SemesterSpring Semester, 2020
DepartmentMA Program of Public Administration, First Year PhD Program of Public Administration, First Year MA Program of Public Administration, Second Year PhD Program of Public Administration, Second Year
Course NameData-Driven Decision Making
InstructorLIAO HSIN-CHUNG
Credit3.0
Course TypeElective
Prerequisite
Course Objective
Course Description
Course Schedule

 



 


































































































































































週次



Week



課程主題



Topic



課程內容與指定閱讀



Content and Reading Assignment



教學活動與作業



Teaching Activities and Homework



學習投入時間



Student workload expectation



課堂講授



In-class Hours



課程前後



Outside-of-class Hours



(3/2)



Introduction and Course Overview


   

3



1



(3/9)



The Big Promise of Big Data






    1. Data science and its relationship to big data and data-driven decision making (Matt)

    2. Digital humanitarians: how big data is changing the face of humanitarian response. 

    3. The age of big data (Oscar)






  1. Disscusions on the papers

  2. GeoDa Lab 1: spatial data handling



3



3



(3/16)



The Challenge of Big Data: Information Blindness




  1. Can big data revolutionise policymaking by governments (Nuran)

  2. Overcoming data blindness; or do shrimp

    chew with their mouths open? 
    (HUAI WEN)

  3. Ten reasons not to measure impact—And what to do instead




  1. Disscusions on the papers

  2. GeoDa Lab 2: basic mapping and rate mapping



3



3



(3/23)



The Challenges of Big Data: Organizational Change




  1.  Big data for social innovation. (Wendell)

  2. Making advanced analytics work for you (Wendell)




  1. Disscusions on the papers

  2. GeoDa Lab 3: univariate and bivariate analysis



3



3



(3/30)



Challenges of Big Data: Ethics and Privacy




  1. Weapons of math destruction: How big data increases inequality and threatens democracy. (Oscar)

  2. China’s date with big data:

    will it strengthen or threaten

    authoritarian rule? 
    (Tsanyu)




  1. Disscusions on the papers

  2. GeoDa Lab 4: multivariate analysis



3



3



(4/6)



Collecting Group Data




  1. Big data for policymaking: fad or fasttrack? (DIN YEAH)

  2. Human-Computer Interaction and Collective Intelligence (Tsanyu)




  1. Disscusions on the papers

  2. GeoDa Lab 5: Space-time exploration



3



3



(4/13)



Using Administrative Data




  1. Challenges in administrative

    data linkage for research

  2. The role of administrative data in the big data revolution in

    social science research

  3. Unlocking data to

    improve public policy 
    (Jeff)

  4. Using linked longitudinal administrative data to identify

    social disadvantage 
    (Jeff)




  1. Disscusions on the papers

  2. GeoDa Lab 6: spatial weights



3



3



(4/20)



Harnessing Social Media Data




  1. Grumble to policy need: deriving public policy needs

    from daily life on social media platform

  2. Impact of social media and Web 2.0 on decision-

    making 
    (Eason)

  3. Social media for social

    change in science (DIN YEAN)




  1. Disscusions on the papers

  2. GeoDa Lab 7: application of spatial weights



3



3



(4/27)



Final Project Proposal Discussion



None



Individual Discussion in Office



3



3



(5/4)



Remote Sensors




  1. Ground level PM2.5 estimates over China using

    satellite-based GeographicallyWeighted Regression

    (GWR) models are improved by Including NO2 and

    Enhanced Vegetation Index (EVI)

  2. Real-time estimation of satellite-derived PM2.5

    based on a semi-physical GeographicallyWeighted

    Regression Model (Becky)

  3. Spatial distribution and opportunity mapping: Applicability of evidencebased

    policy implications in Punjab using remote sensing and global products (Becky)




  1. Disscusions on the papers

  2. GeoDa Lab 8: global spatial autocorrelation



3



3



(5/11)



Challenges of Data Quality




  1. A data quality in use model for big data (Matt)

  2. Can big data improve firm decision quality? The role of data quality and data diagnosticity (Eason)




  1. Disscusions on the papers

  2. GeoDa Lab 9: local spatial autocorrelation



3



3



(5/18)



Static Data Visualization




  1. Basic principles of graphing data

  2.  Graphical integrity

  3. Narrative visualization: telling stories with data (Nuran)




  • Disscusions on the papers

  • GeoDa Lab 10: cluster analysis



3



3



(5/25)



Volunteered Geographic Information (VGI)




  1. Volunteered Geographic Information: towards the establishment of a new paradigm

  2. Volunteered geographic information and crowdsourcing disaster relief

  3. A shared perspective for PGIS and VGI

  4. Mapping with stakeholders: an overview of

    public participatory GIS and VGI in transport decision-making




  • Disscusions on the papers

  • GeoDa Lab 11: spatial regression



3



3



(6/1)



Participatory Mapping




  1. Constructing community through maps? power and praxis in community mapping. 

  2. Making maps that matter: situating GIS within community conversations about changing landscapes




  • Disscusions on the papers

  • GeoDa Lab 12: review



3



3



(6/8)



Final Project Workshop



None



Open Lab



3



3



(6/15)



Final Project Presentation


 

Potluck (Drinks and Snacks)



3



3



(6/22)



Final Project Presentation


 

Potluck (Drinks and Snacks)



3



3



(6/29)



Final Exam


 

Take-Home Final Exam



0



6




 


Teaching Methods
Teaching Assistant
Requirement/Grading

The final semester grade will be computed as:




  • 10% for the oral presentation of the final project

  • 40% for the  final project (3000-5000 words)  

  • 15% for the take-home final exam

  • 15% for the assignment

  • 10% for the classroom discussion

  • 10% for the participation


Textbook & Reference
Urls about Course
Paper Link: https://1drv.ms/u/s!AoacP5CovPLSlxYEU0DJvRWmPyIj?e=6XrhPe
Attachment