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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-temporal data model is proposed that is based on rime as its organizational basis. and is thereby intended to facilitate analysis of temporal relationships and patterns of change through time. This model is named the Event-based Spatio Temporal Data Model (ESTDM). It is shown that temporally-based queries relating to locations can be implemented in an efficient and conceptually straightforward manner using ESTDM by describing algorithms for three fundamental temporally-based retrieval tasks based on this model: (I) retrieving location(s) that changed to a given value at a given time, (2) retrieving location(s) that changed to a given value over a given temporal interval, and (3) calculation of the total area that has changed to a given value over a given temporal interval. An empirical comparison of the space efficiency of ESTDM and compressed and uncompressed forms of the 'snapshot' model is also given, showing that ESTDM is also a compact representation of spatio-temporal information.

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.

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