Extracting Point of Interest and Classifying Environment for Low Sampling Crowd Sensing Smartphone Sensor Data

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by  Billy Pik Lik Lau

Extracting Point of Interest and Classifying Environment for Low Sampling Crowd
Sensing Smartphone Sensor Data
Billy Pik Lik Lau∗, Marakkalage Sumudu Hasala∗, Viswanath Sanjana Kadaba∗,
Balasubramaniam Thirunavukarasu†, Chau Yuen∗, Belinda Yuen∗, Richi Nayak†
Engineering Product Development, Singapore University of Technology and Design, Singapore ∗
School of Electrical Engineering and Computer Science, Queensland University of Technology, Australia †
Email: billy [email protected], [email protected],[email protected]
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1 Abstract—The advancement of smartphones with various sensing environment,there are a wide variety of devices by
0 type of sensors enabled us to harness diverse information differentusers,anditisnotpossibletoretrievegroundtruth
2 with crowd sensing mobile application. However, traditional fromthe users.In thispaper,ourmainfocusis to verifythe
n acopnpsruomacphteiosnhaasveatsruafdfeeroedff tdoraowbtbaainckhsigshucahccuarsachyigdhatbaauttseinryg effectivenessofclusteringalgorithmbasedondatacollected
a high sampling rate. To mitigate the battery consumption, we from volunteer users. Our model require minimal attention
J
proposedlowsamplingpointofinterest(POI)extractionframe- and is suitable for helping elderly to understand their life

6 work,whichisbuiltuponvalidationbasedstaypointsdetection pattern after retirements. These insights can be used for the
(VSPD) and sensor fusion based environment classification
authorities to improve certain location facilities, which will
] (SFEC).Westudiedvariousofclusteringalgorithmandshowed
Y be reported in the continued works.
that density based spatial clustering of application with noise
Variousinformationhasbeencollectedthroughtheappli-
C (DBSCAN) algorithms produce most accurate result among
existing methods. The SFEC model is utilized for classifying cation we have previously developed in [3]. Data collected
.
s the indoor or outdoor environment of the POI clustered from smart-phone are uploaded to the server and off-line
c
[ earlier by VSPD. Real world data are collected, benchmarked analysis are performed to gain insights about POI and
1 usasminpglienxgistriantgecmluosdteelriinnghmigehthnoodisteosdpeantoiatleteefmfepcotirvaelndesastao.f low trajectory. We first extract data from database, and later
perform several preprocessing techniques namely denoise
v
Keywords-Big Data, POI, Clustering, Environment Classifi-
and timeSync. The former mentioned technique allows us
9
cation, Trajectory, Sensor Fusion
7 to remove any potential noise, outliers, and duplicate data;
3 I. INTRODUCTION whereas the latter mentioned technique is used for syn-
3 Data collected using mobile devices such as smart phone chronizingthedatawithdifferenttimestamps.Subsequently,
0
andwearabledeviceshavebeenexplicitlystudiedbyvarious we apply VSPD algorithm to extract the POI, while the
.
1 researchers in [1], [2] as they are able to generate various DBSCAN is used for clustering similar POI. Trajectory can
0
insights about a particular user life pattern. Among all the be obtained through labeling each POI with the time stamp
7
information captured, point of interest (POI), user mobility, based on duration of each stay point. As our main concern
1
: user life pattern, and transportation mode gained a lot of is to determine POI, we further study the characteristic of
v
attention; but major existing crowd sensing applications the POI and classify it into indoor vs outdoor, or private
i
X suffer from the drawbacks of high battery consumption. vspublic usingSFEC technique.TheSFEC is implemented
r The reason behind high battery consumption is due to high using multi sensor data obtained from mobile devices.
a sampling rate as well as limited battery capacity of mobile Our contributions in this paper are listed as follows:
devices,whichrendermostofthe methodsonlysuitablefor • We present VSPD algorithm, which is capable to dis-
short term data collection. tinguishvalidstaypointsduetopoorthedataqualityof
To solve the aforementioned challenges, we proposed low sampling rate. Stay points are validated using the
ultra low sampling architecture for extracting POI utilizing proposedmoduleandlater clusteredinto POI basedon
varioussensor informationavailable on a smart-phone.One temporal data.
of the main challenges of ultra low sampling architecture is • SFEC method is presented to distinguish POI by its
trade off for accuracy in order to obtain data at a very long indoor and outdoor characteristic. In addition, we also
interval compared to traditional method. Another problem categorizethe POI by its property,whetherit is private
that may arise is highly correlated with the data quality, or public premises.
which may produce inaccurate output after computation. The rest of the paper is structured as follows: In Section
The main objective of the paper is to study POI based II, we propose ultra low sampling architecture and discuss
onthe varioussensorinformationgeneratedby smartphone about the data processing techniques. In Section III, VSPD
usinglowsamplingrate.Suchtechniquewillfindapplication algorithm is introduced and benchmarked against existing
in better understanding user life pattern, which in return clustering and stay point detection algorithms. After clus-
can improve user life style. However, in a crowd sourcing / teringsimilar POI, we performedenvironmentclassification
usingSFECmethodandshowtheefficiencyofourmethods
in Section IV. Finally, we conclude our work in Section V.
II. SYSTEMARCHITECTURE AND FRAMEWORK
In this section, we present the ultra low sampling rate
architecture for data collection and processing.
A. User Trajectory and Point of Interest Detection
User mobility has been studied in [4], [5] and several
features extraction show the necessity of extracting stay
points before obtaining insights from user trajectory. Stay
pointsare knownasPOI in ourstudyscenario,whereit can
be a particular user home, work place, leisure spot, and etc.
To study the POI, trajectory data and user mobility are
Figure1. Overview ofthesystemarchitecture
often used to visualize the path a user takes from one
POI to another. Extracting trajectory features yields a lot of for duplicated data is mostly due to the redundantdata kept
importantinformationsuch astraveldistance,duration,POI by mobile phone, which fails to receive acknowledgment
location, transportation mode, and etc. On the other hand, from server upon upload. The timeSync module act as the
transportation mode (TM) is identified through the feature function for linking different data to a similar time stamp
extractionusing methodssuch as probabilityestimation [6]. in order to perform data fusion computation, which will be
Other related works such as [1], [7] use mobile phone as presented in later part. Data alignment starts by aligning
platformfordatacollectionandtrytocapturetrajectorydata the data from different database based on the synchronized
usingvariousinterpolationmethods(linear,cubic,andnear- time for each dataset into single matrices. The overall data
est). Finer detail of TM, such as motion patterns have been processing is shown in the Algorithm 1.
studied in [8], which uses Hidden Markov Model (HMM)
Algorithm 1 Overall System Algorithm
and k-means clustering for obtaining motion patterns; then
Data:{L,Q}whereit=1,2,...,N
it is also serve as anomaly detection.
begin
However, most of the methods only deal with high sam- preprocess({L,Q})
stayPoints =ValidationStayPointAlgorithm(P)
pling rate of data and are not suitable for day long data
clusteredData =DBSCAN(stayPoints)
collection due to limited battery capacity. We incorporated Tx =Trajectory(stayPoints, P,clusteredData)
timeSync({L,Q})
validation function for the stay point detection in order to
SFEC({L,Q})
validate low sampling rate of data.
B. System Architecture and Data Processing We first define set of location data collected from the
ThesystemframeworkforcollectingPOIinformationcan mobile devices as L = {P1,P2,...,PN}, where P can be
be divided into two phases, which are data collection and denoted as Pi = {ϕi,λi,ai,ti}. The latitude and longitude
data processing. Data collection phase involves installation data are represented by ϕ and λ respectively, which is part
of mobile application at volunteers’ smart phone. Data of GPS Information Pi. The t represents the time-stamp of
collected from mobile devices, which are (1) Location in- data obtained and a denotes the accuracy of GPS module.
formation,(2)SocialInformation,and(3)ActivityInforma- We denote Q = {S1,S2,...,SN} as segment of stay
tion. The location informationconsists of user geographical pointsanddefine eachsensorvalueforeach Q asfollowing
positioning data, while the social information is composed Si = {vi,ai,Ni,βi,φi}. Each data are partitioned into
of sound information collected from the mobile devices segments based on the stay points. The vi represents the
microphone. The activity information consists of data such velocity of the user and this is captured using the mobile
as TM and type of activity that users are engaged in. device’s API, and ai is the accuracy of location data. Ni
We have previously developed the application using An- is the normalized noise intensity, and φi represents the
droidframeworksin[3]andisusedfordatacollectioninthis maximum occurred activity within a time segment.
paper. It has been further fine-tuned to adapt the sampling We noticed different mobile devices possess different
rate of 5 minutes and optimized for off-line data collection, noiselevelsduetodifferenthardwarecomponentandmanu-
whenthereisnoreal-timeInternetconnection.Theoverview facturer.Inordertogenerateafaircomparisonofnoiselevel
of the system architecture is shown in Fig. 1. acrossotherdevices,wewillnormalizedthenoisedatausing
The preprocessing of data is performed in order to gen- the Equation (1) below:
erate usable data to extract useful features. First, the raw s smin
=10 − (1)
data will be passed through denoise process to remove any N (cid:18)smax smin(cid:19)
−
duplicaterecords.Thedenoisemodulechecksthedatatime- where s representsthe noise amplitude level. The smin and
stampandeliminatesdatawithsametime-stamp.Thereason smax representminimumandmaximumnoiselevelcaptured
respectivelybasedonuserdataataparticulartimeframe(i.e. Reachability of distance between two points is a crucial
one month data). piece of information required for DBSCAN in order to
III. POINT OF INTERESTDETECTION correctly cluster similar POI. Throughout this paper, the
In this section, we discuss the POI extraction module distance between two points {ϕ1,λ1}, and {ϕ2,λ2} are
calculated using the Harvesine formula as shown in Eqn.
such as stay points detection and data clustering,that group
(2):
similar POI into cluster.
d
hav( )=hav(ϕ2 ϕ1)+cos(ϕ1)cos(ϕ2)hav(λ2 λ1) (2)
A. Point of Interest Clustering r − −
where harvesine function hav() can be found in [14] and
K-means clustering [9] is one of the common clustering
d is the distance between two points and r is the radius of
techniques in handling numerical data since it offers fast
earth. The hav() used in Eqn.(3) is defined as follows:
and easy usage compared to others. However, finding an
optimal k is one of the challenges as it is an NP hard hav(θ)=sin2(θ)= 1−cos(θ) (3)
problem. In [10], they have studied the use of k-means 2 2
clustering on spatial and temporal data for POI clustering. To obtain the distance between two points, we apply the
However, k-means is not capable of identifying arbitrary inverseHarvesinefunctionforEquation(4)byusingarcsine
shape of cluster, which makes it not ideal for our proposed function as follows:
architecture.Alternatecommonclusteringmethodisdensity d=r hav−1(h)=2rarcsin √h (4)
based clustering such as DBSCAN [11] and OPTICS [12]. × (cid:16) (cid:17)
After obtaining the distance between two points, we can
In [13], they have used customized DBSCAN algorithmfor formulate duration difference between two stay points ∆t
geo-taggingphotowhichusesdensitythresholdandadaptive as follows:
density. However, their proposed method only concentrates ∆t=ti+1 ti (5)
−
on spatial information and ignores the temporal data. where ti is the time-stamp for current sample and ti+1 is
Low ultra sampling data collection has a high impact the timestamp for the previous sample.
on the quality of data, which may results inaccurate data Validationfunctionisasacorefunctionindetectingvalid
clustering and wrong POI generated. Therefore, we studied stay points. Ultra low sampling may generate high noise
different kind of clustering technique as well as stay point datafromenvironmentsuchasindoorbuildings,tunnel,and
detection to ensure best features extraction technique is underground. To eliminate potential noise accounted into
chosen. stay points,weformulatethe validationfunctionasfollows:
B. Validation based Stay Points Detection (VSPD) Algo-
validaty(d,∆t)=∆t<Θt and d<Θd (6)
rithm
where Θt is the threshold for time and Θd is the threshold
VSPDalgorithmsutilizethestaypointdetectionalgorithm
for distance.
first proposed in [4] and we added validation function to
Subsequently,theepsandminPtsneedtobedetermined
remove incompetent POI. The validation function in VSPD
before applying DBSCAN algorithm. The eps are derived
algorithm has been deployed in order to adapt ultra low
using the accuracy of GPS location as follows:
sampling architecture, which is presented in Algorithm 2.
eps= ai+ai−1 if ai+ai−1 <Θl where minPts=1
Algorithm2ValidationbasedStayPoint(VSPD)algorithm (cid:26) Θl if ai+ai−1 Θl where minPts=1
≥
(7)
Data:Pi wherei=1,2,...,N
Result:SPx where accuracy of GPS data are calculated is considered
fork←1toN do for checking the validity of the stay points. For example, if
whilej←(k+1)toN do an user entered a tunnel and GPS points remain fixed until
Check(a{i,i+1,...,j},threshold(a)) usingEquation(7)
Calculate dusingEquation(4) he/she exit the tunnel after t time. Our validation model
ifd>athen in Eqn. 6 will deem the period of entering tunnel as part
Caclulate ∆tusingEquation(5)
if∆t>threshold(t) then of traveling because the time taken and distance traveled is
ifValidity(d,∆t)then considered not valid. In Eqn.(7), a threshold of Θl = 200
addStayPoint(Pi,Pj)
wasincludedtopreventGPSaccuracybecometoolargeand
else
break form a potential cluster. The minPts is set to 1 since we
do not treat any POI as outliers at the moment.
Before going into detail of the algorithm, we first define C. Evaluation of Stay Point Detection and Clustering
the following key information, which are stay points and To evaluate the proposed VSPD algorithm, we have few
trajectory.StaypointsaredefinedasSP,whereitrepresents volunteersinstalledtheapplicationontheirsmartphoneand
potential POI a particular user is at. Each stay point can proceedwiththeirdailylife.Afterdatacollectionperformed,
be arranged to trajectory by adding time-stamp, resulting we will requestgroundtruth fromeveryparticipantin order
Tx ={SP1,SP2,...,SPx}. to correctly identify the POI they have been to.
Effect of Different Clustering Algorithms
Figure3. Comparisonofdifferenttimethresholdforstaypointextraction
algorithm
Figure2. ComparisonofDifferentStayPointsExtractionandClustering
Techniques
Effect of Different Mobile Devices
First, we study the POI detection algorithm, which con-
Data collection using differentsmart phonesuch as Sam-
sistsof(1)VSPDalgorithm,(2)staypointalgorithm[4]and
sung Tab 7, Galaxy Alpha A8 , LG G-Pro 2 are tested.
(3) online clustering [3]. Next, We evaluate the clustering
Do note that this is the first phase of our works, we have
method for each stay point detection algorithm using (1)
deployed the application and collected the data more than
DBSCAN, (2) K-Means, (3) Hierarchical. It should be
100 usersin our nextworks. In 4 same POI are labeled and
noted, that the k-means clustering’s k is determined using
colored as same number, where other locations are denoted
Davies Bouldin index. The comparison of different stay
differently.SlightdriftofPOIisobservedthroughalldevices
point extraction algorithm is presented in Figure 2.
tested in the same POI ashighlightedinGrey solidboxand
Basedontheresults,VSPDalgorithmandDBSCANman-
black dash lined box. Despite slight difference in check-in
aged to extract POI correctly compared to others. Original
and check-out time, VSPD algorithm and DBSCAN man-
stay point algorithm clustering wrong results are shown in
agedtoextractthePOIcorrectlyregardlessofdevicevendor.
redsolidlinedbox.ItisunabletodetectthePOIcorrectlyas
The VSPD algorithm has the computational complexity
it is unableto distinguishthe stay pointsdueto poorindoor
GPS accuracy.We manually inspect the GPS points located
in the cluster 10 and found out that indoor low accuracy
led to incorrect stay points extraction. It is observed that
cluster 6 provide incorrect POI compared to ground truth
and we highlighted using dotted grey box. By comparing
the different clustering methods, stay point extraction is
more crucial in term of extracting the correct POI from the
Figure4. Datacollected usingdifferent Mobile Devices
raw database. The clusteringtechniqueused after stay point
extraction algorithm yields significant changes afterwards. of O(N2) where validation function is O(1) step with no
However, by comparing the consistency of the algorithm, extracomputationalcostandisabletoeliminatewrongstay
we prefer DBSCAN overother clustering techniquesdue to points.
itscapabilitiesofformingarbitraryshapecluster,asthedata
IV. SENSORFUSION BASED ENVIRONMENT
collected is never in a consistent shape to begin with.
CLASSIFICATION
A. Indoor and Outdoor Classification
Effect of Different Clustering Parameters
IO classification have been studied by applying several
The next data extraction depends on the duration of stay different approaches. It is can be divided into two ap-
time at a particular POI. We adjust the delta t accordingly proaches, which uses image processing and sensor fusion.
to examine the granulated detail of possible stay point. The In [15], IODetector has been proposed to classify IO
highlightedarea of solid black box and greydash lined box environmentusing three sensors(cellular module,lightsen-
in Figure 3 generates correct POI according to the ground sor, andmagnetic sensor).Theyhave combinedsensorsand
truth. wireless approximation methods to determine whether the
However,there is a slight error as framed in black dotted environment belongs to indoor, outdoor out corridor. They
boxwhichisduetotransitionofGPSaccuracyfromhighto haveutilizedHMMinconjunctionwithViterbialgorithmto
low.Thiscausesa gapbetweentwo pointandthealgorithm estimatetheIOcondition.However,Raduetal.[16]claimed
treat it as two different location and hence error occurred. thattheirsupervisedmethodsarefarsuperiorbyintroducing
After verifying with the volunteers, the reason for such more features into IO classification. They have applied co-
occurrenceisdietotrafficcongestion.Thisledthealgorithm training methods and managed to achieve 92% of accuracy
tothinktheusersisatafixedpositionasitthetimedoesnot compared to GPS and IODetector. Other related work can
exceed the ∆t. They appear to be invalid in the validation be found in [17], [18].
function in VSPD algorithm and does not form a cluster.
In [19], they have proposedIO classification using image For P1, percentage contributions from sensors are, 90%
processing. Waleed T. et al [20] has presented classification by GPS accuracy(G),5% by battery level(β), where β =1
technique that uses GIST descriptor in conjunction with if battery is charging, and β = 0 otherwise, and 5% by
neural network classifiers to decide whether image is taken Activity=’Still’(denotedbyαs,whereαs ={0,1})which
indoor or outdoor. Based on their proposed method, they is returned by location API. In Eqn.(9) and Eqn.(10), ThG
managed to achieve 90.8% of accuracy by extracting GIST is threshold GPS accuracy, and x is average GPS accuracy
features and using feed-forward neural network to feed in the slot.
training samples.
Since image processing requires training and a camera
x Th
to be turned on during data collection, it will end up with P1 =" −ThGG ×0.9 + (β+αs)×0.05 #; if x>ThG
high battery consumption.Hence, it is not suitable for ultra (cid:16) (cid:17) (cid:16) (cid:17)
(9)
low sampling rate. Considering the case, mobile device is For P2, percentage contributions from sensors are, 90%
in pocket, it might end up in inaccurate representation of by G, and 10% by light level (l), where l=1 if light level
the IO. To overcome such scenario, we applied the notion is abovethresholdlevelThl or l=0 otherwise.ThG =30,
of sensor fusion for data collection, in order to achieve ThN =5, and Thl =1000 based on empirical studies.
better battery management and more accurate environment
Th x
classification. P2 =" TGhG− ×0.9 + Thl×0.1 #; if x<ThG (10)
B. SensorFusionbased EnvironmentClassification(SFEC) (cid:16) (cid:17) (cid:16) (cid:17)
The stay points are classified as indoor vs. outdoor and Theconfidencelevelcalculationfortheenvironmenttype
private vs. public, using sensor fusion based environment (2) Private and (3) Public, are presented in Eqn. (11) and
classification. An indoor environment is where the average (12) respectively where, ThN is threshold noise level, and
GPSaccuracyforthePOIisaboveacertainthreshold,while y is average normalized noise level in the slot.
an outdoorenvironmentis where the average GPS accuracy For P , percentage contributions from sensors are, 90%
3
for the POI is below that threshold (here, the GPS accuracy by noise level, and 10% by Activity = ‘Still’(αs).
is returned by Android API, in which, when a higher value
Th y
floowr earc).cuArapcyriviastereetnuvrinreodn,mietntmiesawnshetrheethGePnSoiascecluervaeclyfoisr P3 =" TNhN− ×0.9 + αs×0.1 #; if y<ThN (11)
(cid:16) (cid:17) (cid:16) (cid:17)
the POI is below a certain threshold and, while a public
For P , percentage contributions from sensors are, 90%
4
environment is where the noise level for the POI is above
by noise level, and 10% by Activity = ‘Walking’(denoted
that threshold. Since we cannot obtain a 100% estimation
by w, where αw =0,1).
to determine the type of environmentwhere the POI is, we
assTighnepcelarscseinfiteargerecqounifiredsenmcueltlie-vseenlssoforrddaetate(rim.ei.naGtiPoSn.accu- P4 =" y−ThTNhN ×0.9 + αw×0.1 #; if y>ThN (12)
(cid:16) (cid:17) (cid:16) (cid:17)
racy, Noise, Battery Level, Light etc.), from start and end
C. Evaluation of SFEC
time of the POI. The duration of POI is divided into 5
minute slots, and each of those slots are given a confidence SFEC is evaluated in real-world scenario by collecting
percentagefortypeofabovementionedenvironments.Each multi-sensor informationfrom mobile device of a volunteer
type of environment is labeled into one of the 4 different user. Table I shows calculated percentage confidence levels
categories, such as indoor, outdoor, private, and public, of 4 POI along with their ground truth. When there is no
which are encoded into {1,2,3,4} respectively. data for a particular environment type, it is labeled as ‘-
Total percentage confidence level for a particular type ’. For POI 1, P2 and P4 have substantial difference when
of environment, for each type of slot, is calculated using comparedtoP1 andP3respectively.Similarly,forPOI2,P1
the Equation (8), where, n is number of 5 minute slots and P3 have substantial difference in comparison with P2
in POI, Pc is confidence percentage, Skc is percentage of and P4 respectively. For POI 3, P1 and P4 have substantial
kth slot being type c, and c is type of environment where difference in contrast with P and P respectively. For POI
2 3
c={1,2,3,4},and(1≤k ≤n).Ifthereisnodatainaslot 4, no noise data found possibly due to power management
forthe classification, we use P0 to indicate the environment profile of device which causes absence of data and indoor
type ’Unclassified’. outdoor classification turned out to be incorrect. Moreover,
1 n P1 and P2 have no substantial difference to distinguish the
Pc = n × Skc ; if n>0 (8) environment type. The environment of all POI (except POI
Xk=1 4) were able to estimate correctly, using SFEC method.
The confidence level calculation for the environment type
However,by comparingthe P1 and P2 valuesof POI4,they
(1) Indoor and (2) Outdoor, are presented in the Eqn. (9)
are closed to each another, this may give us hint that the
and Eqn. (10) respectively.
estimation could be in error.
TableI
[7] Y.Zheng,X.Xie,andW.-Y.Ma,“Geolife:Acollaborativeso-
CONFIDENCEPERCENTAGESFORSFEC
cial networking service among user, location and trajectory.”
IEEE Data Eng. Bull., vol. 33, no. 2, pp. 32–39, 2010.
Classification POI1 POI2 POI3 POI4
[8] N. Suzuki, K. Hirasawa, K. Tanaka, Y. Kobayashi, Y. Sato,
P1(Indoor) - 83.94 59.78 35.28 and Y. Fujino, “Learning motion patterns and anomaly de-
P2(Outdoor) 60 2.42 16.24 23.94
tectionbyhuman trajectoryanalysis,”in2007 IEEEInterna-
P3(Private) - 22.35 0.61 -
tionalConferenceonSystems,ManandCybernetics. IEEE,
P4(Public) 73.12 - 29.66 -
2007, pp. 498–503.
Outdoor Indoor Indoor Indoor
Estimation [9] J. A. Hartigan and M. A. Wong, “Algorithm as 136: A k-
Public Private Public Unknown
means clustering algorithm,” Journal of the Royal Statistical
Ground Outdoor Indoor Indoor Outdoor Society.SeriesC(AppliedStatistics),vol.28,no.1,pp.100–
Truth Public Private Public Public 108, 1979.
[10] I.Laptev,“Onspace-timeinterestpoints,”InternationalJour-
nal ofComputerVision, vol.64,no. 2-3, pp.107–123, 2005.
V. CONCLUSION
[11] M. Ester, H.-P. Kriegel, J. Sander, X. Xu et al., “A density-
In a nutshell, this paper focuses on low sampling and
based algorithm for discovering clusters in large spatial
sensorfusiontechniquesforPOIextractionandenvironment databaseswithnoise.”inKdd,vol.96,no.34,1996,pp.226–
classification for smart-phone location data. We introduced 231.
validation function for the POI extraction and clustered [12] M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander,
identical POI to generate user trajectory. Furthermore, we “Optics: ordering points to identify the clustering structure,”
in ACM Sigmod Record, vol. 28, no. 2. ACM, 1999, pp.
implemented SFEC method to identify the type of environ-
49–60.
ment using various sensor input. In future, we will expand
[13] S.Kisilevich,F.Mansmann, andD.Keim,“P-dbscan:aden-
the analysis model to study regional mobility and travel
sity based clustering algorithm for exploration and analysis
pattern with a large scale crow sourcing / sensing activity, ofattractiveareasusingcollectionsofgeo-taggedphotos,”in
where ground truth is hard to obtain. Proceedingsofthe1stinternationalconferenceandexhibition
oncomputing forgeospatial research &application. ACM,
ACKNOWLEDGMENT
2010, p. 38.
Authorswouldliketoexpresstheirsinceregratitudetoall [14] C. Veness, “Calculate distance, bearing and more between
the participants in the study who allowed us to collect their latitude/longitude points,” not dated, http://www. movable-
type. co. uk/scripts/latlong. html, 2010.
location information. This research is supported by the Lee
[15] M. Li, P. Zhou, Y. Zheng, Z. Li, and G. Shen, “Iodetector:
Kuan Yew Centre for Innovative Cities under Lee Li Ming
Agenericservicefor indoor/outdoor detection,”ACMTrans-
Programme in Aging Urbanism.
actions on Sensor Networks (TOSN), vol. 11, no. 2, p. 28,
REFERENCES 2015.
[16] V. Radu, P. Katsikouli, R. Sarkar, and M. K. Marina, “A
[1] S. Hoteit, S. Secci, S. Sobolevsky, C. Ratti, and G. Pujolle, semi-supervised learning approach for robust indoor-outdoor
“Estimating human trajectories and hotspots through mobile detectionwithsmartphones,”inProceedingsofthe12thACM
phonedata,”ComputerNetworks,vol.64,pp.296–307,2014. Conference on Embedded Network Sensor Systems. ACM,
[2] C. Kang, S. Sobolevsky, Y. Liu, and C. Ratti, “Exploring 2014, pp. 280–294.
humanmovementsinsingapore:acomparativeanalysisbased [17] I. Bisio, A. Delfino, and F. Lavagetto, “A simple ultrasonic
on mobile phone and taxicab usages,” in Proceedings of indoor/outdoor detector for mobile devices,” in 2015 Inter-
the 2nd ACM SIGKDD international workshop on urban national Wireless Communications and Mobile Computing
computing. ACM, 2013, p. 1. Conference (IWCMC). IEEE, 2015, pp. 137–141.
[3] S.K.Viswanath,C.Yuen,X.Ku,andX.Liu,“Smarttourist- [18] H.Zou,H.Jiang,Y.Luo,J.Zhu,X.Lu,andL.Xie,“Bluede-
passive mobility tracking through mobile application,” in tect: An ibeacon-enabled scheme for accurate and energy-
International Internet of Things Summit. Springer, 2014, efficient indoor-outdoor detection and seamless location-
pp. 183–191. based service,” Sensors, vol. 16, no. 2, p. 268, 2016.
[4] Q. Li, Y. Zheng, X. Xie, Y. Chen, W. Liu, and W.-Y. [19] L. Zhang, M. Li, and H.-J. Zhang, “Boosting image ori-
Ma, “Mining user similarity based on location history,” in entation detection with indoor vs. outdoor classification,”
Proceedings of the 16th ACM SIGSPATIAL international in Applications of Computer Vision, 2002.(WACV 2002).
conference on Advances in geographic information systems. Proceedings. Sixth IEEE Workshop on. IEEE, 2002, pp.
ACM, 2008, p. 34. 95–99.
[5] Y.Lou,C.Zhang,Y.Zheng,X.Xie,W.Wang,andY.Huang, [20] W.Tahir,A.Majeed,andT.Rehman,“Indoor/outdoor image
“Map-matching for low-sampling-rate gps trajectories,” in classification using gist image features and neural network
Proceedings of the 17th ACM SIGSPATIAL International classifiers,” in 2015 12th International Conference on High-
ConferenceonAdvancesinGeographicInformationSystems. capacityOpticalNetworksandEnabling/EmergingTechnolo-
ACM, 2009, pp. 352–361. gies (HONET). IEEE,2015, pp. 1–5.
[6] Y.Zheng,L.Liu,L.Wang,andX.Xie,“Learningtransporta-
tion mode from raw gps data for geographic applications on
theweb,”inProceedingsofthe17thinternationalconference
on World Wide Web. ACM, 2008, pp. 247–256.

m
877.21
575.14m
6 574.02m 1098.93 m
Note:
actual distance measurement
are performed from the mean
center location of the cluster.
695.14m
307.26m
1082.61m 6
861.81m
8
250.86m
Note:
actual distance measurement
are performed from the mean
center location of the cluster.