Читать книгу GIS Research Methods - Steven J. Steinberg - Страница 74

If you decide that you do want to employ a GIS in your project, you need to accomplish two nuts-and-bolts tasks right away: (1) identify the geographic region and (2) develop a data dictionary (figure 2.12).

First, you should identify the geographic region (or spatial extent) and important features of your study, including the categories, geographic features, and physical environmental features that you want to consider as part of the study. Essentially, this means drawing a study site boundary, along with having a good understanding of your topic and determining which geographic features you want to measure.

Figure 2.12 An example of a section of a data dictionary for the US Census (the Census Bureau website uses the more public-friendly term glossary). Concepts included in the dataset, along with the associated terminology, definitions, and units of measurement, are described in detail. This information is critical to researchers when assessing a dataset to determine its relevance to their research needs and to interpret results obtained from its analysis. US Census.

Developing a data dictionary means developing a set of definitions and criteria for what constitutes your particular categories. In other words, you need to develop descriptions that clearly define each of the attributes that you would be recording in association with your variables. (We explore research design and considerations for incorporating GIS further in chapter 3.)

For example, you might be doing a study of urban communities, but how exactly will you define these? You might use a definition based on population density, the percentage of the landscape covered by concrete, or the number of roads per square mile. Developing and documenting clear definitions for each variable and attribute to be included in your study is an essential step before going forward with data collection and analysis. Doing this in advance will help to ensure that everyone involved in the study understands the data to be collected and that each item will be mutually exclusive, thus avoiding confusion later in the process.