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iMAP: Indirect Measurement of Air Pollution with  Cellphones

iMAP: Indirect Measurement of Air Pollution with  Cellphones. Murat Ali Bay ı r Research Assistant Ubiquitous Computing Laboratory Department of Computer Science and Engineering University at Buffalo mbayir@cse.buffalo.edu. OUTLINE.

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iMAP: Indirect Measurement of Air Pollution with  Cellphones

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  1. iMAP: Indirect Measurement of Air Pollution with  Cellphones Murat Ali Bayır Research Assistant Ubiquitous Computing Laboratory Department of Computer Science and Engineering University at Buffalo mbayir@cse.buffalo.edu

  2. OUTLINE Motivation and Air Pollution Exposure Estimation Problem Mobility Profiler Framework and The Data Set Mobility Path Construction Air Pollution Estimation Experimental Results

  3. Air Pollution Exposure Estimation Problem • Why Air Pollution Exposure Estimation Problem is Important? • The researchers state that two million premature deaths annually are attributable to air pollutants. The death ratio is even high in more developed countries [Brundtland 02]. • Acute and chronic air pollutant exposures increase risks of cardiovascular and respiratory diseases [Brook 07], exacerbate, asthma among children [Sarnat 07], and increase risks of neonatal death, low birthweight [Sarnat 07 and Sorensen 99].

  4. Air Pollution Exposure Estimation Problem • Previous Approaches • To Estimate Air pollution exposure, previous approach [Adar 07] uses residential information. To illustrate if an individual works 9 hours per day. These approaches assumes that an individual stays at work address 9 hours and remaining 15 hours at home address. After this assumption, the average air pollution that current person exposured is estimated by using air pollution data from Department of Environmental Conversation for particular areas containing work and home address.

  5. Our Motivation • Problems of Previous Approaches and Our Motivation • Since the previous approaches uses residential information, they don’t consider time activity of an individual. In real life, It is very common for a person to become mobile between several location like going shopping, go to friends house, go for lunch etc. Since the previous approaches does not consider this conditions, their error in air pollution estimation is increases. • The aim of this project is to use using mobility paths of individual collected via cell phones for increasing the accuracy of air pollution estimation and remove the deficiency of residential approach. We use Mobility Profiler Framework [Bayir 08] for extracting mobility paths of individuals.

  6. OUTLINE Motivation and Air Pollution Exposure Estimation Problem Mobility Profiler Framework and The Data Set Mobility Path Construction Air Pollution Estimation Experimental Results

  7. Topology Construction Mobility Profiler Framework and The Data Set Mobility Profiler Framework [Bayir 08] Path Construction Post Processing Pattern Discovery Mobility paths Mobility Database Interesting Knowledge Rules and Patterns Cell Tower Topology

  8. Mobility Profiler Framework and The Data Set The Data Set • The data set is collected by MIT Reality Mining Group performing experimental study involving 100 people. • Each person uses Nokia N60 series cell phone and runs software which records data about cell phone usage. • All of the data is kept in database spanning 350K hours of data total size of which is about 1GB • The software on cellular phones is written in such a way that it can log data without interrupting user’s process like voice call.

  9. Mobility Profiler Framework and The Data Set The Database Structure • All of the usage data is stored in reality database including 10 tables. From these data set, the following tables are used for mining cell phone user mobility. This is the full schema of the tables used. The core table for mining is cellspan.

  10. Mobility Profiler Framework and The Data Set Example CellSpan Log Cell Transition Time: The time elapsed between any contiguous record of same user 00:02:43 00:03:41 Duration time: Time spent in the area of any cell tower

  11. OUTLINE Motivation and Air Pollution Estimation Problem Mobility-Miner Framework and The Data Set Mobility Path Construction Air Pollution Estimation Experimental Results

  12. Mobility Path Construction • Why do We need Mobility Paths? • Using raw data in cell span table for most of the application is difficult since we don’t have related cell tower connection records together in a set. • What does the related cell tower records means? • The answer is hidden in the semantics of dataset which is related to human mobility. All of human mobility data is collected to during the individuals’ trip from one location to another. • Somehow, we need to construct sets for mobility paths which corresponds to an individuals’ trip from one location to another.

  13. Mobility Path Construction Return to Our raw Data Cell Transition Time: The time elapsed between any contiguous record of same user 00:02:43 Duration time: Time spent in the area of any cell tower 00:03:41 Cell Transition Time for particular two contiguous record or duration time for any record may be very long which corresponds to static state for cell phone user. Therefore, we need to cut mobility paths from these records which corresponds to departure or arrival point for particular trip

  14. Mobility Path Construction • Definition (MobilityPath):A Mobility Path C=[C1, C2, C3,…, Cn] is an ordered sequence of cell tower ids which correspond to cells (active area of cell tower represented by Voronoi diagram) that an individual passed during his/her travel from one location to another location. • Each mobility Path must satisfy the following constraints: • Static Location Rule: (for Observed Static Location) • Ck C satisfying LkdutT > δduration k=1 or k=|C| • Transition Time Rule: (for Hidden Static Location) • Ck, Ck+1 C  L(k+1)start – Lkend δtransition

  15. Mobility Path Construction • Global variables • userSessionSet, tempSessionSet • Procedure CreateNewSession(person_oid, cell, start, end) • cellSequence := (Ci, starti, endi) • tempSessionSet := tempSessionSet U {(person_oid, cellSequence)} • End Procedure • Procedure SessionConstruction(L, δduration, δtransition ) • userSessionSet := {} • tempSessionSet:={} • For each Li of L • durationi := endi - starti • If durationiδdurationthen • If  userSessionk  tempSessionSet with person_oidk = person_oidi then • If (starti - lastEndTime(UserSessionk)) δtransitionthen • userSessionk := (person_oidk, CellSequencek U (Ci, starti, endi)) • Else • userSessionSet := userSessionSet U {userSessionk} • tempSessionSet := tempSessionSet – {userSessionk} • CreateNewSession(person_oidi, Ci, starti, endi) • End If • Else • CreateNewSession(person_oidi, Ci, starti, endi) • End If

  16. Mobility Path Construction • Else • If  userSessionk  userSessionSet with person_oidk = person_oidi then • If (starti - lastEndTime(UserSessionk)) δtransitionthen • userSessionk := (person_oidk, cellSequencek U (Ci, starti, endi)) • userSessionSet := userSessionSet U {userSessionk} • tempSessionSet := tempSessionSet – {userSessionk} • CreateNewSession(person_oidi, Ci, starti, endi) • Else • userSessionSet := userSessionSet U {userSessionk} • tempSessionSet := tempSessionSet – {userSessionk} • CreateNewSession(person_oidi, Ci, starti, endi) • End If • Else • CreateNewSession(person_oidi, Ci, starti, endi) • End If • End If • End Procedure

  17. Mobility Path Construction • [12, 67, 123, 87] • [87, 98, 12] ..(gap).. • [67, 11]

  18. OUTLINE Motivation and Air Pollution Estimation Problem Mobility Profiler Framework and The Data Set Mobility Path Construction Air Pollution Estimation Experimental Results

  19. Air Pollution Estimation • Easy Process after geographical Mapping • We map each cell tower to geographical region in Air Pollution DB of Department of Environmental Conversation. To illustrate of Mobility Path is P = [C1, C2, C3] Pollution Exposured =T1 * P<C1-T1> + T2 * P<C2-T2> + T3 * P<C3-T3> P<CN-TN>: The average air pollution estimated on the region containing cell tower Cn during time interval Tn

  20. OUTLINE Motivation and Air Pollution Estimation Problem Mobility Profiler Framework and The Data Set Mobility Path Construction Air Pollution Estimation Experimental Results

  21. Experimental Results • Remember the Data Set • More than 2M cell span record • It keeps 350K hours of cell span data • Cell span records of 100 mobile users

  22. Experimental Results Determining δduration and δtransition for Mobility Path Construction Duration time of %94 of all logs smaller than 10 minutes

  23. Experimental Results Determining δduration and δtransition for Mobility Path Construction Unlike the analysis of δ_duration time, there is still some visibility problem if we analyze this data without filtering the regular handoffs which takes 0 second. In reality mining data set, nearly, 99.2% of contiguous cellspan records has regular handoff value which is 0 second It is obvious that the user can not be in hidden static location in this time range. Therefore, we filter regular handoff times for analyzing δ_transition time.

  24. Experimental Results • By taking δduration=10 min and δtransition = 10 min, the framework construct 120K mobility paths. • The number of unique cell tower is 32K. • We give Mobility paths of two case study to our domain expert from Department of Social and Preventive Medicine at UB in order to estimate air pollution for two case studies.

  25. References • [Bayir 08 ] Murat Ali Bayir, Murat Demirbas, Nathan Eagle, Mobility Profiler: A Framework for Discovering Mobile User Profiles, 2008 (Under Submission) • [Demirbas 08] Murat Demirbas, Carole Rudra, Atri Rudra, Murat Ali Bayir: IMAP: An Indirect Measurement of Air Pollution via Cell Phone, 2008 (Under Submission) • [Brook 07] R. D. Brook. Is air pollution a cause of cardiovascular disease? Updated review and controversies. Rev. Environ. Health, 22(2):115–137, 2007. • [Brundtland 02] G. H. Brundtland. Reducing risks to health, promoting healthy life. JAMA, 288(16):1974, 2002. From the World Health Organization • [Sarnat 07] J. A. Sarnat and F. Holguin. Asthma and air quality. Curr. Opin Pulm. Med., 13(1):63–66, 2007. • [Sorensen 99] N. Sorensen, K. Murata, E. Budtz-Jorgensen, P. Weihe, and P. Grandjean. Prenatal methylmercury exposure as a cardiovascular risk factor at seven years of age. Epidemiology, 10(4):370–375, 1999. • [Adar 07] S. D. Adar and J. D. Kaufman. Cardiovascular disease and air pollutants: evaluating and improving epidemiological data implicating traffic exposure. Inhal. Toxicol., 19(1):135–149, 2007. • [Barnes 05] B. Barnes, A. Mathee, and K. Moiloa. Assessing child timeactivity patterns in relation to indoor cooking fires in developing countries: a methodological comparison. Int. J. Hyg.. Environ. Health, 208(3):219–225, 2005.

  26. Conclusion Any Questions ??

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