Many researchers seek to understand differences between people and between places. One approach to identifying variations is cluster analysis. The goal typically is to better understand spatio-temporal dynamics of people and places. Development of classifications based on clustering techniques can help researches identify spatial patterns, develop trajectories of change, and develop theories and models of change.
This session seeks to group papers that apply clustering techniques to various topics and themes such as:
- population characteristics, such as age, income, or race
- economic characteristics
- industry characteristics
- migration characteristics
- flow characteristics.
|Presenter||BING SHE*, University of Michigan China Data Center, XINYAN ZHU, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, SHUMING BAO, University of Michigan China Data Center, Knowledge-based Discovery of Industry Data based on chain analysis||20||5:20 PM|
|Presenter||Yu Lan*, University of North Carolina at Charlotte, Elizabeth Delmelle, University of North Carolina at Charlotte, Eric Delmelle, University of North Carolina at Charlotte, Harrison Campbell, University of North Carolina at Charlotte, An interactive web-based system to visualize neighborhood dynamics||20||5:40 PM|
|Presenter||Eric Boschmann*, University of Denver, The urban mobility of older adults: An analysis of travel behaviors and life course||20||6:00 PM|
|Presenter||Robert Keel*, University of Toronto, Steven Farber, University of Toronto, What Factors Influence the Average VKT of Office Employment Clusters? An examination of commuting patterns in the GTHA||20||6:20 PM|
|Presenter||Michael Niedzielski*, University of North Dakota/Institute of Geography and Spatial Organization PAS, Neighborhood economic activity classification: work and non-work opportunities in Poland||20||6:40 PM|
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