Measuring the structural similarity of network time prisms using temporal signatures with graph indices

New publication: Jaegal Y, Miller HJ. (2019) Measuring the structural similarity of network time prisms using temporal signatures with graph indices. Transactions in GIS. 00:1–24. https ://

Abstract.  The network‐time prism (NTP) is an extension of the space‐time prism that provides a realistic model of the potential pattern of moving objects in transportation networks. Measuring the similarity among NTPs can be useful for clustering, aggregating, and querying potential mobility patterns. Despite its practical importance, however, there has been little attention given to similarity measures for NTPs. In this research, we develop and evaluate a methodology for measuring the structural similarity between NTPs using the temporal signature approach. The approach extracts the one‐dimensional temporal signature of a selected property of NTPs and applies existing path similarity measures to the signatures. Graph‐theoretic indices play an essential role in summarizing the structural properties of NTPs at each moment. Two extensive simulation experiments demonstrate the feasibility of the approach and compare the performance of graph indices for measuring NTP similarity. An empirical application using bike‐share system data shows that the method is useful for detecting different usage patterns of two heterogenous user groups.

Geospatial Data for Healthy Places: Building Environments for Active Living Through Opportunistic GIScience

On September 19 2019, I gave a lecture in the Methods: Mind the Gap Webinar Series of the National Institutes of Health Office of Disease Prevention (ODP): Geospatial data for healthy places: Building environments for active living through opportunistic GIScience.  A video of the lecture and slides is posted here

In this lecture, I discuss the role of geospatial technologies and data in facilitating quasi and natural experiments about built environment factors that encourage active living.   I also extend this idea to the concept of geographic information observatories: systems for ongoing data collection and analysis that facilitate opportunistic science that can leverage real-world events via ongoing observation, experimentation, and decision-support.

NSF Sustainable Urban Systems “New Mobility, Cities and Data” Workshop Report

On July 15-16 2019, a diverse group of university researchers and community stakeholders from Columbus, Ohio and Portland, Oregon participated in a workshop to explore issues surrounding new mobility technologies, sustainable urban systems and data.  This event was organized in response to the National Science Foundation Dear Colleague Letter (NSF 19-032; “Concepts for Advancing Sustainable Urban Systems (SUS) Research Networks”) released in December 2018.

Deeper scientific understanding of cities and more nuanced, effective sustainability policy and planning interventions are crucial as we move towards an almost completely urbanized planet by the end of the 21st century.  A pressing concern are questions and needs relating to new technology-enabled services that are disrupting the mobility landscape of cities.  Urban mobility is experiencing a revolution, much of it driven by the private sector, with new technologies and services involving light individual transport (e.g., scooters), shared vehicles, microtransit, mobility as a service and eventually connected and autonomous vehicles.  The impacts of the new mobility revolution on urban sustainability is uncertain: similar to the introduction of cars and highways in the early 20th century, it is possible that mobility technologies and services that individually appear to be sustainable and beneficial may collectively reshape cities to have larger environmental footprints, greater inequality and/or less economic flexibility and resilience.

Urban sustainability data observatories (USDOs) are a means for persistent, ongoing data collection, archiving and analysis to enable new knowledge about complex human and coupled human-natural systems, such as cities. They integrate many of the diverse elements that are needed to significantly advance sustainable urban systems (SUS) science. Importantly, they support new data and methods for understanding current SUS drivers and interactions, advancing comparative studies, developing the science to model the future of SUS, and fostering the science of knowledge co-production. USDOs can also facilitate more sensitive and nuanced understanding of how context and history shape the outcomes of policy and planning interventions in complex urban systems.  Finally, they can go beyond observation to enable platforms and processes for data-enabled engagement and discussions among heterogeneous stakeholders concerned with the environmental, social and economic future of their community.

A report on the workshop is now available here (in PDF) SUS Workshop Report – FINAL 25 Sept 2019.