SemesterFall Semester, 2025
DepartmentInternational Doctor Program in Asia-Pacific Studies, First Year International Doctor Program in Asia-Pacific Studies, Second Year
Course NameGIS for Social Science
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



9/1




  • Introduction

  • Course Overview

  • What is GIS

  • Understanding ArcGIS & GIS Terminology



                       




  • ArcGIS Basics

  • Loading Data

  • Scales

  • Navigation

  • Online Help



3



1



9/8




  • Making Maps



 




  • GIS and Mapping: Pitfalls for Planners(Kent & Klosterman 2000)




  • Types of Maps

  • Elements of Cartography



3



3



9/15




  • Working with Maps & Data I




  • Making a Place for Space: Spatial Thinking in the Social Sciences (Logan 2012)




  • Attribute Query

  • Joining & Relating

  • Data Classification

  • Projection



3



3



9/22




  • Working with Maps & Data II




  • GIS, Public Service_PA (Haque, 2003)

  • Four Ways We Can Improve Policy Diffusion Research_PA(Fabrizio Gilardi1, 2016)




  • Attribute Query

  • Joining & Relating

  • Data Classification

  • Projection



3



3



9/29



Holiday


       

10/6



Holiday



 



 



 



 



10/13




  • Working with Census Data I




  • GIS Education in U S Public Administration Programs Preparing the Next Generation of Public Servants (Nancy J. Obermeyer, Laxmi Ramasubramanian & Lisa Warnecke, 2016)




  • Understanding Census Data & Geometry

  • Accessing Census Data



3



3



10/20




  • Working with Census Data II




  • Spatial data mining and geographic knowledge discovery—An introduction (Mennis & Guo 2009)

  • Spatial analysis and GIS in the study of COVID-19. A review_PH(Ivan Franch-Pardo a,⁎, Brian M. Napoletano b,⁎, Fernando Rosete-Verges a, Lawal Billa c, 2020)




  • Interpreting Census Variables

  • Charts & Graphs for Data Display



3



3


10/27

  • Final Project Proposal Discussion I




  • None




  • Individual Discussion in Office


   

11/3




  • Geoprocessing




  • PPGIS_PA (Ganapati, 2011)




  • Geoprocessing Tools: Buffers, Clips, Unions



3



3



11/10




  • Address Mapping




  • Geographic Information Systems and the Spatial Dimensions of American Politics (Cho & Gimpel 2012)




  • Geocoding



3



3



11/17




  • Final Project Proposal Discussion II




  • None




  • Individual Discussion in Office


   

11/24




  • Network Analysis




  • Measures of Spatial Accessibility to Health Care in a GIS Environment (Luo & Qi 2003)

  • Accessibility, equity and health care_PH(Tijs Neutens, 2015)




  • Spatial Accessibility



3



3



12/1




  • Identifying Statistical Clusters and Exploratory Spatial Data Analysis (ESDA) of Social Data




  • Richardson in the Information Age: Geographic Information Systems and Spatial Data in International Studies (Gleditsch & Weidmann2012)

  • Spatial Big Data Analysis of Political Risks_PS(Chuchu Zhang 1,2,y, Chaowei Xiao 3,*,y and Helin Liu, 2019)

  • Coproduction of Government Services and the New Information Technology: Investigating the Distributional Biases (Clark, Brudney & Jang 2013)

  •  Spatial spillover effects of corruption in Asian_PS(Masoud Khodapanah1 | Zahra Dehghan Shabani2 |

    Mohammad Hadi Akbarzadeh1 | Mahboubeh Shojaeian1, 2020)

  • Explore Spatial Data with GeoDa (Anselin 2003)




  • Spatial Weight Matrix

  • Spatial Autocorrelation

  • Exploratory Spatial Data Analysis

  • Spatial Weighted Regression



3



3



12/8




  • Spatial Heterogeneity




  • Poverty GWR_PH  (Tzai-Hung Wen1, Duan-Rung Chen2, Meng-Ju Tsai3, 2010)




  • Geographically Weighted Regression



3



3



12/15




  • Final Project Presentation and Final Exam


 

  • Potluck (Drinks and Snacks)

  • Take-Home Final Exam



3



3




 



 


Teaching Methods
Teaching Assistant

TBA


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 (Presentation of the assigned article and the output of the Lab practice)

  • 10% for the classroom discussion

  • 10% for the participation


Textbook & Reference

 



 



See the Schedule.


Urls about Course
https://1drv.ms/u/s!AoacP5CovPLS2CUynrfq6Bq6cZbH?e=lu8bw5
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