness via eviction? • Early warning -> early intervention Early Results • A tool that allows social workers and advocates to predict the likelihood of an eviction notice leading to shelter entry, as well as the timeframe available for prevention. What’s Next • Help NGOs use the prediction results to communicate with at-risk families. http://blog.sumall.org/post/88610177356/the-numbers-behind-the-words
  13. 13. http://www.sumall.org/project-overview/homelessness/
  14. 14. http://www.sumall.org/homelessness
  15. 15. http://www.icouldbe.org/ Online Mentoring for Students
  16. 16. Background iCouldBe’s e-mentoring program has served over 19,000 at-risk youth since 2000, providing middle and high school students with an online community of professional mentors that empowers them to stay in school, plan for future careers and achieve in life. Question • Need definitions for their organizational goals and metrics to improve their program. • “What makes a mentoring engagement successful?” Early Results • Defined a “successful” mentee/mentor engagement as one where a mentee completes at least 3 “quests” or learning modules in 3 months. • Identified the characteristics of engagements and interactions. • "I'm here for you.” • A Predictive model to identify key predictors • A framework for text analysis What’s Next • Find more indicators of success/failure • Review current programs http://www.datakind.org/projects/uncovering-the-abcs-of-successful-online-mentoring/
  17. 17. http://www.culturaldata.org/ Help Art Organizations Thrive
  18. 18. Background • The Cultural Data Project (CDP) not only collects financial and programmatic data from over 11,000 arts and cultural institutions across the U.S., it delivers that information back to the organizations themselves, to the funders who support them and into the hands of advocates and policy makers who believe in them. • Each year, organizations ranging from small, all-volunteer dance troupes to multi-million dollar museums across the country submit data to CDP as part of the grant application process with public and private funders. This means CDP has collected a broad dataset with 50,000 records, including up to 1,200 data points on each organization. Question • What makes an art organisation successful? • How can we create more effective tools and training? Early Results • Found clusters of art organizations • Compared the financial success of the five clusters that resulted from the CDP Team's segmentation. • “cluster-4,” is the one cluster that does not achieve financial success. This cluster is a mixed cluster, not dominated by any one type of organization. What’s Next • Improve the categorisation of art organizations • Develop targeted services to organizations and enabling them to benchmark themselves to understand how they’re doing in relation to their peers. http://www.datakind.org/projects/clustering-arts-organizations-to-help-them-thrive/
  19. 19. http://www.culturaldata.org/
  20. 20. http://www.globalgiving.org/ Help Great Causes Get Funded
  21. 21. Background • GlobalGiving is the world's first and largest crowdfunding community for nonprofits. Since 2002, more than 400,000 donors have given $150 million to more than 10,500 projects in 160 countries. • GlobalGiving also helps them learn fundraising and operational best practices to improve their efficiency and increase their impact. Question • GlobalGiving wanted to help their community be even more successful by looking at their past fundraising campaigns or “projects” to determine what factors lead to projects being successfully funded. • They wanted to know - was there a formula for project success? Early Results • Success factors: project title, funding amount, photos, speed of funding? • Projects focused on hunger did better than projects focused on economic development and nearly 50% of donors skip the predefined donation values, choosing instead to enter their own donation amount. • A correlation between specificity of language and project success. • “arts” < “photography exhibit" What’s Next • Take a deeper look at the data • Improve data quality http://www.datakind.org/projects/helping-great-causes-get-funded/
  22. 22. http://www.powerpoetry.org/ Power of Poetry
  23. 23. Background • The literacy gap poses the greatest threat to the future of America. Nearly two-thirds of fourth graders don't meet reading proficiency standards. • Power Poetry is a social platform that brings young poets together. Users can write, post, share, and comment on each other’s poems. For some youth in low-income communities, community support is next to impossible in real life, but with Power Poetry, an expansive network of young poets can instill a sense of empowerment and motivation to change. Question • Can we measure literacy through poetry? Early Results • Those who publish at least 10 poems on Power Poetry saw a visible progression of their language scores • It was possible to map poems and their language scores to respective low-income and high-income zip codes. • Among powerpoetry.org users the literacy gap between low-income and high-income neighborhoods appears to be about 9 percentiles. • Active poets seemed to be able to advance their language score by about 4-percentiles which is the equivalent of half of the gap between more affluent and less affluent zipcodes. What’s Next • To create an assessment tool through which parents, educators, policymakers can use to make informed decisions for their families, schools, and society. http://www.sumall.org/project-overview/power-poetry/
  24. 24. http://www.cookcountylandbank.org/ Land Bank for Urban Regeneration
  25. 25. Background • Boarded up buildings and overgrown lots have plagued Chicago’s low-income neighborhoods for decades. • Over the past five years, however, vacant and abandoned properties have spread beyond the inner city and into the suburbs, disrupting formerly stable working and middle class communities and prompting the creation of a county-wide land bank, a new tool for fighting blight. • Properties become vacant or abandoned because of weak real estate markets in impoverished neighborhoods or because of the recent region-wide foreclosure crisis. Question • The Cook County Land Bank has one job: to acquire vacant and abandoned properties throughout Cook County and return them to productive use. • There are tens of thousands of boarded up homes and overgrown lots in Cook County, and the land bank’s budget is limited. • How will the agency figure out which of these properties to acquire, and what to do with them? Early Results • A database to search and analyze vacant properties. • A model to compare the quality of neighborhoods. What’s Next • Engage stakeholders in communities to come up with mutually acceptable criteria. • A clear, justifiable plan of action for putting vacant properties back to work. http://dssg.uchicago.edu/2014/01/20/cclb-real-estate-finder-for-vacants.html
  26. 26. http://dssg.uchicago.edu/2014/01/20/cclb-real-estate-finder-for-vacants.html
  27. 27. https://www.huridocs.org/ International Human Rights Case Law
  28. 28. Background • HURIDOCS is an international NGO that helps other human rights organizations liberate, illuminate and manage this kind of data to make a positive impact on the human rights situation worldwide. • They had already collected over 40,000 processed judgments from the ECHR HUDOC database and fed them into their Caselaw Analyzer to make it easier for people to access. Question • How could they use this data to improve the human rights situation in Europe and further address the obfuscation of states' accountability? • How judges were ranking cases as important and look for potential patterns? • linked to data from another ECHR website to show whether the case judgment was ultimately enforced. Early Results • Scraped data from the Council of Ministers website showing the execution of case decisions. • Made data connections between case judgement and enforcement • One-stop shopping for human right law cases What’s Next • Enhance government accountability • Study the trends of cases
  29. 29. 更多的國際案例
  30. 30. http://www.donorschoose.org/ Which Project Should Get Funded?
  31. 31. http://datatools.dcactionforchildren.org/ Understand Gaps in Child Resources
  32. 32. http://dssg.uchicago.edu/2014/01/16/mesa-undermining-undermatching.html Better Matching of College Applications
  33. 33. http://www.crisistextline.org/ Texting Lifeline
  34. 34. http://jsbin.com/diduwule
  35. 35. Code for Healthcare
  36. 36. 傳統保守的醫療產業,如何運⽤用並⾯面 對新興熱⾨門的資料科學熱潮呢? ! 例如: ! • ⾝身體數據 (quantified self) • 資料分析 -> 醫療品質改善 • 醫療資料視覺化 • 時空地理資訊 -> 透視醫療問題 • 如何⾛走出醫院,⾛走⼊入社會? • 更多的開放資料腦⼒力激盪? http://codefortomorrow.org/portfolio/partnership/141 Data Science in Healthcare: Hot or Hype?
  37. 37. 活動流程分享主題 • 13:20 - 報到 • 14:00 - 主持⼈人 * 2 開講 • 14:15 - 各主題 pitching (15 mins each * 6) • 16:00 - 現場登記 (5 mins each * 6 ) • 16:30 - 開放交流 • 17:00 - 掰掰 • 18:00 - 會後⾃自由餐敘 • 開放、資料、創新 • DSP 資料科學計畫 • 以時空地理觀解構⾼高雄市到院前救護資料 • 微觀之開放資料: Gene Expression Omnibus • 體感技術對醫療照護之前瞻展望 • 可量化的⾃自我 (Quantified Self) - 由消費 者引領的電⼦子運動 “Unconference” (2014.04)
  38. 38. “Hackathon” (2014.08)
  39. 39. 資料集 ⾼高屏澎區域緊急醫療應變聯盟資料! • 提供單位:KAMERA 執⾏行團隊 • 說明:急診現場(每30分鐘忙碌情形)歷史資料 ! 急診轉診資料! • 提供單位:⾼高雄市衛⽣生局、屏東縣衛⽣生局 • 說明:⾼高屏區急救責任醫院病患轉診資訊 ! 外傷登錄資料! • 提供單位:台灣外傷醫學會 • 說明:外傷病患完整就醫資訊 資料授權 1. 參與本活動即視為同意本資料授權條款。 2. 主辦單位所提供的各種資料,僅限本次活 動使⽤用,不得另作他⽤用。 3. ⽇日後若有任何資料使⽤用需求,必須和主辦 單位重新申請。 活動規劃 活動發起單位 • ⾼高屏澎區域醫療網 • 台灣外傷醫學會 • KAMERA @ KMUH • ⾼高雄市政府衛⽣生局 • 屏東縣政府衛⽣生局 • Code for Tomorrow
  40. 40. 活動流程 活動流程 • 13:00 - 報到 • 13:30 - 開場致詞 • 13:40 - 活動與資料說明 • 13:50 - 上台提案 (3 mins each * 10) • 14:20 - 開⼯工:團隊討論、交流、開始實作 • 18:00 - 參加者晚餐餐敘 • 09:00 - 報到、開⼯工 • 12:00 - 午餐 • 16:30 - 各組成果分享與交流 (5 mins each * N) • 18:00 - 賦歸 08/16(六) 08/17(⽇日)
  41. 41. 參加者來源
  42. 42. Medical Data Analyst Software Engineer Researcher Designer Government Workers 0 0.1 10 0.2 20 0.3 30 0.4 40 ⼈人數⽐比例 (%) 參加者專業背景
  43. 43. 活動現場
  44. 44. 活動現場
  45. 45. 活動現場
  46. 46. 219 14,992 16,250 1,272 5 Hospitals Patients Cases Transfer Paths Groups 急診室的社交網路
  47. 47. 1. ⼤大量傷病患檢傷暨後送 APP
  48. 48. 1. ⼤大量傷病患檢傷暨後送 APP
  49. 49. 2. ⼼心⼒力交瘁
  50. 50. 2. ⼼心⼒力交瘁
  51. 51. 3. 全台六區 ISS 分析
  52. 52. 全國外傷登錄統計 3. 全台六區 ISS 分析 ⼈人 次 年齡
  53. 53. 各區 ISS 與死亡率 3. 全台六區 ISS 分析
  54. 54. 4. 轉診流程與系統旅程
  55. 55. 4. 轉診流程與系統旅程
  56. 56. 4. 轉診流程與系統旅程
  57. 57. 5. 搖滾吧!爺奶
  58. 58. 5. 搖滾吧!爺奶
  59. 59. 對系統的建議 • 系統防呆 • 教育訓練 • 希望給回饋,譬如建議 病患可以往哪邊送 • 資訊公開,以了解醫療 體系整體的趨勢 6. 轉診系統初探
  60. 60. 研究⺫⽬目標: ! 1. 估計每間醫院平常承載容量,並計算最⼤大承載量。 2. 可額外承載嚴重外傷的病⼈人數 7. 氣爆與地震:如何合理分配⼤大量傷患
  61. 61. 簡報似顏繪
  62. 62. 簡報似顏繪
  63. 63. ⼀一個提案
  64. 64. 提升公共服務提供者 的資料聽說讀寫能⼒力 Improve Data Literacy of Public Service Providers
  65. 65. 公益資料 駭客松 資料培⼒力 訓練計畫 資料掃盲運動 https://c4t.hackpad.com/SkSNy3pr7ID
  66. 66. https://c4t.hackpad.com/SkSNy3pr7ID
  67. 67. ⼀一次性 -> 規模化 BYOD* 配對 對應⾏行動初步成果 Scale * BYOD: Bring your own data. 1 2 4 3

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