An assessment of NASA master directory/catalog interoperability for interdisciplinary study of the global water cycle

The most important issue facing science is understanding global change; the causes, the processes involved and their consequences. The key to success in this massive Earth science research effort will depend on efficient identification and access to the most data available across the atmospheric, oceanographic, and land sciences. Current mechanisms used by earth scientists for accessing these data fall far short of meeting this need. Scientists must as a result frequently rely on a priori knowledge and informal person to person networks to find relevant data. The Master Directory/Catalog Interoperability Program (MC/CI) undertaken by NASA is an important step in overcoming these problems. The stated goal of the MD project is to enable researchers to efficiently identify, locate, and obtain access to space and Earth science data

Advanced techniques for the storage and use of very large, heterogeneous spatial databases

Progress is reported in the development of a prototype knowledge-based geographic information system. The overall purpose of this project is to investigate and demonstrate the use of advanced methods in order to greatly improve the capabilities of geographic information system technology in the handling of large, multi-source collections of spatial data in an efficient manner, and to make these collections of data more accessible and usable for the Earth scientist

Advanced techniques for the storage and use of very large, heterogeneous spatial databases. The representation of geographic knowledge: Toward a universal framework

A new approach to building geographic data models that is based on the fundamental characteristics of the data is presented. An overall theoretical framework for representing geographic data is proposed. An example of utilizing this framework in a Geographic Information System (GIS) context by combining artificial intelligence techniques with recent developments in spatial data processing techniques is given. Elements of data representation discussed include hierarchical structure, separation of locational and conceptual views, and the ability to store knowledge at variable levels of completeness and precision

An Ensemble Approach to Space-Time Interpolation

There has been much excitement and activity in recent years related to the relatively sudden availability of earth-related data and the computational capabilities to visualize and analyze these data. Despite the increased ability to collect and store large volumes of data, few individual data sets exist that provide both the requisite spatial and temporal observational frequency for many urban and/or regional-scale applications. The motivating view of this paper, however, is that the relative temporal richness of one data set can be leveraged with the relative spatial richness of another to fill in the gaps. We also note that any single interpolation technique has advantages and disadvantages. Particularly when focusing on the spatial or on the temporal dimension, this means that different techniques are more appropriate than others for specific types of data. We therefore propose a space- time interpolation approach whereby two interpolation methods – one for the temporal and one for the spatial dimension – are used in tandem in order to maximize the quality of the result. We call our ensemble approach the Space-Time Interpolation Environment (STIE). The primary steps within this environment include a spatial interpolator, a time-step processor, and a calibration step that enforces phenomenon-related behavioral constraints. The specific interpolation techniques used within the STIE can be chosen on the basis of suitability for the data and application at hand. In the current paper, we describe STIE conceptually including the structure of the data inputs and output, details of the primary steps (the STIE processors), and the mechanism for coordinating the data and the 1 processors. We then describe a case study focusing on urban land cover in Phoenix Arizona. Our empirical results show that STIE was effective as a space-time interpolator for urban land cover with an accuracy of 85.2% and furthermore that it was more effective than a single technique.

An Ensemble Approach to Space-Time Interpolation

There has been much excitement and activity in recent years related to the relatively sudden availability of earth-related data and the computational capabilities to visualize and analyze these data. Despite the increased ability to collect and store large volumes of data, few individual data sets exist that provide both the requisite spatial and temporal observational frequency for many urban and/or regional-scale applications. The motivating view of this paper, however, is that the relative temporal richness of one data set can be leveraged with the relative spatial richness of another to fill in the gaps. We also note that any single interpolation technique has advantages and disadvantages. Particularly when focusing on the spatial or on the temporal dimension, this means that different techniques are more appropriate than others for specific types of data. We therefore propose a space- time interpolation approach whereby two interpolation methods – one for the temporal and one for the spatial dimension – are used in tandem in order to maximize the quality of the result. We call our ensemble approach the Space-Time Interpolation Environment (STIE). The primary steps within this environment include a spatial interpolator, a time-step processor, and a calibration step that enforces phenomenon-related behavioral constraints. The specific interpolation techniques used within the STIE can be chosen on the basis of suitability for the data and application at hand. In the current paper, we describe STIE conceptually including the structure of the data inputs and output, details of the primary steps (the STIE processors), and the mechanism for coordinating the data and the processors. We then describe a case study focusing on urban land cover in Phoenix, Arizona. Our empirical results show that STIE was effective as a space-time interpolator for urban land cover with an accuracy of 85.2% and furthermore that it was more effective than a single technique.

National Kriging Exposure Estimation: Liao et al. Respond

Szpiro et al. suggest that our findings Liao et al. (2006) do not adequately support using national-scale, log-normal ordinary kriging to estimate daily mean concentrations of PM10 (particulate matter with aerodynamic diameter ≤ 10 µm) at unmonitored locations in the contiguous United States. They posit that the absence of the cross-validation SE prevents evaluating the validity of kriging estimation, as we implemented in this context, and the comparability of both regionalversus national-scale kriging and manually modified versus semiautomated, defaultcalculated semivariograms

Searching correlated objects in a long sequence

National Science Foundatio

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

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 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 US states in 1999-2004. The acute and subacute exposures were estimated as mean, geocoded address-specific PM concentrations on the day of, 0-2 days before, and averaged over 30 days before the 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) years (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 non-smokers. 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