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When analyzing pharmacokinetic data, one generally employs either model fitting using nonlinear regression analysis or non-compartmental analysis techniques (NCA). The method one actually employs depends on what is required from the analysis. If the primary requirement is to determine the degree of exposure following administration of a drug (such as AUC), and perhaps the drug's associated pharmacokinetic parameters, such as clearance, elimination half-life, T (max), C (max), etc., then NCA is generally the preferred methodology to use in that it requires fewer assumptions than model-based approaches. In this chapter we cover NCA methodologies, which utilize application of the trapezoidal rule for measurements of the area under the plasma concentration-time curve. This method, which generally applies to first-order (linear) models (although it is often used to assess if a drug's pharmacokinetics are nonlinear when several dose levels are administered), has few underlying assumptions and can readily be automated.In addition, because sparse data sampling methods are often utilized in toxicokinetic (TK) studies, NCA methodology appropriate for sparse data is also discussed.

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The participatory creation of maps, above and beyond their interpretation, started in the late 1980s. At that time, development practitioners were inclined to adopt Participatory Rural Appraisal (PRA) methods such as sketch mapping (Mascarenhas et al. 1991) rather than the more complex and time consuming scale mapping. Preference was given to eliciting local knowledge and building on local dynamics to facilitate communication between insiders (e.g. villagers) and outsiders (e.g. researchers, government officials, etc.). This approach placed little emphasis on charting courses of action that would enable ordinary people to interact efficiently with policymakers (Rambaldi 2005). The situation was further compounded by state control of aerial photography, satellite imagery and large-scale topographic maps under the pretext of national security concerns.The state of affairs in mapping changed in the '90s, with the diffusion of modern spatial information technologies (including geographic information systems (GIS), global positioning systems (GPS), remote sensing image analysis software and open access to spatial data and imagery via the Internet into the industry. With the steadily decreasing cost of computer hardware and the availability of user-friendly software, spatial data that were previously controlled by government institutions became progressively more accessible 2 to, and mastered by non-governmental and community-based organisations, minority groups and sectors of society traditionally disenfranchised and excluded from spatial decision making processes (Fox et al. 2003). The new environment facilitated the integration of geo-spatial information technologies and systems (GIT&S) into community-centred initiatives. GIT practitioners and researchers around the world were able to adopt a range of GIT&S to integrate multiple realities and diverse forms of information with the objective of empowering underprivileged groups, promote social learning, support two-way communication and thereby broaden public participation across socio-economic contexts, locations and sectors. This merging of community development with geo-spatial technologies for the empowerment of less privileged communities has come to be known as Participatory Geographic Information Systems or PGIS.