Research

My research focuses on the development of analytical methods, spatial-temporal simulation and modeling approaches, and visualization techniques for movement and spatiotemporal processes. My current research projects are as follows:

Exploring trip patterns and equitable access of Nice Ride bike sharing in Twin Cities

Launched in 2010, Nice Ride Minnesota is a nonprofit organization running the Twin Cities bike sharing system between June and November each year. It currently offers over 1800 bikes through 201 stations across Minneapolis and Saint Paul. This project aims to analyze spatiotemporal patterns of bike sharing trips in the Twin Cities to determine if access to the bike sharing service is equal for neighborhoods of different demographic characteristics. This knowledge is essential for modeling and designing equitable and accessible bike sharing systems.

This project is supported by the MPC’s Summer 2018 Diversity Fellowship. Student fellows working on this projects are: Wing Yi (Pinki) Wong and Jueyu (Olivia) Wang.

Computational movement ecology: Agent-Based Simulation of Tiger Movement

I am currently part of an international interdisciplinary team, together with Prof. James D. Smith (tiger biologist from the UMN Department of Fisheries, Wildlife and Conservation Biology), Prof. Sean Ahearn (Hunter College — CUNY), and the Department of National Parks, Wildlife and Plant Conservation of Thailand. Our research is aimed at generating new insights into the movement behavior of tigers and their interactions in their ecosystem in the Thailand Western Forest Complex, a United Nations World Heritage site. Our research is designed to develop analytical approaches and simulation models to understand how tigers interact with their environment. We are developing an agent-based simulation system which integrates the different components of tiger movement to assess the role that environment (e.g. habitat, prey abundance, co-occurrence of leopards, and human factors) plays in the movement of tigers in their ecosystem and their behavioral variability.

This video animates GPS tracking data of a tiger collected between December 2009 and July 2010 with a sampling rate of one hour. The red line represent the tiger movement track. The thickness of the track indicates the movement speed of the tiger.

DynamoVis: Dynamic Visualization of Animal Movement Data

DynamoVis is an interactive and exploratory visualization tool that allows for the creation of intuitive, interactive, and high quality animations suitable for presentations of animal movement data. The visualization serves as a preliminary and exploratory analysis tool, allowing biologists and movement ecologists to investigate their data and identify environmental drivers of movement and potential correlations, and generate new hypotheses on the relationships between animal’s movement behavior and landscape use.

This project has been supported by the Student-Faculty Research Awards of the Dean of College of Letters, Arts, and Sciences at the University of Colorado, Colorado Springs, and the University of Minnesota’s Undergraduate Research Opportunities Program.

You may download DynamoVis and find more information about the tool here.

Generated using DynamoVis, these videos shows how movement of Galapagos Albatrosses are supported by wind patterns. More information here.

This video presents a demo of DynamoVis tool:

Past Projects

Movebank Env-DATA Project

envdata-wordcloud

As part of the NASA Earth Science Division Ecological Forecasting Program, in a multidisciplinary collaboration, directed by Prof. Gil Bohrer (Associate Professor for Ecological Engineering), between the Ohio State University (USA) and the Max Planck Institute for Ornithology (Germany), I participated in the development of the Environmental Data Automated Track Annotation System (Env-DATA) as a free online tool that allows users to link animal tracking data with ambient atmospheric observations and underlying landscape information. The new Env-DATA system enhances Movebank, an open portal of animal tracking data, by automating access to environmental variables from global remote sensing, weather, and ecosystem products from open web resources. The general aim of this project is to develop tools and methods to investigate the links between the Environment and animal movement patterns. This research involves handling and analyzing Big Data; such as diverse and multivariate remote sensing data of the environment, together with long-term animal tracking data, obtained from GPS, and Argos satellites, Geo-sensors, or RFID tags.

More info: Dodge, S., Bohrer, G., Weinzierl, R., Davidson, S.C., Kays, R., Douglas, D., Cruz, S., Han, J., Brandes, D., and Wikelski, M., (2013). The Environmental-Data Automated Track Annotation (Env-DATA) System: Linking Animal Tracks with Environmental Data. Journal of Movement Ecology, July 2013, 1:3, BioMed Central Ltd. doi:10.1186/2051-3933-1-3.