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Professor Shih-Lung Shaw, Fleming Lecture, 2009 AAG Meeting

Individual-based Tracking Data: Potentials and Challenges to Transportation Geography

Shih-Lung Shaw

Professor and HeadDepartment of GeographyUniversity of Tennessee

Knoxville, TN 37996-0925 U.S.A.E-mail: [email protected]

Fleming Lecture, 2009 AAG Meeting, March 24, 2009

Acknowledgement:This research is funded by NSF Grant #BCS-0616724

and Microsoft Research Asia.

mailto:[email protected]


“Imagine that your business had a complete log of your customers’ wanderings – every trip to the grocery store, every work commute, every walk with the dog. What could you learn about them? ….. Just as important, ….., would customers concerned about privacy …..? This isn’t science fiction. A nascent industry extending from the laboratories of Google and Nokia to a host of data-fueled startups is wrestling with these very questions.”

(“The Next Net” by Stephen Baker, BusinessWeek, March 9, 2009, p. 42)

“Location-aware phones and apps now deliver the hidden information that lets users make connections and interact with the world in ways they never imagined. The future is here and it’s in your pocket.”

(“Inside the GPS Revolution” by Erin Biba, Wired, February 2009, p. 64)


Individual-based tracking data– Modern tracking technologies– Accuracy and privacy– Selected applications

Time geography for individual tracking data in a hybrid physical-virtual space

A space-time GIS in support of representation, analysis, and visualization of human activities and interactions in physical and virtual spaces

A preliminary list of research challenges

Outline of this presentation:


What’s new about individual tracking data?


Individual-based tracking data sets have been widely used in research and various applications for many decades. For example,

– Telemetry tracking data used in fisheries and wildlife research and management,

– Travel/Activity diary data used in transportation studies,

– Barcode asset/shipment tracking data used in business and logistics,

– Credit card transactions data, telephone logs, …

Individual-based tracking data:


Goodchild and Janelle (1984) used space-time diaries of 1,500+ respondents in Halifax, Canada to reveal distinctive diurnal patterns in the city’s social geography. They argued that:

– “An integrated space-time framework for gathering and analyzing such information would be of advantage to many professions and disciplines concerned with the description, explanation, and control of urban processes. However, the principal impediment to incorporating these considerations in practical analyses, …, has been lack of data.” (p. 808)

(emphasis added)


With modern information and communications technologies (ICT) and location-aware devices, it is becoming easier to track individuals and their activities.

Global positioning system (GPS) provides a low-cost way of tracking moving objects.

Modern tracking technologies:


Professor Shih-Lung Shaw, Department of Geography, UTK



Global positioning system (GPS) provides a low-cost way of tracking the locations of moving objects.

Cellular phones are quickly becoming an important source of collecting individual tracking data.


MIT SENSEable City Labhttp://senseable.mit.edu/projects/graz/graz.htm

http://senseable.mit.edu/projects/graz/graz.htm



Global positioning system (GPS) provides a low-cost way of tracking the locations of moving objects.

Cellular phones are quickly becoming an important source of collecting individual tracking data.

RFID, Internet surfing, …


Accuracy and Privacy


Global Positioning System (GPS):– Horizontal positional accuracy is about 5 m (16 ft) to

15 m (50 ft)– Requires clear line of sight

Cell Phone Towers:– Horizontal positional accuracy ranges from 200 m

(660 ft) to 1,000 m (3,300 ft)

Wi-Fi Positioning System (WPS):– Horizontal positional accuracy is, on average, about

30 m (100 ft) depending on the density of Wi-Firouters.

Positional accuracy of tracking technologies:


Skyhook Wirelesshttp://www.skyhookwireless.com/

http://www.skyhookwireless.com/


It’s clear that we are constantly tracked by various service providers:

– Internet service providers– Cellular phone service providers– Google, Yahoo, Microsoft, …– Credit card banks, …

Concerns are:– Who has access to the data?– How are the data being used?– How can we protect privacy while fully enjoying the

convenience provided by the modern ICT?

Privacy concerns:


Some observations:– Someone must pay for the free services on the

Internet, such as Google Mail, Google Maps, Google Desktop, YouTube, …

– Google’s goal is to index all kinds of information. For free?

– It appears that an increasing number of people are willing to trade privacy for convenience and/or fun (either knowingly or unknowingly).

The bottom line is:– These technologies are here now and are likely to

grow in the foreseeable future.


Selected Example Applications


Sense Networkshttp://www.sensenetworks.com/

According to Baker (BusinessWeek, March 9, 2009),

– “… a startup called Sense Networks is pouring over the movements of nearly 4 million cell-phone users over the course of a year. They have been tracked by global positioning systems, by cell towers …, or by local Wi-Finetworks …”

– “Phone companies and advertisers provide Sense raw data on people’s movements and behavior.”

– “To understand what Sense is doing, it’s easiest to think about – what else? – Google. The search engine has dissected the behavior of Web surfers, including which sites they visit and which they link to.”

http://www.sensenetworks.com/


Google Latitudehttp://www.google.com/latitude/intro.html

http://www.google.com/latitude/intro.html


Fire Eaglehttp://fireeagle.yahoo.net/

http://fireeagle.yahoo.net/


LifeAwarehttp://www.lifeaware.net/

http://www.lifeaware.net/


Android Applicationshttp://www.android.com/

ShopSavvy, nru, Loopt, Lacale, GeoLife

http://www.android.com/


The above examples indicate that many user applications based on individual tracking data are becoming available.

– Combination of tracking and ICT technologies has reached a level that is capable of providing some useful location based services (LBS) applications.

– These applications appear to have attracted certain groups to agree to be tracked in exchange for convenience, useful tools, and/or fun.

– Individual tracking data collected by these service providers has many potential uses for both research and applications.


Potentials of Individual-based Tracking Data


Individual-based tracking data today have the following characteristics:

– Finer spatial resolution (“points” vs. “zones”)– Finer temporal resolution ( “seconds/minutes” vs.

“hours/days”)– Larger data volume– Real-time and dynamic changes– Complex interactions among individuals and the

environment– Easier to cross-reference with other digital databases– …

Characteristics of individual tracking data:


Individual tracking data have been studied in various fields, such as:

– human behavior– transportation and logistics– evacuation/emergency management– animal behavior and habitats– marketing and business– data mining– computational geometry– simulation– …


Hägerstrand’s (1970) time geography provides a useful framework for studying individual activities under different constraints (capability, authority, and coupling constraints) in a space-time context.

Some key concepts of Time Geography:– Space-time path (ST path)– Space-time prism (ST prism)– Potential path area (PPA)

Basic Concepts of Time Geography:


time

space

12

3

Space-Time Path

time

space

t2

t1

Space-Time Prism

Potential Path Area


Information and communications technologies (ICT)have introduced important changes to the ways that people carry out their activities (and travel).

For example,

– Cellular phones provide people with new freedom in space because we are no longer constrained by the fixed locations of landline phone service.

– We now can purchase plane tickets and many other items on the Internet even when travel agencies and stores are closed for business. This represents new freedom in time (as well as in space) due to the use of ICT.

Information and communication technologies (ICT):


While transportation serves as the means of carrying out activities in physical space, ICT provide the means for individuals to perform activities in virtual space.

Information and communications technologies (ICT) allow people to interact with others beyond the physical proximity in virtual space (i.e., the concept of extensible agents; Janelle, 1973; Adams, 1995; Kwan, 2000a).


Based on the concept of extensible agents, we extend Hägerstrand’sspace-time path concept to represent both physical and virtual activities, with virtual activities represented as relations reaching out from a space-time path.

ST Path

phone call

Instant messaging

email

time

space

travel

travel


Four types of interactions:

With ICT, people can participate in activities through tele-presence in addition to the conventional physical presence.

Four types of communication modes based on their spatial and temporal characteristics (Janelle, 1995; Harvey and Macnab, 2000).

SpatialTemporal Physical presence Tele-presence

Synchronous SP

Face-to-face meetingST

TeleconferenceInstant messages

Asynchronous APPost-it noteBulletin board

ATE-mailVoice mail


Four types of human interactions based on their spatio-temporal relationships:

time

space

t2

t1

ST (co-location in time)

AP (co-location in space)

time

space

SP (co-existence)time

space

t2

t1

time

space

AT (no co-location in either space or time)


Representation of individual activities in GIS:

Represent individual activities on a space-time path using spatio-temporal linear referencing and dynamic segmentation.

space-time path

space

time

1

2

4

6

8

9

7

Physical activities:1. Drive to work3. Walk to lunch4. Have lunch5. Walk back from lunch6. Drive back home8. Grocery shopping9. Return home

Virtual activities:2. Instant messaging with colleagues7. Receive a cell phone call from

spouse to do grocery shopping

3

5

Professor Shih-Lung Shaw, Fleming Lecture, 2009 AAG Meeting(Cell phone tracking data provided by Christian Licoppe, Ecole Nationale des Telecommunications, France.)



Find locations of ST paths at time T

time

space

TL1

L2

Locate time for point A on the ST path

T interpolate

A

time

space

T6T5

T4T3

T2T1

Find ST paths that visited location L

time

spaceL

time

space

P

Find ST paths interacting with person P

Exploratory spatio-temporal analysis functions:


AT(no co-locationIn either space or time)

AP(co-location in space)SP

(co-existence)

ST(co-location in time)


Network-based space-time prism(different O-D)



Potential AP interactionsPotential SP interactions

space

time

t1

t2

l1 l2 space

time

l1 l2

t11

t12

t21

t22

Potential ST interactions

space

time

t1

t2

l11 l12 l21 l22

Potential AT interactions

space

time

t

l11 l12 l21 l22


initiatorreceiver

initiator

receiver

A case for potential SP interactions

A case for potential ST interactions A case for potential AT interactions

A case for potential AP interactions

For additional information, please see Yu & Shaw (2008) in IJGIS.


Uncover Space-Time Patterns in Large Data Sets:

Migration Data Set: Northern Plains survey dataarea of wide-spread and persistent population losssurvey of socio-demographics & attitudesmigration history data:

– roughly 400 respondents per state– over 2800 individuals– more than 11,000 migration moves

Survey data collected by the Bureau of Business and Economic Research at The University of Montana and funded by Congressional Appropriation secured by Sen. Byron Dorgan (North Dakota). Data set was provided by Dr. Christiane von Reichert at The University of Montana.


Temporal view vs. Spatial view


Professor Shih-Lung Shaw, Fleming Lecture, 2009 AAG MeetingThis visualization function was developed with assistance from Yibin Zhao.


Professor Shih-Lung Shaw, Fleming Lecture, 2009 AAG MeetingFor additional information, please see Shaw, Yu & Bombom (2008) in Transactions in GIS.



Five-year interval

Three-year moving window

Individual-year interval

Generalized Space-Time Paths

For additional information, please see Shaw, Yu & Bombom (2008) in Transactions in GIS.

Professor Shih-Lung Shaw, Fleming Lecture, 2009 AAG MeetingFor additional information, please see Shaw, Yu & Bombom (2008) in Transactions in GIS.



Human activities are often organized in certain temporal orders such that we can accomplish various tasks.

It is important to view human activities as processesrather than independent events.

“Projects are composed of a series of tasks and act as the vehicle for goal achievement. ..… The pursuit of projects involves events and actions that are incorporated into an individual’s path.” (Golledgeand Stimson, 1997, p. 271)

Representation of processes in space-time:


For additional information, please see Shaw & Yu (2009) in Journal of Transport Geography.

Professor Shih-Lung Shaw, Fleming Lecture, 2009 AAG MeetingFor additional information, please see Shaw & Yu (2009) in Journal of Transport Geography.



For additional information, please see Shaw & Yu (2009) in Journal of Transport Geography.


Do we have adequate theories/models/methods to deal with the “dynamically changing and real-time individual tracking data”?

– Transportation geographers’ interests have centered on “the location and geographic pattern of transport systems and the magnitude of the movement or spatial interaction over the elements of such systems”(Black, 2003, p. 3).

– Time has received relatively minor attention in transport geography, with exceptions such as studies of time as an impedance measure and time-space convergence (Janelle, 1969,1975; Knowles, 2006).

A preliminary list of research challenges:


– To what extent are some of the classical theories and models in transport geography, such as gravity models, facility location models, and spatial choice models, still valid in today’s world with modern ICT?

– Lyons (2009) guest editorial article of “The reshaping of activities and mobility through new technologies”for a special issue of Journal of Transport Geographyoffers a good overview of the challenges we are facing.

– With the dynamic and real-time tracking data, it’s time to ask “What about time in transportation geography?” (Shaw, 2006)


How can we go from individual tracking data to “geography of connections”?

– There exist complex relationships in individual tracking data, both among activities of the same individual and among activities of different individuals.

– Our space-time GIS offers some basic exploratory spatio-temporal analysis functions to examine these hidden complex relationships. There remain many challenging research questions. For example, how to track and analyze interactions between physical and virtual activities over time?

– This also is relevant to applications such as social networking.


What are the “locations in virtual space” when tracking individuals?

– For example, what is the location of a person who is playing the World of Warcraft game on the Internet?Is it the physical location of the person, the server location he/she connects, or something else? How do we represent such virtual locations to best support analysis of human activities and interactions?

– How do we define and represent the concept of space-time prism in virtual space?

– There is a need to encourage more intellectual discussions such that we can develop a theoretical framework for locations/interactions in virtual space.


What can we do with the “historical individual tracking data”?

– Most applications have focused on the real-time use of individual tracking data.

– Analyzing archived individual tracking data could tell us a lot about patterns, changes, trends, anomalies, etc. Such information can enhance our understanding of the increasingly dynamic society and can be useful for planning, marketing, and other purposes.

– What data should be archived? How to extract hidden spatio-temporal patterns and relationships in the data sets?


How can we manage and analyze “large and dynamic individual tracking data” effectively and efficiently?

– Approaches of managing and analyzing individual tracking data must be scalable since the data volume could be huge.

– How do we ensure adequate performance for real-time, near-real-time, or even offline analyses and applications? Spatio-temporal indexing? High performance computing (HPC)? …


The changes in today’s world are pushing us to:

– move from an “aggregate” approach towards a “disaggregate” approach;

– move from a “static” approach towards a “dynamic and real-time” approach; and

– move from a “space-centric” approach towards a “space-and-time-centric” approach,

I am sure that I have left out some other important research challenges.

One fundamental research question is how spatial interactions today are different from those studied by Edward L. Ullman in the 1950s.

Conclusion


Thank you!


Adams, P. 1995. A reconsideration of personal boundaries in space–time. Annals of the Association of American Geographers, 85, 267–285.Asakura, Y. and Hato, E. 2004. Tracking survey for individual travel behavior using mobile communication instruments. Transportation Research C, 12, 273-291.Baker, S. 2009. The next NET. BusinessWeek, March 9, 2009, 42-46.Black, W.R. 2003. Transportation: A Geographical Analysis. New York: The Guilford Press.Buliung, R. and Kanaroglou, P. 2004. On design and implementation of an object-relational spatial database for activity/travel behavior research. Journal of Geographical Systems, 6, 237–262.Buliung, R. and Kanaroglou, P. 2006. A GIS toolkit for exploring geographies of household activity/travel behavior. Journal of Transport Geography, 14, 35–51.Golledge, R. and Stimson, R. 1997. Spatial Behavior: A Geographic Perspective. New York: The Guilford Press.Goodchild, M.F. and Janelle, D.G. 1984. The city around the clock: space-time patterns of urban ecological structure. Environment and Planning A, 16, 807-820.

Selected references:


Hägerstrand, T. 1970. What about people in regional science? Papers of Regional Science Association, 24, 1-12.Harvey, A. and Macnab, P. 2000, Who’s up? Global interpersonal accessibility. In: Janelle, D. and Hodge, D. (Eds.), Information, Place, and Cyberspace Issues in Accessibility. Springer, Berlin, 147–170.Janelle, D.G., 1969. Spatial reorganization: a model and concept. Annals of the Association of American Geographers 59 (2), 348–364.Janelle, D.G. 1973. Measuring human extensibility in a shrinkingworld. The Journal of Geography, 72, 8–15.Janelle, D.G., 1975. Time space convergence and urban transportation issues. In: Blong, C.K. (Ed.), Systems Thinking and the Quality of Life. The Society for General Systems Research, Washington, DC, 594–600.Janelle, D.G. 1995. Metropolitan expansion, telecommuting, and transportation. In: Hanson, S. (Ed.), The Geography of Urban Transportation. The Guilford Press, New York, 407–434.Janelle, D.G. 2004. Impact of information technologies. In: Hanson, S., Giuliano, G. (Eds.), The Geography of Urban Transportation, third ed. The Guilford Press, New York, 86–112.Knowles, R.D. 2006. Transport shaping space: differential collapse in time-space. Journal of Transport Geography, 14, 407-425.


Kwan, M.-P. 2000a. Human extensibility and individual hybrid-accessibility in space–time: a multi-scale representation using GIS. In: Janelle, D.J., Hodge, D.C. (Eds.), Information, Place, and Cyberspace. Issues in Accessibility. Springer-Verlag, Berlin, Germany, pp. 241–256.Kwan, M-P. 2000b. Interactive geovisualization of activity-travel patterns using three dimensional geographical information systems: a methodological exploration with a large data set. Transportation Research C, 8, 185–203.Kwan, M-P. 2002. Time, information technologies, and the geographies of everyday life. Urban Geography, 23, 471–482.Kwan, M-P. 2004. GIS methods in time-geographic research: computation and geovisualization of human activity patterns. Geografiska Annaler B, 86, 267–280.Kwan, M-P. and Lee, J. 2003. Geovisualization of human activity patterns using 3D GIS: a time-geographic approach. In: Goodchild, M., Janelle, D. (Eds.), Spatially Integrated Social Science. Oxford University Press, Oxford, pp. 48–66.Laube, P., Imfeld, S. and Weibel, R. 2005. Discovering relative motion patterns in groups of moving point objects. International Journal of Geographical Information Science, 19, 639-668.


Lyons, G. 2009. The shaping of activities and mobility through new technologies. Journal of Transport Geography, 17, 81-82.Miller, H.J. 1991. Modeling accessibility using space–time prism concepts within geographical information systems. International Journal of Geographical Information Systems, 5, 287–301.Miller, H.J. 2005a. A measurement theory for time geography. Geographical Analysis, 37 (1), 17–45.Miller, H.J. 2005. Necessary space-time conditions for human interaction. Environment & Planning B, 32, 381-401.Mintsis, G., Basbas, S., Papaioannou, P., Taxiltaris, C. and Tziavos, I.N. 2004. applications of GPS technology in the land transportation system, European Journal of Operations Research, 152, 399-409.Shaw, S-L. 2006. What about “time” in transportation geography? Journal of Transport Geography, 14, 237-240. Shaw, S-L. and Yu, H. 2009. A GIS-based time-geographic approach of studying individual activities and interactions in a hybrid physical-virtual space. Journal of Transport Geography, 17, 141-149.


Shaw, S-L., Yu, H. and Bombom, L. 2008. A space-time GIS approach to exploring large individual-based spatiotemporal datasets. Transactions in GIS, 12, 425-441. Shoval, N. and Isaacson, M. 2006. Application of tracking technologiesto the study of pedestrian spatial behavior. The Professional Geographer, 58, 172-183.Ullman, E.L. 1974. Geography as spatial interaction. In: Hurst, M.E.E.(Ed.), Transporttaion Geography: Comments and Readings. McGraw-Hill, New York, 29–40.Yu, H. 2006. Spatio-temporal GIS design for exploring interactions of human activities. Cartography and Geographic Information Science,33, 3-19.Yu, H. and Shaw, S-L. 2008. Exploring potential human interactions in physical and virtual spaces: A spatiotemporal GIS approach. International Journal of Geographical Information Science, 22(4), 409-430. Zhou, J. and Gollege, R. 2007. Real-time tracking of activity scheduling/schedule execution within a unified data collection framework. Transportation Research A, 41, 444-463.

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