Somayeh Dodge recently gave a talk entitled “WhereNext: Towards a Cartographic Framework for Movement” at AutoCarto 2020 on Wednesday, November 18, 2020.
AutoCarto 2020 (cartogis.org/autocarto) was the 23rd International Research Symposium at the intersection of cartography and GIScience, presented by the Cartography and Geographic Information Society (CaGIS). The meeting was held virtually in an open access format. The theme in 2020 was WhereNext: “In this symposium, we not only look at where we are and where to go next, we also challenge the ideas of where and next in terms of meaning, communication, visualization, and reasoning in the new age of automation, robotic revolution, and artificial intelligence (AI).”
The full recording of the IEEE VIS Workshop on Information Visualization of Geospatial Networks, Flows and Movement (MoVIS 2020), held on October 26th, 2020, is now available to watch on-demand. View the recording here on YouTube.
Researchers frequently access and visualize data that represent spatial connections. These include GPS trajectories, migration movement, mobile phone-based movement, social networks, flights, commutes, SMS/phone call connections, human and animal movement trajectories, commuter flows, international trade, disease transmission flows, remittance flows, online friendship connections and transportation magnitudes. They are important in the study of globalization, wealth distribution, information spreading, epidemiological modeling, economics and infrastructure management. At the same time, analyzing these data requires advanced visualization methods. The objectives of this workshop were to explore the best ways to visualize large, spatio-temporal flow systems with both node and edge attribute data; describe the best visualization methods to unearth meaningful results; facilitate the use of diverse big data sets, communicate these results through visualization and description; and reflect upon how visualization is best communicated for urban and regional studies, geography, urban planning, public health, civil engineering, sociology and related fields.
The MoVIS ’20 workshop is situated in a larger body of research on geovisualization, geospatial data analytics and exploratory spatial data analysis (ESDA). This body of work includes mapping spatial datasets using different visual techniques, and assessing how humans interact and gain insights from spatial data. Spatial flow data is a special type of data that presents issues like the “haystack” problem of overlapping flows, edge effects, issues of using polygon centroids as origins or destinations, and issues of scale and modifiable areal units.
More info:
The MoVIS 2020 workshop was organized by Somayeh Dodge along with Clio Andris (Georgia Institute of Technology) and Alan MacEachren (Pennsylvania State University). Full program committee and more details about the workshop are available on the MoVIS 2020 page.
Modeling of the effects of NPIs in COVID-19 transmission
Browse visualizations here: covid19-mobility-vis
MOVE starts a new interdisciplinary collaboration with the UCSB Geography, Computer Science, Statistics and Applied Probability Departments, and the Information Systems and Modeling Group of the Los Alamos National Lab. The research is funded by VCR COVID-19 Seed Grants Program. The aim is to develop data-driven and knowledge-driven approaches to learn and model spatial and socioeconomic heterogeneity in exposure to COVID-19 and study the relationships between non-pharmaceutical interventions (NPIs) in terms of mobility and interaction and virus transmission at multiple geographic scales. Graduate students who are involved in this projects are: Evgeny Noi, Zijian Wan, and Vania Wang.
The MOVE@UCSB Laboratory at the Geography Department at UC Santa Barbara is seeking PhD students. We are looking for talented students with a strong quantitative background in GIScience, Geography, Computer Science, or Data Science, and interest in movement and mobility. Our group focuses on the study of movement using data science and geo-computational approaches, including movement data analytics, pattern mining, machine learning, geovisualization, -AI, modeling, and simulation. Please see our research areas for more information. The students will be engaged in multidisciplinary research to develop data science approaches to study movement in different contexts including human mobility, migration, health geography, movement ecology, and natural hazards and earth system sciences. Please find more information about the application process and the UCSB Geography Department here. The application deadline is December 15th, 2019. If you are interested in this opportunity, please contact Dr. Somayeh Dodge (sdodge[at]ucsb.edu) before submitting your application.
Analysis and Modeling of Movement of Tigers in Thailand
Through international interdisciplinary collaboration with the Department of National Parks, Wildlife and Plant Conservation of Thailand, MOVE Lab develops analytical approaches and simulation models to understand how tigers and leopards interact with their environment in their ecosystem in the Thailand Western Forest Complex. The aim is to increase our understanding of species resilience through the analysis and modeling of the conspecific and predator-predator interactions across a range of environmental conditions and levels of human impact. The outcomes of this study will contribute to better understanding, and ultimately prediction of the survivability and behavioral changes of tigers in a changing environment. This is especially important with the increase of human-wildlife interaction as a result of greater intensity of land-use change and urbanization.
This research focuses on understanding how humans perceive movement visually. Movement is realized in both a three- (or four-) dimensional space (i.e. location and time) and a multidimensional attribute space (i.e. context variables). The syntheses of these two spaces need new effective tools for dynamic visualization of the traversal of a moving individual through these dimensions. Our research advances cartographic theories for movement and creates innovative visualization methods to enhance fundamental knowledge on how motion should be represented in space and time and across scales. For a prototype of this research please see the DYNAMOvis project.
The aim is to develop scalable computational data analytics, simulation, and prediction models to relate movement to its context (i.e. behavior, geography, environment, and interactions). This helps us predict individuals’ responses to environmental and behavioral variabilities at multiple scales. This research breaks new ground in computational movement data analytics for integrating heterogeneous data sets to inform and calibrate more realistic context-sensitive simulation and predictive models.
Somayeh Dodge has been awarded an NSF grant as the sole PI of the project “Visualizing Motion: A Framework for the Cartography of Movement” (NSF award # 1853681, amount $328,756). Evgeny Noi will be working on this project.
This project will examine how motion as a dynamic phenomenon, with complex space and time dimensions, is effectively represented in geographic visual displays. It will develop visualization methods and tools to map movement patterns and interaction between individuals.
