Atsushi Nara

Space-time analytics of tracks for the understanding of patterns of life

Location-aware devices have enabled the recording of personal whereabouts at fine spatial and temporal resolutions. These temporal sequences of personal locations provide unprecedented opportunities to explore patterns of life through space-time analytics of movement and stops of individuals. At a disaggregated level, patterns of life reveal the activities and places as well as the development of routines for individuals. At an aggregate level, patterns of life suggest potential social networks and social hot spots for interactions. Moreover, the concept of “neighborhood” can become personalized and dynamic with space-time analytics to identify the spatial extent to which an individual operates and how the extent varies with temporal granularity. This chapter starts with an overview of space-time track analysis. While time geography has proven useful for analysis of space-time paths and space-time constraints on human activities, its scalability to large data sets is questionable. This chapter provides a conceptual framework and methodology for conducting space-time analysis with a massive number of space-time tracks including over a million points of moves and stops over the course of a year. The examples demonstrate the usefulness of the proposed conceptual framework and methodology to distill complex patterns of life at both disaggregate and aggregate levels that can lead to research opportunities for space-time integration in GIScience for an improved understanding of geography.

Language Services In Hospitals Vary By Ownership And Location

Twenty-four million people in the United States have limited English proficiency. They experience barriers to health care because of their inability to communicate effectively with providers. Hospitals are required to provide language services that reflect the needs of people in their communities, but these services are not available systematically.

The Opportunities and Challenges with Social Media and Big Data for Research in Human Dynamics

Geographers and scientists can collect and analyze social media and Big Data via smartphones, sensors, and mobile devices with locational contents, such as global positioning system tags, check-ins, place names, and user location profiles. The dynamic characteristics of social media and Big Data offer geographers research opportunities for examining and modeling human behaviors, communications, and movements. To discuss this emerging research themes in the field of geography and GIScience, a series of special paper sessions were organized at AAG annual meetings in 2015 and 2016, Human Dynamics in the Mobile Age: Linking Physical and Virtual Spaces and Symposium on Human Dynamics Research: Social Media and Big Data. This short viewpoint paper first reports on a summary of papers presented in these AAG sessions. Then we discuss the current state-of-the-arts in human dynamics research and highlight their key concepts, opportunities, and challenges.

A meta-modeling approach for spatio-temporal uncertainty and sensitivity analysis: an application for a cellular automata-based Urban growth and land-use change model

ABSTRACT The paper presents a computationally efficient meta-modeling approach to spatially explicit uncertainty and sensitivity analysis in a cellular automata (CA) urban growth and land-use simulation model. The uncertainty and sensitivity of the model parameters are approximated using a meta-modeling method called polynomial chaos expansion (PCE). The parameter uncertainty and sensitivity measures obtained with PCE are compared with traditional Monte Carlo simulation results. The meta-modeling approach was found to reduce the number of model simulations necessary to arrive at stable sensitivity estimates. The quality of the results is comparable to the full-order modeling approach, which is computationally costly. The study shows that the meta-modeling approach can significantly reduce the computational effort of carrying out spatially explicit uncertainty and sensitivity analysis in the application of spatio-temporal models.

Surgical Phase Recognition using Movement Data from Video Imagery and Location Sensor Data

The automatic recognition of surgical phases has strong potential to help medical staff understand individual and group patterns, optimize work flows, and identify potential work flow risks that lead to adverse medical events in an operating room. In this chapter, we investigate the performance of context recognition on the movement of operating room staff throughout their work environment, which was measured by imaging and tracking. We employed an optical flow algorithm and trajectory clustering techniques to extract movement characteristics of surgical staff from video imagery and time-stamped location data collected by an ultrasonic location aware system, respectively. Then we applied a Support Vector Machine to time-stamped location data, optical flow estimates, trajectory clusters, and combinations of these three data to examine the intraoperative context recognition rate. Our results show that the integration of both video imagery and location sensor data improves context awareness of neurosurgical operations.

Exploring the imprint of social media networks on neighborhood community through the lens of gentrification

Gentrification, the rise of affluent socioeconomic populations in economically depressed urban neighborhoods, has been accused of disrupting community in these neighborhoods. Social media networks meanwhile have been recognized not only to create new communities in neighborhoods, but are also associated with gentrification. What relation then does gentrification and social media networks have to urban communities? To explore this question, this study uses social media networks found on Twitter to identify communities in Washington, DC. With space-time analysis of 821,095 geo-tagged tweets generated by 77,528 users captured from 15 October 2015 to 18 July 2016, we create a location-based interaction measure of tweets which overlays the social networks of the comprising users based on their followers and followees. We identify gentrifying neighborhoods with the 2000 Census and the 2010–2014 American Community Survey at the block group level. We then compare the density of location-based interactions between gentrifying and nongentrifying neighborhoods. We find that gentrification is significantly related to these location-based interactions. This suggests that gentrification indeed is associated with some communities in neighborhoods, though questions remain as to who has access. Making novel use of big data, these results demonstrate the important role built environment has on social connections forged “online.”

Big data: techniques and technologies in geoinformatics, edited by Hassan A. Karimi, Boca Raton, FL, CRC Press, 2014, 312 pp., US

Baltimore, MD: Johns Hopkins Press. Jefferies, M.E. and Yeap, W.K., eds., 2008. Robotics and cognitive approaches to spatial mapping. Berlin Heidelberg: Springer. Knauff, M., 2013. Space to reason: a spatial theory of human thought. Cambridge, MA: The MIT Press. Montello, D.R., 2010.The role of landmarks is exaggerated. Paper presented at Las Navas 2010: cognitive and linguistic aspects of geographic space, 5 July 2010, Las Navas del Marques, Spain. Newcombe, N.S. and Huttenlocher, J., 2000. Making space: the development of spatial representation and reasoning. Cambridge, MA: The MIT Press. Waller, D. and Nadel, L., eds., 2012. Handbook of spatial cognition. Washington, DC: American Psychological Association.