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Data for Social Good - 由資料驅動的公益新浪潮

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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。本演講將針對多種類型的國際公益計畫,進行個案分享。

第一次發表場合:「網路星期二」(http://nettuesday.tw/) 。(Nov. 2014)
第二次發表場合:「非營利組織資訊科技運用」座談會 (http://apply.frontier.org.tw/2015/introduce.htm) (April-May 2015)

Published in: Data & Analytics
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Data for Social Good - 由資料驅動的公益新浪潮

  1. 1. 由資料驅動的公益新浪潮 Data for Social Good 劉嘉凱 (CK) ck@dsp.im April-May 2015
  2. 2. ⼀一個關於廁所的故事 http://goo.gl/yDr1Mg
  3. 3. 肯亞
  4. 4. http://goo.gl/1Zowm 學⽣生表現與廁所⽐比例之關係
  5. 5. 怎麼做到的?
  6. 6. 開放發展 資料驅動發展多邊合作夥伴
  7. 7. 在地記者 國際媒體組織 國際慈善機構 在地公⺠民社群國際發展組織 跨國科技公司 在地媒體組織 在地政府
  8. 8. “If it works in Africa, it will work anywhere.” - Erik Hersman
  9. 9. 智庫驅動 12
  10. 10. http://dsp.im/
  11. 11. DSP 智庫驅動 14 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/
  12. 12. 激 發 資 料 價 值 資 料 科 學 教 育 DSP 時間線 12 團訓班 2014 1 團訓班結業 暨資料狂歡節 3 團訓班 資料爬理析班 5 6 資料爬理析班 8 9 10 看資料找故 事⼯工作坊 PIXNET Hackathon SmartGov Conference 7 Code for Healthcare Workshop 外傷醫學會 11 12 政府標案 ⿊黑客松 ⼤大數據時代 的管理策略 R語⾔言 探索之旅 1 2015 資料科學 冬令營 資料科學 愛好者年會 ⾏行政院網路 趨勢研習營 A1 資料分析 基礎班 4 E1 資料⼯工程 基礎班 A1 資料分析 基礎班 7 資料科學 夏令營 8 資料開竅論壇 政府 團訓班 Data for Social Good 5 6 A2 資料分析 進階班
  13. 13. Data for Social Good 發展趨勢 16
  14. 14. Social Good A good or service that benefits the largest number of people in the largest possible way. 古典案例 乾淨空氣、清潔⽤用⽔水、教育、社福、⼈人權 現代案例 醫療、網路 相關概念 Common good, public service, corporate social responsibility
  15. 15. forBig Data Social Good Data Data Science “Good people using data to do good things.” ⼀一個概念
  16. 16. 資源有限,需求無窮 辨先後、抓重點 找對象、改⽅方向 複製成功經驗 找出成功模式 ⼯工作⺫⽬目標
  17. 17. BYOD* 配對 初步成果對應⾏行動 最佳實務 * BYOD: Bring your own data. (⾃自備資料) 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 公益⼈人 + 資料⼈人
  18. 18. 個案探討 21
  19. 19. http://www.dosw.gov.taipei/ct.asp?xItem=88137862&ctNode=71216&mp=107001
  20. 20. http://www.appledaily.com.tw/appledaily/article/headline/20111225/33912274/
  21. 21. http://udn.com/news/story/3/844485
  22. 22. 預防勝於治療 25
  23. 23. http://goo.gl/4AEEEI 街友問題:防患於未然
  24. 24. 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
  25. 25. http://www.sumall.org/project-overview/homelessness/ 紐約市街友⾼高危險群
  26. 26. http://www.sumall.org/homelessness 收到驅離通知後第 4 個⽉月 進⼊入收容所者佔⽐比 ~10%
  27. 27. 個案成功⽅方程式 30
  28. 28. http://www.icouldbe.org/ 弱勢族群之數位學習
  29. 29. 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/
  30. 30. Health Leads 基本資源,讓⽣生活更健康
  31. 31. http://dssg.uchicago.edu/2014/12/15/health-leads.html
  32. 32. http://www.culturaldata.org/ 藝⽂文團體,萌芽開花
  33. 33. 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/
  34. 34. http://www.culturaldata.org/
  35. 35. 募款勝利組 39
  36. 36. http://www.globalgiving.org/ 公益計畫,群眾募資
  37. 37. 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/
  38. 38. 資源有限,要選哪⼀一個? 42
  39. 39. http://www.cookcountylandbank.org/ 都市更新,⼟土地銀⾏行
  40. 40. 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
  41. 41. http://dssg.uchicago.edu/2014/01/20/cclb-real-estate-finder-for-vacants.html
  42. 42. 即時通只能聊天? 46
  43. 43. http://www.crisistextline.org/ ⽣生命線之即時通訊
  44. 44. http://jsbin.com/diduwule
  45. 45. 天上的案例
  46. 46. http://goo.gl/krB8XN
  47. 47. http://goo.gl/krB8XN
  48. 48. http://goo.gl/krB8XN
  49. 49. http://goo.gl/VoUxcz
  50. 50. Code for Healthcare
  51. 51. Data Science in Healthcare: Hot or Hype? http://codefortomorrow.org/portfolio/partnership/141 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?
  52. 52. “Unconference” (2014.04) • 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 活動流程 分享主題 • 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
  53. 53. “Hackathon” (2014.08)
  54. 54. 活動規劃 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 資料集 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
  55. 55. 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)
  56. 56. 參加者來源
  57. 57. 參加者專業背景 Medical Data Analyst Software Engineer Researcher Designer Government Workers 0 0.1 0.2 0.3 0.4 ⼈人數⽐比例 (%) 10 20 30 400
  58. 58. 活動現場
  59. 59. 活動現場
  60. 60. 219 14,992 16,250 1,272 5 Hospitals Patients Cases Transfer Paths Groups 急診室的社交網路
  61. 61. 1. ⼤大量傷病患檢傷暨後送 APP
  62. 62. 1. ⼤大量傷病患檢傷暨後送 APP
  63. 63. 2. ⼼心⼒力交瘁
  64. 64. 3. 全台六區 ISS 分析
  65. 65. 3. 全台六區 ISS 分析 ⼈人 次 年齡 全國外傷登錄統計 19歲 57歲
  66. 66. 3. 全台六區 ISS 分析 各區 ISS 與死亡率
  67. 67. 4. 轉診流程與系統旅程
  68. 68. 4. 轉診流程與系統旅程
  69. 69. 4. 轉診流程與系統旅程
  70. 70. 6. 轉診系統初探 • 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 對系統的建議
  71. 71. 7. 氣爆與地震:如何合理分配⼤大量傷患 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
  72. 72. 簡報似顏繪
  73. 73. 簡報似顏繪
  74. 74. ⼀一個提案
  75. 75. 公共服務提供者 資料聽說讀寫能⼒力 升級⼤大作戰 Data Literacy
  76. 76. 兩種做法
  77. 77. 資料培⼒力 ⼯工作坊 公益資料 ⿊黑客松
  78. 78. 資料培⼒力⼯工作坊
  79. 79. 公益資料⿊黑客松
  80. 80. 資源配置 員⼯工/志⼯工資源配置 捐款分析 個案分析 趨勢預測 … 可能的題⺫⽬目
  81. 81. BYOD* 配對 初步成果對應⾏行動 ⼀一次性 規模化 * BYOD: Bring your own data. (⾃自備資料) 擴展 1 2 34
  82. 82. 名額有限,先搶先贏

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