On design and implementation of an object-relational spatial database for activity/travel behaviour research

Abstract. The development of micro level models of urban processes haspartially been facilitated by increased availability of detailed activity/travelsurvey data. Managing and exploring these data can be resource intensiveand time consuming. Researchers and municipal planning organizationsincreasingly face information management challenges. This paper reviews anexperiment in design and implementation of an object-relational geographicdatabase using the principles of object-orientation. A data model is specifiedusing the Unified Modeling Language notation and a visual modeling tool,and then implemented as an object-relational spatial database. The resultingdatabase acts as an information foundation capable of supporting empiricalresearch and application development. The experience suggests that a formalapproach to information management can enhance our understanding ofcomplex activity/travel data contributing to informed application of thesedata to transportation research questions.

Key words: activity/travel behaviour, object-orientation, unified modelinglanguage, Geodatabase, database design

JEL Classification: C81, R40, C88

1 Introduction

While object technology is not new its use in description and simulation ofcomplex natural processes and human behaviour is relatively new. Consid-erable effort has recently been extended to object-oriented simulation anddescription of natural processes (Westervelt and Hopkins 1999; Bian 2000;Davis and Maidment 2000). In contrast, the literature reveals few studies

*Corresponding author

The authors would like to thank Metro for the provision of data and supporting documentation.

They are also thankful to three anonymous reviewers for their constructive comments. The

second author, holder of the Canada Research Chair in Spatial Analysis, is grateful for the

support of the SSHRC Canada Research Chairs program.

J Geograph Syst (2004) 6:237–262

DOI: 10.1007/s10109-004-0139-y

On design and implementation of an object-relationalspatial database for activity/travel behaviourresearch

Ronald N. Buliung and Pavlos S. Kanaroglou*

School of Geography and Geology, McMaster University, 1280 Main St. West, L8S 4K1,

Hamilton, Ontario, Canada (e-mail: {buliungr, pavlos}@mcmaster.ca)

Received: 14 November 2003 / Accepted: 10 July 2004

using object-orientation to describe and simulate human behaviour. Exam-ination of recent applications to activity/travel modeling and other appliedresearch revealed an opportunity to improve modeling efficiency within theactivity/travel behaviour research community. In this paper we describe anobject-oriented approach to information modeling that can potentiallyreduce redundancy of information management and shift resources towardsubstantive research issues.Object-orientation provides a framework to simultaneously represent

properties and behaviours of discrete entities that together and through theirinteractions can be used to model systems or processes. Within the object-oriented paradigm the world is comprised of objects, characterized bydescriptive attributes or properties, and operations that represent behaviour.Objects can engage in relationships with other objects, aggregate to complexobjects, and generalize to specific types. Activity/travel behaviour is ideallysuited to object-orientation due to the presence of several interacting entitiesthat may be expressed as hierarchical forms. These include households,activity patterns, tours, and schedules. Object-orientation provides anopportunity to model activity/travel objects and their relationships at a moreintuitive manner reflecting the intrinsic complexity of activity/travel behav-iour (Makin et al. 1997; Claramunt and Theriault 2001; Frihida et al. 2002).To demonstrate these ideas an approach to information management,

founded on the principles of object-orientation and using the process ofobject-oriented analysis and design (OOAD) is discussed. We demonstratethe approach by reviewing our experience with data model development anddatabase implementation using concepts from activity/travel behaviourresearch (e.g. activities, persons, households etc.) and data primarily drawnfrom the Portland Metro 1994/1995 Household Activity and TravelBehaviour Survey. While the procedure is demonstrated using data fromPortland it is applicable where similar data are available. The Portlandsurvey has been used in several studies with researchers having individuallymanipulated the original survey data to a form compatible with researchobjectives. Aggregating these efforts across the research community repre-sents a relatively high cost for early stage information management. Wesuggest that the data modeling exercise will provide researchers with anadvanced starting point to spatial database creation, saving the time andeffort required to independently study and understand microdata that issimultaneously used to support multiple studies.We are essentially mapping, in data space, spatial and non-spatial elements

of the activity/travel problem domain and implementing them in ageographic database. Logically and physically different from earlier work(e.g. Claramunt and Theriault 2001; Frihida et al. 2002) our data model hasevolved from a desire to develop linkages with longer-term, exploratory andexplanatory research objectives. A relatively simple structure has beenadopted to demonstrate core concepts and to potentially ease data modeltransferability to other researchers looking to use the same information.

2 Background

The City of Portland has continued to maintain an important role in the stateeconomy of Oregon since the mid 19th century and is a major urban center

238 R. N. Buliung and P. S. Kanaroglou

with a resident population of approximately 536,240 (Gibson and Abbott2002). The regional government established in 1978, known as Metro, is theonly elected metropolitan government in the United States and maintains ajurisdiction that includes 1.3 million people (Gibson and Abbott 2002).Urban planning in Metro benefits from the use of spatial informationtechnologies. Metro’s Data Resource Centre (DRC), TransportationDepartment, and the Regional Land Information System (RLIS) make useof geographic information system (GIS) software for storage and retrieval ofregional base-data to support transport and land use modeling (Perone andCulp 1998). According to Metro, benefits are distributed throughout theagency and include elimination of data redundancy, cost savings, delivery ofspecialized services, and advocacy work leading to data sharing initiatives.Metro and Portland have historically received considerable attention from

the activity/travel behaviour research community. Some studies haveexamined timely planning strategies such as adjusting jobs:housing balanceto mitigate negative externalities related to urban travel. Using data from anearlier Metro travel survey Peng (1997) develops an alternative approach toestimating the jobs:housing ratio using GIS while Sun et al. (1998) considerthe policy indirectly examining household demographic and land usepredictors of household travel behaviour. Sun et al. (1998) conclude thatland use balance could reduce congestion and mobile emissions contrastingPeng’s finding that the relationship between vehicle miles traveled (VMT)and jobs:housing balance is subject to local variations.In other research the activity/travel patterns of individuals have been

studied. Interested in exploring spatial and spatiotemporal characteristics ofactivity participation, Kwan (2000) tests the data visualization capabilities ofgeographic information systems (GIS). Space and space-time densitysurfaces are used to generate hypotheses and for exploring aggregateoutcomes of individual activity participation. Buliung (2001) explores spatialpatterns of work, work-related and non-work out of home activities withinthe Portland region. He discovers differences in work and non-work patternsand the relative importance of downtown Portland as both a hub for workand non-work participation. While this discovery seems intuitive, theapproach used to draw this conclusion was interesting as it relied on remotestudy of activity/travel behaviour using GIS and spatial statistical techniquesin the absence of detailed prior knowledge of the city’s urban form.Weber and Kwan (2002) study individual accessibility to the Portland CBD

and twelve regional centers by examining relationships between spatialaccessibility as well as the configuration of spatial opportunities and activity/travel behaviour. They use various accessibility metrics that separatelycontrol for congested and uncongested travel times under a set of assumptionswith respect to business operating hours. Interpreting the results frommonocentric and polycentric perspectives they find little support formonocentrism with higher levels of accessibility associated with suburbanlocations and locations adjacent to regional centres. They also provideevidence suggesting that spatial accessibility is jointly determined byindividual activity/travel behaviour and the geographical organization ofspatial opportunities.Timmermans et al. (2003) and Golob (2000) provide contrasting perspec-

tives concerning the impact of space and the configuration of spatial

On design and implementation of an object-relational spatial database 239

opportunities on activity/travel behaviour. Timmermans et al. (2003) findlittle evidence of a relationship between spatial context, transportationsystems and space-time activity patterns, after controlling for differencesbetween selected cities and regions. While the study is interesting it does notdiscount the potential role of spatial context within one of the regions in theircomparison group. Golob (2000) estimates four separate models for Portlandusing different measures of spatial accessibility demonstrating a positivespatial accessibility effect on participation in out-of-home non-work activ-ities and simple home-based non-work trip chains. Studies at the metropol-itan scale are potentially more useful because it is at this scale that urbanplanning and demand management strategies are implemented.Other studies have attempted to understand the impact of interactions

between household members on activity/travel outcomes. Golob andMcNally (1997) and Golob (2000) have used structural equation modeling(SEM) to investigate interactions between household members and trade-offsbetween in and out-of-home activity participation. Golob and McNally(1997) find differences in activity participation and trip chain formation bygender and the presence of potential intra-household trade-offs in respon-sibilities. Golob (2000) confirms earlier findings and discovers that improve-ments to commuter travel times can potentially induce more non-worktravel. Unlike previous work the household is the unit of analysis withactivity duration (participation) aggregated across household members.Lastly, there are a few recent examples of activity-based travel demand

forecasting models developed for Portland and the larger metropolitanregion. These include Wen and Koppelman’s (1999) two-stage model systemthat predicts frequency of maintenance stops, auto and stop allocation tohousehold members, and home-based tours. McNally (1997) adopts amicrosimulation approach developing a prototype activity pattern system tobe implemented using the Portland survey data. Mark Bradley Research andBowman (1998) develop a comprehensive daily scheduling model withendogenous activity patterns and tours. The effort is a refinement of earlierresearch for Boston (e.g. Bowman and Ben-Akiva 2001). Portland has alsoserved as a test bed for the Transportation Analysis and Simulation System(TRANSIMS) (Los Alamos 2002). TRANSIMS is an integrated system oftravel forecasting models designed to model traffic impacts, congestion, andpollution (Bush 1999).Activity/travel research on Portland and the surrounding region is quite

diverse and has arguably been enhanced by the presence of detailedmicrodata. Processing these data to support research will have, in certaininstances, resulted in independent database development. Aggregatingindividual data management efforts across these papers suggests an oppor-tunity exists to improve efficiency through better data managementconducted early on with circulation of results to other researchers interestedin using the same information. Adoption of a formal object-oriented analysisand design procedure for database construction could potentially facilitatecost reductions and improve the efficiency of the data acquisition/integrationphase of research. The approach that we are advocating could positivelyinfluence current information management strategies both in practice and inacademic research. In the next section we consider recent experiences withobject-oriented simulation and database modeling.


Recent experiences with object-orientation

Beginning with Jackson’s (1994) paper on the efficacy of object-orientationfor regional science we begin to observe the emergence of a small number ofstudies that adopt the approach for urban modeling and the development ofgeocomputational tools for urban analysis. For example, Makin et al. (1997)develop a microsimulaton model of shopping behaviour within a time-geographic framework. The model endogenizes shopping time subject towork schedule, facility hours of operation, personal mobility, and transportsupply constraints. Spatial and temporal entities and constraints areimplemented using SmallWorld GIS�. Scott (2000) constructs an object-oriented simulation model of household activity participation. The modelgenerates daily out-of-home activity episodes for the heads of five householdtypes and is applied to investigate adoption of a compressed workweekstrategy.Large-scale urban models have also been developed using the object-

oriented approach. Examples include UrbanSim (Waddell 2002; Noth et al.2003) and ILUTE (Miller et al. 2004). UrbanSim is a metropolitan scale landuse model that can be coupled with transport models to address metropolitangrowth from a land use/transport perspective (Waddell 2002). Its structureincludes an Object Store of urban entities represented as individual objectsthat can be updated (create, modify, delete) as UrbanSim is exercised throughtime (Noth et al. 2003). UrbanSim’s objects and real-world counterparts arehighly correlated making the process of model abstraction readily under-standable (Noth et al. 2003). ILUTE has been designed to simulate urbanevolution over an extended period of time and replace state-of-practiceaggregate, static models characterized by limited behavioural content (Milleret al. 2004).Data modeling initiatives focused on transport supply management

include the Unified Network for Transportation (UNETRANS) and theMulti-Dimensional Location Referencing System (MDLRS). UNETRANSis designed to primarily support the spatial database requirements oforganizations managing road and rail systems. Implementation of the datamodel depends on ESRI’s Geodatabase (Curtin et al. 2000). The traveldemand side of UNETRANS has been designed to support state-of-practiceUTMS or four-stage modeling applications. Developers have used UNE-TRANS as an object model for database and application development tosupport network asset management software (Kratzschmar 2001).MDLRS is a comprehensive location referencing system developed to

support the spatial database requirements of transportation organizations(Koncz and Adams 2002). It is essentially a conceptual expression oftransport supply management with attention given to the issues of scale,temporal change, navigation, and multi-dimensional location referencing.Recognition of the temporal dimension is innovative in the presence of otherlocation referencing systems that have not generally been able to manage thedynamics of change related to transport supply objects. Among the manytypes of descriptions included in the data model are the multi-dimensionalrepresentation of transportation events (e.g. construction, maintenance etc.)and objects (e.g. highways, bridges, signs etc.) MDLRS is currently at theconceptual level with the authors citing implementation and testing as a


future endeavor. It appears to offer more comprehensive management oftemporal characteristics of transport supply than UNETRANS.Prototype database systems and geocomputational tools for query and

retrieval of activity-based microdata have also been developed. Resultingsystems are powerful activity-based spatial database management systemsintegrated with GIS software that can be used to support exploratory spatialdata analysis (ESDA) of individual activity/travel patterns. Claramunt andTheriault (2001) implement their data model as an ArcGIS Geodatabase thatcan be accessed and manipulated using tools available in the ArcGIS suite.Frihida et al. (2002) develop a more comprehensive system that representsindividuals as complex aggregates of several activity/travel entities includingactivity locations, trips, plans, and schedules. The system responds tospatiotemporal queries and has been implemented using Smallworld GIS�.While all development objectives have not been realized the data model hasbeen translated into a database that can be queried using a custom interface.Adopting a different focus, Shaw and Xin (2003) develop a GIS-based

ESDA tool for studying transport/land use interactions in space and time.The foundation of their system is a spatiotemporal Geodatabase that usersaccess through custom software tools developed using ArcObjects. Similar toUNETRANS and Claramunt and Theriault (2001) their application alsodepends on ESRI’s Geodatabase. Users can query the database for differentversions of transport and land use objects (e.g. roads and dwellings) and canhold instances and attributes fixed in time or space while performing querieson others. For example, one can easily determine all of the land use featuresthat existed within a certain distance of a transport feature over a particulartime period. The application demonstrates that current technology providesan opportunity to overcome traditional limitations of GIS with respect todiscrete temporal treatment of spatial data.Jackson (1994) suggests that object-oriented modeling can improve the

conceptual representation and understanding of problems within regionalscience. He also suggests that separation between conceptual and operationalmodels can be minimized using object technology. Taking advantage of coreobject-oriented constructs, the literature reviewed in this paper lends supportto Jackson’s position. All examples share in adoption of an object-orientedapproach and are substantively connected. Similarly, some type of graphicalnotation or formalism is used to express, at a minimum, general conceptualoverviews of each system or behavioural model. In virtually all casesresearchers have adopted the Unified Modeling Language (UML) asgraphical notation to support object-oriented modeling and applicationdevelopment. In the next section we provide a general overview of theapproach we are suggesting for understanding and managing activity/travelbehaviour data.

3 Approach to modeling activity/travel behaviour data

Our interest in studying and applying state-of-the-art approaches toinformation modeling and management developed in response to usingactivity/travel microdata in empirical research (e.g. Buliung 2001; Buliungand Kanaroglou 2002). Studying activity and travel processes at the microlevel typically requires access to detailed information on behavioural units of


interest (e.g. households, persons) and behavioural outcomes (e.g. activities,trips). Understanding and maintaining conceptual or logical connectionsbetween behavioural entities (e.g. persons, households), supply side aspects(e.g. roads, vehicles), and corresponding detailed spatial, temporal, and non-spatial descriptions presents a challenging and interesting informationmanagement problem.Figure 1 illustrates one solution to this problem that essentially recasts

many principles of object-oriented analysis, design, and data modeling toactivity/travel behaviour research. We describe an information acquisitionand management process that is motivated by behavioural questions fromthe activity/travel research domain. The first phase involves detaileddescription of research questions, followed by the conceptual modeling ofactivity/travel survey data. Modeling within this framework refers todescribing in detail, the behavioural and non-behavioural units of interestand their interactions or relationships. This should not be confused withstatistical and econometric methods for explanation and prediction. Theconceptual model is refined during the database design phase producing astructural or schematic representation of the database that complies with thestandards of some type of database technology. The process concludes withimplementation of a fully documented spatial database, designed to supportshort and long-term research endeavors. This database then feeds back intothe research process as the initial questions of interest are studied usingappropriate methods and database content. Iteration of the entire process orspecific components of the process may occur several times before asatisfactory data management solution emerges. As a result of the approach,and tools used, the database has the flexibility to be extended or refined as

Real World

Activity/Travel Behaviour:Research Questions

Database Design:Classes, RelationshipsVisual Modeling

Conceptual ModelingConcept Identification:e.g. Households, Persons,Activities,Travel

Implementation:Spatial Database

Fig. 1. Information management process


the need arises. In the remainder of the paper we will elaborate on variousaspects of this framework. To illustrate the approach we have used data fromthe Portland Metro 1994/1995 Household Activity and Travel BehaviourSurvey and additional geographic data layers from Metro’s Data ResourceCentre. The next section describes these data in greater detail.

Data sources

Metro implemented a set of transportation modeling improvement initiativesthat included disaggregate data collection to encourage innovations in traveldemand modeling. One result was the region’s 1994/1995 Household Activityand Travel Behaviour Survey. These data have been used extensively inacademic research including many of the examples cited earlier in this paper.The bulk of the survey was completed in the spring and autumn of 1994 withadditional sampling in the winter of 1995. A two-day activity diary surveyinstrument was used to collect data on households, individuals, vehicles, andthe activity and travel behaviour of household members (CambridgeSystematics 1996). Activity variables include detailed location descriptionsand activity start and end times. Activity locations have been geocoded atvarious resolutions (e.g. intersection, address etc.) allowing researchers toconstruct and study geographic patterns of activity participation forindividuals and households (e.g. Kwan 2000; Buliung 2001). Trip variablesinclude mode, start and end times, number of people taking the trip with therespondent, and information on mode switching. The sampling frame wasgeographically stratified and designed to capture non-motorized travel. Of20,161 households contacted, 7,090 were recruited with 4,451 of thesehouseholds completing the surveys. Estimates of activity/travel participationindicate 9,471 persons reporting 122,348 activities and 67,891 trips (Cam-bridge Systematics 1996).We have combined the activity/travel survey data with additional spatial

data from Metro’s Regional Land Information System (RLIS). RLIS is anintegrated regional spatial database that includes data from 24 cities and 3counties and is managed by Metro’s Data Resource Centre (DRC). It wasoriginally designed in the late 1980’s to support transportation and regionalmodeling and planning but now supports additional uses. We haveincorporated several geographic data layers from RLIS including regionaltraffic analysis zones, and political boundaries. The survey and RLIS dataare available through Metro in archival form in a variety of data formats andcan also be explored on the Internet using the DRC’s MetroMap GISapplication.We have integrated data from two principal sources resulting in a database

that includes behavioural entities and their characteristics (e.g. persons,households etc.), revealed patterns of activity/travel behaviour, and thespatial context within which such behaviour occurs. While we have imposeda limit on our data integration exercise there is no practical constraint. Wecould incorporate data from other sources such as the U.S. Census Bureau’sTIGER program. The flexibility of the adopted approach allows us tointegrate additional information as the need arises. In the next section wereview the principles of object-orientation that we apply in the remainder ofthis paper.


4 Conceptual modeling and database design

The conceptual and design sections of the information managementframework we have introduced adhere to the principles of object-orientedanalysis and design (OOAD). This approach focuses on enumeration andimplementation of concepts relevant to a problem domain. A problem domainmay refer to system requirements or a specific research question. In thecontext of our research the problem domain is activity/travel behaviour.Concepts within this problem domain include, but are not limited to:households, persons, activities, and vehicles. Structurally, OOAD makes useof objects or instances of classes and their specified interrelationships in themodeling of software systems (Booch 1994). The analysis (OOA) and design(OOD) aspects of OOAD are not necessarily mutually exclusive. Iterationswithin and between phases are common during development (Jacobson andChristerson 1995; Bedard 1999a). Within our framework, conceptualmodeling is equivalent to OOA while database design is equivalent to OOD.Object-oriented analysis (OOA) addresses conceptual expression of

real-world processes or systems within the object-oriented paradigm (Boochet al. 1999). The method or tools for technological implementation are notnecessarily considered at this stage. From a database perspective this leads toan understanding and description of what users require as content (Booch1994; Bedard 1999b). Analysis can produce a conceptual or logical data modelthat represents user reality using object-oriented constructs, such as classescomprised of instances or objects and relationships between classes. It is notuncommon to consider types of behaviour that might later extend thedescriptions of classes.Complementing OOA is object-oriented design (OOD) or the expression of

OOA results using a formal, technology dependent specification (Booch1994; Bedard 1999b). OOD can be used to decompose a problem intomanageable parts and depict logical, physical, static or dynamic aspects of asystem using consistent notation (Booch 1994). The purpose of design isrefinement of a conceptual/logical data model using rules and guidelines thatenable implementation using a specific technology. Typically design modelswill be similar, but not necessarily identical, to their logical counterparts(Zeiler 1999). During design, attention is given to internal database structureand the means of translating concepts into a software system or database(Bedard 1999b).This process of OOADusually employs some kind of language or formalism,

comprised of specific constructs, notation, and rules to communicate thestructure of an object-based model of information (Bedard 1999a, b). Analysisand design can be supported by the same formalism implemented in a visualmodeling tool. We have used the diagrammatic notation of the UnifiedModeling Language (UML) and the Microsoft� Visio visual modeling tool.UML is a visual modeling language used to specify, visualize, construct, anddocument software systems (Rumbaugh et al. 1999). The emergence of UML,its structure and capabilities, and relationship to object-orientation has beencovered in detail elsewhere (Booch 1994; Rumbaugh et al. 1999). What isrelevant here is that UML can be used to develop conceptual models ofprocesses and information, and depending upon the computational environ-ment within which it is applied, facilitate implementation of databases and


systems. We are not limited to the expression of ‘‘software’’ when using UMLand can use the notation to describe other types of complex human behaviour,systems, and natural processes.In this paper we focus on describing those aspects of UML relevant to our

application. We are not explicitly addressing dynamic aspects of a system butrather focus on developing a static view of activity/travel concepts that canbe implemented as a spatial database using the data sources previouslyintroduced. Describing the static view of a system using UML is a processthat relies on class diagrams. Classes and relationships are used in classdiagrams to communicate information about database structure and content.Following an OOAD approach we use a class diagram to express logical anddesign data models of core activity/travel concepts. Due to similaritiesbetween our logical and design models, our discussion in the next sectionfocuses on the final database structure or schema that has been implementedas an object-relational geographic database.

5 A Data model for activity-based research

The current version of our data model, shown in Fig. 2, is an example of aUML class diagram and expresses a static, detailed description of severalactivity travel concepts and their interrelationships. These include persons,households, activity descriptions, activity locations, and other spatial andnon-spatial concepts. Level of detail varies across the diagram to clearlydescribe core concepts in the presence of the larger data model. This modelhas been implemented as an ESRI Geodatabase using data from sourcespreviously discussed. Extending the model to include other concepts anddata is not difficult in the presence of this initial model. The remainder of thepaper uses our data model as an explanatory tool in a discussion dealing withthe application of object-oriented principles and OOAD to supportinformation management.

5.1 Classes and objects

A class can be thought of as a blueprint for a collection of physical or logicalthings uniformly described using the same attributes, operations, relation-ships, and semantics (Rumbaugh et al. 1999). Classes are the primaryorganizational unit of object-oriented systems, providing a means fordescribing the structure and behaviour of individual system objects. Withinthis framework an object is a single instance of a class. A class will have aunique name, attributes or properties shared by all of its objects, andoperations (Fig. 3). Attributes defined for a class describe object state andcan have values that may or may not remain fixed in time. While all objectswithin a class have the same attributes they may have different attributevalues. Operations are the behaviour of objects within a class and define thetypes of things that can be done to an object within a class.The Activity Location class is an example of a custom ESRI Feature class

that we have programmed to store geocoded respondent activity locationsusing simple point geometries (Fig. 3). The Geodatabase implementation of


geographic data uses the feature class concept to model features that havespatial properties (e.g. shape, location etc.). These spatial characteristics arestored in a single geometry property referred to as the shape field (Zeiler2001). Other user-defined properties and those inherited from the ArcObjectsdata model are stored in separate data items alongside the shape field foreach object of a feature class. This contrasts with earlier topological, entity-relation approaches where spatial and attribute information were stored inseparate but related data files.Attributes of the ActivityLocation class include a unique identifier (UID),

point coordinates (X_COORD, Y_COORD), an in or out-of-home locationidentifier (HOME), and a traffic analysis zone identifier (TAZ) to connectactivity locations with host traffic zones. Objects of the Activity Locationclass can be combined to form revealed point patterns of respondent andhousehold activity participation. As with all classes in our data model theoperations box remains empty; we have not implemented any custombehaviours. These types of extensions can be added incrementally as the needarises. All of our custom classes are, however, inheriting default propertiesand operations (methods) from ESRI Feature and Object creatable classes(e.g. Zeiler 2001).

Operations

HouseHold

+OBJECTIDESRI Classes::Object

Mode

Person

1

*

HhldWgt

PlaceOfResidence

ShapeESRI Classes::Feature

ActivityDescription

-AREAMetro

SurveyStratum

-AREA-TAZ

TrafficAnalysisZone

-AREA-CITY_NO-CITYNAME

City

-AREAUrbanGrowthBoundary

-TAZ-HHLDS-REmp-OEmp-TotalEmp

LuDesc

-AREA-TAZ

TrafficAnalysisZone

-TAZ-Empacc-Hhacc

AccInd-UID-X-Y-Case_ID-Freq-TAZ-SID-TotEmp94-RetEmp94

HhldEmpDesc

-UID-X_COORD-Y_COORD-SAMPNO-PERSNO-PID-DAYNO-ACTNO«SubtypeField» -HOME= 0-TAZ-DIST8-MDPT_ID

ActivityLocation

InHome

OutOfHome

ActivityDescription

1

*

Spatial Classes

Non Spatial Classes

Subtypes

-UID-SAMPNO-PID-X_COORD-Y_COORD-START-STOP-DURATION-DAYNO

SpaceTimePath

PersonToMode

PersonToActivityDescription

Class Name

Attributes

1one and only one object of a classzero to many objects of a class*

AssociationComposition

Generalization (e.g. 1 object of the Person class isassociated with 0..many objects of theMode or Activity Description classes)

Fig. 2. Activity/travel database design model


5.2 Superclass and subclass

The expression of a parent-child relationship between classes invokes adistinction between subclasses and superclasses. A subclass (child) is a morespecific representation of a particular class and receives its structure(attributes, operations), relationships, and behaviour from a superclass(parent). A class can have one or more parents and one or more children.The definition of a subclass is not restricted to inherited characteristics andcan be altered to further distinguish it from its superclass (Booch 1994;Booch et al. 1999; Rumbaugh et al. 1999). In our data model, ActivityLo-cation is a subclass of the ESRI Feature class inheriting properties andoperations defined on the parent ESRI Feature class (Fig. 2).

5.3 Subtypes

A special case, unique to Geodatabase, is support for class subtypes. Subtypesare a categorical expressionof objectswithin a class and canbeused to uniquelyidentify a subset of instances within a class that are similar along somedimension. For example, in-home and out-of-home activities have beenspecified as separate subtypes (InHome andOutOfHome) of ActivityLocationusing the HOME field (Fig. 4). The HOME field is defined on the parent classActivityLocation and subtypes InHome and OutOfHome. The databaseautomatically segments ActivityLocation using defined subtypes uponimplementation. This distinction is substantively useful (Golob 2000) andautomatically recognized by theGIS software in the simplest case displaying inand out-of-home activity locations using different symbols. Subtyping is, in asense, analogous to assigning cases to a particular category of a dichotomousvariable. Subtypes possess computational advantages over subclasses partic-ularly with respect to the mapping application’s use of system resources.

5.4 Relationships

Relationships are connections between things and appear on UML classdiagrams as lines connecting classes (Booch et al. 1999). Different line typesand ends are used to distinguish relationship types and capabilities. Whileseveral kinds of relationships can exist between classes we have developedour data model using generalization, association, and a special type ofassociation called composition. Generalization is the process of sharingdescriptions between classes and is the relationship type that facilitates

Anatomy of a Class

-UID : esriFieldTypeDouble-X_COORD : esriFieldTypeDouble-Y_COORD : esriFieldTypeDouble-HOME : esriFieldTypeInteger-TAZ : esriFieldTypeSmallInteger

ActivityLocation ClassName

Operations

Attributes

Fig. 3. Structural elements of a class


endowment of attributes, operations and relationships between superclassesand subclasses (Booch et al. 1999; Rumbaugh et al. 1999). The application ofgeneralization enables description of systems and processes using a hierarchyof classes (Taylor 1998). Generalization engenders certain advantagesincluding reduction in model size, and increased efficiency and consistencyof model specification and updating (Rumbaugh et al. 1999).In our data model the ESRI Feature class inherits an OBJECTID property

from the ESRI Object class. Inheritance is cumulative with all objects of theActivityLocation class also containing OBJECTID and Shape attributespassed through the class hierarchy (Fig. 5). Several activity/travel classes areinheriting properties and methods from ESRI Feature classes (Fig. 2).Objects of these classes will have specific geometries and location (e.g.TrafficAnalysisZone, City, Metro, etc.). Several other classes inherit prop-erties and methods from the ESRI Object class only. These classes aretabular containing descriptive information that is in some cases associatedwith spatial classes (e.g. Mode, ActivityDescription, Person, Household etc.).We have organized the class diagram to differentiate feature and objectclasses.The second type of relationship that we are using in our data model is the

association (Booch et al. 1999; Rumbaugh et al. 1999). Associations are themost frequently occurring type of relationship in our data model. As adescriptive example, consider the relationships programmed between thePerson, Mode, and ActivityDescription classes (Fig. 6). Associationsbetween classes typically have adornments (e.g. descriptive name, multiplic-ities etc.) to describe relationship properties (Booch et al. 1999; Rumbaughet al. 1999). Inspection of Fig. 6 illustrates that single instances of the Personclass (survey respondents) are linked to zero or more instances of the Modeand ActivityDescription classes. Survey respondents are connected to owned-vehicles and detailed activity descriptions. These relationships can betraversed in both directions when browsing the physical database.As a second example, consider the binary association between the

ActivityLocation and ActivityDescription classes (Fig. 2). Respondentactivity locations are linked with corresponding detailed descriptive data(e.g. start and end times, mode, activity type etc.). The ActivityDescriptionclass appears twice in our data model (Fig. 2). When implemented, a singleActivityDescription relation appears in the physical database complete withall relationships defined at any location in the design model. That is, it ispossible to navigate from ActivityLocation to ActivityDescription and fromActivityDescription to the Person and SpaceTimePath classes. ActivityLo-cation objects can be linked to SpaceTimePath objects indirectly through

Subtypes

-UID-X_COORD-Y_COORD«SubtypeField» -HOME-TAZ

ActivityLocation

-HOME = 1

InHome

-HOME = 0

OutOfHome

Class

Fig. 4. In-home and out-of-home activities

modeled as subtypes of the

ActivityLocation class


related ActivityDescription objects. This indirect link eliminates the need todefine a separate relationship between the two classes.Classes can exist in isolation but this independence is likely unrealistic and

functionally limiting when describing systems. For example, in an activity/travel context, it is generally accepted that persons are part of householdsand that household level behaviour impacts the activity participation ofindividuals (Golob and McNally 1997; Golob 2000; Scott 2000). This whole(household), part (person) connection between activity/travel concepts canbe logically expressed using aggregation or composite relationships (Booch

Generalizationand Inheritance

-UID-X_COORD-Y_COORD-HOME-TAZ

ActivityLocation

+Shape : esriFieldTypeGeometry

ESRI Classes::Feature

+OBJECTID : esriFieldTypeOID

ESRI Classes::Object

Fig. 5. Feature is a subclass of Object and

ActivityLocation is a subclass of Feature

-VEHNUMBE-VEHOWNER-YR-ACQUIRED-REPLACE-MAKE-MODEL-CLASS-VEHTYPE-FUEL-BEGODOM-ENDODOM-MILES

Mode

-RELATION-GENDER-AGE-RACE-HOMELANG-OTHLANG-SPEAKENG-LICENSED-EMPLOYED-WORKHRS-OCCUPAT-INDUSTRY

Person

1

PersonTo Mode

ActivityDescription1

*PersonToActivityDescription

one and only one object of a class1

zero to many objects of a class*

Association

*

Fig. 6. Binary association

between activity/travel classes


et al. 1999; Rumbaugh et al. 1999). Composites are powerful relationshipswhereby objects of a ‘‘whole’’ class exercise varying degrees of control overobjects of a ‘‘part’’ class (Booch et al. 1999; Rumbaugh et al. 1999). This typeof control is not a characteristic of aggregation. One weakness ofGeodatabase is that aggregation, while supported as a logical modelingconstruct, cannot be maintained through the database design and imple-mentation phases. Aggregation relationships specified in conceptual datamodels must be removed or changed to binary associations or compositerelationships during design. With these concepts and technical limitations inmind we have adopted composites in several cases to characterize conceptualand functional ‘‘whole-part’’ relationships between activity/travel classes(Fig. 2).As an illustrative example, consider the composite relationships defined

between the TrafficAnalysisZone (TAZ), ActivityLocation, and PlaceOfRes-idence feature classes (Fig. 7). Logically this suggests that each TAZ containszero or more activity locations and residential sites. The TAZ class containspolygon data representing TAZ geometry and location within the survey’sgeographical extent. This use of composition has useful implications whenimplemented in the database. From a data maintenance perspectivemanipulation of TAZ objects impacts related instances from the Placeof-Residence and ActivityLocation classes. Deletion of a TAZ results indeletion of point objects in associated classes. Movement of a TAZ forcespoint objects in attached classes to move as well. Messaging between objectsof participating feature classes operationalizes these types of spatialbehaviours. Messages can propagate forwards, backwards or in bothdirections. The type of directed messaging controls the functional charac-teristics of spatial behaviour in the database. This connectivity andcommunication is useful for database maintenance and exists by default aspart of the ESRI Geodatabase design.Having discussed the types of relationships that we have specified between

several activity/travel concepts in our database it is useful to consider thetransition of the design level data model to a physical database. While recententity relation (Shaw and Wang 2000) and extended entity relation (Wangand Cheng 2001) activity/travel data modeling initiatives illustrate the usefulproperties of conducting a formal database modeling process our approachpresents certain advantages. In these earlier examples, conceptual or logical

-AREA-TAZ

TrafficAnalysisZone-UID-X_COORD-Y_COORD-HOME-TAZ

ActivityLocation

1*-X_COORD : esriFieldTypeDouble-Y_COORD : esriFieldTypeDouble-TAZ : esriFieldTypeSmallInteger

PlaceOfResidence

1 *

one and only one object of a class1zero to many objects of a class*

Composition

Fig. 7. Composition is a strong form of aggregation where objects of a whole class

(TrafficAnalysisZone) control the lifetime of objects in a part class or classes (ActivityLocation,

PlaceofResidence)


data models were translated to physical databases through a largely manualprocess. Modeled relations and relationships were created using the databasecapabilities of the chosen GIS software. In the approach that we haveadopted, the database design model (Fig. 2) has two roles. First, it is anillustrative tool for communicating the structure and content of the activity/travel database. Second, it acts as a database specification that is translated,automatically, by a visual modeling tool into a Geodatabase for use in theGIS software. That is, the analyst does not have to engage in a potentiallylengthy process of creating the database structure in the GIS software, thisstructure is exported from the visual modeling tool. With this discussion inmind, the next section addresses implementation of our activity/traveldatabase and the tools used to accomplish this task.

6 Implementation and modeling tools

Implementation involves translation of the database design model into aphysical database schema that is then loaded with data from the sources thatwe described earlier in the paper. We have chosen the ESRI Geodatabasesolution for implementation purposes. Geodatabase has been described as anobject extension of relational technology with enhanced functionality formanaging complex spatial data (Worboys 1999; Zeiler 1999). Object-relational models combine capabilities and features from object andrecord-based approaches (Worboys 1999). The alternative is a true object-oriented database built around an object-oriented programming language(Worboys 1999). In Geodatabase, the logical or conceptual view ofinformation is entirely object-oriented but is transformed, at design time,into a schema for implementation using relational technology. That is,physical database implementation assumes object-relational form. Thishybrid approach benefits from dependence on the widely accepted relationalmodel and its theoretical basis (Zeiler 1999; Bedard 1999b).Making the information modeling and management framework that we

have described operational relies on several tools. The Microsoft� Visiovisual modeling tool provides an environment for developing conceptual anddesign level data models. Visual modeling tools provide a means forconceptualizing and designing software systems in graphical form (Taylor1998). They incorporate object-oriented notation like UML, generate object-oriented computer code, and support object-oriented analysis and designmethodologies (Harmon and Sawyer 1999). These tools can check designwork and maintain and control classes and relationships within and betweendiagrams when classes are moved or copied.Upon completion of database design, the data model is exported from

Visio to the Microsoft� Repository. This is a required intermediate steppreceding translation into a physical Geodatabase. The Microsoft Repos-itory stores parts of an application (e.g. objects, components, relationships,documentation) and makes them available to other development tools(Harmon and Sawyer 1999). The contents of the repository are thentransformed into appropriate Geodatabase feature, object, and relationshipclasses. This is accomplished using the database schema creation capabilitiesof the ArcCatalog component of ESRI’s ArcGIS Suite. This procedure


translates classes into relations capable of managing spatial and non-spatialdata. The relations are then loaded with the appropriate data from theactivity/travel survey and RLIS using the object loading capabilities ofArcCatalog and ArcMap. Once the data have been loaded into theappropriate classes they can be used, in combination with associationsspecified in our data model, to explore database content. The final result is aGeodatabase containing an abstraction of the core activity/travel conceptsidentified as being relevant to the overarching research area. This database isopen for manipulation and analysis using standard and custom toolsavailable in ESRI’s ArcGIS, software suite. The database content can now beused to address substantive research questions that originally stimulated thedata modeling and management process.

Metadata and documentation

Extensive class, attribute, and relationship documentation has been pro-grammed into the data model using the visual modeling tool (Fig. 8). Thisresource is useful for immediate and long-term use of database contentbecause it can potentially reduce dependence on the presence of the originalprogrammer (Bedard 1999a, b). Documentation is used to communicatedetails of class structure, attributes, and the purpose and cardinality ofrelationships. We have used documentation to indicate the connectionbetween classes in our data model and the original geographic and surveydata files. This eases implementation and supports future revisions to thedatabase schema.Class documentation also includes general descriptions, metadata con-

cerning geographic reference for classes containing spatial information (e.g.projection, coordinate system and datum), and the type of GIS data format.Attributes are documented with all relevant coding schemes for nominal andordinal variables and detailed descriptions for interval/ratio variables. Thevisual modeling tool can be used to generate a comprehensive reportincluding all documentation programmed for classes, attributes, andrelationships. The modeling environment supports acquisition of a visualunderstanding of database content, supported by detailed documentation.This can be a useful point of reference for further application developmentand in empirical research that relies on the database content.

7 Applications

TheGeodatabase schema (Fig. 2) has been implemented inArcGIS and loadedwith data from the Portland activity/travel survey andRLIS. The database canbe used to support a number of activity/travel research applications. Theseinclude, exploration of content using the existing capabilities of the GISsoftware implementation environment, providing support for econometricand statistical analysis of activity/travel behaviour, and acting as an informa-tion foundation for developing software designed to support ExploratorySpatioTemporal Data Analysis (ESTDA) of activity/travel data. Theremainder of this section expands on the econometric and softwaredevelopment applications of the database.


Econometric and statistical analysis of individual and household levelactivity/travel outcomes can benefit from the presence of a highly structured,detailed, and documented spatial database. For example, evidence suggeststhat the composition of households (e.g. presence of children, income,household size) has an impact on various activity/travel outcomes (e.g.Hanson and Hanson 1981; Golob and McNally 1997; Wen and Koppelman1999; Golob 2000). In our database the HouseHold class does not containdata on several useful indicators of family status (e.g. presence of school agechildren) and mobility (e.g. number of licensed drivers). These data arestored at the individual level in the Person class. The separation results fromhousehold and respondent representation in the original survey. Therelationship (binary association) between these two classes (Fig. 2) can beused to summarize person level attributes at the household level producinghousehold characteristics for econometric models of activity/travel out-comes. When combined with the summary and storage of activity/traveloutcomes at the household level the process can generate a table for exportfrom the GIS environment to be used for econometric and statisticalanalysis.The resulting Geodatabase can also act as a foundation for developing

ESTDA tools. For example, Buliung and Kanaroglou (2004) have developeda set of tools for exploring individual and household activity/travelbehaviour. The tools have been built upon the database discussed inthis paper with additional programming of ArcObjects. So far, theanalytic environment includes spatial statistical measures of central

Fig. 8. Documentation and metadata development using a Visual Modeling Tool


tendency and dispersion, an area-based representation of household activitypatterns, and household space-time trajectories (Buliung and Kanaroglou2004).Exploration begins with retrieval of household activity patterns from the

underlying activity/travel database. A user interacts with the database byspatially selecting an instance of the PlaceOfResidence class. This eventtriggers automated enumeration of relationships programmed for thePlaceOfResidence class (e.g. TrafficAnalysisZone and HouseHold). Theselected PlaceOfResidence object is then automatically related to itsmatching HouseHold object. The HouseHold object is then related toSpaceTimePath objects. The application constructs point layers of householdactivity patterns for survey days using selected SpaceTimePath objects.Activities in the resulting pattern can be related to detailed descriptionscontained in ActivityDescription objects.We know, from the time-geographic literature, that space-time paths are

comprised of references to daily activities occurring at various locations inspace and time (Hagerstrand 1970; Lenntorp 1978; Kwan 2000). TheSpaceTimePath class has been programmed to include spatiotemporallocation references for recorded respondent activities. The PID and SAM-PNO properties of the SpaceTimePath class are used to identify individualsand households respectively that are associated with each SpaceTimePathobject. Spatial and spatiotemporal data structures are assembled fromgeographic (X_COORD, Y_COORD) and temporal (START, STOP)references stored as properties of each object. These data structures can beused to explore individual and household level activity/travel behaviour(Buliung and Kanaroglou 2004). The geocomputational problem of creatingspace-time paths for individuals and households, in addition to other spatialstructures, becomes an object-oriented programming task.We have summarized the spatial and temporal behaviour of two

households from the Portland survey to provide a descriptive example ofsome of the capabilities of our activity analysis tools (Fig. 9). We are notattempting to make broad generalizations concerning activity/travel behav-iour with these examples. The cases are being used as a demonstrative deviceonly, as part of an exploratory exercise characterized by emergence ofactivity/travel hypotheses for further examination. Standard deviationalellipses and household level space-time paths have been constructed for thesehouseholds using analytic tools discussed in Buliung and Kanaroglou (2004).Both households have two adults (20 £ age £ 59) with paid employment (fullor part-time), and young children (age £ 12). Inspection of the figure suggeststhat these households, although similar with respect to composition, havedissimilar activity patterns. Here, we have constructed standard deviationalellipses from household activity locations stored as SpaceTimePath objects inthe underlying database. The ellipse with the longer major-axis belongs to ahousehold living in the west suburban portion of the region, while the secondellipse belongs to a household located adjacent to downtown Portland in anarea known as East Portland.Ellipses are biased toward the home location in the presence of multiple in-

home activities recorded for survey households (Buliung and Kanaroglou2004). This is particularly true for the East Portland household. This initialexploration begins to suggest that spatial patterns of household activities are


potentially affected by patterns of land development and the distribution ofspatial opportunities.Household space-time paths are collections of individual space-time paths

programmatically assembled from SpaceTimePath objects (Fig. 9). It ispossible to extract and study the paths of individual household membersindependent of the household. These structures can be used to exploreactivity-based interactions between household members and to generallycharacterize the spatiotemporal complexity of daily household activitypatterns. The households in the example participate in activities further fromhome, later in the day. Closer inspection of the survey data for the householdfrom the west suburbs suggests that these distant activities are associatedwith family responsibilities that are attended jointly by the female member ofthe household and the children. The household located in East Portland isalso participating in joint activities, late in the day, with all householdmembers participating in some type of out-of-home amusement activity

Fig. 9. Case study showing standard deviational ellipses and household space-time paths

assembled from recorded activities for two households living in the Portland, Oregon

metropolitan area. Ellipses are used to describe directional dispersion around the unweighted,

bivariate mean centre of daily household activity locations


followed by a meal. Together, these exploratory examples begin to suggestthat the presence of children may influence the occurrence of out-of-homeactivities later in the day. In a manner similar to the elliptical characteriza-tion of household activities, the household trajectories also suggest thatsuburban locations could be associated with participation in more distantactivities.The software development and exploratory analysis examples that we have

discussed demonstrate the usefulness of the GIS-based data modelingapproach for providing a foundation that supports development of explor-atory tools. We are able to programmatically access and employ databaserelationships and object properties in developing software that facilitatesexploration of activity/travel behaviour data. In addition, a separate exercisecomparing the use of the described tool with manual construction ofhousehold activity patterns suggests that our approach is more efficient interms of the amount of time required to produce results. Overall, combiningthe underlying activity/travel database with the set of analytic tools that wehave developed provides a flexible, efficient environment for exploring theactivity-based spatiotemporal behaviour of households and individuals.

8 Discussion and concluding remarks

A general framework for information modeling and management has beenproposed and applied using data from Portland Metro’s 1994/1995 House-hold Activity and Travel Behaviour Survey and additional geographic datafor the same region. The approach recasts principles of object-orientationand object-oriented analysis and design to demonstrate their use in anactivity/travel setting. We provide a data model comprised of detailedstructural descriptions of several core activity/travel concepts. The datamodel is implemented as an object-relational geographic database and servesas an information foundation for analytic tools that support spatiotemporalexploration of daily household activity patterns. Development within anobject-oriented framework facilitates re-use of analytic components and theunderlying database for future research. We expect to revise the data modeland the exploratory tools that we have developed to support integration andmigration of activity/travel data collected for other cities.There exist a limited number of examples in the literature where data

modeling approaches have been applied in an activity/travel research setting.These data modeling experiments have been conducted using eitherrelational (Shaw and Wang 2000; Wang and Chen 2001) or object-oriented(Makin et al. 1997; Claramunt and Theriault 2001; Frihida et al. 2002; Shawand Xin 2003) approaches while database implementation has been carriedout using relational (Shaw and Wang 2000; Wang and Chen 2001), object-relational (Claramunt and Theriault 2001; Shaw and Xin 2003), or object-oriented (Makin et al. 1997; Frihida et al. 2002) forms. The presence of thisliterature suggests that data modeling and database design is an area ofresearch that is receiving increasing attention within the transportationresearch community. In addition, the documentation of alternativeapproaches suggests that we do not yet fully understand the costs andbenefits associated with data modeling and database design. The experimentdiscussed in this paper contributes to the existing literature in several ways.


Although recent examples of data modeling and database design provideuseful insights to the processes involved there is relatively cursory treatmentof specific details involved in mapping activity/travel concepts to object-oriented constructs using an object-based notation. This is important on twofronts. First, while concepts discussed in the paper may be familiar tocomputer scientists and database developers they are not necessarily widelyunderstood as yet by geographers and others interested in transportationresearch. Second, data modeling and database design concerns can have apivotal role in the development of tools designed to enhance ourunderstanding of individual and household level spatiotemporal behaviour.Exploratory applications discussed in the paper serve to demonstrateconnections between activity/travel concepts, their modeling and implemen-tation in an object-relational database, and application in an activity analysiscontext.Contrasting entity-relation and extended-entity relational modeling exam-

ples in the literature, we demonstrate that the selected modeling environmentfacilitates physical database implementation without extensive additionalmanual operations. With respect to the modeling environment, the mostrecent example of activity/travel database design and application (Frihidaet al. 2002) applied the capabilities of SmallWorld GISTM. Our applicationhas been developed using a different technological platform providingresearchers with an alternative solution. Lastly, we also demonstrate that theintersection of object-oriented database design and object-oriented program-ming provides an opportunity to develop complex, informative, spatiotem-poral data structures (e.g. household space-time paths) from very basicobjects (e.g. SpaceTimePath objects) stored in an underlying database. Thisrepresents an alternative approach to existing examples of conceptually richspatiotemporal data models (e.g. Shaw and Wang 2000; Wang and Chen2001; Frihida et al. 2002).Two themes that have not explicitly been addressed in our data modeling

experiment involve the dynamics or behaviour of activity system objects, andthe spatially continuous nature of certain activity/travel processes andoutcomes. From a behavioural perspective, several of the concepts (e.g.households, individuals etc.) are likely to interact and make decisions,stimulating activity participation in space and time. In addition, certainclasses contain mobile objects, or objects that move through a city on a dailybasis to satisfy personal or household objectives (e.g. individuals, vehiclesetc.). Specific types of UML diagrams (e.g. activity, state charts) can be usedfor conceptual modeling of dynamic or behavioural aspects of systems(Booch et al. 1999).Implementation of dynamics for object-based simulation typically relies on

convergence of behavioural theory, empirical evidence, data modeling, andobject-oriented programming (e.g. Makin et al. 1997; Westervelt andHopkins 1999; Bian 2000). Extension to static descriptions to includedynamic behaviour (e.g. scheduling and performing activities) will likelyemerge at the intersection of activity-based theoretical and empiricalresearch, data modeling, and object-oriented programming. While we haveexperimented with the study of revealed patterns of activity/travel behaviour,conceptualization and implementation of a dynamic, predictive frameworkremains a longer-term research objective.


The second theme that we have not addressed concerns the deficiency ofthe object view as an abstraction for spatially continuous processes. While werecognize that activity/travel behaviour effects are not necessarily discrete,we have not attempted to manage this in our data model. The field view isgenerally considered a more appropriate abstraction for spatially continuousprocesses and can be implemented in GIS using data structures such asrasters and triangulated irregular networks (Bian 2000; Cova and Goodchild2002). The utility of the field view for representation of certain urbanprocesses (e.g. air and noise pollution) has been documented in the urbanmodeling literature (Wegener and Furst 1999; Spiekermann and Wegener2000).Recent research provides interesting examples of convergence between field

and object views. These efforts include dynamic simulation of mobile objectswithin heterogeneous environments modeled as rasters (Westervelt andHopkins 1999; Bian 2000), and attempts to build hybrid, object-field,relational data structures (Cova and Goodchild 2002). These developmentscould potentially facilitate joint representation of discrete activity/travelconcepts (e.g. individual activities) and spatially continuous outcomes (e.g.mobile source emissions) within a single data model. Experimentation of thissort has yet to be conducted in an activity/travel setting. One challengeconcerns conceptual modeling of joint object-field structures. This capacitydoes not presently exist within standard object-based notations like UML(Bian 2000).The object-oriented data modeling approach has been adopted in our

research in the presence of certain conceptual and technical limitations.Advocates suggest that object-orientation facilitates representation of com-plex systems in a more intuitive manner (Booch 1994; Jackson 1994; Makin etal. 1997; Frihida et al. 2002). This advantage persists in the presence of richconstructs for documenting and modeling static and dynamic aspects ofsystems. During the course of the data modeling exercise we discovered thatGeodatabase, at the design and implementation levels, does not supportaggregation. This presents a limitation with respect to mapping activity/travelconcepts to object-oriented constructs and the physical database implemen-tation that follows. Conceptual adjustments to the model were required in theface of a technical constraint. From a representational perspective, thisgenerates increased abstraction as objects participating in aggregate relation-ships are modeled using complementary but functionally different types ofrelationships at the design level. As an alternative to abandoning the conceptof aggregation altogether, we elected to use composite relationships in certaincases. We have not found this to be a practical limiting factor in thedevelopment of exploratory tools for activity analysis (e.g. Buliung andKanaroglou 2004) but recognize that it could present challenges whenapplying our data model elsewhere. The task of re-specifying relationships tosupport other applications is not onerous in the presence of the design modeland the visual modeling environment.We have built a relatively basic, comprehensive data model with

implementation as an object-relational geographic database for use inempirical research and application development. The approach is flexible andintuitive and the data model can be modified as the need arises. In addition,by modeling the data we are able to more clearly observe and specify


connections between activity/travel concepts facilitating construction ofexogenous inputs for explanatory and predictive activity/travel models. Theextensive documentation that has been built into the data model reducesdependence on the original designer, encourages continued use of the data,and promotes a more flexible environment for future database extension andmodification. We expect, as Bedard (1999b) suggests, that the approach willdecrease longer run maintenance costs. Adopting a formal method forstudying, modeling, and managing information has refined our understand-ing of the survey and its data. As a result, we expect that our use of these datain an empirical setting will be considerably more efficient than otherexperiences in the past. We suggest that the approach can add value to datathat are used in transportation research by reducing the time researchersindependently allocate to understanding and in some cases constructing basicdatabases. Perhaps activity/travel survey design initiatives should beexpanded to include data modeling with resulting models, survey data, andpost-survey databases bundled to ease the transition toward substantiveconcerns.

References

Bedard Y (1999a) Visual modelling of spatial databases: Towards spatial PVL and UML.

Geomatica 53: 169–186

Bedard Y (1999b) Principles of spatial database analysis and design. In: Longley PA, Goodchild

MF, Maguire DJ, Rhind DW (eds) Geographical Information Systems, vol. 1, Principles and

Technical Issues. John Wiley and Sons, New York, pp. 413–424

Bian L (2000) Object-oriented representation for modelling mobile objects in an aquatic

environment. International Journal of Geographical Information Science 14: 603–623

Booch G (1994) Object-oriented analysis and design with applications [2nd ed.]. Addison-Wesley

Publishing, Massachusetts

Booch G, Rumbaugh J, Jacobson I (1999) The unified modeling language user guide. Addison-

Wesley Publishing, Massachusetts

Bowman B, Ben-Akiva M (2001) Activity-based disaggregate travel demand model system with

activity schedules. Transportation Research A 35: 1–28

Buliung RN (2001) Spatiotemporal patterns of employment and non-work activities in Portland,

Oregon. In: Proceedings of the 2001 ESRI International User Conference, San Diego [CA], July

2001, http://gis.esri.com/library/userconf/proc01/professional/papers/pap1078/p1078.htm (ac-

cessed July 2004)

Buliung RN, Kanaroglou PS (2002) Commute minimization in the Greater Toronto Area:

Applying a modified excess commute. Journal of Transport Geography 10: 177–186

Buliung RN, Kanaroglou PS (2004) An Exploratory Spatial Data Analysis (ESDA) Toolkit for

the Analysis of Activity/Travel Behaviour Data. Lecture Notes in Computer Science 3044:

1016–1025

Bush BW (1999) The TRANSIMS framework. Presented at TRANSIMS Opportunity Forum,

Santa Fe [NM]

Cambridge Systematics (1996) Data collection in the Portland, Oregon Metropolitan Area: Case

study. Report DOT-T-97-09. U.S. Department of Transportation

Claramunt C, Theriault M (2001) A UML-based modelling approach for analysing travel

behaviour. Presented at Eurosim, Delft, NL

Cova TJ, Goodchild MF (2002) Extending geographical representation to include fields of

spatial objects. International Journal of Geographical Information Science 16: 509–532

Curtin K, Noronha V, Goodchild M, Grise S (2000) UNETRANS data model reference – Draft:

ArcGIS Transportation Data Model (DRAFT), http://www.geog.ucsb.edu/�curtin/unetrans/UNETRANS_index.htm (accessed September 2002)


Davis KM, Maidment DR (2000) Object-oriented modeling of rivers and watersheds in geographic

information systems. Master of Science in Engineering thesis, Department of Civil Engineer-

ing, The University of Texas, Austin

ESRI (2001) Designing geodatabase (For ArcEditor 8 and ArcInfo 8) vol 1. ESRI, Redlands [CA]

Frihida A, Marceau DJ, Theriault M (2002) Spatio-temporal object-oriented data model for

disaggregate travel behaviour. Transactions in GIS 6: 277–294

Gibson K, Abbott C (2002) City profile Portland Oregon. Cities 19: 425–436

Golob TF (2000) A simultaneous model of household activity participation and trip chain

generation. Transportation Research B 34: 355–376

Golob TF, McNally MG (1997) A model of activity participation and travel Interactions

between household heads. Transportation Research B 31: 177–194

Hagerstrand T (1970) What about people in regional science? Papers of the Regional Science

Association 24: 7–21

Hanson S, Hanson P (1981) The travel-activity patterns of urban residents: Dimensions and

relationships to sociodemographic characteristics. Economic Geography 57: 332–347

Harmon P, Sawyer B (1999) UML for Visual Basic 6.0 Developers. Morgan Kaufmann [CA]

Jacobson I, Christerson M (1995) Modelling with use cases: A confused world of OOA and

OOD. Journal of Object-Oriented Programming 8, (September) 15–20

Jackson RW (1994) Object-Oriented Modeling in regional science: An advocacy view. Papers in

Regional Science 73: 347–367

Koncz NA, Adams TM (2002) A data model for multi-dimensional transportation applications.

International Journal of Geographical Information Science 16: 551–569

Kratzschmar M (2001) Implementing the UNETRANS Data Model. In: Proceedings of the 2001

ESRI International User Conference, San Diego [CA], July 2001, http://gis.esri.com/library/

userconf/proc01/professional/papers/ pap975/p975.htm (accessed July 2004)

Kwan M-P (2000) Interactive geovisualization of activity-travel patterns using three-dimensional

geographical information systems: A methodological exploration with a large data set.

Transportation Research C 8: 185–203

Lenntorp B (1978) A time-geographic simulation model of Individual activity programmes. In:

Carlstein T, Parkes D, Thrift N (eds) Human activity and time geography. John Wiley and

Sons, New York, pp. 162–180

Los Alamos National Laboratory (2002) TRansportation ANalysis SIMulation System

(TRANSIMS) Portland Study Reports Volume One – Introduction/Overview. Report LA-

UR-01_5711. Los Alamos National Laboratory

Makin J, Healey RG, Dowers S (1997) Simulation modelling with object-oriented GIS: A

prototype application to the time geography of shopping behaviour. Geographical Systems 4:

397–429

Mark Bradley Research and Consulting, Bowman J (1998) A system of activity-based models for

Portland, Oregon. Report FHWA-PD-99-003. FHWA, U.S. Department of Transportation

McNally MG (1997) An activity-based micro-simulation model for travel demand forecasting.

In: Ettema DF, Timmermans HJP (eds) Activity based approaches to travel demand analysis.

Permagon, London, pp. 37–54

Miller EJ, Hunt JD, Abraham JE, Salvini PA (2004) Microsimulating urban systems. Computers,

Environment and Urban Systems 28: 9–44

Noth M, Borning A, Waddell P (2003) An extensible, modular architecture for simulating urban

development, transportation, and environmental impacts. Computers, Environment and Urban

Systems 27: 181–203

Peng Z-R (1997) The jobs-housing balance and urban commuting. Urban Studies 34: 1215–1235

Perone S, Culp M (1998) Case study 2: Portland Metro, Oregon – GIS database for urban

transportation planning. Report FHWA-PD-98-065. FHWA, U.S. Department of Transpor-

tation

Rumbaugh J, Jacobson I, Booch G (1999) The unified modeling language reference manual.

Addison-Wesley, Massachusetts

Scott DM (2000) Toward an Operational Model of Daily Household Activity-Travel Behavior.

Unpublished PhD thesis, School of Geography and Geology, McMaster University, Hamilton


Scott DM, Kanaroglou PS (2002) An activity-episode generation model that captures

interactions between household heads: Development and empirical analysis. Transportation

Research B 36: 875–896

Shaw S-L, Wang D (2000) Handling disaggregate spatiotemporal travel data in GIS.

GeoInformatica 4: 161–178.

Shaw S-L, Xin X (2003) Integrated land use and transportation interaction: A temporal GIS

exploratory data analysis approach. Journal of Transport Geography 11: 103–115

Spiekermann K, Wegener M (2000) Freedom from the tyranny of zones: Towards new GIS-

based spatial models. In: Fotheringham AS, Wegener M (eds) Spatial models and GIS: New

potential and new models [=GISDATA 7]. Taylor and Francis, London, pp. 45–61

Sun X, Wilmot CG, Kasturi T (1998) Household travel, household characteristics, and land use:

An empirical study from the 1994 Portland activity-based travel survey. Transportation

Research Record 1617: 10–17

Taylor DA (1998) Object technology: A manager’s guide [2nd ed.]. Addison-Wesley Longman,

Massachusetts

Timmermans H, van der Waerden P, Alves M, Polak J, Ellis S, Harvey AS, Kurose S, Zandee R

(2003) Spatial complexity of daily travel patterns: An international comparison. Journal of

Transport Geography 11: 37–46

Waddell P (2002) UrbanSim: modeling urban development for land use, transportation and

environmental planning. Journal of the American Planning Association 68: 297–314

Wang D, Cheng T (2001) A spatio-temporal data model for activity-based transport demand

modelling. International Journal of Geographical Information Science 15: 561–585

Weber J, Kwan M-P (2002) Bringing Time Back In: A Study on the Influence of Travel Time

Variations and Facility Opening Hours on Individual Accessibility. Professional Geographer

54: 226–240

Wegener M, Furst F (1999) Land-use transport interaction: State of the art. Deliverable 2a of the

project TRANSLAND of the 4th RTD Framework Programme of the European Commis-

sions: Institut fur Raumplanung (IRPUD), Dortmund

Wen C-H, Koppelman FS (1999) Integrated model system of stop generation and tour formation

for analysis of activity and travel patterns. Transportation Research Record 1676: 136–144

Westervelt JD, Hopkins LD (1999) Modeling mobile individuals in dynamic landscapes.

International Journal of Geographical Information Science 13: 191–208

Worboys MF (1999) Relational databases and beyond. In: Longley PA, Goodchild MF,

Maguire DJ, Rhind DW (eds) Geographical Information Systems Vol. 1: Principles and

Technical Issues. John Wiley and Sons, New York, pp. 373–384

Zeiler M (1999) Modeling our world: The ESRI guide to geodatabase design. ESRI Press,

Redlands [CA]

Zeiler, M. (ed) (2001) Exploring ArcObjects. ESRI Press, Redlands [CA]


Documents

On design and implementation of an object-relational spatial database for activity/travel behaviour research