paper).
Data collection can come in a variety of forms. It may involve going into the field for face-to-face contact with the people in your study or examining in detail the secondary data. This can involve distributing and collecting surveys or traveling to a faraway location to conduct a case study. Data collection is part of the research process that motivated many of us to go into natural or social sciences in the first place, and the part we have always found to be fun.
One should begin the data collection process with a good understanding of (1) what type of data are necessary for testing the hypothesis, (2) an idea of where to find this information (agencies, corporations, universities, etc.), and (3) clearly identified geographic components that best fit within the study. In this technological age, and given security concerns, some organizations and agencies are somewhat guarded about sharing detailed geographic information. However, a wealth of free, preexisting geographic information is available from a variety of data providers, libraries, government agencies, Internet resources, and others. Where you go looking for information depends on which geographic features you are looking for. (See chapter 5 for a more detailed discussion of measurement and GIS.)
Ground truth (verify) the data
Because a GIS involves technology that can use data acquired without ever leaving your office, it is theoretically possible to complete an entire analysis without ever leaving your desk. However, it would be foolish to believe such an analysis would be without flaws (especially considering the variable quality, scale, projections, data formats, etc., we discuss throughout this book). Even if all of your data can be acquired without fieldwork, it is a good idea to ground truth at key points in the process. Whenever you use GIS, you want to make sure that you ground truth, not only to ensure that the data you are using are appropriate to your question but also to validate that the results obtained in the GIS match what you find in the field. (Note that ground truthing of results, by necessity, comes after the analysis described in step 9, “analyze the data,” but we discuss it here because the issues are similar for both input data and results.)
In most cases, ground truthing means actually traveling to the place where the study is located to get a visual on the data or results. In situations where physically visiting the site may be difficult, alternative sources might be used to cross-validate your data against a second, reliable source. For example, if you are studying urban development patterns, you might be able to use current aerial photography or satellite images to confirm that a particular location has or has not been developed. If you can go to the field, you would want to take along a map or maps that represent data you are going to use in your GIS analysis and make sure your maps are both valid and accurate. Of course, you cannot check everything; but simply spot-checking or sampling from the map can go a long way toward ensuring that you start with good data.
In our pollution example discussed earlier in the chapter, ground truthing could help to answer questions about the form of the pollution and where it travels. For example, observing a smoke plume from a factory may show that a prevailing wind carries the smoke in a particular direction most of the time. Or a drive through each census block could tell you if the residents are evenly distributed within the polygon or if they are clustered in specific portions.
Finally, ground truthing is essential at the conclusion of an analysis when you are evaluating the results. Most GIS-based studies will pinpoint certain locations as having met a set of criteria. Again, spot-checking in the field can go a long way toward telling you if the results look correct and if they make sense. If your study is large, you may want to spot-check a number of sites to assess the overall accuracy of the analysis (e.g., if nine of ten results are correct when checked on the ground, it would be reasonable to estimate accuracy at 90 percent). The topic of accuracy assessment in spatial data analysis could fill an entire book, so we only mention it here and encourage those who are interested to pursue this topic independently.
Analyze the data
In this step in the research process, you must again refer back to your primary research question and conceptual framework. The form of the analysis you select will in part be determined by the type of data you have collected and prepared in the GIS. Some analysis tools work exclusively on vector data and others exclusively on raster data. Of course, you can accomplish many operations with either. Furthermore, if you collected quantitative data, chances are you will use some form of statistical analysis. Many of the common, descriptive statistics are available within the GIS software. However, it is also common to enlist other statistical programs for some portions of the analysis. This is accomplished by extracting key information from the GIS using the spatial tools and exporting the raw numbers into a program such as SPSS, S-PLUS, R, or Excel to further analyze the information before returning it to the GIS to make maps of the results.
If you are working with qualitative data, you may require use of a data program designed specifically for qualitative analysis, such as HyperRESEARCH by Researchware, NVivo, or ATLAS.ti. In identifying your key variables, you can decide which types of contextual or geographic information might aid in studying the relationships between these variables. You can then analyze the geographic information you have collected as part of your study using a variety of methods.
Regardless of the type of data, the most important thing to keep in mind at this stage is to let your project dictate the analysis rather than let the software drive the analysis simply because the software contains a menu option or button. Retuning to your conceptual framework will be essential at this stage because the framework is your guide in the analysis. Chapter 11 provides more detailed discussion of completing the data analysis using GIS.
Share the results
For applied research, this is perhaps the most important step in the entire research process. You can share results through two basic avenues and for two different audiences: laypeople and members of the scientific community. If you are interested in sharing your findings with laypeople, you should package the results in a manner that is easily understood without a lot of scientific jargon.
We have found that producing a report that discusses the methods and highlights the main findings of a study to be most effective. You might also consider developing a visual presentation, which could be given in a variety of settings that highlight both your research methods and your main findings. If you plan to share your results with members of the scientific community, you will most likely want to write a paper for submission to a scientific journal and possibly also present your results at a professional conference.
GIS can be very useful in sharing your results because it allows for presenting data in a visual format. Most commonly, the visual output is a map, but GIS can also provide output as charts or graphs or, if done as an interactive presentation, as animated or interactive maps. Not surprisingly, any of these visualizations can have wide-ranging applicability in a variety of contexts—“a picture is worth a thousand words.”
Grounded theory: GIS using an inductive approach
As a researcher, you also have the option of employing an inductive model in your research design. This type of approach begins with a different series of steps than those traditionally used for a deductive approach. An inductive approach begins with the data and proceeds to gleaning an understanding of themes and patterns. From this information, theory is then generated.
Grounded theory is an inductive research approach characterized by its sequencing: data collection followed by theory generation. It is called “grounded” because of its strong connection to the reality that the data represent. This inductive research approach is qualitative in nature. Grounded theory is an appropriate research method for assessing case studies, transcripts, oral histories, and archival data.
Glaser