Projects

Findings: Despite a devastating N-S Civil War, phone calls still connect N-S cities, moreso than E-W cities. Coastal cities host most of the calling.

Method: mobile phone call flows from city to city.

Good for: understanding a nationwide economic system.

Findings: Given it’s ability here to find geographic variances in students admitted to a university, the Support Vector Machine is a good tool for recognizing hidden patterns in a dataset.

Method: the paper tests Support Vector Machine against the more traditional method of Linear Discriminant Analysis.

Good for: Researchers looking for new or additional pattern recognition tools.

Findings: It’s hard to create a chart showing where a million people were picked up and dropped off. We can use clusters to simplify visualization.

Method: Spacial clustering of massive GPS points and mapping cluster-based flow measures to discover spacial and temporal patterns. Results are demonstrated through taxi cabs in Shenzhen, China.

Good for: If you are interested in traffic analysis or would like a user-friendly system to show where large groups of people are moving at different times.

Findings: Understanding how people travel through Singapore and how successful the existing travel options are in connecting those people to important places like their homes, schools and workplaces will be important in planning for an increasingly urbanized future.

Method: Activity-based modeling.

Good for: Understanding mobility in Singapore. Improving ride-share services.

Findings: A technique called Weighted Radial Variation makes it easier to visualize migration connections created from a node-edge matrix.

Method: Extracting stars where the node is the center. Creating a signature vector comprised of an edge weight circling around the node from 0-360 degrees.

Good for: If you are looking to publish maps that are easier to understand.

Findings: People’s social ties, not just cost and distance, play a role in deciding where people migrate.

Method: A Bayesian place-pair mode tested against other existing models used to predict migration like the traditional gravity model.

Good for: Understanding inter-city migration in the United States, which impacts the economic lives of cities, urban planning and more. If you are considering how to prepare for fluctuations in housing or employment based on inflows and outflows.