5/6/2023 0 Comments The eyes of ara map![]() ![]() An example of an inconsistency would be to record the dominant species of one forest as Pinus taeda, and the dominant species of another forest in the same map as P. This may require adherence to data collection and processing standards by all involved in the map development process ( Fürst, 2002). Within a single map, one might expect that information assigned to landscape features (the attributes of features) should be presented in a consistent manner. Logical consistency of mapped information refers to an adherence of the data to conceptual, logical, or physical data structure rules. Map consistency problems can be viewed from two perspectives: consistencies within a single map and consistencies across similar maps. Department of Agriculture, Natural Resources Conservation Service (2017). Soil management units of an area in Antrim County, Michigan. In addition, natural features may not have clearly defined land area boundaries some exceptions perhaps being the intersection of water bodies and land, or certain rock formations (e.g., cliffs).įigure 8.8. Each field in the study of science may classify nature in a different manner, and these differences in perception can cause ambiguity in map interpretation ( Schaffer et al., 2016). Vagueness or ambiguity among landscape features being represented in a map can lead to error when the boundaries of these features are fuzzy. 8.8 likely contain some error because a change from one soil type to another is usually not as evident as the location of a centerline of a road or the edge of an orchard. The shape of the polygons representing soil management units (MUTYPE) in Fig. Poorly defined features might be considered those with fuzzy boundaries, such as the transition between soil types or natural forests. The error might be associated with incorrect georeferencing or drawing of the feature, or with incorrect attribution (assigning the feature the wrong label or class). 8.7 may contain errors even though they are very clearly identifiable on the aerial image. The polygon and line representations of orchards and roads described in Fig. Well-defined features might be thought of as those with sharp edges, such as roads, buildings, or other clearly defined features. Thus, we might characterize map error in this sense as errors associated with well-defined features, and errors associated with poorly defined features ( Fisher, 1999). However, map error may propagate through the use of poorly defined landscape features, those that were initially vague and ambiguous in character and quality before a map was developed. Generalization and simplification of geographic features and their attributes (as features get combined) often are applied to features we assumed were well-defined before these processes were employed. Given the slight changes in direction of the road found here, about how far apart are the vertices that describe the road, on average? Assuming the road was generalized, what issues may arise if you were to use this data on a map? ![]() Within the layers sidebar window, enable the viewing of roads. ![]() As a subtle example of this, using Google Earth Pro, navigate to US Forest Service road FS 323 on the De Soto National Forest in Mississippi (31.078127 degrees north latitude, 88.932214 degrees west longitude). For example, line simplification (reduction in vertices that describe lines) can result in lines that change in direction more abruptly than you might expect in a carefully created geographic database. Prior to the delivery of geographic information through digital means, an organization may have already generalized landscape data to ensure that the computer processing time and effort are reasonable for users of their services. With respect to linear features or edges of closed areas (polygons), map errors can arise from error propagated by the generalization and positioning of the vertices that represent changes in direction along these lines ( Liu and Shi, 2016), as we described in Chapter 7, Map Development and Generalization. In small-scale printed maps (e.g., 1:100,000), as compared to large-scale printed maps (e.g., 1:10,000), one may find that roads are drawn smoother (a simpler shape with fewer curves) and that fewer landscape features are present given the limited printable or displayable space. With respect to a digital map, one might envision a reduction in content as they zoom out (increase the eye altitude) of a landscape. It might seem obvious that reductions in content and resolution can occur as map scale decreases (e.g., from 1:24,000 to 1:100,000). Further, map error may be propagated by map generalization processes as well. Maps can easily be developed using a scale that is inappropriate for the purpose of the map. ![]()
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