GIS methods in time-geographic research: Geocomputation and geovisualization of human activity...

GIS METHODS IN TIME-GEOGRAPHIC RESEARCH

Geografiska Annaler · 86 B (2004) · 4 267

GIS METHODS IN TIME-GEOGRAPHIC RESEARCH: GEOCOMPUTATION AND GEOVISUALIZATION

OF HUMAN ACTIVITY PATTERNS

byMei-Po Kwan

Kwan, M.P., 2004: GIS Methods in Time-Geographic Research:Geocomputation and Geovisualization of Human Activity Pat-terns. Geogr. Ann., 86 B (4): 267–280.

ABSTRACT. Over the past 40 years or so, human activities andmovements in space-time have attracted considerable research in-terest in geography. One of the earliest analytical perspectives forthe analysis of human activity patterns and movements in space-time is time geography. Despite the usefulness of time geographyin many areas of geographical research, there are very few studiesthat actually implemented its constructs as analytical methods upto the mid-1990s. With increasing availability of geo-referencedindividual-level data and improvement in the geo-computationalcapabilities of Geographical Information Systems (GIS), it is nowmore feasible than ever before to operationalize and implementtime-geographic constructs. This paper discusses recent applica-tions of GIS-based geo-computation and three-dimensional (3-D)geo-visualization methods in time-geographic research. The use-fulness of these methods is illustrated through examples drawnfrom the author’s recent studies. The paper attempts to show thatGIS provides an effective environment for implementing time-geographic constructs and for the future development of opera-tional methods in time-geographic research.

Key words: time geography, space-time paths, GIS, geocomputa-tion, geovisualization

IntroductionIn the past 40 years or so, human activities andmovements in space-time have attracted consider-able research interest in geography. This researcharea includes a wide range of themes such as mi-gration, residential mobility, shopping, travel andcommuting behaviour. One of the earliest analyti-cal perspectives for the analysis of human activitypatterns and movements in space-time is time geo-graphy. Developed by a group of Swedish geogra-phers – including Torsten Hägerstrand, TommyCarlstein, Bo Lenntorp and Don Parkes – the time-geographic perspective has inspired generations ofgeographers and transportation researchers in theanalysis of human activities in space-time.

Time geography not only highlights the impor-tance of space for understanding the geographies ofeveryday life. It also allows the researcher to exam-

ine the complex interaction between space and timeand their joint effect on the structure of human ac-tivity patterns in particular localities (e.g. Kwan,1999a). This perspective has been particularlyfruitful for understanding women’s everyday livesbecause it helps to identify the restrictive effect ofspace-time constraints on their activity choice, joblocation, travel, as well as occupational and em-ployment status (Dyck, 1990; England, 1993; Fri-berg, 1993; Kwan, 2000a; Laws, 1997; Palm, 1981;Tivers, 1985). Time geography has also been usedas a framework for the study of migration and mo-bility behaviour (Odland, 1998), exposure to healthrisk (Löytönen, 1998), and the everyday life of chil-dren, dockworkers and homeless people (Mårtens-son, 1977; Pred, 1990; Rollinson, 1998).

Despite the usefulness of time geography inmany areas of geographical research, there are veryfew studies that actually implemented its con-structs as analytical methods up to the mid-1990s– with the notable exception of Bo Lenntorp’s(1976, 1978) Program Evaluating the Set of Alter-native Sample Path (PESASP) simulation model.The limited development of time-geographic meth-ods may be attributed to the lack of detailed indi-vidual-level data and analytical tools that can real-istically represent the complexities of an urban en-vironment (e.g. the transportation network and spa-tial distribution of urban opportunities). Anotherdifficulty is that individual movement in space-time is a complex trajectory with many interactingdimensions. These include the location, timing, du-ration, sequencing and type of activities and/ortrips. This characteristic of activity patterns hasmade the simultaneous analysis of its many dimen-sions difficult.

However, with increasing availability of geo-referenced individual-level data and improvementin the geo-computational capabilities of Geograph-ical Information Systems (GIS), it is now more fea-sible than ever before to operationalize time-

GIS METHODS IN TIME-

© Swedish Society for Anthropology and Geography, 2004

MEI-PO KWAN

Geografiska Annaler · 86 B (2004) · 4268

geographic constructs. Furthermore, the use of GISalso allows the incorporation of large amounts ofgeographic data that are essential for any meaning-ful analysis of human activity patterns. Because ofthese changes, time-geographic methods are un-dergoing a new phase of development as several re-cent studies indicate (e.g. Dijst and Vidakovic,2000, Dijst et al., 2002; Kim and Kwan, 2003;Kwan, 1998; 1999a, 1999b; Miller, 1999; Ohmoriet al., 1999; Takeda, 1998; Weber, 2003; Weber andKwan, 2002, 2003). Although the primary focus ofthese studies is on individual accessibility, there aremany areas where time geography may be fruitful-ly applied.

This paper explores the value of GIS-basedtime-geographic methods in the description andanalysis of human activity patterns in space-time. Itdiscusses recent developments in GIS-based geo-computation and three-dimensional (3-D) geo-vis-ualization methods. Usefulness of these methods isillustrated through examples drawn from the au-thor’s recent studies. The paper attempts to showthat GIS provides an effective environment for im-plementing time-geographic constructs and for thefuture development of operational methods intime-geographic research. An important premise ofthe paper is that the development and implementa-tion of time-geographic concepts had been limitedby the computational intensity of time-geographicmodelling as well as the lack of digital geographicdata. But the drastic increase in computer powerand availability of digital geographic databases inrecent years will greatly facilitate the applicationand development of time-geography methods inthe future. The paper begins with a discussion ofearly implementation of time-geographical con-cepts, especially Lenntorp’s PESASP simulation.Examples of GIS-based geo-computation and geo-visualization methods are discussed in subsequentsections.

Operationalization of time-geographic constructsTime geography conceives and represents an indi-vidual’s activities and travel in a 24-hour day as acontinuous temporal sequence in geographicalspace. The number and location of everyday activ-ities that may be performed by one person are lim-ited by the amount of time available and the space-time constraints associated with various obligatoryactivities (e.g. work) and joint activities with oth-ers. Important constructs in time geography such as

stations, projects, space-time paths and prism con-straints are well articulated in Carlstein et al.,(1978), Hägerstrand (1970), Parkes and Thrift(1975) and Thrift (1977). However, the first in-depth analytical treatment and operationalizationof time-geographic constructs were provided byLenntorp (1976).

In that study Lenntorp provided a formulation ofindividual accessibility that centers on the notion ofan individual’s reach, which is the physically ac-cessible part of the environment in space-time giv-en the individual’s constraints. This accessible por-tion is the space-time prism or potential path space(PPS). The projection of the prism on to planar geo-graphic space depicts the reachable area by the in-dividual and is called potential path area (PPA).Based on these constructs, the volume of the space-time prism (or potential path space) and the area de-limited by its projection on to planar space (PPA)are used as accessibility measures. Prism volumesand areas of PPA under various conditions were de-rived based on the geometrical relations betweenthe locations of origin and destination, travel speed,activity duration and time budget. Lenntorp (1976)then conducted a simulation exercise that attempt-ed to identify all the potential space-time paths ofindividuals in the study area.

The study area in Lenntorp (1976) was theVällingby-Bromma area in Sweden. The activity-travel survey data of 980 households used in thestudy were collected in 1967. At that time therewere no digital geographic data of any sort thatcould be used to provide the computational frame-work for the simulation exercise. All the geograph-ic data were therefore manually constructed fromthe ground up. As Lenntorp (1976, p. 82) reported,among the 5 500 items of information assembledfor running the PESASP simulation, there weredata for 496 individual stations and 138 food stores.The digital road network (called the ‘link-net’ (p.84)) was also constructed from the ground up. Ithad a total of 100 nodes and 246 links. To constructthe link-net, distances were measured manuallywith a distance-recording instrument on a map witha scale of 1:20 000. A total of 246 distances weremeasured, and they became the input for a compu-ter program that calculated the shortest distancesbetween all pairs of nodes.

Through this process, Lenntorp (1976) success-fully established a digital multi-modal transporta-tion network that included public transport, bicycleand walking as alternative travel mode. This wasperhaps the first digital transport network con-



structed for the study of human activity patterns inspace-time. Demand for computational resources,however, was tremendous. The data on the environ-ment (e.g. stations and the transport network) to-talled approximately 14 000 data items, and the 230activity programs constructed contained a total ofabout 6 000 data items. Computing time totalledfive hours and 44 minutes, and approximately 80%of this time was required for the 62 activity pro-grams where public transport is combined withwalking.

The PESASP simulation conducted by Lenntorpin the late 1970s was remarkable in the light of thelimited digital geographic data and computationalresources at that time. To contextualize his achieve-ment, one may compare it with another study byBach (1981) that evaluates accessibility using lo-cation-allocation models (the study did not usetime-geographic constructs) a few years later.Bach’s study dealt with 286 demand points and amuch larger street network that had 939 nodes and2 395 arcs. As reported by Bach (1981), the codingof the geographic coordinates of the demand pointstook two days, but the manual construction of thedigital street network took about three months – al-though the computation of the 286 × 286 distancematrix of shortest paths in the network took only afew minutes of computer time. The time needed forconstructing the geographic data for both Lenntorpand Bach had been considerable. But the durationneeded for PESASP computation was in the orderof 60 to 70 times longer than one that did not in-volve the simulation of potential space-time pathsor alternative activity programs.

Implementation of time-geographic concepts iscomputationally demanding. Although analyticaltime-geographic constructs were further expandedand elaborated by Burns (1979) and Landau et al.,(1982), there was no operational method for imple-menting these constructs using data that realisticallycapture the complex geographies of the urban envi-ronment or the transportation network. There weresome attempts to operationalize time-geographicconstructs (e.g. Villoria, 1989), but they all used ge-ometrical methods based on simplifying assump-tions about the urban environment and none of themused real geographic data of urban opportunities ortransportation network. There are a number of dif-ficulties with this approach. First, mobility and trav-el speed were assumed to be uniform throughout theurban environment. Second, urban opportunitieswere assumed to be evenly distributed in the urbanenvironment (Kwan and Hong, 1998).

For example, the space-time prism or potentialpath area (PPA) derived using geometric methodtakes the shape of a spatial ellipse whose geometricproperties are determined only by the locations ofa person’s home and workplace and the given timebudget (e.g. Kondo and Kitamura, 1987). While theellipse may be a good appromixation of the PPA inmetropolitan areas where mobility is relatively uni-form, it is highly unrealistic in other urban con-texts. As shown in Kwan and Hong (1998) and We-ber (2003), the spatial configuration and extent ofpotential path area are heavily shaped by the geom-etry of the transportation network, often stretchingalong routes of high speed of travel. When mobilityand travel speed are not equal in all areas and di-rections in a city, the spatial configuration of thePPA is far from any convenient geometric shapessuch as the ellipse used to approximate the PPA inpast studies.

Although Burns (1979) considered the effect oftransport network on the configuration of space-time prisms, and Miller (1991) first developed anetwork-based algorithm for computing space-time prism and accessibility, it was not until the late1990s that Kwan (1998) first operationalized net-work-based accessibility measures using a geo-computational algorithm and digital geographicdata of the transportation network and urban op-portunities in the study area. From this point on-ward, GIS-based time-geographic research seemsto have evolved into an active phase of develop-ment, especially in the areas of geo-computationand 3-D geo-visualization. The following two sec-tions discuss selected examples in these two areasfrom the author’s recent studies.

Geo-computation of space-time accessibility measuresThe term geo-computation refers to an array of ac-tivities involving the use of new computationaltools and methods to depict geographical varia-tions of phenomena across scales (Longley, 1998).It encompasses a wide range of computer-basedtechniques, including expert systems, fuzzy sets,genetic algorithms, cellular automata, neural net-works, fractal modelling, visualization and datamining. Many of these methods are derived fromthe field of artificial intelligence and the more re-cently defined area of computational intelligence(Couclelis, 1998). The availability of affordablehigh-speed computing and the development ofGIS technologies in recent years have greatly fa-

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cilitated the application of geo-computation intime-geographic research.

The use of geo-computation as a time-geograph-ic method is most visible in recent research on in-dividual accessibility (Kwan, 1998, 1999b; Kimand Kwan, 2003; Miller, 1999; O’Sullivan et al.,2000; Weber, 2003; Weber and Kwan, 2002, 2003).It involves the development and application of de-dicated algorithms for computing space-time ac-cessibility measures within a GIS environment.Space-time accessibility measures are based large-ly on the analytical framework formulated by Len-ntorp (1976) and Burns (1979). They are based onthe time-geographic construct of potential path ar-ea, which is the geographic area that can be reachedwithin the space-time constraints established by anindividual’s fixed activities. It is the area that an in-dividual can physically reach after one fixed activ-ity ends while still arriving in time for the next fixedactivity. All space-time accessibility measures arederived from certain measurable attribute of thisarea (e.g. number of opportunities it includes).

Because of the need to represent real-worldcomplexities and to deal with the large amount ofgeographic data, GIS provides an effective envi-ronment for implementing geo-computational al-gorithms for space-time accessibility measures.With modern GIS technologies and increasinglyavailable disaggregate data, highly refined space-time measures of individual accessibility may beoperationalized. Several studies in recent yearshave developed and implemented geo-computa-tional algorithms based on the time-geographicperspective. Drawing upon the author’s recentsearch, several examples are discussed below to il-lustrate the application of geo-computation intime-geographic research.

Kwan’s algorithmThe first major effort in the geo-computation ofspace-time accessibility measures is that by Kwan(1998). The study examined individual access tourban opportunities for a sample of 39 men and 48women in Columbus (Ohio, USA). Data for thestudy came from three main sources. The firstsource is an activity-travel diary dataset collectedby the author through a mail survey in 1995. In ad-dition to questions about the activity-travel charac-teristics of the respondent, data of the street ad-dresses of all activity locations and the subjectivespatial and temporal fixity ratings of allout-of-home activities were collected (Kwan,

2000a). The second source of data is a digital geo-graphic database of the study area that provides de-tailed information about all land parcels, their at-tributes, and other geographical features of thestudy area. Among the 34 442 non-residential par-cels in the database, 10 727 parcels belonging toseven land-use categories were selected as the ur-ban opportunities in the study. The third data sourceis a detailed digital street network of the study area.The network database contains 47 194 arcs and36 343 nodes of Columbus streets and comes withcomprehensive address ranges for geo-coding lo-cations.

Using these data, 18 conventional measures ofthe gravity and cumulative opportunity variantswere evaluated using the home locations of the 87individuals as origins and 10 727 property parcelsas destinations. Distances were computed usingpoint-to-point travel times through a digital streetnetwork. Three space-time measures were alsocomputed for each individual using a geo-compu-tational algorithm written in ARC Macro Lan-guage (AML) and implemented in ARC/INFOGIS. These three measures evaluate the size of thespace that can be reached, the number of opportu-nities that can be reached, and the size or attrac-tiveness of those opportunities. The algorithmused in the study was based on the one developedin Kwan and Hong (1998). Although it providesonly an approximate solution to the exhaustive setof reachable opportunities, it is computationallymore tractable. It uses the intersection of a seriesof paired arc-allocations to generate individualnetwork-based PPAs, each of which is defined bythe space-time coordinates of two fixed activities(see Fig. 1 for a schematic representation of the al-gorithm).

The results of the study reveal the contrast be-tween conventional and space-time measures.While the values produced by most gravity and cu-mulative opportunity measures were highly corre-lated and produced similar spatial patterns, space-time measures were very different. Gravity meas-ures tended to replicate the geographical patterns ofurban opportunities in the study area by favouringlocations near major freeway interchanges andcommercial developments, while cumulative op-portunity measures emphasized centrality withinthe city by showing the downtown area to be themost accessible place. In contrast, space-timemeasures produced different spatial patterns, andthe patterns for men resembled the spatial distribu-tion of opportunities in the study area while the



women’s patterns were considerably different. Thestudy shows that space-time measures are capableof revealing individual differences that are invisiblewhen using conventional accessibility measures.

Weber and Kwan’s algorithmTo take into account the effect of the spatial andtemporal variations in travel speeds and facilityopening hours on individual accessibility, Weberand Kwan (2002, 2003) developed a second-gen-eration algorithm for computing space-time acces-sibility measures. The study used a new geographicdatabase with the enhanced geo-computational al-gorithm. The activity-travel diary dataset used wascollected through the Activity and Travel Survey inPortland Metropolitan Area in Oregon (USA) in1994 and 1995. The data set logged a total of129 188 activities and 71 808 trips undertaken by10 084 respondents. Among the respondents, 101men and 99 women were selected for the study. Be-sides, a digital street network with estimates of freeflow and congested travel times (with 130 141 arcsand 104 048 nodes) and a comprehensive geo-graphic database of the study area were used. A dig-

ital geographic database containing 27 749 com-mercial and industrial land parcels was used to rep-resent potential activity opportunities in the studyarea.

The analytical procedures involved creating a re-alistic representation of the temporal attributes ofthe transport network and urban opportunities inthe study area, as well as developing a geo-compu-tational algorithm for implementing space-time ac-cessibility measures within a GIS environment.The algorithm was developed and implemented us-ing Avenue, the object-oriented scripting languagein the ArcView 3.2 GIS environment. Five space-time accessibility measures were computed. Thefirst is the length of the road segments containedwithin the daily potential path area (DPPA). Thesecond is the number of opportunities within theDPPA. The total area and total weighted area of theland parcels within the DPPA is the third and fourthspace-time accessibility measures computed. Fi-nally, to incorporate the effect of business hours onaccessibility measures, opportunity parcels wereassumed to be available (and could therefore be ac-cessible to an individual) only from 9 a.m. to 6 p.m.This creates the fifth accessibility measure.

The results show that link-specific travel timesproduce very uneven accessibility patterns, withaccess to services and employment varying consid-erably within the study area. The time of day whenactivities were carried out has also been shown tohave an effect on accessibility, as evening conges-tion sharply reduced individuals’ access through-out the city. The effect of this congestion on mobil-ity is highly spatially uneven. Furthermore, the useof business hours to limit access to opportunities atcertain times of the day shows that non-temporallyrestricted accessibility measures produce inflatedvalues by treating these opportunities as beingavailable at all times of the day. It is not only thatincorporating time reduces accessibility, but that italso produces a very different, and perhaps unex-pected, geography of accessibility (Weber andKwan, 2002). This geography depends very muchon individual behaviour and so cannot be discernedfrom the location of opportunities or congestionalone. The study observed that the role of distancein predicting accessibility variations within cities isquite limited (Kwan and Weber, 2003).

Kim and Kwan’s algorithmIn an attempt to render earlier geo-computationalalgorithms more realistic, several enhancements

Fig. 1. A schematic representation of Kwan’s algorithm.Source: Kwan (1999). Used by permission of the Association ofAmerican Geographers.

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were conceived and implemented by the third-gen-eration algorithm developed by Kim and Kwan(2003). First, space-time accessibility is extendedas a measure of not only the number of accessibleopportunities, but also the duration for which thesefacilities may be enjoyed given the space-time con-straint of an individual and facility opening hours.Second, more realistic travel times are incorporatedthrough better representation of the transportationnetwork, such as one-way streets in downtown ar-eas and turn prohibition – besides incorporating theeffect of congestion, and location- and segment-specific travel speeds. Third, ways are developed tobetter incorporate other factors such as facilityopening hours, minimum activity participationtime, maximum travel time threshold, and delaytimes. The study seeks to enhance space-time ac-cessibility measures with more rigorous represen-tation of the temporal and spatial characteristics ofopportunities and human activity-travel behaviour.

A new GIS-based geo-computational algorithmwas developed to implement these enhancements.The key idea of the algorithm is to efficiently iden-tify all of the feasible opportunities within thespace-time prism using several spatial search oper-ations in ArcView GIS, while limiting the spatialsearch boundary with information about the traveland activity participation time available betweentwo fixed activities. This algorithm was developedbased upon numerous tests of the computational ef-ficiency of different methods and a series of exper-iments using a large activity-travel diary datasetand a digital street network. The GIS algorithm forderiving the potential path area (PPA) and for cal-culating space-time accessibility was implementedusing Avenue in ArcView GIS. The study showsthat space-time accessibility measures that do notconsider the effect of facility opening hours and ac-tivity duration threshold will tend to overestimateindividual accessibility.

3-D geo-visualization of human activity patterns in space-timeGeo-visualization (visualization of geographic in-formation) is the use of concrete visual representa-tions and human visual abilities to make spatialcontexts and problems visible (MacEachren et al.,1999). Through involving the geographical dimen-sion in the visualization process, it greatly facili-tates the identification and interpretation of spatialpatterns and relationships in complex data in thegeographical context of a particular study area. For

the visualization of geographic data, conventionalGIS has focused largely on the representation andanalysis of geographic phenomena in two dimen-sions (2-D). Although 3-D visualization pro-grammes with advanced 3-D modelling and ren-dering capabilities have been available for manyyears, they have been developed and applied in ar-eas largely outside the GIS domain (Sheppard,1999). Only recently has GIS incorporated the abil-ity to visualize geographic data in 3-D (althoughspecialized surface modelling programmes haveexisted for many years). This is the case not only inthe digital representation of physical landscape andterrain of land surfaces, but also in the 3-D repre-sentation of geographic objects using various datastructures.

Despite the use of GIS-based 3-D geo-visuali-zation in many areas of research in recent years, itsapplication in time-geographic research is ratherlimited to date. In many early studies, 2-D mapsand graphical methods were used to portray the pat-terns of human activity-travel patterns in space-time (e.g. Chapin, 1974; Tivers, 1985). Individualdaily space-time paths were represented as linesconnecting various destinations. Using such kindsof 2-D graphical methods, information about thetiming, duration and sequence of activities and tripswas lost. Even long after the adoption of the theo-retical constructs of the time-geographic perspec-tive by many researchers in the 1970s and 1980s,the 3-D representation of space-time aquariumsand space-time paths seldom went beyond theschematic representations used either to explain thelogic of a particular behavioural model or to putforward a theoretical argument about human activ-ity patterns. They were not intended to portray thereal experience of individuals in relation to the con-crete geographical context in any empirical sense.

There have, however, been noticeable changesin recent years. As more geo-referenced activity-travel diary data become available, and as moreGIS software has incorporated 3-D capabilities,GIS-based 3-D geo-visualization has become amore feasible approach to time-geographic re-search. For instance, Forer (1998) implementedspace-time paths and prism on a 3-D raster datastructure for visualizaton and computational pur-poses. Their method is useful for aggregating in-dividuals with similar socioeconomic characteris-tics and for identifying behavioural patterns.Kwan (2000b) and Kwan and Lee (2004) imple-mented 3-D visualization of space-time paths andaquariums using vector GIS methods and activity-



travel diary data. These studies indicate that GIS-based geo-visualization can be a fruitful methodfor time-geographic research. Furthermore, im-plementing 3-D visualization of human activitypatterns can be an important first step in the deve-lopment of GIS-based geo-computational proce-dures that are applicable in many areas of geo-graphical research. The following subsectionsprovide an overview of some of these methodsfrom the author’s recent research.

Space-time paths and the space-time aquariumThe earliest 3-D method for the visualization of in-dividual space-time paths is the space-time aquar-ium conceived by Hägerstrand (1970). In a sche-matic representation of the aquarium, the verticalaxis is the time of day and the boundary of the hor-izontal plane represents the spatial scope of thestudy area. Individual space-time paths are por-trayed as trajectories in this 3-D aquarium. Al-though the schematic representation of the space-time aquarium was developed long ago, it has neverbeen implemented using real activity-travel diarydata. The main difficulties include the need to con-vert the activity data into ‘3-Dable’ formats thatmay be used by existing visualization software, andthe lack of comprehensive geographic data for rep-resenting complex geographic objects of the urbanenvironment.

Kwan (1999a) was the first to implement thespace-time aquarium and space-time paths in a 3-D GIS environment using individual-level activity-travel diary data. Based on a subsample of 72 Eu-ropean Americans from the dataset she collected inColumbus (Ohio, USA), the study examines the ef-fect of gender on the space-time patterns of out-of-home non-employment activities. Visualization ofthe space-time paths of three groups of researchparticipants reveals their distinctive activity pat-terns, and this insight guided the structural equa-tion modelling in the later phase of the study. Theresults of the study show that the structure of one’sdaily activity patterns and daytime fixity constraintdepends more on one’s gender than on some con-ventional variables of household responsibilitiessuch as the presence or number of children in thehousehold.

Given the limited 3-D visualization capabilitiesof GIS when the study was conducted, the geo-vis-ualization performed by Kwan (1999a) did not usea large geographic database of the study area (butit did incorporate a transportation network). The

first study to incorporate a comprehensive geo-graphic database for the 3-D geo-visualization ofspace-time paths was that of Kwan (2000b). Thisstudy used the Portland activity-travel diary datasetmentioned above and a comprehensive geographicdatabase of the Portland metropolitan region (Ore-gon, USA). The GIS database provides compre-hensive data on many aspects of the urban environ-ment and transportation system of the study area. Ithas data for about 400 000 land parcels in the studyarea. The digital street network used, with 130 141arcs and 104 048 nodes, covers the four counties ofthe study area (i.e. Clark, Clackamas, Multnomahand Washington).

These contextual data allow the activity-traveldata to be related to the geographical environmentof the region during visualization. To implement 3-D geo-visualization of the space-time aquarium,four contextual geographic data layers are first con-verted from 2-D map layers to 3-D format and add-ed to a 3-D scene. These include the metropolitanboundary, freeways, major arterials and rivers. Forbetter close-up visualization and for improving therealism of the scene, outlines of commercial and in-dustrial parcels in the study area are converted to 3-D polygons and vertically extruded in the scene. Fi-nally, the 3-D space-time paths of the African andAsian Americans in the sample are generated andadded to the 3-D scene. These procedures finallycreated the scene shown in Fig. 2.

The overall pattern of the space-time paths forthese two groups shown in Fig. 2 indicates heavyconcentration of daytime activities in and arounddowntown Portland. Using the interactive visuali-zation capabilities of the 3-D GIS, it was observedthat many individuals in these two ethnic groupswork in downtown Portland and undertake a con-siderable amount of their non-employment activi-ties in areas within and east of the area. Space-timepaths for individuals who undertook several non-employment activities in a sequence within a singleday tend to be more fragmented than those whowork long hours during the day. Furthermore, eth-nic differences in the spatial distribution of work-place are observed using the interactive capabilitiesprovided by the geo-visualization environment.The space-time paths of Asian Americans are morespatially scattered throughout the area than those ofthe African Americans, whose work and non-em-ployment activities are largely concentrated in theeast side of the metropolitan region.

A close-up view from the west of the 3-D sceneis given in Fig. 3, which shows some of the details

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of downtown Portland in areas within and aroundthe ‘loop’ and along the Willamette River. Portionsof some space-time paths may also be seen. Withthe 3-D parcels and other contextual layers in view,the figure gives the researcher a strong sense aboutthe geographical context through a virtual reality-like view of the downtown area.

Space-time paths based on GPS dataAlthough the 3-D space-time paths shown in Figs2 and 3 are helpful in understanding the activitypatterns of different population subgroups, thesepaths are not entirely realistic since they only con-nect trip ends with straight lines and do not tracethe travel routes of an individual. This limitationis due to the lack of route data in the Portland da-taset. When geo-referenced activity-travel datacollected by GPS are available and used in thegeo-visualization environment, the researcher canexamine the detailed characteristics of an individ-ual’s travel pattern, since actual travel routes canbe revealed by these kinds of data (Kwan, 2000c;Kwan and Lee, 2004). Fig. 4 illustrates this pos-sibility using the GPS data collected in the Lex-ington Area Travel Data Collection Test conduct-ed in 1997 (Battelle, 1997). The original datasetcontains information on 216 licensed drivers (100male, 116 female) from 100 households with anaverage age of 42.5 years. In total, data of 2 758

GPS-recorded trips and 794 861 data points of lat-itude–longitude pairs and time were collected fora six-day period for each survey participant.

To prepare for 3-D geo-visualization, three con-textual geographic data layers of the Lexingtonmetropolitan area are first converted from 2-D maplayers to 3-D format and added to a 3-D scene.These include the boundary of the Lexington met-ropolitan region, highways and major arterials. Asan illustration, the 3-D space-time paths of thewomen with no children under 16 years of age inthe sample are generated and added to the 3-Dscene. These procedures finally created the sceneshown in Fig. 4. The overall pattern of the space-time paths for these women indicates that their tripswere undertaken using largely highways and majorarterials. There is some regularity as indicated bythe daily repetition of trips in more or less the sametime throughout the six-day survey period. Thissuggests that distinctive activity-travel patterns canbe revealed by 3-D geo-visualization.

There are difficulties in the analysis of theseGPS data due to the computational intensity ofprocessing and visualizing large space-time data-sets (Kwan, 2001a; Lowe, 2003). For instance, theoriginal GPS data file for the 100 households con-tains 794 861 data points of latitude–longitudepairs and time (Kwan, 2000c; Murakami and Wag-ner, 1999). It takes up about 230 megabytes of diskspace in the format provided on the data CD. Ma-

Fig. 2. Space-time aquarium showing thespace-time paths of African and AsianAmericans in the sample.Source: Kwan and Lee (2004). Used by per-mission of Oxford University Press, Inc



nipulating files of this size can be taxing for thecomputer hardware normally available to socialscientists. Although improvement in computingpower in the near future will reduce this problem,more research is still needed to develop more effi-cient algorithms and data manipulation methodsfor handling large GPS datasets.

Human extensibility and cyberspatial activities in space-timeThree-dimensional geovisualization has also beenapplied to visualize human activities in both thephysical world and cyberspace based on the notionof human extensibility (Kwan, 2000d). The con-cept of the individual as an extensible agent wasfirst formulated by Janelle (1973), where extensi-bility represents the ability of a person to overcomethe friction of distance through using space-adjust-ing technologies, such as transportation and com-munication. Human extensibility not only expandsa person’s scope of sensory access and knowledge

acquisition; it also enables a person to engage indistantiated social actions whose effect may extendacross disparate geographical regions or historicalepisodes (Kwan, 2001b). To depict human extensi-bility that includes activities both in the physicalworld and cyberspace, Adams (1995) developedthe extensibility diagram using the cartographicmedium. The diagram, based on Hägerstrand’sspace-time aquarium, portrays a person’s daily ac-tivities and interactions with others as multiple andbranching space-time paths in three dimensions,where simultaneity and temporal disjuncture ofdifferent activities is revealed. This method may beused to represent a diverse range of human activi-ties both in the physical and virtual worlds, includ-ing telephoning, driving, e-mailing, reading, re-membering, meeting face-to-face and televisionviewing.

Although the extensibility diagram is largely acartographic device, most of its elements are ame-nable to GIS implementation. Kwan (2000d) de-veloped a method for implementing the extensi-

Fig. 3. A close-up view of downtown Portland.Source: Kwan and Lee (2004). Used by permission of Oxford University Press, Inc.

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bility diagram using 3-D GIS. The study used realdata about a person’s physical activities and cy-berspatial activities (e.g. e-mail messages andweb-browsing sessions). The focus is on incorpo-rating the multiple spatial scales and temporalcomplexities (e.g. simultaneity and disjuncture)involved in individual hybrid accessibility. Thefollowing example, derived from the case exam-ined in Kwan (2000d), illustrates the proceduresfor constructing the multi-scale 3-D extensibilitydiagram and its use in a GIS-based 3-D geo-visu-alization environment. The first step is to deter-mine the most appropriate spatial scales and to ex-tract the relevant base maps from various digitalsources.

Consider a person who lives and works in Fran-klin County (Ohio, USA), and engages in cyber-spatial activities (e.g. sending and receiving e-mail) involving cities in the northeastern region ofthe USA and other countries (e.g. South Africa andJapan). To prepare the GIS base maps at thesethree spatial scales (local, regional and global), amap of Franklin County and a regional map of 15US states in the northeastern part of the countrywere first extracted from a commercial geographicdatabase. Franklin County is the home county ofthe person in question, whereas the US region ex-tracted will be used to locate the three Americancities involved in her cyber-transactions. Thesecities are Chicago in Illinois, Maywood in NewJersey and Charlotte in North Carolina. At the glo-bal scale, the world map layer was derived fromthe digital map data that came with ArcView GIS

(many high-latitude regions and islands in theworld map layer were eliminated to improve vis-ual clarify).

After performing map-scale transformations toregister these three map layers to the person’s homelocation in Franklin County, these 2-D map layerswere converted to 3-D shape files and added to anArcView 3-D Analyst scene as 3-D themes. Afterpreparing these map layers, 3-D shape files for theperson’s space-time paths were generated usingAvenue scripts and added to the 3-D scene. Theseprocedures finally created the multi-scale extensi-bility diagram shown in Fig. 5. It shows how vari-ous types of transactions at different spatial scalesmay be represented in a 3-D GIS environment.

Figure 5 shows five types of activities undertak-en on a particular day. On this day, the person inquestion worked from 8.30 a.m. to 5.30 p.m., andhad a one-hour lunch break at a nearby restaurant(c in the diagram). She subscribes to a Web-castingservice where news is continuously forwarded toher web browser. On this day, she read some newsabout Yugoslavia, South Africa and Nashville, TN(a in the diagram) before she started work. An hourlater she sent an e-mail message to three friends lo-cated in Hong Kong, Chicago and Vancouver (b).The friend in Chicago read the e-mail two hours lat-er and the friend in Vancouver read the email fivehours later. The friend in Hong Kong read the e-mail 13 hours later and replied immediately (e).The reply message from this friend, however, wasread at 2.00 a.m. at the person’s home (g). In the af-ternoon, she browsed web pages hosted in New

Fig. 4. Space-time paths based on GPS datacollected in Lexington, Kentucky.Source: Kwan and Lee (2004). Used by per-mission of Oxford University Press, Inc.)



York, Charlotte and Anchorage in Alaska (d). Shefinished work at 5.30 p.m. and spent the evening athome. At 9.00 p.m. she started an ICQ (real-timechat) session with friends in Tokyo, Melbourne,Memphis, TN, and Dublin, OH (f on the diagram).

As shown in Fig. 5, very complex interactionpatterns in cyberspace can be represented usingmultiple and branching space-time paths. These in-clude temporally coincidental (real-time chat) andtemporally non-coincidental (e-mailing) interac-tions; one-way radial (web browsing), two-way dy-adic or radial (e-mailing), and multi-way (chat) in-teractions; incoming (web casting) and outgoing(e-mailing) transactions. The method is thus capa-ble of capturing the spatial, temporal and morpho-logical complexities of a person’s extensibility incyberspace.

Furthermore, the visualization functions availa-ble in ArcView 3-D Analyst also enable one to ex-plore interactively the 3-D scene in a very flexiblemanner (e.g. the scene is visible in real-time whilezooming in and out, or rotating). This allows for theselection of the best viewing angle and is a veryhelpful feature, especially when visualizing verycomplex space-time paths. To focus on only onetype of transaction or activity at a particular spatialscale, one can select the relevant themes for displaywhile keeping the other themes turned off. Further-more, when the three sets of paths and base mapsare displayed at the same time, they may be colour-coded to facilitate the visualization. In the originalcolor 3-D scene, each segment of the space-timepaths is represented using the same colour as therelevant base map (e.g. blue for Franklin Countyand local activities), conveying a rather clear pic-ture of the spatiality and temporal rhythm charac-terizing the person’s activities on that day. But inthe black-and-white version presented in Fig. 5,spike lines are used to identify the location involvedin each transaction.

ConclusionDespite the usefulness of time geography in manyareas of geographical research, there are very fewstudies that actually implemented its constructs asanalytical methods up to the mid-1990s. Howev-er, with increasing availability of geo-referencedindividual-level data and improvement in the rep-resentational and geo-computational capabilitiesof Geographical Information Systems (GIS), it isnow more feasible than ever before to operation-alize and implement time-geographic constructs.

Due to these recent changes, GIS-based time-geo-graphic methods are undergoing a new phase ofdevelopment. Drawing upon the author’s recentresearch, this paper discusses two major areas ofsuch development: geo-computation of individu-al space-time accessibility and 3-D geo-visuali-zation of human activity patterns.

Geocomputation of individual accessibility hasprogressed from Kwan’s first generation algo-rithm to Kim and Kwan’s third generation algo-rithm. There are changes in certain aspects ofthese recent exercises: (1) the amount of digitalgeographic data used has become much larger; (2)the computing platform has moved from UNIXplatform to desk-top PCs; and (3) the GIS soft-ware used has evolved from text-based ARC/INFO to Windows-based ArcView 3.x. Althoughthe computing power available today is much bet-ter than before, the data requirement and compu-tational intensity of the spatial search involved inthe geo-computation of individual accessibility isstill a major difficulty in this area of research. Bothstudies by Weber and Kwan (2002, 2003) and Kimand Kwan (2003) involved several months of in-

Fig. 5. An extensibility diagram rendered using 3-D GIS.Source: Kwan (2000d). Used by permission of Springer-Verlag.

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tensive geo-computation. Although Kim andKwan’s current research is adopting concepts ofmassively parallel processing in the computation-al part of the work, there seems to be no simple so-lution to the problem in the near future, and thechallenge remains significant for researchers in-volved in this kind of computational work.

With regard to GIS-based 3-D geo-visualizationof human activity patterns, there are also severaldifficulties. First, there is the challenge of convert-ing many types of data into ‘3-Dable’ formats fora particular geo-visualization environment. Sinceevery visualization software may have its uniquedata format requirements, and the activity and geo-graphic data currently available are largely in 2-Dformat, the data preparation and conversion proc-ess can be time consuming and costly. Second, theresearcher may encounter barriers to the effectivevisualization of large and complex activity-traveldatasets. These barriers include problems associat-ed with the limitation in human ability to identifypatterns and relations when many layers, themes orvariables are simultaneously viewed; the complex-ity associated with the vast range of possibilitiesthat a visualization environment provides; and theorientation of the user in a visualized scene or vir-tual world.

Besides, rendering speed can also be a signifi-cant challenge to researchers who want to use thiskind of method. For instance, interactive geo-visu-alizations of the Portland dataset were conductedwith the help of a professional-grade graphic card,which has a dual-pipeline architecture and a highlytuned geometry engine, and is capable of rendering24.2 million solid-colour 3-D vectors per secondand has a tri-linear textured fill rate of 332 millionpixels per second. But rendering 400 000 parcels orbuildings (typical of a medium-sized city) in 3-Dhas been quite taxing, even with this high-endgraphic card. Third, the use of individual-level ac-tivity-travel data geo-coded to street addresses, giv-en their reasonable degree of positional accuracy,may lead to considerable risk of privacy violation.Researchers using the 3-D geo-visualization meth-ods discussed in the paper should pay particular at-tention to this potential risk.

To conclude, Lenntorp has laid much of the an-alytical foundation of today’s GIS-based time-geo-graphic research. His implementation of the PE-SASP simulation model was remarkable in manyregards. It has inspired many of the time-geograph-ic methods discussed in this paper. Operationaliza-tion of analytical time-geographic methods, how-

ever, has been hindered by the computational de-mand of the tasks. Recent developments have over-come many hurdles. But it is often easy to forgetpast achievements that were stunning even by to-day’s standards. Similarly, it is often easy to under-estimate the breakthroughs achieved by recentGIS-based time-geographic studies that involve anenormous amount of digital data and months ofcomputation.

AcknowledgementsThe author thanks the Association of AmericanGeographers, Oxford University Press, and Spring-er-Verlag for kindly permitting the use of previous-ly published illustrations.

Mei-Po KwanDepartment of Geography1036 Derby Hall154 North Oval MallThe Ohio State UniversityColumbus, OH 43210-1361, U.S.A.E-mail: [email protected]

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