PubMed · 15460222
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Evelyn J S Hovenga, Walter Sermeus. 2002. Data analysis methods.. https://pubmed.ncbi.nlm.nih.gov/15460222/
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The brackish groundwater production fields in Kuwait have been drilled intensively during the past three decades. However, poor spatial distribution of some of the measured parameters and limited measurements of others are forcing extended extrapolation during numerical simulations of management schemes. On the other hand, operational, time and budget constraints allow only limited additional field-generated data, raising the question of the significance of their impact on numerical modeling results. This study evaluates this significance by using nearest neighbor statistics and a synthesized data set to assess the impacts of a proposed field investigation on the spatial distribution and kriged values of transmissivity and storage coefficient arrays of the brackish groundwater production fields. A scale for the significance of the impacts was prepared based on the sensitivity of the aquifer system towards the studied properties. The transmissivity data array indicated a 25% reduction in the maximum distance and an 8% increase in the interquartile of the nearest neighbor analysis. The storage coefficient data arrays indicated the extension of the covered area to reach the southern boundary of the study area, which was reflected in more than doubling the maximum distance between the nearest neighbors. For the kriged values of the transmissivity, about 35% of the study area was significantly changed due to the addition of the new data points, while only 10% of the study area was significantly changed for the storage coefficient values. Overall, while the spatial distribution indicators were not conclusive, the significance of the new data was clear in the changes in the kriged values of the aquifer properties. Additionally, this approach highlights the aquifer test locations where the generated data had no impact on the kriged values, which may be utilized for optimizing the allocation and number of the aquifer tests.
The concept of a sampling scale triplet of spacing, extent and support is used to define the spatial dimensions of a monitoring network or a field study. The spacing is the average distance between samples, the extent is the size of the domain sampled and the support is the averaging area of one sample. The aim of this paper is to examine what is the bias and the random error (uncertainty) introduced by the sampling scale triplet into estimates of the mean, the spatial variance and the integral scale of a variable in a landscape. The integral scale is a measure of the average distance over which a variable is correlated in space. A large number of two dimensional random fields are generated from which hypothetical samples, conforming to a certain sampling scale triplet, are drawn which in turn are used to estimate the sample mean, spatial variance and integral scale. The results indicate that the biases can be up to two orders of magnitude. The bias of the integral scale is positively related to the magnitude of any of the components of the scale triplet while the bias of the spatial variance is different for different components of the scale triplet. All sampling scale effects are relative to the underlying correlation length of the variable of interest which is closely related to the integral scale. The integral scale can hence be used for sampling design and data interpretation. Suggestions are given on how to adjust a monitoring network to the scales of the variables of interest and how to interpret sampling scale effects in environmental data.
AIMS: To explore issues in the collection and analysis of ethnicity data in primary care, and discuss the implications of this for health services research. METHODS: Data routinely collected by 25 Wellington Independent Practice Association (WIPA) general practices in 2001. RESULTS: Practices varied in the level of ethnicity data coverage achieved, ranging from less than 10% to greater than 90% of patients. Combining practice data with National Health Index (NHI) ethnicity data increased coverage to at least 70% for registered patients at 23 of 25 practices. There were differences between the data collected in general practices and ethnicity recorded on NHI. However, with the exception of the 'Other' category, this disagreement was not systematic. Practices had lower proportions of patients with ethnicity recorded as 'Other' (not further specified) than NHI ethnicity records. CONCLUSIONS: The study demonstrated that it is possible to collect quality ethnicity data in general practices, although there are challenges. Merging practice-collected ethnicity data with NHI ethnicity data increases coverage. However, there is some mismatch between data sources. The findings support the need for standardised, consistent approaches to ethnicity data collection and analysis, as well as systems and policies that facilitate the collection of high quality data.