Advances in Spatial Information Science
Research Symposium
May 24 & 25, 2007
Abstracts
Contextualization of Geospatial Data Semantics (Abstract)
Human interpretation of geographical information are contextualized by problem-solving activities which endow meaning to geospatial data and processing. However, existing spatial data models have not taken this aspect of semantics into account. Our research have approached this problem by extending spatial data semantics to include not only the contents and schemas, but also the contexts of their use. In this talk, I will define such a semantic model in terms of three related components: context knowledge, contextualized ontology base, and context-sensitive interpretation. The contextualization of spatial data semantics allows the same underlying data to take multiple semantic forms, and enables disambiguation of spatial concepts based on localized contexts.
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Louise Comfort, Pittsburgh
Managing Risk in Real-Time: Vulnerability Assessment for Flooding in the Mon Valley (Abstract)
The Interactive, Intelligent, Spatial Information System (IISIS) Project of the Graduate School for Public and International Affairs has developed a prototype assessment model that will enable communities to make decisions regarding the vulnerability of existing infrastructure, the threat to local populations, and the capacity of local resources to respond effectively to flooding in the Pittsburgh metro region. This model, known as the Dynamic Assessment Model (DAM), is an innovative component of the IISIS prototype for decision support and needs to be tested in a real world setting. For that purpose, Allegheny County Emergency Management Agency and twenty-six municipalities along the Monongahela River are willing collaborate with IISIS to test the model. This presentation will briefly address the goals, objectives, and progress of the project.
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William Harbert, Pittsburgh
Geospatial Analysis of Geohazards (Abstract)
William Harbert & David Davis
The Geologic hazards loom all around us. As population growth forces more
communities to expand into areas at risk from these ominous threats,
concern increases about the danger that geohazards pose to people,
property, and the environment. As part of the Environmental Systems Research Incorporated (ESRI) Virtual Campus program, a course was designed to present the benefits of Geographical Information Systems (GIS) based spatial analysis as applied towards a variety of geohazards. We created this on-line ArcGIS -based course to aid the motivated student or professional in his or her efforts to use GIS in determining where geohazards are likely to occur and for assessing their potential impact on the human community. Our course is broadly designed for earth scientists, public sector professionals, students, and others who want to apply GIS to the study of geohazards. Participants work with ArcGIS software and diverse datasets to display, visualize and analyze a wide variety of data sets and map a variety of geohazards including earthquakes, volcanoes, landslides, tsunamis, and floods. Following the GIS-based methodology of posing a question, decomposing the question into specific criteria, applying the criteria to spatial or tabular geodatasets and then analyzing feature relationships, from the beginning the course content was designed in order to enable the motivated student to answer questions. For example, to explain the relationship between earth quake location, earthquake depth, and plate boundaries; use a seismic hazard map to identify population and features at risk from an earthquake; import data from an earthquake catalog and visualize these data in 3D; explain the relationship between earthquake damage and local geology; use a flood scenario map to identify features at risk for forecast river discharges; use a tsunami inundation map to identify population and features at risk from tsunami; use a hurricane inundation map to identify the population at risk for any given category hurricane; estimate accumulated precipitation by integrating time-series Doppler radar data; and model a real-life landslide event. The six on-line modules for our course are Earthquakes I, Earthquakes II, Volcanoes, Floods, Coastal Geohazards and Landslides. Earthquake I can be viewed and accessed for no cost at http://campus.esri.com.
Toward A Framework for Geographic Event Conceptualization (Abstract)
This contribution discusses the elements of a framework for geographic event conceptualization from a cognitive perspective. Dynamic aspects of geographic phenomena have been realized as an important aspect of current and future approaches in spatial information theory. Several formalism have been developed that capture temporal aspects in spatial information theory and event oriented approach undergo a renaissance as technical abilities to capture time as well as space increase. The perspective adopted in this presentation deviates from the main stream in that it evaluates formal approaches from a cognitive perspective and tests whether formal characterization of temporal phenomena hold against a cognitive validation. Exemplarily, research is reported that evaluates the omnipresent conceptual neighborhood approach for topological relationships. The spatio-temporal changes that two geographic regions can undergo can bee captured as paths through a conceptual neighborhood graph. Different characteristics of the changes that the regions undergo, for example, if we only allow the topological transformation of translation, lead to unique paths through the conceptual neighborhood graph. The question answered here is whether this formal characterization is sufficient to capture how a cognitive agent would conceptualize the very same changes that the two regions in question undergo. In a behavioral experiment employing a grouping paradigm these questions have been evaluated. The results clearly show that the cognitive conceptualization is influenced by other—perceptually and conceptually—salient factors such as the identity of the regions, the characteristics of the reference object (is a larger or a smaller region mobbing), and the general dynamics (are both regions moving at the same time).
Visual Analysis of Human Activities (Abstract)
A major difficulty in the analysis of disaggregate human activity behavior arises from the fact that individual movement in space-time is a complex trajectory with many interacting dimensions. These include the location, timing, duration, sequencing and type of activities and/or trips. This characteristic of human activity behavior has made the simultaneous analysis of its many dimensions difficult. Two approaches to overcoming this difficulty are often pursued by researchers. One is to decompose activity patterns into their component dimensions and focus only on a few dimensions at a time. The other is to treat activity behavior as a multidimensional whole and use multivariate methods to derive generalized activity patterns from a large number of variables. For instance, through the use of multivariate group identification methods (such as clustering or pattern recognition algorithms), complex behavioral patterns can be represented by some general characteristics and organized into relatively small number of homogenous classes.
While these conventional methods are useful for modeling purposes and for discovering the complex interrelations among variables, they also have serious limitations. First, few of these methods were designed to handle real geographical locations and context of human activities and trips. Often, the spatial dimension is represented by some measures derived from real geographical locations (e.g. distance or direction from a reference point such as home or workplace of an individual). Further, locational information of activities and trips was often aggregated with respect to a zonal division of the study area (e.g. traffic analysis zones). Using such zone-based data, measurement of location and/or distance involves using zone centroids where information about activity locations in geographic space and their spatial relations with other urban opportunities is lost.
Second, since many analytical methods (e.g. log-linear models) are designed to deal with categorical data, organizing the original data in terms of discrete units of space and time has been a necessary step in most quantitative analyses of activity-travel patterns in the past. Discretization of temporal variables, such as the start time or duration of activities, involves dividing the relevant span of time into several units and assigning each activity or trip into the appropriate class (e.g. dividing a day into 8 or 12 temporal divisions into which activities or trips are grouped). Discretization of spatial variables, such as distance from home, involves dividing the relevant distance range into several “rings”. Since both the spatial and temporal dimensions are continuous, results of any analysis that are based upon these discretized variables may be affected by the particular schema of spatial and/or temporal divisions used. The problem may be serious when dealing with the interaction between spatial and temporal variables since two discretized variables are involved.
Third, the amount and complexity of activity-travel data have increased considerably in recent years. Without effective methods for exploring these data, the researcher may need to model activity patterns without a preliminary understanding of the behavioral characteristics or uniqueness of the individuals in the sample at hand. This can be costly in later stages of a study if the model’s specifications fail to take into account of the behavioral anomalies involved. Since exploratory data analysis (EDA) can often lead to more focused and fruitful methods or models in later stages of a study, the recent development and use of geovisualization suggest a possible direction for overcoming the problem.
In this paper I draw from my recent studies to suggest that GIS-based 3D geovisualization offers considerable promise in overcoming these three limitations of past studies on human activity behavior. Specifically I seek to illustrate that 3D geovisualization is effective for enhancing our understanding of the interaction between space and time in human activity behavior, for exploring human hybrid activity-travel patterns (i.e. the interaction between of people’s activities and/or travel in both the physical and virtual worlds), and for taking qualitative information and human emotions and feelings into account. These examples are drawn from my recent studies that used three different activity-travel diary data sets I collected.
The first set of examples illustrates how interactive 3D geovisualization based on the time-geographic framework can be fruitfully deployed for enhancing our understanding of human activity behavior (Kwan 2000; Kwan and Lee 2004). Several geovisualization methods for dealing with the spatial and temporal dimensions of human activity-travel patterns at the same time while avoiding the interpretative complexity of multivariate pattern generalization or recognition methods will be described. Advantages of the methods will be highlighted. For instance, significance of the temporal dimension and its interaction with the spatial dimension in structuring the daily space-time trajectories of individuals can be clearly revealed (Kwan 2004). Further, using 3D geovisualization as an exploratory tool may lead to more focused analysis in later stages of a study. They can also help the formulation of more realistic computational or behavioral travel models.
Since the Internet now plays an important role in shaping people’s daily activities and travel, we can no longer ignore the effect of the Internet in any analysis of human activity behavior. The second set of examples illustrates how our understanding of human hybrid activity patterns can be enhanced using 3D geovisualization (Ren and Kwan 2006). Two geovisualization approaches using 3D and 2D GIS techniques will be discussed: information cube and hybrid 3D space-time paths, and a new representation of 2D space-time paths that incorporates parallel coordinate plots. How these two methods allow us to uncover important patterns in hybrid human activity-travel behavior will be described.
As shown in recent studies of human activity behavior, approaches that rely solely on quantitative activity data tend to offer limited understanding of individual behavior and the situational factors that help explain such behavior. The third example addresses the need for understanding people’s emotions and feelings and for GIS to analyze qualitative material, especially textual material. It explores the possibility of incorporating qualitative data analysis capabilities in GIS, and involves the conceptualization, design and implementation of a 3D visualization and qualitative analysis component (called 3D-VQGIS) in GIS (Kwan and Ding 2006). This illustration shows that capabilities for the recursive and interactive analysis of qualitative data can be incorporated into 3D geovisualization environment of a GIS.
References
Kwan, Mei-Po. 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.
Kwan, Mei-Po. 2004. GIS methods in time-geographic research: Geocomputation and geovisualization of human activity patterns. Geografiska Annaler B, 86(4): 205-218.
Kwan, Mei-Po and Jiyeong Lee. 2004. Geovisualization of human activity patterns using 3D GIS: A time-geographic approach. In Spatially Integrated Social Science, 48-66, Michael F. Goodchild and Donald G. Janelle (eds). New York: Oxford University Press.
Kwan, Mei-Po and Guoxiang Ding. 2007. 3D-VQGIS: Extending GIS for Qualitative and Mixed-Method Research. Manuscript.
Ren, Fang and Mei-Po Kwan. 2007. Geovisualization of Human Hybrid Activity-Travel Patterns. Transactions in GIS, forthcoming.
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Jonathan Raper, City University (UK)
Situated Searching on Mobile Location-Aware Guides: What People Want to Know, Where (Abstract)
The EU Webpark research project (http://www.webparkservices.info/) produced a mobile GIS platform for use by tourists on rented mobile devices with GPS in national parks, nature reserves, parks and open air museums. Webpark was succeeded by the project spinoff Camineo SAS (http://www.camineo.com/) which is now sucessfully marketing and installing this technology at a variety of European sites along with the partner Universities, including City University, London.
Under a User Agreement these mobile devices record where the visitors actually took the device and what information requests they made on it. As the software is web-based these requests are URLs for applications like map and trail guides, and content pages describing natural history. There are full location-aware search tools in the system that allow users to ask 'What's around me?', customised by their movement style and by their choice of search area. The visitor logs have been collected together in a GIS and have been analysed by user, through time and space, or in aggregate terms. This presentation will explore the actual questions asked by some of the 200+ visitors on their device in the Swiss National Park in the autumn of 2006, and will focus on the implications for the design of public GIS and location-based services from this experience.
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Ken Sochats, Pittsburgh
Dynamic Discrete Disaster Decision Simulation System (D4S2) (Abstract)
Carey Balaban, Bopaya Bidanda, Matthew Kelley, Larry Shuman, Ken Sochats, Lin Wang & Shengnan Wu
D4S2 seamlessly integrates ESRI’s ArcGIS 9.2, Rockwell Automation’s Arena discrete event simulation, a custom built rule based decision modeling system and a control interface that mirrors an emergency operations center. The geo-database contains over 100 layers of geographic, asset and other geo-referenced information. The simulation model is built dynamically from the geo-database and situational data about the event allowing us to create any number, type and size of emergency events. The decision model contains rules that codify standards, training, best practices, exercises and research on first responders, emergency managers, dispatchers, the public, terrorists, other actors and environmental factors. Each component informs the others continuously of decisions, status changes, and other situational variables that have changed as the event(s) unfold. D4S2 provides a circumstance independent laboratory for testing how the type and scale of an event, situational variables and command decisions affect responders’ efficiency and effectiveness in dealing with disasters.
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