Geog 458: Map Sources and ErrorsJanuary 27, 2006Midterm ReviewIsn’t this course for arguing the importance of good spatial data? Why do we have to dig into errors in spatial data? Why do we need high-quality spatial data anyway?Many overriding concerns of today are spatial in nature (e.g. climate change, environmental protection, homeland security, disaster management). Thus obtaining relevant and accurate spatial information is essential to addressing these issues. The world is complex. Consensus has reached how such complex issues can be better examined. It gets increasingly relevant to examine issues from multidisciplinary view. Geospatial data (i.e. georeferenced data of different themes) can help us see how differentthemes are related. Geospatial data provides the framework in which different themes canbe integrated. Geographic framework can serve as glue for integrating views. High-quality data precedes making the right decision. How have all important decisions been justified? Aren’t they reliant on some kind of analysis? Prior to addressing the accuracy of analysis, shouldn’t we address whether data is relevant and accurate enough? Garbage in garbage out. This course leads you to the journey to devising the best practice for evaluating the fitness of data for given use. Please do not miss the big picture – wherewe are: we are trying to bridge rather weak (in the sense that it’s been largely ignored) link between data and its use where data quality plays a significant role.In technology-driven world, there are open opportunities for enhancing our knowledge ofthe world. Spatial data is collected, manipulated, and analyzed differently from yesterday.It is well said that we live in data-rich and high-performance computing environments. Such things can be well examined all along geospatial activities from data collection to data analysis. For example, elevation data is collected by digitizing paper map, differential GPS receivers, and enhanced remote sensing technology (such as LiDAR). Better data helps validate and revise our process models that have formed the way to understand the world. Good data provides a unique opportunity for us to grasp the complexity of the world indeed.Geographic representation: its building blockSome geographic phenomenon is well placed in discrete object while other phenomenon is well placed in continuous field. It requires different measurement scheme. For example, continuous field provides challenge for measuring space as its values vary in a continuous manner. It is necessary to devise sampling scheme for measuring variations invalue for continuous field. That’s why it’s common to store information in tessellation format. On the other hand, the spatial dimension of discrete object can be measured ratherprecisely. For that reason, it is common that dimensionality (point, line, polygon) is well 1associated with discrete object rather than continuous field. It is also important to know that human conceptualization of geography is not only domain-specific (such as continuous vs. discrete), but also task-driven. For example, Tornado can be better (more usefully) seen as a path (or line object) than continuous fields (accumulated air mass) if you’re interested in its direction. Scale can complicate the issue of geographic representation. Haven’t you ever thought about how come complex reality in a geographic scale is reduced to table-top object (like something in your table or lines in ArcGIS)? It’s the scale: table-top object is conceivable while geographic phenomena and things are not much so. To make it conceivable, we had to transform the reality into objects to be manipulated. This is where we have to think about the scale-dependency of geographic features because it has been transformed into scale-free constructs in essence. In other words, to represent geography correctly, we should go back where it exists, that is in which scale. If we miss the scale, we miss the essence of its reality. Multiple representation can be understood as technical implementation of associating scale with geographic features in GIS context. Multiple representation is an important mechanism for better geographic representation (or data modeling). (As a side, more uncertainty inherent in geographic phenomenon may be because it is beyond our scope of conception – go back to unit of analysis in the Longley book Uncertainty chapter for more).Data model, data structure, and data formatHow the geographic reality is put into the computer can be viewed from varying degree of abstraction. Data model refers to the representation of reality in the form that can be understood by humans. Humans have devised many convenient concepts that can be usedto represent geographic things. Relevant concepts among what we have achieved so far inthe context of geographic representation include Euclidean geometry, Cartesian coordinate, graph theory, not to mention number. Geographic objects portrayed on maps somewhat emulates what we perceive as point, line, and area as studied in Euclidean geometry. Even though the earth is round, there have been needs for portraying them in flat sheets of maps so that we can carry them along. Now the precise location of geographic features portrayed in flat sheet (2-dimension) can be identified by the intersection between x-value and y-value. Human concepts such as Cartesian coordinate explain the popularity of planar coordinate system. In a sense, data model is nothing morethan fitting the reality into “existing” human concepts. Digital technology has driven needs for putting (human conceptualization of) reality into the computer. Data structure can be seen as the digital representation of the world that canbe understood by computers. For example, lines are stored as a set of points where points have x, y coordinates. Unlike other kinds of information, geographic information should necessarily be represented by spatial attribute as well as non-spatial attributes. Storing non-spatial attribute (number, name, and so on) is conveniently stored in table format. However, graphic elements of spatial data (its shape, direction, size, density) are not adequately stored in table. Most of proprietary GIS over the last twenty years have stored graphic elements in a file (whether binary file or ASCII file) separate from attribute stored in a table (relational database). Continuous fields have been mainly stored in 2gridded format where grid
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