Its not always possible to get all the data you want for your project off-the-shelf. Archives and Old Reports are one of a number of incredibly important GIS data sources. In fact, many GIS projects would not be possible without the aid of old archives and reports.
I’ve used old reports as GIS data sources to help with projects about Planning Conflict, Land Use Change over time, Demographic Changes over time, Soils, and Archaeology. Archives that I’ve used have dated as far back as to the late 1800s, but more commonly have been from the 1940s thru 1970s.
The first thing you have to do when you look at these maps is to read the data descriptions for the reports. That will give a clue as to how useful the data within them can be, and to some of the limitations that might apply. Here are some examples of problems you may experience with GIS data sources such as groundwater, soil and demographic archives.
A groundwater archive might contain many data issues you should be aware of, particularly if it has been compiled over a long period of time. For example…
- Seasonality of Data Collection: Errors are introduced when data are collected at different times. Data collected in both winter and summer, or during wet years and dry years can be expected to be inconsistent in terms of groundwater depth and in terms of chemical and pathogen concentrations.
- Data collection technique: Datums (reference position coordinates and height) often vary from project to project. You need to understand whether the same datum has been used for all the data collected and if not, whether or not that’s important, or how to bring all the data points into a common datum.
A soil archive might contain a different set of issues. I must begin by saying that, as a general rule, I have found archival soil maps to be a very useful basis for soil reinterpreting studies. Although the boundaries in old studies are spatially inaccurate, they are generally the correct shape, and the better quality spatial and descriptive data we have at hand these days generally leads to vast improvements in quality at a fraction of the cost of start-from-scratch interpretation.
- Scale: Never forget the scale of an interpreted map relates to the scale of collection more than it does to the scale of display. By this statement I mean you should think of a 2km2 soil map that takes 10 days to interpret versus the same 2km2 soil map that takes the same scientist 1 day to interpret. Which map will be more reliable?
- Technology: What technologies were used to create the map? Very old maps may not have been produced using air photography. Even not-so-old maps might not have been produced with good ground control and so might be spatially displaced.
- Purpose: Why was the soil map produced in the first place? Does its purpose align with yours? A soil map produced for engineering purposes cannot be directly used for agricultural work.
A demographic archive will contain issues relating to boundary compatibility and data definition.
- Boundaries: Census boundaries change through time. This is often in response to population shifts that result from events such as commercial buildings becoming high-rise apartment buildings or farmland becoming urban land.
- Data definition: When mapping demographic change its important to understand whether or not the theme that you’re comparing has a consistent definition through time. For example, in one census period being unemployed might be defined as working less than 10 hours per week and in another unemployed might be defined as working less than 5 hours per week.
Clearly there can be many issues relating to GIS data sources. The examples and issues I mention above should not prevent you from using GIS datasets, but rather they are issues you should keep in mind when using any GIS dataset.
I touch on these issues in my GIS for Beginners #1: QGIS Orientation course and deal with them in depth within my GIS for Beginners #2: Learn Digitizing using QGIS course. CLICK HERE FOR DISCOUNT COUPONS
And…you might also be interested in this blog post about GIS data sources.
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