Learning
Goals:
This
course will introduce graduate students to the research strategies for the application
of GIS and Big Data to issues in a variety of research areas, including
identifying the variable and nature of data, designing quantitative and
qualitative methods, and preparing and presenting research results. Review of
current research on 3S application, Big Data, and emerging debates will also be
included. Active learning techniques will be applied to engage
students with the material, participate in the class, collaborate with each
other, and learn from each other. This course combines lectures, readings,
discussions, and hands-on exercises and projects to help students with 3S and
Big Data background to develop more knowledge and techniques for critical
thinking, problem solving and decision-making.
July 2, 2018: Morning | Introduction to the Class;
Introduction to Research: an Overview |
July 3, 2018: Morning | Research Design |
July 3, 2018: Afternoon | Laying out a 3S Research Project:
From Non-spatial to Spatial |
July 3, 2018: Evening | Health GIS |
July 4, 2018: Morning | Big Data
Applications and Challenges for the Understanding of Cities |
July 5, 2018: Morning | Lab and Discussion |
July 5, 2018: Afternoon | Lab and Discussion |
July 5, 2018: Evening | Report your Research |
July 6, 2018: Morning | Group Presentation & Discussion |
Reading List
1. Frank, L., J. Sallis, T. Conway, J. Chapman, B. Saelens, and
W. Bachman. 2006. Many pathways from land use to health. Journal of the American Planning Association 72 (1): 75-87.
2. Raja, S., Yin, L., Roemmich, J., Ma, C., Epstein, L., Yadav,
P. and Ticoalu, A. 2010. “Food environment, Built Environment, and Women's BMI:
Evidence from Erie County, New York” Journal
of Planning Education and Research, 29(4), pp444-460.
3. Yin, L., Raja, S., Li,
X., Lai, Y., Epstein,
L. H., and Roemmich, J. N. 2013.
Neighborhood for Playing: Using GPS, GIS, and Accelerometry to Delineate Areas
within which Youth are Physically Active. Urban
Studies, 50(14), pp2922-2939
4. Hajrasouliha, A. and Yin,
L. 2015. The Impact of Street Network Connectivity on Pedestrian Movement. Urban Studies.
52(13). pp2483-2497
5. Mao, L. and
Bian, L. 2011. Massive agent-based simulation for a dual-diffusion process of
influenza and human preventive behavior. International
Journal of Geographical Information Science. 25(9) pp1371-1388
6. Yin, L. 2013. Assessing Walkability in the City of Buffalo: An
Application of Agent-Based Simulation, Journal
of Urban Planning and Development. 139(3),
pp. 166–175
7. Solhyon,
B. Raja, S., Park, J., Epstein, L., Yin, L., and Roemmich, J. 2015. Park Design
and Children’s Active Play: A Micro-Scale Spatial Analysis of Intensity of Play
in Olmsted’s Delaware Park. Environment and Planning B 42(6). pp1079-1097
8. Kim, H.M. and Kwan, M. 2003. Space-time Accessibility
Measures: A Geocomputational Algorithm with a Focus on the Feasible Opportunity
Set and Possible Activity Duration. Journal
of Geographical Systems, 5(1):71-91.
9. Purciel, M.,
Neckerman, K. M., Lovasi, G. S., Quinn, J. W., Weiss, C., Bader, M. D. M., et
al. 2009. Creating and validating GIS measures of urban design for health
research. Journal of Environmental Psychology, 29, 457e466.
10. Yin, L. 2017.
Street Level Urban Design Qualities for Walkability: Combining 2D and 3D GIS
Measures Computers, Environment and Urban
Systems 64, pp288-296
11. Goodchild, M. F. and
Li, L. 2012. Assuring the quality of volunteered geographic information. Spatial Statistics. 1. pp110-120
12. Miller, H. and
Goodchild, M. F. 2015. Data-driven geography, GeoJournal. 80(4), pp449-461
13. Arribas-Bel, D. 2014.
Accidental, open and everywhere: Emerging data sources for the understanding of
cities. Applied Geography, 49. pp45-53
14. Yin, L.,
Cheng, Q., *Wang, Z. and Shao, Z. 2015.
‘Big Data’ for Pedestrian Volume: Exploring the use of Google Street View
Images for Pedestrian Counts. Applied Geography 63,
pp337-345
Yin, L. and Wang Z. 2016. Measuring Enclosure for Street
Walkability: Using Machine Learning Algorithms and Google Street View
Imagery Applied Geography 76, pp147-153.
16. Schweitzer, L. 2014. Planning and Social Media: A Case Study of
Public Transit and Stigma on Twitter, Journal
of the American Planning Association, 80:3, 218-238
