教學大綱 Syllabus

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

（2/14）

Introduction and Course Overview

3

1

（3/7）

The Challenge of Big Data: Information Blindness

1. Can big data revolutionise policymaking by governments 
2. Overcoming data blindness; or do shrimp
   chew with their mouths open? 
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

（3/14）

The Challenges of Big Data: Organizational Change

1.  Big data for social innovation. 
2. Making advanced analytics work for you 

1. Disscusions on the papers
2. GeoDa Lab 3: univariate and bivariate analysis

3

3

（3/21）

Challenges of Big Data: Ethics and Privacy

1. Weapons of math destruction: How big data increases inequality and threatens democracy. 
2. China’s date with big data:
   will it strengthen or threaten
   authoritarian rule? 

1. Disscusions on the papers
2. GeoDa Lab 4: multivariate analysis

3

3

1. Disscusions on the papers
2. GeoDa Lab 5: Space-time exploration

（4/11）

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 
4. Using linked longitudinal administrative data to identify
   social disadvantage 

1. Disscusions on the papers
2. GeoDa Lab 6: spatial weights

3

3

（4/18）

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 
3. Social media for social
   change in science 

1. Disscusions on the papers
2. GeoDa Lab 7: application of spatial weights

3

3

（4/25）

Final Project Proposal Discussion

None

Individual Discussion in Office

3

3

（5/2）

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 
2. Real-time estimation of satellite-derived PM2.5
   based on a semi-physical GeographicallyWeighted
   Regression Model 
3. Spatial distribution and opportunity mapping: Applicability of evidencebased
   policy implications in Punjab using remote sensing and global products 

1. Disscusions on the papers
2. GeoDa Lab 8: global spatial autocorrelation

3

3

（5/9）

Challenges of Data Quality

1. A data quality in use model for big data 
2. Can big data improve firm decision quality? The role of data quality and data diagnosticity 

1. Disscusions on the papers
2. GeoDa Lab 9: local spatial autocorrelation

3

3

（5/16）

Static Data Visualization

1. Basic principles of graphing data
2.  Graphical integrity
3. Narrative visualization: telling stories with data 

• Disscusions on the papers
• GeoDa Lab 10: cluster analysis

3

3

（5/23）

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

（5/30）

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

（6/13）

Final Project Presentation and Take Home Exam

Potluck (Drinks and Snacks)

0

6