Donna J. Peuquet

An event-based spatiotemporal data model (ESTDM) for temporal analysis of geographical data

Abstract Representations historically used within GIS assume a world that exists only in the present. Information contained within a spatial database may be added-to or modified over time, but a sense of change or dynamics through time is not maintained. This limitation of current GIS capabilities has recently received substantial attention, given the increasingly urgent need to better understand geographical processes and the cause-and-effect interrelationships between human activities and the environment. Models proposed so-far for the representation of spatiotemporal data are extensions of traditional raster and vector representations that can be seen as location- or feature-based, respectively, and are therefore best organized for performing either location-based or feature-based queries. Neither form is as well-suited for analysing overall temporal relationships of events and patterns of events throughout a geographical area as a temporally-based representation. In the current paper, a new spatio-tem...

A Conceptual Framework and Comparison of Spatial Data Models

This paper examines the major types of spatial data models currently known and places these models in a comprehensive framework. This framework is used to provide clarification of how varying data models, as well as their inherent advantages and disadvantages, are interrelated. It also provides an insight into how these conflicting demands may be balanced in a more systematic and predictable manner for practical applications, and reveals directions for needed future research. On examine les principaux types de modeles de donnees spatiales actuels, et on les place dans un cadre global. Ce cadre est utilise pour eclaircir comment ces divers modeles spatiaux, de meme que leurs avantages et desavantages, sont interrelies. Le cadre laisse aussi voir comment ces demandes contradictoires peuvent etre equilibrees dune facon plus systematique et previsible pour des applications pratiques, et revele les directions que doit prendre la recherche future.

Making Space for Time: Issues in Space-Time Data Representation

Even with much activity over the past decade, including organized efforts on both sides of the Atlantic, the representation of both space and time in digital databases is still problematic and functional space-time systems have not gone beyond the limited prototype stage. Why is this the case? Why did it take twenty years from the first GIS for the for representation and analysis in the temporal, as well as the spatial dimension, to begin? I explore the answers to these questions by giving a historical overview of the development of space-time representation in the geographic information systems and database communities and a review of the most recent research. Within the context of this perspective, I also question what seems to be a spirit of self-accusation in which the lack of functional space-time systems has been discussed in the literature and in meetings of GIS researchers. I close by offering my own interpretation of current research issues on space-time data models and languages.

GIS Approaches for the Estimation of Residential-Level Ambient PM Concentrations

Spatial estimations are increasingly used to estimate geocoded ambient particulate matter (PM) concentrations in epidemiologic studies because measures of daily PM concentrations are unavailable in most U.S. locations. This study was conducted to a) assess the feasibility of large-scale kriging estimations of daily residential-level ambient PM concentrations, b) perform and compare cross-validations of different kriging models, c) contrast three popular kriging approaches, and d ) calculate SE of the kriging estimations. We used PM data for PM with aerodynamic diameter ≤10 μm (PM10) and aerodynamic diameter ≤ 2.5 μm (PM2.5) from the U.S. Environmental Protection Agency for the year 2000. Kriging estimations were performed at 94,135 geocoded addresses of Women’s Health Initiative study participants using the ArcView geographic information system. We developed a semiautomated program to enable large-scale daily kriging estimation and assessed validity of semivariogram models using prediction error (PE), standardized prediction error (SPE), root mean square standardized (RMSS), and SE of the estimated PM. National- and regional-scale kriging performed satisfactorily, with the former slightly better. The average PE, SPE, and RMSS of daily PM10 semivariograms using regular ordinary kriging with a spherical model were 0.0629, −0.0011, and 1.255 μg/m3, respectively; the average SE of the estimated residential-level PM10 was 27.36 μg/m3. The values for PM2.5 were 0.049, 0.0085, 1.389, and 4.13 μg/m3, respectively. Lognormal ordinary kriging yielded a smaller average SE and effectively eliminated out-of-range predicted values compared to regular ordinary kriging. Semiautomated daily kriging estimations and semivariogram cross-validations are feasible on a national scale. Lognormal ordinary kriging with a spherical model is valid for estimating daily ambient PM at geocoded residential addresses.

ICEAGE: Interactive Clustering and Exploration of Large and High-Dimensional Geodata

The unprecedented large size and high dimensionality of existing geographic datasets make the complex patterns that potentially lurk in the data hard to find. Clustering is one of the most important techniques for geographic knowledge discovery. However, existing clustering methods have two severe drawbacks for this purpose. First, spatial clustering methods focus on the specific characteristics of distributions in 2- or 3-D space, while general-purpose high-dimensional clustering methods have limited power in recognizing spatial patterns that involve neighbors. Second, clustering methods in general are not geared toward allowing the human-computer interaction needed to effectively tease-out complex patterns. In the current paper, an approach is proposed to open up the “black box” of the clustering process for easy understanding, steering, focusing and interpretation, and thus to support an effective exploration of large and high dimensional geographic data. The proposed approach involves building a hierarchical spatial cluster structure within the high-dimensional feature space, and using this combined space for discovering multi-dimensional (combined spatial and non-spatial) patterns with efficient computational clustering methods and highly interactive visualization techniques. More specifically, this includes the integration of: (1) a hierarchical spatial clustering method to generate a 1-D spatial cluster ordering that preserves the hierarchical cluster structure, and (2) a density- and grid-based technique to effectively support the interactive identification of interesting subspaces and subsequent searching for clusters in each subspace. The implementation of the proposed approach is in a fully open and interactive manner supported by various visualization techniques.

An examination of techniques for reformatting digital cartographic data. Part 2: the vector-to raster process.

Current graphic devices suitable for high-speed computer input and output of cartographic data are tending more and more to be raster-oriented, such as the rotating drum scanner and the color raster display. However, the majority of commonly used manipulative techniques in computer-assisted cartography and automated spatial data handling continue to require that the data be in vector format. This situation has recently precipitated the requirement for very fast techniques for converting digital cartographic data from raster to vector format for processing, and then back into raster format for plotting. The current article is part one of a two-part paper concerned with examining the state-of-the-art in these conversion techniques. In part one, algorithms to perform all phases of the raster-to-vector process are systematically outlined, and then compared in general terms. Examples of existing implementations of the raster-to-vector process are also described and evaluated. Part two will outline and compare ...

Geobrowsing: creative thinking and knowledge discovery using geographic visualization

In the modern computing context, the map is no longer just a final product. Maps are now being used in a fundamentally different way - as a self-directed tool for deriving the desired information from geographic data. This, along with developments in GIScience and computer graphics, have led to the new field of geographic visualization. A central issue is how to design visualization capabilities that, as a process, facilitate creative thinking for discovering previously new information from large databases. The authors propose the term geobrowsing to designate this process. A number of interrelated ways that visualization can be used to spark the imagination in order to derive new insights are discussed and a brief example provided. Based upon the cognitive literature, specific properties of a visual image that promote discovery and insight are discussed. These are known as preinventive properties, and include; novelty, incongruence, abstraction, and ambiguity. All of these properties, either individually or in combination, tend to produce features that are unanticipated by the viewer, and often not explicitly created or anticipated by the person generating the visual display. While traditional (i.e. non-computer generated) images can also possess these properties, as shown in the historical examples in this discussion, it is the capability of the viewer to directly and quickly manipulate these properties that provides the real power of geobrowsing for uncovering new insights.

Opening the black box: interactive hierarchical clustering for multivariate spatial patterns

Clustering is one of the most important tasks for geographic knowledge discovery. However, existing clustering methods have two severe drawbacks for this purpose. First, spatial clustering methods have so far been mainly focused on searching for patterns within the spatial dimensions (usually 2D or 3D space), while more general-purpose high-dimensional (multivariate) clustering methods have very limited power in recognizing spatial patterns that involve neighbors. Secondly, existing clustering methods tend to be closed and are not geared toward allowing the interaction needed to effectively support a human-led exploratory analysis. The contribution of the research includes three parts. (1) Develop an effective and efficient hierarchical spatial clustering method, which can generate a 1-D spatial cluster ordering that preserves all the hierarchical clusters. (2) Develop a density- and grid-based hierarchical subspace clustering method to effectively identify high-dimensional clusters. The spatial cluster ordering is then integrated with this subspace clustering method to effectively search multivariate spatial patterns. (3) The above two methods are implemented in a fully open and interactive manner and supported by various visualization techniques. This opens up the black box of the clustering process for easy understanding, steering, focusing and interpretation. At the end a working demo with US census data is presented.

Ambient particulate air pollution and ectopy - The environmental epidemiology of arrhythmogenesis in women's health initiative study, 1999-2004

The relationships between ambient PM2.5 and PM10 and arrhythmia and the effect modification by cigarette smoking were investigated. Data from U.S. Environmental Protection Agency (EPA) air quality monitors and an established national-scale, log-normal kriging method were used to spatially estimate daily mean concentrations of PM at addresses of 57,422 individuals from 59 examination sites in 24 U.S. states in 1999–2004. The acute and subacute exposures were estimated as mean, geocoded address-specific PM concentrations on the day of, 0–2 d before, and averaged over 30 d before the electrocardiogram (ECG) (Lag0; Lag1; Lag2; Lag1–30). At the time of standard 12-lead resting ECG, the mean age (SD) of participants was 67.5 (6.9) yr (84% non-Hispanic White; 6% current smoker; 15% with coronary heart disease; 5% with ectopy). After the identification of significant effect modifiers, two-stage random-effects models were used to calculate center-pooled odds ratios and 95% confidence intervals (OR, 95% CI) of arrhythmia per 10 μg/m3 increase in PM concentrations. Among current smokers, Lag0 and Lag1 PM concentrations were significantly associated ventricular ectopy (VE)—the OR (95% CI) for VE among current smokers was 2 (1.32–3.3) and 1.32 (1.07–1.65) at Lag1 PM2.5 and PM10, respectively. The interactions between current smoking and acute exposures (Lag0; Lag1; Lag2) were significant in relationship to VE. Acute exposures were not significantly associated with supraventricular ectopy (SVE), or with VE among nonsmokers. Subacute (Lag1–30) exposures were not significantly associated with arrhythmia. Acute PM2.5 and PM10 exposure is directly associated with the odds of VE among smokers, suggesting that they are more vulnerable to the arrhythmogenic effects of PM.

Visual Analysis of Historic Hotel Visitation Patterns

Understanding the space and time characteristics of human interaction in complex social networks is a critical component of visual tools for intelligence analysis, consumer behavior analysis, and human geography. Visual identification and comparison of patterns of recurring events is an essential feature of such tools. In this paper, we describe a tool for exploring hotel visitation patterns in and around Rebersburg, Pennsylvania from 1898-1900. The tool uses a wrapping spreadsheet technique, called reruns, to display cyclic patterns of geographic events in multiple overlapping natural and artificial calendars. Implemented as an improvise visualization, the tool is in active development through a iterative process of data collection, hypothesis, design, discovery, and evaluation in close collaboration with historical geographers. Several discoveries have inspired ongoing data collection and plans to expand exploration to include historic weather records and railroad schedules. Distributed online evaluations of usability and usefulness have resulted in numerous feature and design recommendations