Geography
Sample Location
Sample location refers to the specific geographic location where data is collected for research or analysis. It is important to carefully choose sample locations to ensure that they are representative of the larger population or area being studied. Factors such as accessibility, diversity, and variability should be considered when selecting sample locations.
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3 Key excerpts on "Sample Location"
- Nigel Walford(Author)
- 2011(Publication Date)
- Wiley(Publisher)
2 . Selecting observations for inclusion in a sample as part of a geographical or Earth scientific investigation needs to distinguish whether it is the spatial units or the phenomena that they contain that are of interest (i.e. are the relevant observations). For example, an investigation into the extent of sorting in lateral moraine material needs to focus on the characteristics of the stones, pebbles and other measurable items rather than the grid squares; and a project on the reasons for passengers and other visitors to a major rail station is concerned with individual people rather than their location in the concourse. However, location, in the sense of where something is happening, frequently forms a context for geographical and Earth scientific enquiries. Thus, the location of the lateral moraine as adjacent to the western flank of Les Bossons glacier in the European Alps and the situation of Liverpool Street station close to the City of London and as a node connecting with North Sea ports and Stansted airport provide geographical contexts in these examples.No matter how perfect the design of a particular a sample, unforeseen events can occur that lead to the data collected being less than perfect. Consider the example of a company running the bus service in a large city that decides to carry out a simple on-board survey of passengers to discover how many people use a certain route, their origins and destinations and their reason for making the journey. The survey is carefully planned to take place on a randomly selected date with all passengers boarding buses on the chosen route being given a card to complete during the course of their journey, which will be taken from them when they alight. Unfortunately, on the day of the survey one of the underground lines connecting two rail termini is closed temporarily during the morning commuter period due to trains being unexpectedly out of service. An unknown proportion of commuters who usually take the underground between the rail stations decide to travel by buses on the survey route. Unless the survey team become alert to this unanticipated inflationary factor, the estimated number of passengers normally travelling the bus route is likely to be exaggerated. This unfortunate change of conditions is not as fanciful as it might at first seem, since this situation arose when the author participated in such a survey on his normal commute through London when this text was being written.
eBook - PDFResearch Methods in Geography
A Critical Introduction
- Basil Gomez, John Paul Jones, John Paul Jones, III, Basil Gomez, John Paul Jones, Basil Gomez(Authors)
- 2010(Publication Date)
- Wiley-Blackwell(Publisher)
They have and will continue to contribute important theoretical ideas about how the world works, but to the extent that this work ultimately relies on information derived from 78 Ryan R. Jensen and J. Matthew Shumway observations, geography as a discipline has a strong empirical focus. As Gersmehl and Brown (1992: 78) suggest, the purpose of geographic observation is to “record the traits and locations of features with enough precision for valid pattern visualization and analy- sis.” One of the most intellectually challenging facets of geographic research concerns the issue of how best to represent different aspects of the world. Representation commonly involves measurement, the process of assigning a number to different objects in a popula- tion (both human and non-human objects, including spatial partitions). Measurement functions as a conduit for information and helps link the empirical and the theoretical (see Chapter 4). Often, however, the population as a whole is much too large for us to obtain information about in its entirety and must necessarily be sampled. Even censuses – catalogs of entire populations – can contain samples . During the year 2000, for example, the US Census Bureau attempted a count of every person in every housing unit; each person was asked basic information such as age and gender. Information on educational attainment and length of residence, however, was derived from a sample of one-in-six persons and housing units. Sampling thus involves selecting a small piece of reality or a small number of objects from a population for specific investigation. This chapter summarizes the methods geographers use to sample the world, and discusses some of the inherent limita- tions in sampled data. The chapter also describes secondary data and some of its limitations as well as data standards and data errors.
eBook - PDF- Jingxiong Zhang, Peter Atkinson, Michael F. Goodchild(Authors)
- 2014(Publication Date)
- CRC Press(Publisher)
A representative sample is one that is meant 37 Geospatial Measurements to provide accurate, in statistical terms, characterization of the underlying popula-tion. This is how a sampling design can contribute (Cochran 1977; Czaplewski 2003; Edwards et al. 2006; Stehman et al. 2011). For instance, spatial sampling in the con-text of forest surveys was described by Köhl et al. (2006). As mentioned previously, sampling scheme, sampling density, and sample size define the spatial coverage of samples and boil down to the set of distance and direc-tion vectors between the observations of a sample set, as described by Sk ø ien and Bl ö schl (2006). Sampling scheme refers to the spatial pattern of the sample observa-tions. Sampling density refers to the number of observations per unit area (Sk ø ien and Bl ö schl 2006). Over vast spatial extents and, indeed, the globe, the Earth’s curva-ture will have effects on areal, distance, and other measures. This will be discussed later in this subsection. Finally, sample size is the total number of observations, which should be determined on statistical grounds, but is often subject to compro-mise owing to practical constraints (e.g., time, money, and so on). Below, we will review major sampling schemes, followed by the sampling theorem as the theoretical basis for further discussion about spatial scales in measurement and the underlying data-generating processes. For survey sampling, probability theory and statistical theory are employed to guide the practice of sampling design. Thus, probability sampling schemes are preferred, by which every element or unit in the population has a chance of being selected in the sample, enabling unbiased estimates of population totals by weighting sampled elements according to their probability of being selected. Probability sam-pling includes simple random sampling, systematic sampling, stratified sampling, and cluster sampling (Köhl et al. 2006), as described below.
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