Seeking structure in records of spatio-temporal behaviour...

Computational Statistics & Data Analysis 43 (2003) 581–603www.elsevier.com/locate/csda

Seeking structure in records of spatio-temporalbehaviour: visualization issues, e-orts and

applications�

J.A. Dykes∗ , D.M. MountainDepartment of Information Science, City University, Room A217a Northampton Square,

London EC1V 0HB, UK

Abstract

Information that contains a geographic component is becoming increasingly prevalent and canbe used both to analyse relatively complex behaviours in time and space and to combat thepotential for information overload by assessing the geographic relevance of information. Suchanalysis can be combined with mobile communications technology to fuel location-based servicesthat o-er information pertinent in terms of geography, time, experience and preference. This paperaims to raise some issues relating to these advances and describes novel representations designedfor interactive graphical exploratory data analysis (EDA). A number of graphical techniques andrepresentation methods are introduced to establish the nature of the kinds of data that are beingcollected and the suitability of visualization for EDA of spatio-temporal data. These include theinteractive views provided by the Location Trends Extractor, ‘spotlights’—continuous densitysurfaces of recorded spatio-temporal activity, networks of morphometric features derived fromcontinuous surfaces representing density of activity and geocentric parallel plots presented in aspatial multimedia environment for data exploration. Some of the bene9ts and limitations of thetechniques are outlined along with suggestions as to how the visualization tools might be utilizedand developed to improve our understanding of behaviour in time and space and evaluate andmodel geographic relevance.c© 2003 Elsevier B.V. All rights reserved.

Keywords: Geographically relevant information; Exploratory data analysis; Visualization; Spatio-temporaldata; Spotlight; Morphometric surface features; Geocentric parallel coordinates plot

� Colour 9gures: http://www.soi.city.ac.uk/∼jad7/csda/∗ Corresponding author. Fax: +44-02-7040-8584.E-mail address: [email protected] (J.A. Dykes).

0167-9473/03/$ - see front matter c© 2003 Elsevier B.V. All rights reserved.PII: S0167 -9473(02)00294 -3

http://www.soi.city.ac.uk/~jad7/csda/

mailto:[email protected]

582 J.A. Dykes, D.M. Mountain / Computational Statistics & Data Analysis 43 (2003) 581–603

1. Introduction

Massive volumes of data are being produced by the information society. Recent es-timates suggest that between one and two exabytes (1 million terabytes) of digital dataare generated every year, equating to approximately 250 megabytes for every humanalive (Lyman et al., 2000). Under these conditions the opportunities for individuals andorganizations to gain advantageous knowledge are great. But such opportunities relyincreasingly upon the e-ective organization of information in order to ensure that usersgain access to that which is relevant. The challenge of achieving relevance (Raper, inpress) is increasingly diHcult as the size and number of data sets grow, but the ge-ographic aspects of information may o-er considerable potential. Estimates as to theprevalence of geographic information (GI) suggest that up to 85% of all informationhas a spatial component (MacEachren and Kraak, 2001). This situation has resulted inan increasing number of collaborations between information scientists and geographers,whereby the geographic component of information is used to generate an organizationalstructure for a wide range of information (e.g. Butten9eld and Goodchild, 1996). Alter-natively, a spatial metaphor can be used to graphically represent an information source(Fabrikant, 2000).The recent and continuing advances in mobile communications technology have had

a signi9cant impact on the nature and availability of GI. Mobile devices are an in-creasingly important means of accessing information. Until relatively recently, mobilecommunications have mainly relied upon sending and receiving audio, but now allsorts of additional digital data are being transmitted to people on the move. The rapidevolution of mobile technologies means that access to such devices is burgeoning,bandwidth is increasing and ‘always on’ status is imminent. Just as users of the Webon static desktop machines require 9ltering and searches to avoid information overload,mobile communications device users require analogous techniques to ensure that theinformation they receive is relevant. Geography can be employed in a number of waysto ensure that information served on the move is pertinent to the current position, likelyfuture position, past spatial experience and anticipated behaviour.Mobile communications device owners are also contributing signi9cantly to the mas-

sive increase in the volumes of spatial data that are being generated. This is occurringboth deliberately as data are recorded on the ground and added to centralized databasesand inadvertently as a result of an increasing number of devices that automatically de-termine an individual’s location on the surface of the Earth. This ability to record timeand position in parallel at almost any moment means that a unique spatio-temporalrecord can be generated for any device, and by inference it’s user. This trend has beenadvanced by investment in the global positioning system (GPS) and alternative satel-lite positioning systems, the development of terrestrial solutions that exploit existingmobile communications infrastructures to gauge device location, legal imperative (theFederal Communications Commission E911 initiative) and the potential for 9nancialgain (particularly in Europe and Japan).There is potential for using these data for a variety of purposes. Of relevance here

is the opportunity to analyse the data in order to generate knowledge about behaviourin time and space. User pro9les that enable us to describe and predict behaviours and

J.A. Dykes, D.M. Mountain / Computational Statistics & Data Analysis 43 (2003) 581–603 583

Fig. 1. Time Geography approach. A 2D model of space is used with an additional (third) independentcontinuous dimension employed to represent time. The key concept is the trajectory of mobile individualsover (2D) space and through time; static phenomena (e.g buildings, transport networks) occupy the samelocation in space through time. Here three stages are shown for an individual moving from one staticphenomenon to another via a network link. A traditional planimetric representation (with the link as a greyline and the individual a black cross) is contrasted with the trajectory through time (the black line).

even information needs over time and across space can be developed from recordsof spatio-temporal behaviour. The knowledge from this spatio-temporal signature canthen be applied to address the issue of information overload and potentially gauge ge-ographic and personal relevance. In order to achieve these aims preliminary knowledgeis required concerning the nature of the data that are collected and the way that theyrecord behaviour in time and space. As the data sets are large and typical structuresare generally unknown a degree of exploratory data analysis (EDA) is appropriate atthis stage. Due to the geographic nature of the data and the evolution of graphicaltechniques for exploratory spatial data analysis, visualization provides a solution thato-ers exciting possibilities. Some recent responses to the need for exploratory graphicsfor visualization, a process of investigation through ideation prompted by the use ofhighly interactive graphics, are presented here.

2. Graphical representations of spatio-temporal information

A considerable body of work exists in academic geography regarding the natureof spatio-temporal behaviours and interactions, a discipline often referred to as ‘TimeGeography’. The tradition is exempli9ed by the Lund School from which much of thework originates (HNagerstrand, 1968, 1970). Typically, a two-dimensional (2D) modelof space is used whilst an additional (third) independent continuous dimension is em-ployed to represent time. The key concept is the trajectory of each individual over(2D) space and through time (see Fig. 1). At any point in time the location of anindividual can be recorded and mapped. All other locations that are accessible fromthis point over any given period of time can also be delimited based on the user’slimiting maximum speed. Members of the Lund School represented this informationgraphically by projecting these sets of one-dimensional (1D) points and 2D spaces


into a third dimension representing time. This results in spatio-temporal geometries forindividuals such as the time line and the activity volume or ‘prism’ (Lenntorp, 1976).Viewed obliquely, these spatio-temporal boxes are sometimes referred to as aquariums.Despite this important work, traditional GI systems are not designed to record the

temporal component of GI (Langran, 1992; Raper, 2001a). The static nature of printedmaps and the inertia resulting from the development of early GI systems to generateconventional cartographic output are likely reasons (Fisher, 1998). Many attempts havebeen made to incorporate the temporal component into GI systems, and these usuallyrely upon the addition of a temporal attribute to geometry based upon the map model.The three-dimensional (3D) modelling capabilities of many modern GI systems en-

able us to produce 3D data structures that correspond to the Lund School models.For example, Forer (1998) generates a series of time–space volumes that he terms‘taxels’ in a standard GI system and represents the results graphically using scien-ti9c visualization technology. An alternative is to take advantage of the advances in2D representation, speci9cally the high levels of interactivity that are now available indata analysis software that e-ectively utilize the coordinates of the plane and additionalretinal visual variables. Elegant software such as REGARD (Unwin, 1994) MANET(Unwin et al., 1996) and Mondrian (Theus, 1996) demonstrate the utility of this ap-proach, and cartographic applications such as ‘cdv’ (Dykes, 1996) have extended itinto the realm of GI by incorporating geographic data and representations.Capabilities for this kind of exploratory analysis are rare in GI systems as a con-

sequence of the reliance upon the map metaphor for structuring GI and the inertiaassociated with investment in this model. As a result, the systems that are currentlyavailable are inappropriate for detecting structure in novel, large and unexplored datasets and particularly unsuitable for the exploratory analysis of data describing behaviourin time and space. New models, techniques and software are required to store, representand analyse the products of the spatio-temporal data explosion.

3. Visualization requirements and data

We describe a number of techniques and software solutions that have been developedspeci9cally to meet these requirements in order to perform exploratory analysis onspatio-temporal data using interactive graphics. The 3D approach is a popular one touse for representing such information graphically (Forer, 1998) and is appropriate incertain circumstances. Here, however, we employ the 2D techniques epitomized by thesoftware introduced above that uses the co-ordinates of the plane to ensure that the useris able to discern the information distinctly, identify regularity and patterns and estimatemagnitudes from the representation clearly without obstruction (Cleveland, 1993). Suchtechniques are better suited to the kinds of selection and interrogation options requiredfor detecting structure in large data sets than 3D alternatives, which imply a linearand continuous conceptualization of time. This may be inappropriate when searchingfor patterns in spatio-temporal data sets that are periodic, such as those describingbehaviours that are repeated on a daily, weekly, and even annual basis. Such patternsare particularly important for modelling behaviour in time and space in order to address


the issues associated with information overload by assessing geographic relevance anduser’s geographic experience through a personal pro9le.The data used here were recorded by a number of individual volunteers who carried

GPS receivers to create data sets that contain patterns at a variety of spatial andtemporal scales. These logs simulate the kinds of spatio-temporal data that can berecorded by mobile communications devices. Each record in a GPS track log consistsof a 2D spatial co-ordinate, a temporal tag indicating the time at which the positionwas logged and an assessment of the positional accuracy. Logs describing activity overa whole year typically contain 40,000 such entries for each individual. Currently e-ortsare focussed on identifying structure in the data, eliciting trends and identifying meansof generating user pro9les that summarize the spatio-temporal information about anyindividual. Such summaries could be vital tools for assessing geographic experienceand inferring information relevance. Once the nature of this higher level informationhas been established its derivation should be automated and may ultimately be used todescribe a user’s past spatio-temporal behaviour and model likely future trends.

4. Visualization with points in time and space

The location trends extractor (LTE) is an application designed to begin the task ofinterpreting data of this format (Mountain and Raper, 2001).

4.1. Data subsetting

Initial objectives of the software include the basic need to see the data in order toconceptualize the types of information that might be generated from it. The softwareprovides a planimetric view of the point-history contained in any spatio-temporal log.In this ‘map view’ a symbol is positioned according to the longitude and latitudeand shaded according to either the time at which the position was recorded or pointattributes (see Fig. 2). The temporal information is also displayed by plotting thefrequency of collection on the y-axis against absolute time on the x-axis in a reciprocal‘time view’. Once again, symbols for each logged position are shaded to show the timeat which they were recorded or other attributes of the datum.More advanced requirements include the need to identify ‘episodes’ of spatio-tempo-

ral behaviour describing di-erent kinds of activities. These may relate to various distinctbehaviours and information demands. As GPS signals do not usually penetrate build-ings, breaks in the longitudinal record of position are a feature of the method of datacollection that can be utilized to de9ne episodes. These are often evident from thebasic spatial and temporal displays described (for example, in Fig. 2, several peaks ofactivity can be seen to be separated by gaps of inactivity). However, interactive tech-niques are also extremely useful. Subset functionality (‘focusing’, Becker et al., 1987)is available in LTE so that groups of cases can be selected from the spatial and tem-poral views. Brushing between views is supported and views can be re-con9gured sothat the spatial boundaries of the map view are set to a particular time period selectedinteractively from the time view or so that the temporal data range is set to a selected


Fig. 2. Graphical output from the location trends explorer. A 2-year log of 80,000 points collected by asingle individual is shown shaded by time. The map (left) covers most of Europe and some of North Africa.The height of the bars on the time view (right) indicates the frequency at which points were logged. Aninteractive selection tool is being used to focus in on a particular period (see Fig. 3).

spatial subset. The former of these can be termed a ‘time–space’ where spatial rangesare de9ned by temporal limits (where a user is during a particular period), and thelatter a ‘space–time’ where temporal ranges are de9ned by spatial limits (when a useris in a particular space). Fig. 3 demonstrates by focussing in on a particular period oftime from an annual log and then a particular spatial section of the route taken.Dealing with data interactively in this graphical and user-centered manner allows

the analyst to gain an appreciation of any periodicity or pattern in the data and thetemporal and spatial scales at which these forms occur through a process of visual-ization. Several subsetting operations of each type may be required to gain knowledgefrom a large (spatial and temporal) scale data set such as an annual log. The interac-tive software can be used to extract signi9cant episodes and higher level informationsuch as familiar and unfamiliar locations, ranges of movement, repeat and periodicbehaviour.

4.2. Episode summaries and indices

A number of measurements can be made from logs of data of this sort, at a varietyof temporal scales. These include calculations of absolute speed, direction and sinuosityand measurements of their variation. Data selection techniques have been incorporatedinto LTE to perform focus operations on sections of a log with particular speed, di-rections and sinuosity. Sudden changes in spatial co-ordinates, time or any of thesederived indices can be used to identify break points between episodes of homogenousspatio-temporal behaviour. The detection of break points enables us to apply limits totime–spaces and space–times that are derived from the data, rather than more arbi-trary considerations such as particular scales or periods. The nature of these points in


Fig. 3. Graphical output from the location trends explorer. A subset covering the Sinai peninsular is shownshaded by speed; this is revealed to be slower in general for node points where much time is spent andfaster for the links between them. In the time view, the temporal subset selected in Fig. 2 is shown. Thefocus tool has been used to select a particular period of activity for which additional detail is evident. Themap is re-scaled to show the selected ‘time–space’ (the spatial range de9ned by the user de9ned temporallimits). Additional focusing using the map can select ‘space–times’ as shown by the lasso tool created withthe cursor. When such a space is selected the time view is updated appropriately.

relation to the data log as a whole has been investigated through visualization, whichcan reveal various kinds of information:Firstly, episodes allow us to identify frequently occurring patterns. These can then

be formalized into enclosing envelopes that de9ne time–spaces and space–times. Thisinformation allows us to identify regularly visited ‘bases’ that are evidently known, andto delimit spatial envelopes of experience and periodic patterns of day, week and year.This can be achieved using a range of Boolean or fuzzy techniques that take advantageof simple regular geometries such as an enclosing rectangle or more complex ones suchas the phases of the sun or moon.Secondly, conjectured activity can be measured from episodes to summarize the

likely means by which a user is travelling through time and space. In LTE mea-surements of sinuosity, average speed and envelope are used to ascribe episodes tofour di-erentiating classes using a fuzzy membership function. These are shown inTable 1.The derivation of these kinds of categories provides information about likely activity

and so allows information relevance to be assessed. The ‘range’ and ‘freedom’ columnsin Table 1 demonstrate. A user with a high fuzzy membership function to ‘low-speedmotor’ mode is more likely to be able to respond to information with a geographicfootprint that is close in any direction. Whilst episodes with a high membership functionto ‘high speed motor’ are likely to require information at a higher spatial range, in thedirection of travel. The information needs of those corresponding closely to a ‘Pight’class are likely to be almost entirely dependent upon the 9nal destination rather than


Table 1Conjectured activity classes derived from spatio-temporal log with LTE

Speed Sinuosity Spatial range Freedom of movement

Foot Low High Low High (self-powered, unpredictable)Low-speed motor Higher Less sinuous Higher HighHigh-speed motor Fast Low High Lower (constrained by network)Flight Very high Lower Very high Entirely constrained. Information

provision will depend upon endpoint,which cannot be changed whenPying with an airline

the current location or speed due to the spatial constraints put upon them by the natureof the mode of transport.

4.3. Additional exogenous information

Additional knowledge can be gained from episodes by adding appropriate exogenousinformation (Cleveland, 1998). This may be relatively simple, such as the addition ofsome form of backdrop map that identi9es physical constraints or a limited series ofpotential destinations (such as airports). More complex examples may include the con-tinuous analysis of transportation networks to match locations to known arcs (Tayloret al., 2001), enable additional attribute information to be incorporated into any analysisor use predictive models that rely upon similar networks. For example, the relationshipbetween the user’s speed and the local legal restriction might be used to determine avery real information requirement. The analysis of an episode in relation to a trans-portation network enables likely and unlikely destinations to be derived from a broaderset of possibilities, such as the destinations o-ered by a road network. The log shownin Fig. 4 represents one individual’s activity over a period of 1 week. The sinuosityand structures of the basic patterns suggest that a number of distinct types of activityhave occurred and particular bases are known and visited. The addition of some verybasic GI can help explain. Simply adding the boundaries of land and sea shows thatmovement in time and space di-ers signi9cantly in each situation. In this particularexample the constraints of the islands dominate the behaviour. There is a tendencyfor the user to circumnavigate islands when on land. Sea trips tend to be routed moredirectly and link a relatively low number of known locations (suitable for boardingboats). Certain islands, such as those to the top right and bottom right appear as themain centres of activity.

4.4. Emphasising the temporal component

The inescapably linear nature of our experience of time is an additional factor. Whenplaces were visited is important as prior knowledge a-ects our behaviour considerably.A tool like LTE allows us to assess the way in which a user’s behaviour changes astheir experience of a new region increases. If detected, such information on changing


Fig. 4. (a) Weekly spatio-temporal log and (b) Weekly spatio-temporal log with exogenous information.In these two 9gures lines are used to connect successive points. The shapes and patterns displayed in Fig.4a allow particular episodes and behaviours to be identi9ed. The background map showing the extent ofland and sea adds context and helps explain the patterns. More sophisticated GI allows us to make moreadvanced inferences.


behaviour could be used to predict future responses to visiting new areas (which arelikely to be locations in which users have high information needs) and to determinelikely information requirements. For example, a temporal assessment of the data shownin Fig. 4 reveals that the 9rst visit to a new island results in the user travellingwidely before focussing on areas of interest. Noting this behaviour and using exogenousinformation to identify the kinds of locations that are selected may enable us to meet theuser’s requirements more successfully. When tailoring location-based services (LBS) itis likely that the user will require the most generic information on their 9rst visit to anarea. During subsequent visits more speci9c and detailed information may be required,such as points of interest that are ‘o- the beaten track’. Recording a user pro9le ofspatio-temporal activity allows us to identify the ‘beaten track’ with some precision.Learning about the data recorded by individuals on the move through visualization mayhelp us automate the process.

4.5. Identifying constraints

The physical constraints placed upon users are relatively extreme in the island-basedexample introduced here. Yet our continual operation under the constraints of time,money, need, right of way, knowledge, experience, fear, safety, transportation net-works, transport services and a range of other factors that inPuence our daily livesare also likely to shape our spatio-temporal behaviour. Some of these factors, such astransportation networks, can be incorporated into geographic analyses. For example, itis possible to determine which road a user is on from relatively few successive GPSreadings (Taylor et al., 2001). Likely (and unlikely) destinations can then be derived,with more con9dence if an individual’s time–space pro9le is consulted. It is a majorchallenge to model some of the less well-de9ned constraints and incorporate them intospatio-temporal investigation. Whilst geographic relevance can be assessed to a degreewith current technology, the incorporation of these additional factors would lead toexciting developments in information provision.

5. Visualization with continuous point density surfaces

The LTE software enables us to detect episodes of spatio-temporal behaviour throughan exploratory process of interactive focusing or sifting. A series of simple indices canbe measured from the point data to provide numeric descriptions of spatio-temporalsub-sets of a data set. In addition to this vector-based representation of the spatio-temporal log, use of the software and familiarity with the data type suggests that ad-ditional synoptic views are also bene9cial for a number of reasons associated withrepresenting large data sets graphically. These include over-plotting and perceived dif-9culties in detecting structure and form in large point clouds. In Fig. 2, for example,over 40,000 points are plotted in a relatively small space and it is both impossible toinfer the magnitudes of the densities and diHcult to visually synthesize this informationwith ancillary graphical data.


5.1. The spotlight metaphor and representations

One solution that addresses these problems is the use of surfaces to provide estimatesof point density at each location. The approach is relatively standard and has beenapplied in an EDA context to show point density in scatter plots (Haslett et al., 1990).In this case a regular grid of pre-determined resolution is imposed over the pointsoccurring within a particular time–space. The number of points in each cell is calculatedand the density computed resulting in a spatial familiarity surface. An element ofsmoothing may be appropriate depending upon the accuracy of the spatial information.A graphical advantage of the technique is that point density can be represented with asingle visual variable, such as colour lightness, allowing other colour variables to beused for additional graphical information. In Fig. 5a colour lightness is used to representdensity of occupation of space over time. Fig. 5b also uses this visual variable, butvariations of hue and saturation reveal underlying exogenous information suitable forexploratory and explanatory purposes. We refer to these views as ‘spotlights’ becauseof the way that they focus attention on the contextual information at locations with thegreatest density of spatial activity, and inferred spatial familiarity.

5.2. Visualization with morphometric surface feature networks

An opportunity exists for generating feature networks from continuous 9elds suchas those described by density surfaces (Pfaltz, 1976; Wolf, 1984). These represent-ing the morphology and topology of a surface and can provide useful summaries ofform for visualization (Bajaj et al., 1998). Such networks consist of peaks (maxima),pits (minima), channels (linear minima), ridges (linear maxima) and saddles (chan-nels crossing ridges). They can also o-er insight into the nature of continuous datasurfaces at various speci9ed scales. The morphometric features can be derived withappropriate analytical software (Wood, 1996). These summarize variations in surfaceform, indicating in this instance variations in the regularity with which locations arerecorded in a spatio-temporal log. Detected peaks are likely to relate to locations thatare visited often and pits to areas of relative local knowledge dearth. Ridges may re-late to preferred or regularly used routes between peaks and channels to locations androutes that are avoided. These features can be mapped and analysis of the relationshipbetween these measured characteristics and the spatio-temporal record may reveal thatthe peaks, pits, channels and ridges of activity relate to some of the constraints imposedupon our spatio-temporal behaviour introduced in Section 4.5. This knowledge may beusefully applied to address some of the key issues relating to the analysis and use ofspatio-temporal logs.Feature network measurements indicate the presence of features as nominal classes

but not their magnitude. Graphical representations of the networks can show both themagnitude at any point on the surface and the category of feature detected with thecombinations of visual variables used in our spotlight examples. Such representationsshould also reveal the scale at which surface features are detected as the measuredmorphology is scale dependent (Fig. 6).


Fig. 5. Use of the spotlight metaphor: (a) spotlight showing weekly spatio-temporal log and (b) spotlightwith superimposed exogenous information. Varying colour lightness to show density of occupation of spacecan be useful to ‘throw light upon’ additional exogenous data in areas that are frequently visited whengraphically exploring time–space data sets. Here the full week’s activity is represented by colour lightness(Fig. 5a). We are able to use colour saturation and hue to show contextual spatial information that help usinterpret the time–space data. The data can be split into various temporal periods (e.g. days of the week,hours of the day, daytime/nighttime) and sequenced to throw light on relevant locations in a suitable order.Interactive or animated representations reveal dynamic changes in the focus of activity and the relevantcontextual spatial data is revealed to aid interpretation—hence the spotlight metaphor. A drawback of the


Surface feature networks have great potential for emphasizing the information contentof continuous 9elds, an important consideration when a user is presented with a largenumber of complex surfaces (Bajaj et al., 1998; Rana and Dykes, 2002). This is likelyto be the case when performing the iterative process of visualization. It is especiallyso when data vary over time and animation may well be appropriate. The networkscan be generated at a range of spatial scales, a feature that can be used to identifydi-erent types of events in spatio-temporal data surfaces as illustrated in Fig. 7.The kind of visualization demonstrated by Fig. 7 indicates that a series of surface

feature networks covering a range of scales may successfully summarize the informa-tion contained in large logs of spatio-temporal behaviour. E-orts to substantiate these9ndings and apply them are ongoing.

6. Visualization and data synthesis: multivariate geocentric views and multimedia

A number of views and techniques are thus available for representing and exploringlogs of spatio-temporal positions in an attempt to extract information about the natureof the data and ways of encapsulating the important activities of users in time andspace. Whilst this knowledge can be applied to the issue of assessing and ensuringthe geographic relevance of ancillary information, the data can also be used to anal-yse behaviour in time and space by employing supplementary information. We havetouched on this in our consideration of transport networks and conventional maps toprovide exogenous context for spotlights. A series of more novel interactive views canbe generated to combine sequences of positions in time and space with additional in-formation about the physical and social environments. Visualization using such toolsmay o-er insights into the reasons why particular spatio-temporal activity is recordedand aid our attempts to model and predict.

6.1. Review

Combining spatio-temporal activity, such as a section of a track log, with mea-surements of independent underlying geographic phenomena, such as physical fea-tures or socio-economic data, enables analysts to identify dominant attributes of anyroutes taken through time and space. Relationships can be considered, requirementsassessed and models generated from any insights that are achieved (Wills et al., 1989;Wilhelm and Sander, 1998). Graphic representations can be implemented through localparallel co-ordinates plots (Dykes, 1998). Novel representations such as radial dis-tance function (RDF) plots (Imfeld, 2000) can combine user-centered and spatially

←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−technique is the requirement of large amounts of computing power to create and manipulate the rasterstructures used. EDA with high levels of interaction and quick response is less easy to achieve with suchbulky continuous data structures than the discrete points used in LTE. The surfaces reported here werecalculated independently in a non-interactive environment before being loaded into more interactive software.This is a stopgap approach and a more thoroughly integrated solution is required so that density surfacescan be incorporated into the suite of real-time visualization tools o-ered by EDA software such as LTE andspotlights derived from them.


Fig. 6. (a) Surface feature network and (b) Surface feature network and magnitude (spotlight). Surface featurenetworks and spotlights derived from northern section of weekly log. Feature categories are symbolised withcolour hue: peaks in red, pits in gray, ridges in yellow, channels in blue, passes or saddles in green.The densities (surface value) use colour lightness in ‘spotlight’ fashion, highlighting the densest areas. Thewidth of the border at the edge of the surface reveals the scale of kernel used to calculate the networkof features.


distributed geographic information. The temporal RDF plots generated from such func-tions depict the way in which the immediate surroundings of a moving object varyover time. The plots epitomize a conceptual shift from one that is space-centered toa method of analysis that balances the spatial and temporal characteristics of GI. Inhis implementation Imfeld (2000) reports that temporal RDF plots can take severalhours to generate, o-ering a serious challenge to developers of real-time visualizationsoftware in this 9eld.

6.2. Geocentric parallel plots

Each of these techniques for combining spatio-temporal data with geo-referencedinformation rely upon GI that is quantitative. In addition to the explosion of numericinformation, a mass of varied qualitative GI is becoming available due to develop-ing data types, new capture devices, automated methods of geo-referencing and newcommunication mechanisms. E-orts have been made to incorporate these multimediasources into functionally rich tools for both data presentation (Shi-er, 1995) and anal-ysis (Dykes et al., 1999).In the panoraMap software, visualization functionality incorporates a range of data

types, including both quantitative GI geo-referenced at point locations or by associa-tion with de9ned areas, and qualitative multimedia data such as imagery, sound andvideo (Dykes, 2000). Three screen shots from a panoraMap session are displayedin Fig. 8. Each view shows a map with the locations of panoramic images sym-bolized. In each case a di-erent image has been opened for display (bottom rightwindow). The positions and directions of any open view are symbolized on the map.Dragging the symbol or the image results in the view panning and the map symbolis updated accordingly. The map view on the left of Figs. 8a–c includes polygonssymbolized according to various quantitative attributes. The polygons can be interro-gated for data values and are dynamically linked to additional views for EDA suchas interactive parallel co-ordinates plots (Wegman, 1990; Inselberg, 1995). These areused to analyse multivariate quantitative data and relate the statistical and spatial dis-tributions. Additional multimedia items are symbolized on the interactive map andcan be viewed in appropriate external software by clicking the symbols. The soft-ware provides a means of synthesizing disparate data types in a spatial structure forexploratory analysis.Spatial information can be represented in parallel plots by graphically symbolizing

each of the parallel lines according to the distance of the spatial unit with which eachcase is associated from any particular location of interest. This technique is akin tothose identi9ed in the previous section, the di-erence being that it is implementedin software that responds instantly, and provides access to a range of qualitative andquantitative data through an explicitly geographic user interface. The locations can beselected interactively by the user, or loaded from a spatio-temporal log (or pro9le).Fig. 8 demonstrates by showing three successive shadings of a geocentric parallel plotbased upon a route derived from a GPS log.The geocentric parallel plots combine the spatial and statistical components of quan-

titative attribute data in interpretable graphics that are updated instantly in response to


a user’s movement of the cursor. They share many of the characteristics of RDF plots.The user may well be interested in following geographic features identi9ed on one ofthe base maps and assessing the multivariate nature of this interactive trace through thegeocentric parallel plots. Shading could also be used to summarize a range of positions,rather than a single ‘current’ location. The result is a unique graphical representation inwhich the interplay between a series of measured geographic attributes and position intime and space can be assessed in relation to auxiliary multimedia data. Synthesizinga range of data types in this way through a geographic framework and via a highlyinteractive map-based GUI o-ers plenty of opportunity for exploring spatio-temporalbehaviour and ultimately generating knowledge about processes.

7. Conclusion

As is often the case when visualization is successful, the images and techniques pre-sented here give rise to more questions than answers. However, the graphics generateddo provide us with some clues as to how we might address the information overloadand begin to assess geographic relevance. The data that we are now able to collectmake many new representations possible which can help achieve the initial objectivesof searching for structure in the spatio-temporal logs and summarizing them to gener-ate user pro9les. The visualization enables us to move towards longer-term objectivessuch as the user-centered organization of information that relies upon these pro9les ofspatio-temporal behaviour. Prototypes have been developed to implement this objectiveby using appropriate data and communications structures to measure geographic rel-evance and deliver relevant information to mobile users based upon this information(Mountain and Raper, 2001).But more needs to be done to enable us to re9ne our analysis, and improve our as-

sessment of geographic relevance. We must also consider interactions between groupsof individuals and in relation to signi9cant events in space and time that a-ect be-haviours. The graphics generated by the Lund School were initially used to addressthe movement of individuals through the two dimensions of space and one of time,as are those presented here. Later e-orts used these ‘aquariums’ to link communalactivity to signi9cant positions in time and space known as spacemakers (Parkes andThrift, 1980). Corresponding e-orts could be made to relate a number of user pro9lesto each other and to additional contextual data about events in time and space. This

←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−Fig. 7. (a) 5 cell window (500 m); and (b) 7 cell window (700 m); (c) 9 cell window (900 m); and (d) 15cell window (1500 m). Surface feature networks and spotlights generated from northern section of weeklylog. Deriving networks at a variety of spatial scales reveals di-erent structural components in the data.Calculating the networks at a range of spatial scales results in the identi9cation of features that relate toevents at di-erent spatio-temporal scales in this instance. The 500 m kernel reveals peaks at stopping pointson a series of recreational walks recorded in the log. The peaks identi9ed in the 900 m kernel highlightareas of repeat activity such as ‘home’, quays and favourite locations for lunch. The channels de9ne ‘nogo areas’ that are not navigable by boat due to shoals, or foot due to vegetation and access restrictionsimposed following an outbreak of disease. The surface network derived from the 1500 m kernel identi9esthe locations at which most time was spent: ‘home’ (the centre of the peak) and the main tracks along theisland. This network of four nodes and three vertices describes the activity extremely succinctly.


Fig. 8. Geocentric parallel plots—each parallel plot (top right frame) shows four variables relating to occu-pancy levels of households in enumeration districts (EDs) in an area of Leicester, UK. The vertical axes arescaled and, from left to right, relate to the percentages of households in each unit with: less than 0.5 personsper room (ppr); 0.5–1:0 ppr; 1.0–1:5 ppr; at least 1:5 ppr. Each of the three 9gures uses shading to relatethe map polygons to corresponding lines on the parallel plot. Colour lightness represents the distance froma single geographic point of interest to each of the EDs in each case. It varies from highly visible black forclose EDs to faint/nonvisible white for distant cases. The location of interest is represented by the red targetsymbol on the map in each 9gure. For a route loaded directly from a GPS receiver, spatial/statistical trendscan be observed as the target point moves through the scene. An early location along the route (Fig. 8a)shows an area with relatively low levels of overcrowding. As the GPS track is followed the pattern changesand the local EDs, shaded in darker greys on the map and in the parallel plot, display greater variation andmore overcrowding (see Fig. 8b). At the end of the track the geocentric shading of the multivariate parallelplot (Fig. 8c) reveals a very di-erent neighbourhood. In each 9gure the panoramic images located closeto the target are displayed to provide local qualitative information to the analyst. The plots can be used toshow variation amongst independent as well as dependent variables.


Fig. 8. (Continued.)

kind of visualization would require additional functionality in EDA software such asLTE. Whilst the availability of new data allows us to reassess, modelling these kindsof interactions is likely to be extremely complex. Forer (1998) identi9es at least fourmajor obstacles that aRicted the progress of initial advances in the geography of timeand space. These must be re-addressed to achieve the aims identi9ed here.Providers of relevant information also need to consider the ways in which users

remember information about places. Do our mental spotlights fade over time? Dousers require prompts or summaries when they re-visit places after a certain period?Are these user and/or use dependent? And importantly, are they predictable? Thisarea is one of several that require analysis and knowledge of the cognitive skills of


Fig. 8. (Continued.)

information users. Another is in the presentation of information. A user’s selection andassimilation of the relevant data with which they are provided may in itself requireinteractive graphics that draw upon the exploration metaphor to “evoke the visualizationexperience” (DiBiase, 1999). Novel representations and forms of interaction may berequired to provide this kind of facility to information users who are on the moveand using mobile systems with potentially limited screen resolution, bandwidth andperipherals (Fairbairn et al., 2001). It is imperative that some assessment is made of anygraphic techniques and software interfaces that are developed. The usability approachhas been suggested as an appropriate methodology (Slocum et al., 2001). Potentialalso exists to re9ne the concept of geographic relevance by incorporating additional


up-to-date information about transportation networks and to account for user-feedbackon the value of the information that is served.The data that are currently being recorded and processed o-er signi9cant insight and

potential to improve our knowledge and to increase the relevance of the informationwe send and receive. EDA that takes account of time and space, is allowing us toassess the opportunity to achieve these aims. It is important, however, to rememberthe technical characteristics and limitations of many of the positioning devices consid-ered here. Issues relating to the accuracy of terrain-based solutions, the coverage ofsatellite-based solutions, the scale-dependent nature of many of the measurements andthe geo-referencing of information itself are continually under consideration and themodels that we are using are elementary and inadequate. Personal privacy is also akey concern (Raper, 2001b). Yet the development of practical prototypes and explo-ration of the data that are available and the purposes that they can serve are importantsteps forward in our search for understanding and relevance. Informed and critical anal-ysis of some of the Utopian scenarios predicted as being just around the corner arerequired however, in order to ensure that the expectation for personally and geograph-ically relevant location-based services (LBS) does not exceed achievable functionality.Visualization of spatio-temporal logs, the derivation or various forms of summary infor-mation and the synthesis of spatio-temporal data with other qualitative and quantitativesources are important 9rst steps that provide the opportunity to generate ideas aboutthe relationships between people, places, time, spaces and events. These ideas can takeadvantage of novel and interactive graphics in an exploratory scenario to further thebounds of our geographic knowledge and aim to improve our access to the masses ofinformation at our disposal.

Acknowledgements

The GI Science Group, Department of Information Science, City University—in par-ticular Prof. Jonathan Raper and Dr. Jo Wood; The HyperGeo project; Delegates of theData Viz II workshop; the University of Leicester; the Virtual Field Course Project;the Joint Information Systems Committee; Sanjay Rana of CASA; Pete Boyd of CityUniversity; George Mason University; Several maps are based on data provided withthe support of the ESRC and JISC and use boundary material which is copyright ofthe Crown, Post OHce and the EDLINE consortium.

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