exploring mobility indoors: an application of sensor-based and gis systems

Exploring Mobility Indoors: an Application ofSensor-based and GIS Systems

Anastasia Petrenko*, Anton Sizo*, Winchel Qian†, A. Dylan Knowles†,Amin Tavassolian†, Kevin Stanley† and Scott Bell*

*Department of Geography and Planning, University of Saskatchewan†Department of Computer Science, University of Saskatchewan

AbstractThe popularization of tracking devices, such as GPS, accelerometers and smartphones, have made it pos-sible to detect, record, and analyze new patterns of human movement and behavior. However, employingGPS alone for indoor localization is not always possible due to the system’s inability to determine loca-tion inside buildings or in places of signal occlusion. In this context, the application of local wireless net-works for determining position is a promising alternative solution, although they still suffer from anumber of limitations due to energy and IT-resources. Our research outlines the potential for employingindoor wireless network positioning and sensor-based systems to improve the collection of tracking dataindoors. By applying various methods of GIScience we developed a methodology that can be applicablefor diverse human indoor mobility analysis. To show the advantage of the proposed method, we presentthe result of an experiment that included mobility analysis of 37 participants. We tracked their move-ments on a university campus over the course of 41 days and demonstrated that their movement behaviorcan be successfully studied with our proposed method.

1 Introduction

Over the last ten years there have been significant advances in tracking technology that pro-vides a wide range of georeferenced disaggregate spatial behavior data (Jung et al. 2012). Suchinformation provides researchers with an opportunity to augment our understanding of therelationship between space, time, human mobility and spatial behavior, more generally. Tradi-tional collection tools for movement data include paper diaries, sketch maps, controlledexperiments, and participant observations. More recently, location-based services (Kwan 2004;Andrienko et al. 2008; Orellana et al. 2010) have been employed to better understand humanmobility, and GPS data collection devices have become one of the most important tools fordetecting, analyzing, and modeling human movement behavior. However GPS is often unreli-able indoors, so this technology is not necessarily suitable for all environments. Moreover,precise localization of moving objects inside large buildings is rarely possible with currentcommercial technology, which makes accurate indoor tracking and navigation an interestingproblem.

The goal here is to evaluate the potential for WiFi-based positioning, coupled with anexisting smartphone-based multi-sensor tracking system, as a means to offer better trackingand analysis of human indoor mobility. Modern indoor environments contain a number ofelements that are difficult to locate or capture (Giaglis et al. 2003); examples include such

Address for correspondence: Anastasia Petrenko, Department of Geography and Planning, University of Saskatchewan, 117 Science Place,Saskatoon SK S7N 5C8, Canada. E-mail: [email protected]

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Research Article Transactions in GIS, 2014, 18(3): 351–369

© 2014 John Wiley & Sons Ltd doi: 10.1111/tgis.12102

mailto:[email protected]

spaces as large supermarkets, museums, libraries, and airports. Introducing a universal methodfor indoor pedestrian tracking has numerous benefits and can be employed in different fields ofstudy and environments. First, monitoring indoor human mobility and position can improveemergency response applications. By modeling visitor densities or waiting time, this data canhelp to explain and reduce congestion, and to facilitate evacuation procedures in the case of anemergency. Secondly, indoor mobility analysis can be beneficial for design, facility allocationplanning, or even used to rectify/facilitate public event management (Liebig et al. 2012). Forexample, enclosed environments such as shopping malls can record and analyze indoor track-ing data for future marketing campaigns as well as improve the arrangement of products andservices (Kourouthanassis and Roussos 2003). Such findings can also be adapted to improvethe design of the facilities and enhance the efficiency of their customers or patrons. Finally,indoor population mobility can be important in healthcare research (Stanley and Osgood2011), in particular as a crucial factor that influences the transfer and spread of pathogens. Inthis case, analysis of population mobility can enhance our knowledge of how human behaviorinteracts with the built indoor environment to drive the spread of contagious disease.

We carried out our experiment on the heavily utilized campus of the University of Sas-katchewan in a variety of environmental settings. We employed the iEpi (Hashemian et al.2012) system to collect sensor data, SaskEPS (Bell et al. 2010) to determine the position of theindividuals, and the Walkable CentreLINE Network (WCN) (Jung et al. 2012) as a tool tolimit the scope of possible positions to a single dimensional manifold. To process and analyzethe data we applied various tools from GIS. Our methodology included three steps. First wecreated an algorithm to correct and improve the enormous positioning data from SaskEPS,then we applied a geometric point-to-curve map-matching to facilitate indoor mobility analy-sis, afterwards we a performed a spatio-temporal data analysis and positioning validation,which enabled the depiction of participants’ movements indoors. Researchers have studied thevarious aspects of indoor localization, including advanced variants of both fingerprinting andtrilateration, have performed automated generation of navigable hallways, and have combinedthe techniques for more accurate localization. We believe we are the first to report this particu-lar combination of features combined in such a way as to make deployment for populationsand locations of interest simple and automated. The data collection device is simply an app,which can be downloaded onto any suitably equipped Android phone, which will automati-cally collect and synchronize its data with a specified server over the Internet. The process forWCN generation is highly automated, and can be performed easily with little human interven-tion on ArcGIS readable blueprints. The post-analysis of the collected data can be performedin an automated fashion by sequentially moving data between ArcGIS and analysis tools. Theresult is a precise, flexible, and easily deployed system for probing the use of indoor spaces,particularly in institutional settings.

This article is organized as follows: the relevant literature on iEpi, SaskEPS systems, andWCN is briefly reviewed in Section 2; study methods, including the description of data collec-tion and analysis, are presented in Section 3; results are provided in Section 4; Section 5 dis-cusses the potential impact of the technique; and conclusions are drawn at the end of thearticle.

2 Research Background

Depsey (2003) defined an indoor localization system as a system that can continuously deter-mine the position of the object in a physical space in real time. Indoor positioning forms an

352 A Petrenko, A Sizo, W Qian, A D Knowles, A Tavassolian, K Stanley and S Bell

© 2014 John Wiley & Sons Ltd Transactions in GIS, 2014, 18(3)

important aspect of GIScience (Kaemarungsi and Krishnamurthy 2004). However, indoorspaces present challenges for all types of positioning technologies. Although there is no singleaccepted solution for accurate indoor localization, several technologies have become popularsuch as: Bluetooth, WiFi, Radio Frequency ID (RFID), ultra wide band, and many others.Additionally, wireless networks (of some of the preceding technologies) can be a solution forseamless positioning or navigation. Currently, wireless networks can be considered as an inde-pendent technology to be applied with different objectives in diverse places. There are at leastthree primary benefits of wireless networking as a solution to determining indoor position (Janet al. 2010): (1) no need to install additional infrastructure for localization; (2) relatively widephysical coverage range of WiFi; and (3) presence of WiFi in most institutional facilities andpublic buildings (campuses, airports, hotels, shopping areas, urban cores). Moreover sinceWiFi signal level can be measured freely even for secure but visible WiFi networks, WiFi is apromising solution for indoor global localization systems that offer a good accuracy-costtrade-off (Alvarez-Alvarez et al. 2013).

WiFi is susceptible to environmental influences that affect the precision, performance,and reliability of the technology (Jung and Bell 2013); the effect that environmental variancehas on WiFi is the primary reason there is not a single accurate and reliable positioningsystem in development today. Many of the existing systems have received limited adoptionbecause they are targeted at consumers as navigation aids (Kourouthanassis and Roussos2003). In this case, the quality of the system must be based on its instantaneous positionalaccuracy, or users will begin to distrust or ignore the positioning system (Giaglis 2002).Over the past few years, many researchers have experimented with indoor positioningsystems using wireless networks (Thiagarajan et al. 2011; Woo et al. 2011; Zhou et al. 2014;Radu and Marina 2013). Although the results reported in these papers demonstrate highlyaccurate results, they cannot be easily implemented in a large indoor environments due tothe fingerprinting algorithm that is used to determine the location; fingerprinting requiresactive and accurate scanning of all possible locations for an individual. In our application,we employ a variation on a classic WiFi-based trilateration scheme (Bell et al. 2010). We arenot trying to provide instantaneous feedback to users, but are instead, in aggregate attempt-ing to represent and understand the use of a particular space, by a particular population.This makes our application of the technology much less sensitive to instantaneous errors,and allows us to perform analysis on trajectories that are semantically (approximately at thislocation along a hallway) rather than numerically (at a precise northing and easting) correct.However, while we lose some accuracy (see Bell et al. 2010 for an analysis of accuracy andrepeatability of the trilateration algorithm), we gain the ability to quickly deploy the systemfor performing spatial analysis. By using the approach we not only investigate the feasibilityof a WiFi-based indoor positioning system but also demonstrate spatial patterns (in particu-lar spatio-temporal moving patterns) that can be revealed when a sensor-based and GIS arecombined.

2.1 iEpi

Meaningful human activity and positioning data can be acquired with a smartphoneemployed as a sensor platform (Stanley and Osgood 2011). Similar systems employingsmartphones (Eagle and Pentland 2006; Aanensen et al. 2009), and custom sensors (Salathéet al. 2010; Cattuto et al. 2010) exist. Smartphone applications can create multi-sensorrecords regarding movement and behavior of the carrier, including GPS, accelerometer,Bluetooth, and WiFi. One of these advanced systems is iEpi (Hashemian et al. 2012). iEpi is

Exploring Mobility Indoors: an Application of Sensor-based and GIS Systems 353


a large-scale data collection system that can provide high resolution measurements of aparticipant’s activity, location, and person-person and person-place contacts. However, animportant trade-off when using such a system is battery life, which must be balanced withthe demands of the researcher’s objectives. iEpi consists of several components: smartphone-based modules for data collection, server-side architectures for data recording, and aggregatepost-processing capabilities (Hashemian et al. 2012). The backbone of iEpi is HealthLogger– an Android Java-based program that provides extensible sensor data acquisition compo-nents, stable encryption, and opportunistic uploading. The application runs on the back-ground of the smartphone and collects detailed information related to user behavior.Coupled with GPS or/and indoor positioning system, such information can provide insightto human mobility and enhance our knowledge of spatial dependency in human activity.

2.2 SaskEPS

An example of an indoor positioning system that can be employed to represent the locationinformation collected with iEpi is SaskEPS (Bell et al. 2010). SaskEPS was proposed andproduced in 2010 by researchers at the University of Saskatchewan. It uses a trilaterationalgorithm that can calculate location on-device or on an external server. Distances fortrilateration are measured according to signal strength from “visible” access points (APs).The exact locations of APs are stored in a server-located database. To ensure correct posi-tioning, the system also calibrates the signal strength and assesses whether an AP signal isexperiencing interference from structures such as floors or walls (Jung and Bell 2013).SaskEPS produces 2.5 dimensional positioning information that consists of X, Y coordinates,and floor information. The system can provide a GPS-like indoor positioning accuracy,although it is limited by the arrangement of WiFi routers and the structural characteristics ofthe buildings. These shortcomings are consistent with the physical nature of the trilaterationprocess and the arrangement of routers in the built environment based on ease of dataaccess, not ease of trilateration.

2.3 Walkable CentreLINE Network (WCN)

Accuracy of indoor positioning systems can vary according to techniques and algorithms. Per-formance of the system may change in different testing environments due to signal propagationand attenuation. For positioning systems that use WiFi signal strength to determine a location,the influence of these negative effects is particularly acute. Although such negative effectscannot be completely eliminated, incorporation of indoor networks can be applied to rawpositioning data in order to improve accuracy and route generation.

Presently, the generation and analysis of the complex multidimensional transformationalnetworks is possible using the capabilities of a number of commercial software packages (suchas ArcGIS, see Lim et al. 2009) or open source solutions (such as PostGIS spatial objects forthe PostgreSQL database, see Liu et al. 2010).

To correct and improve the large volume of positioning output, map-matching algo-rithms are employed; specifically, a network-based dataset of navigable indoor spaces wasused (Jung et al. 2012). Called the Walkable Centre Line (WCN), it is based on a 3Dnetwork that connects campus buildings and represents a topological and navigable repre-sentation of the University. The network consists of nodes and edges, where nodes corre-spond to the central position of a room (or points at which edges meet), and edges represent



the medial axis of hallway polygons, physically connecting rooms (Figure 1). If necessary ordesirable, edges can be regularly subdivided into additional nodes to provide regular snap-tofeatures. A similar node-link approach is applied along stairs and elevators to connect build-ing floors. Each of the 20 interconnected buildings that are included in the network isrepresented as a multi-level structure and they are separated according to their floor number.The WCN was created based on CentreLINE (CL) with the purpose of capturing andsimplifying the geometry of building layout, improving positioning, and supporting indoornavigation (Jung and Bell 2013; Petrenko et al. 2013). The University of Saskatchewan is ina climatic zone characterized by harsh winters. Most of the buildings on campus can bereached by transiting almost entirely indoors. This leads to a natural experimental setting fortesting the efficacy of indoor localization systems and the insights that can be drawn fromthem.

Figure 1 A: Generation of WCN from CAD files: A1: initial CAD file, A2: only wall and window struc-tures. A3: position of the Voronoi polygons created based on the room points of the corridor edges.A4: script output – initial representation of the centre line. A5: final representation of the centre lineafter editing. B: generation of the room points from CAD annotations. C: final network representa-tion in 3D with the example of the shortest path calculation



3 Methods

3.1 Study Experiment

Data was collected over a 41-day period from October to November 2012. Android OSsmartphones were programmed with the iEpi application and used to detect and locate partici-pants’ movement at the University of Saskatchewan. Thirty-seven undergraduate students,recruited from the same Arts and Science class, took part in our experiment. Phones were dis-tributed to participants during three different sessions. In these sessions they were instructedabout their privacy and use of the phone, and were asked to fill in pre-survey questionnaires,draw a number of sketch-maps indicating their familiarity with the University of Saskatch-ewan campus (Figure 2), and sign informed consent documents, in keeping with the studyapproval from the institutional Ethics Review Board.

Participants were asked to carry the phones with them at all times during the day. Theywere instructed to charge their phone on a nightly basis and participate in daily on-line phonesurveys. At the end of the experiment trial all the phones were returned to the Department ofComputer Science for data analysis.

3.2 Data Analysis

The flowchart in Figure 3 depicts a general overview of our methodology, which describes theprimary steps required to produce the indoor tracking data.

Phase I: Data collection was consistent with earlier applications of iEpi (Hashemian et al.2012). The phones had the iEpi application installed and were programmed to collect data attwo minutes intervals; these intervals are referred to as ‘Duty Cycles’ and contained a numberof Epoch Cycles with the activation periods starting at the beginning of the duty cycle. Each

Figure 2 Study area on the University of Saskatchewan campus



epoch cycle in our study lasted five seconds during each 30 second activation period; duringevery duty cycle the phones collected 30 seconds of accelerometer records, Bluetooth contacts,WiFi contacts, and 10 records of battery state. The number of records collected for WiFi andBluetooth depended on the beacon rate of the fixed nodes, which is outside our control. Typi-cally WiFi nodes beacon several times per second and Bluetooth nodes beacon once every 8–16seconds. The phones also regularly uploaded data using the University of Saskatchewan secureWiFi network. Accumulated data was parsed at regular intervals to be later inserted into aMicrosoft SQL Server database.

During the study, if we noted that a participant was not returning data, we contactedthem via email and asked them to visit a study organizer to have their smartphone checked forpossible problems. Due to technical problems with some phones, weak compliance ratesamongst some participants, and the absence of other participants from the University campus,we were able to record reliable data from 32 of the 37 participants.

Phase II: The SaskEPS algorithm employed a trilateration technique to estimate the loca-tion of the participant. It used the received signal strength (RSS) from routers to calculate thedistance from the AP to the phone (Bell et al. 2010). Using the iEpi system, the received signalstrength indicators (RSS) value from all WiFi records, for every participant within each DutyCycle, were converted to meters and grouped into a tuple according to the signal’s origin AP(AP, distance). For every duty cycle, it estimated a participants’ location at time t by applyingSaskEPS’s trilateration algorithm to all possible combinations of APs recorded for a single par-ticipant. This returned a set of points corresponding to the probability distribution of a partic-ipant’s coordinates at time t.

Phase III: For further data post-processing, the server database with the output of theSaskEPS algorithm was connected directly to GIS software (ArcGIS 10.1). Although prelimi-nary data analysis demonstrated relatively high accuracy, several post-processing steps wererequired. SaskEPS data contained a number of points that appeared to be outliers from thedata. Outliers were removed by identifying the geographic center for a set of recorded pointsunique to every participant over a one second interval for all recording epochs. Such data fil-tering significantly reduced the number of the points used in the later stage of data post-processing. Figure 4 shows SaskEPS data before and after the removal of the outliers for theground floor of the Murray building. Because the data was collected for the first 30 secondswithin each two-minute interval, gaps in participant mobility records were evident. To addressthis problem a map-matching algorithm was applied (Figure 4). In general such algorithms canbe categorized into four different groups (Quddus et al. 2007): probabilistic, advanced, topo-logical analysis based, and geometric analysis based algorithms. The last is employed in thisstudy. This type of map-matching employs the geometric information of the spatial roadnetwork data and considers only the shape of the links (Greenfeld 2002); in other words, the

Figure 3 Methodology flowchart



map matching employs a technique that snaps a position to the closest node or edge of a roadsegment. Geometric map-matching algorithms are easily and efficiently implemented, they canbe executed using the capabilities of existing ArcGIS tools and provide accurate results(Bernstein and Kornhauser 1996). However they are highly dependent on the network repre-sentation and may perform poorly when network errors exist. Nevertheless, several solutionsexist to improve the accuracy of the method (Bernstein and Kornhauser 1996; White et al.2000). In particular, problems with the method can often be addressed by including more ver-texes for every arc (used for snapping positions to the network). The drawback of this solutionis increased computational time; this, however, has a limited impact in our particular case dueto the relatively small size of the network. Similar techniques could be employed in larger net-works using simple network-partitioning algorithms to manage complexity.

We employed a geometric map-matching algorithm (Quddus et al. 2007) to link filteredSaskEPS points to a corresponding position on the WCN. Prior to snapping, vertexes were insertedalong the WCN (with one meter distance between neighboring vertexes) to simplify the snappingprocess to WCN vertexes rather than segments. This allowed for calculation of snapped SaskEPSpoints based on WCN vertexes and produced more consistent representation of the participants’movements.

Figure 4 Filtering the SaskEPS points. A: non-filtered SaskEPS points. B: SaskEPS points after clip-ping with corresponding building floor. C: filtered SaskEPS points, corresponding to position of theparticipant within one second intervals. Application of geometric map-matching to SaskEPS points.D: raw SaskEPS data points. E: after implementing map-matching



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4 Results

Data was collected over 41 days from 37 participants every two minutes for 30 seconds. Thisresulted in a significant quantity of data on the location contact patterns and activity of the partici-pants (Figure 5). The number of recordings significantly varied for different days. As expectedalmost no recordings were collected on weekends, in contrast, the highest activity on campusoccurred on Tuesdays and Thursdays. The participants’ presence on campus was significantlyhigher in the first three weeks of the experiment. The fourth week was characterized by the drop ofthe number of the recorded positions; therefore, less data was collected in the last week of theexperiment (Figure 6). Such variation in recording results can be explained by the academic sched-ule of the study participants since the last two weeks corresponded to the reading and mid-termexam weeks respectively. Summary statistics of the three primary sensor streams (WiFi, Bluetooth,and Accelerometer) are shown in Table 1, where the number of records includes all records of thattype across all participants, and the number of unique addresses is the number of unique devices(either WiFi or Bluetooth) observed (Table 1). It should be noted that the number of WiFi devices isskewed higher as all University of Saskatchewan routers support four distinct addresses, indicatingthat the number of addresses is not the same as the number of devices. This was accounted for

Figure 6 Sum of the daily recordings during the study experiment. Red vertical bars correspond tothe data collected during regular academic weeks, green to the study week, and black to themidterm exam week. The X axis represents single days of the experiment, the Y axis the sum of dailyduty cycles with data

Table 1 Number of records from primary sensors during the experiment

Data TypeTotal Numberof Records

UniqueAddresses

WiFi 2,715,396 14,803Bluetooth 546,975 4,903Accelerometer 35,625,370 –



during analysis, prior to trilateration, as University of Saskatchewan routers have a known addressprogression. Overall results are replicated from Petrenko et al. (2013) to provide overall context,and for the reader’s convenience.

The scope of the data collection is evident in Table 1. While having orthogonal datastream opens the potential for analyzing interesting composite queries, such as where is a par-ticipant most likely to be active with other people (where proximity to a Bluetooth cell phoneis a proxy for person), we focus in this work entirely on position-based measurements, andconstrain ourselves to the analysis of the WiFi data.

Using ArcGIS 3D Analyst we created a 3-dimensional model of the university campus(Figure 7). This model represents the locations that were visited by study participants. Thevisualization indicates that the most popular destination for participants was the first floor ofthe Physics building. This location is not surprising as it is the location of the undergraduateclass from which participants were recruited. Apart from the Physics building, there was alsoconsiderable presence in the Arts building (first floor), Thorvaldson (second floor), and Agri-culture (first floor). For a more in depth analysis of how recorded locations matched with par-ticipants’ perceptions of their habits see Petrenko et al. (2013).

To further explore the data, we performed spatio-temporal analysis of popular campus loca-tions. By calculating the number of SaskEPS points that were snapped to WCN we determined thecampus location with the longest visit duration. Figure 8 shows an example of this analysis. Weselected four different buildings (Physics, Arts, Thorvaldson, and Murray Library) and created heatmaps using the point density function for the entire duration of the experiment. Colors close to redindicate a higher number of tracked points, whereas colors closer to blue are related to lowernumbers. By visualizing this data in an ArcGIS environment and overlaying it with campus blue-prints, it was possible to detect that room 107 in the Physics building was the location where par-ticipant spent most of the time. This visualization demonstrates that computer rooms in the Artsand Thorvaldson buildings as well as the lounge area of Murray library were also characterized bya high number of visits, probably during independent study time or for tutorials. Created mapsalso enable us to depict the high concentration of recordings in relatively small spatial areas, whichwe believe is a defining characteristic of participant visitor patterns.

Figure 7 3D model of campus that represents the building floors where the participants mainlyspent their time. Colors close to red correspond to the locations with a high number of daily dutycycles with data, blue to those with a low number



Temporal analysis of the data (Figure 9) demonstrated that the highest number of record-ings over the duration of the experiment was collected between 8 and 10 a.m. on the first floorof the Physics building. These results were consistent with the academic schedule of partici-pants because 8.30 and 10 a.m. was the lecture schedule for participants’ common class.Furthermore, temporal analysis of participant mobility was performed for three buildingfloors. Unlike the first floor of Physics, no discrete daily patterns were observed. In general, wecan detect a relatively high number of participants’ visits with the highest concentration ofrecordings in and around computer laboratories of Arts between 2 and 3 p.m. Overall, it ispossible to detect that the duration of the visits is evenly distributed, meaning that participantsspend considerable time in different computer laboratories of the Arts building.

The Thorvaldson Building and Murray Library are characterized by different patterns. Inparticular, most locations with a high number of individual participant recordings correspondto places where participants did not spend much time; these place were used as transitional

Figure 8 Heat maps representing the location of highest concentration of the daily duty cycles withdata. A: Physics building 1st floor. B: Arts building 1st floor. Thorvaldson 2nd floor. Murray Library 1st

floor



spaces, to get from one location to another. This is consistent with the topology of the campus,as the Thorvaldson Building is a major intersection between the Physics/Geology buildings andthe Arts Tower and Murray library and contains the sole covered walkway permitting indoortransit between these destinations. The Murray library is connected via a heated tunnel toPlace Riel, which hosts student services and the bus depot, and to Marquis Hall, which con-tains the primary cafeteria on the campus.

5 Results Validation

Although the results were good enough to track individual spatio-temporal positions, data vali-dation was still required. We decided to run two separate tests to understand the reliability of thedata. The first test was based on the sampling method we selected for our research. Since thestudy participants were recruited from the same Arts and Science class, we expected to observecommon spatio-temporal moving behavior twice per week. In particular, room 107 of thePhysics building was the location of the morning class that should have been attended by everyparticipant on Tuesdays and Thursdays. In order to check the validity of our method, we visual-ized the number of the recordings between 8.30 and 10 a.m. on those days. We did not select theexact time of the lecture class (8.30–9.50 a.m.) in order to depict whether we could observe anychanges in data recordings before, during, and after the lecture and to examine how participantscame to and departed Physics 107. Locations outside class during class time could depict stu-dents skipping the lecture, a participant compliance issue where the phone was left at another

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Figure 9 Temporal analysis of the daily duty cycles with data for the entire duration of the experi-ment. A: Arts building 1st floor. B: Murray building 1st floor. C: Thorvaldson 2nd floor. D: Physics Build-ing 1st floor



location, such as a residence, or a technical or algorithmic error where our localization techniqueproduced unreliable results. The first failure mode actually indicates interesting spatial behaviorthat we would like to capture. The second and third failure modes represent shortcomings in theexperimental design and analysis techniques, respectively. Figure 10 shows the results of thisvisualization. Colors close to blue correspond to very low/zero recordings, while those close todark red represent a high number of recordings. Approximately 77% of the collected partici-pants’ locations were tracked around Physics 107 before 8.30am, 88% during the scheduledtime of the lecture and only 10% after 9.50. We checked the location of the recordings that cor-responded to the 12% that was expected to be collected inside Physics 107 during the scheduledclass. These recordings were found in other parts of the campus, such as computer classes in theArts buildings (various days for Participant IDs 20 and 23) as well as recreation areas ofMarquis Hall (Participant IDs 8 and 16). Based on the frequency and positions of the recordings,we concluded that they most probably belonged to students who were skipping the classes ratherthan to incorrectly calculated positions. Few of the records were indicative of an abandonedphone. Interestingly, and important for our validation, no recordings indicated that participants

Figure 10 Spatio-temporal visualization of the of daily duty cycles with data collected during theregular academic weeks (study breaks and exams weeks excluded) on Tuesdays and Thursdaysbetween 8 and 10 a.m. Colors close to blue correspond to a low number of recordings; those closeto red, to a high number. Graphs on the right side depict the overall distribution of the recordingsfor different buildings before, during, and after the shared class



were near, but not in, Physics 107. Overall, we concluded that the data corresponds to thespatio-temporal moving behavior of the study participants and that all participants who were inthe classroom were recorded as such.

The second test was a spatial-temporal validation for individual tracks. Although, byusing the WCN we constrained the spatial validity of the results (a participant would not beable to move from one room to another unless there was a door), we introduced a thresholdbased on maximum walking speed. To check our findings, an ArcGIS model that calculates theduration of all movement trajectory fragments was created. The purpose of this model was toidentify the length of every path that connected successive locations recorded with iEpi andcalculate the time taken to travel the corresponding distance (assuming the average walkingspeed around 5 km/h). We connected sequential tracking points and determined those tracks’segments, where the length exceeded the possible walking speed. Introducing these segmentsallowed for the identification of false trajectories – paths imputed as indoor movement whenoutdoor movement was more likely based on movement speed.

To run this test we selected a single day and used the top five participants by logged time oncampus (Participant IDs = 10, 15, 30, 31, 32). We selected a particular day for each individual(not necessarily the same for every participant) where: (1) the participant had a high number ofrecords; and (2) she/he visited various buildings on campus. The analysis of this data revealedthat speed threshold was exceeded for only 4% of the generated trajectories. Moreover, out ofthis 4%, 12% were likely taking place outdoors (Figure 11). The next figure (Figure 12) repre-sents the example of two wrongly classified trajectories for participants 30 and 32. According tothe results, it took approximately two minutes for participants to travel between areas locatedrelatively far apart. However, this is hardly possible, even with a high walking speed. It is moreprobable that participants left the building through one of the doorways (indicated in blue inFigure 12) and took a shorter outdoor path. Although this accuracy check is preliminary, it givesan opportunity to identify the trajectories were more likely to occur outdoors. More preciseaccuracy estimation can be performed only if the chosen tracking method allows for the collec-tion and estimation of position in an outdoor environment as well.

Figure 11 Distribution of the of daily duty cycles with data according to the speed threshold



6 Discussion

In this article we have presented a method for combining high temporal and spatial fidelitymeasurements of indoor location obtained through commodity electronic devices and existinginfrastructure with an efficient roadmap system and sophisticated GIS tools to obtain a novelview of human behavior in indoor spaces. Our contribution in this article corresponds pri-marily to the methodology itself, and not the insights gained from this methodology alreadydiscussed in other works (Petrenko et al. 2013; Jung and Bell 2013). This kind of techniquepermits a new level of precision in the understanding of indoor behavior without the need todeploy new systems either for the space or the individual. We believe the number of spaceswhere this type of system could be deployed will increase with the advent of iBeacon and

Figure 12 Example of false positive path for Participant IDs 30 and 32



similar technologies, as the precision available in the environment will only increase. Indeed,this system could easily be reversed such that the participant’s phone passively pings everysecond, and the owner of an institution-wide WiFi network combs the data collected at therouter for relevant patterns of individual phones. The system described here is particularlyeasy to deploy for experimentation. In a work yet to be published, the system has been used toanalyze patient mobility in a hospital setting and contact patterns during flu season in a dormi-tory. The flexibility and ease of deployment, while maintaining the required precision forserious analysis of the system constitute the primary contribution of the work, and foreshadowa point in the not-to-distant future where automated analysis of indoor mobility will be thenorm, not an engineering curiosity. While we do not require additional analysis of the space, inpractice a visual confirmation of WiFi router locations is required due to inaccuracies in thereported locations in the building plans.

This technological combination of automatic indoor position tracking, post-processing statisti-cally large samples and integration with existing tools has the potential to change how we analyzethe interaction of people with indoor spaces. While this technique is not meant to supplant moretraditional data collection systems such as direct observation, questionnaires or structured inter-views, this kind of technique can be readily adapted to provide deeper data on observed behavior.Preliminary results such as our observation that students frequented the areas associated with theirclasses, or employed the Murray Library as a transient node due to use of the tunnel, not visits tothe chain coffee shop on the first floor, or to the stacks are illustrations of the kinds of insights thatcan be obtained with this kinds of system. We have also demonstrated that by aggregating largenumbers of records we can obtain insights from positioning systems which exhibit error modeswhen used for navigation. The large number of samples washes out the outliers, and most localrandom variation is addressed with the grid snap.

Like most studies, our work is characterized by some shortcomings that should be addressedin future work. The first and foremost of these is sample size and bias. A relatively small number ofstudents were selected from a single class. While some initial inferences can be drawn from theirbehavior, many of the findings, such as their use of the Physics Building, is an artifact of the samplewe chose. For a more representative analysis of the use of the facility, a more representative popu-lation must be chosen. Secondly, we chose geometric mean and geometric-based map correction.While these approaches are computationally efficient and reliable, greater precision might beobtained from more sophisticated techniques. While sufficient to demonstrate the methodology,our work could benefit from more advanced positioning algorithms, such as particle filter-basedestimation. We had to trade-off battery conservation and data volume; the scanning schedule of thedataset was set to collect data for 30 seconds every two minutes. By not recording data at all timesthe movement of participants is represented with some uncertainty. Finally, we tested a reasonablylong window of six weeks, but only during the early winter. We would expect differences in navi-gational choices and use of facilities based on the climatic swings experienced in Saskatchewan,therefore the temporal generalizability of our work is limited. However, given that the purpose ofthis article was to demonstrate the approach, these shortcomings are generally outside the scope ofthe work.

7 Conclusions

Our research demonstrates the potential for using iEpi and SaskEPS for understanding indoormovement and spatial behavior. The results of our study reveal that our methodology wassuccessful in the collection of indoor movement data. The study outlined certain limitations



related to the applicability of using automated data collection methods for spatio-temporalanalysis. However, by demonstrating data through visual representations, we have shown thatcollected tracking data accurately corresponded to the academic schedule of the participantsand is consistent with the building layout. This proves that iEpi and SaskEPS system alongwith GIS processing can be successfully used for understanding the movement behavior anddetecting movement patterns.

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