Metadata education suggestions and materials for:
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Projections and Scale |
Learning Material | Preparatory topics | Complementary topics | Vocabulary
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General
The management, analysis, and reporting of all GIS data requires that it be carefully referenced by position on the Earth's surface. Many different coordinate systems are used to record location. Some systems such as latitude and longitude are global systems that can be used to record position anywhere on the Earth's surface. Other systems are regional or local in coverage and intended to provide accurate positioning over smaller areas. The system of locational reference used in a particular GIS project will depend on the purpose of the project and how the positions of the source data have been recorded.
It is often the case that the data needed for a particular GIS project will be recorded in two or more of these reference systems. Combining the information of these sources will require that positions be carefully converted, transformed, or projected from one system to another. This is a reason why GIS practitioners must usually be familiar with a variety of commonly used coordinate systems.
When utilizing data from different sources, and in potentially different reference systems, metadata is the key to determining which reference system the data is in (see the example of conflicting shorelines provided by NOAA's Coastal Services Center). It also provides information on the parameters of the reference system, which are needed to accurately convert the data to another reference system. Some GIS software systems can automatically read what coordinate system the data is in and display this information, but the information may be incomplete (for instance, what geodetic datum is used by the coordinate system?) If the GIS software you are using cannot read/display this information, or if the information is not recorded in the metadata, there is a great risk associated in using this data. Even if two data sets "look" like they are in the same reference system (in other words, they overlay each other), you could still be introducing error into analysis based on overlay of the data sets if you don't have metadata about the specific parameters or datum of the data's reference system. If only one parameter is slightly off, such as false northing or easting, or the central meridian differs, or one data set is in the 1927 North American datum and the other is in the 1983 North American datum, there could be an offset between the two data sets that could easily be overlooked, unless you are zoomed in close enough on the common borders to detect the difference.
Education materials for coordinate systems and projections:
Position on the Earth: Introduction to coordinate systems from the NCGIA Core Curriculum
Overview of Coordinate systems from the Geographer's Craft
Overview of Map Projections from the Geographer's Craft
GIS depend upon the abstraction and classification of real-world phenomena. Data creators determine what amount of information is used and how it is classified into appropriate categories. The amount of information used, in spatial terms, relates to the scale and the resolution of the data.
Scale is the ratio of distances represented on a map or photograph to their true lengths on the earth's surface. A scale-bar on a map may be represented in terms of specific units: meters, kilometers, miles. Scale may also be represented without specific units. For instance, a scale of 1:24,000 indicates that one unit on the map corresponds to 24,000 of the same units (whether inches or centimeters) on the ground.
In creating maps, deciding on scale is a matter of deciding what level of detail is appropriate to be mapped. Small-scale maps (1:1,000,000) can generally cover a large area, but are not very detailed. Large-scale maps (1:24,000) cover smaller areas at greater detail. A city on a 1:1,000,000 scale map will probably be represented as a point or a dot. The same city on a 1:24,000 scale map may be represented as an area having shape or even greater detail such as a street network. Within a GIS, scale takes on two dimensions: display scale, and source scale. Display scale changes as the user "zooms in" or "zooms out". But the source scale, the scale that the data was originally mapped at, does not change. No matter how close you zoom into a city digitized from a 1:1,000,000 scale map, that city will still be a point, even when the display scale is 1:24,000 or larger.
Resolution is a description for how much information is contained on a map. Maps with a larger or denser amount of information on them have a higher level of resolution. Resolution is sometimes also referred to as a map's "minimum mapping unit". Technically, resolution is the content of the geometric domain of a map divided by the number of observations represented on the map, normalized by the spatial dimension (domain being the area covered by the observations). For instance, if the area of the United States is approximately 6 million square kilometers, and there are 50 states (observations), then the mean resolution element of a map portraying the states is 346 km. In this example, the smallest state represented on the map is the "minimum mapping unit". A map of all the 3141 counties in the U.S. would have a much higher resolution, approximately 43 km, using the same formula. In this case, the smallest county represented on the map is the "minimum mapping unit".
Resolution is also applies to the density of sampling used to collect the data. For instance, samples taken every 1 km will miss any variation at resolutions less than 1 km. If data is needed for an application that requires knowing locations within meters, rather than kilometers, then 1 km resolution data is inappropriate for this type of application.
Metadata about datasets' source scale or resolution is important in evaluation the fitness-for-use of data for a particular application. This is especially true when using multiple data sets for a GIS application, it is important to know the source scale or resolution of the data sets. For instance, it could very well be inappropriate to compare very accurate GPS locations (+/- 10 meters) of breeding bird nests to a forest cover data set at a minimum mapping unit of 100 hectares for the purpose of correlating species' nests to vegetation types. A vegetation map with a resolution of 30 meters would be much more appropriate. On the other hand, accurate GPS locations of vegetation samples may overlayed with the 100 hectare resolution vegetation types to determine a percentage of accuracy of the vegetation map, useful metadata to help determine this data set's fitness-for-use for other applications.
The University Consortium for Geographic Information Science has identified scale as one of their ten major research priorities. Research priorities involving scale include using scale information in judging the fitness of data for a particular use and how scale and change in scale affect information content, analysis, and conclusions about patterns and processes.
In the metadata content standard, there is no single element that contains information about the data set's scale. This is because a single data set may actually be composed of several different sources, each with their own respective scale. There are several places in the metadata content standard, under the Data Quality Section, that should be checked to determine the data set's fitness-for-use as far as scale and resolution. Attribute Accuracy and Horizontal Positional Accuracy are two important elements that can describe the scale/resolution of the data set (either directly or indirectly). Under the Lineage subsection, for each contributing source to the data set pertinent information must be included such as the source's title, media (paper, digital), and source scale. Finally, it is also important to check the Process Steps in the Data Quality section. In well-documented metadata, the Process Steps will describe not only how the data set was created (e.g. from paper maps), but also provide information or links to other documents containing information on how the contributing sources were created, as well. For a more detailed look at these issues please refer to the unit on Data Quality.
Exercises used in GIS classes to illustrate projections usually include one variation of these two themes:
How to enhance exercises to emphasize the importance of metadata:
Here are two examples of metadata taken from the Wyoming Natural Resources Data Clearinghouse:
In the metadata content standard, this reference information is stored under the Spatial Reference Information section.
In the "Question/Answer" format of standardized metadata, this information is found under How does the data set represent geographic features?
Examples of the Spatial Reference Information section for data sets in:
Question: What does Abscissa and Ordinate resolution refer to?
Question: Why does the State Plane coordinate system have Lambert Conformal Conic parameters listed in it? Isn't this a different projection?
In the metadata content standard, this reference information is stored under the Data Quality Information section, under the sub-section Lineage, as Source-Scale Denominator.
In the "Question/Answer" format of standardized metadata, this information is found under Where did the data come from? in this format.
Please review the previous section on How to read metadata for coordinate systems/projections and scale, and then proceed to the topic Implementing Metadata for a discussion of methods and tools for creating metadata.
