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27 Cards in this Set
- Front
- Back
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Error and analysis - why are they important? |
Error can... - invalidate the results of spatial analysis - be injected at many points in the process One of the largest sources of error starts with DATA With every introduction of a new dataset, new error possibilities are introduced |
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Accuracy definition |
Is an issue pertaining to the quality of data and the number of errors contained in a dataset or map.
Also the degree to which information on a map or in a digital database matches true or accepted values. The degree to which information on a map or digital database matches true or accepted values.
Horizontal and vertical accuracy. Attribute, conceptual, and logical accuracy. |
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Precision definition |
Refers to the granularity of measurement and exactness of description in the GIS database. The detailed measurement and exactness of description in a GIS database
Precise data, no nature how carefully measured, may be inaccurate. |
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Precision locational data |
May measure position to a fraction of a unit (like meters or inches) |
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Precise attribute information |
May specify the characteristics of features on great detail. |
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Levels of precision |
Varies greatly from project to project. Highly precise data may be costly to collect. |
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Accuracy and precision |
High precision does not indicate high accuracy. High accuracy does not imply high precision.
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Data quality |
Refers to the relative accuracy and precision of a particular GIS database. Often documented in data quality reports. |
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Error |
Both the imprecision and inaccuracies of data. |
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Types of Error |
Positional Attribute Conceptual |
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Positional error |
Applies to horizontal and vertical positions. Accuracy and precision are a function of the scale at which a map was created. Location on a map = probable location |
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Attribute accuracy |
The non-spatial data linked to location may also be inaccurate or imprecise. Inaccuracies may result from many mistakes. Can also vary greatly in precision. Precise attribute information describes phenomena in great detail. |
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Conceptual accuracy |
GIS depend upon the abstraction and classification of real- world phenomena. The user determines what amount of information is used and how it is classified into appropriate categories. (Discretization) Sometimes may use inappropriate categories or misclassify information. |
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Misclassification |
An act of wrongly assigning someone or something to a group or category. For example, classifying cities by voting behaviour would probably be an ineffective way to study fertility patterns. For example, failing to classify power lines by voltage would limit the effectiveness of a GIS designed to manage an electric utilities infrastructure |
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Sources of imprecision and error |
Many sources of error that affect the quality of a GIS dataset. Chloropleth maps are misrepresentations of reality |
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Imprecision associated with cartography |
Starts with the projection process and it's necessary distortion of some of the data - an imprecision that may continue throughout the GIS process. |
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Recognition of error |
And what level of error is tolerable and affordable must be acknowledged and accounted for by GIS users |
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Sources of error |
1. Obvious sources of errors 2. Errors resulting from natural variation or from original measurements 3. Errors arising through processing |
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Obvious sources of error |
Age of data. Areal cover. Density of observations - sufficient # of observations to perform spatial analysis Relevance - valid relationship must exist between data and the phenomena Format - formatting for transformations, storage, and processing may introduce error Accessibility - what is readily available in one place may be unobtainable elsewhere Cost - effective and reliable data is often quite expensive to obtain or convert |
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Obvious sources of data - age of data |
Data sources may be too old to be useful or relevant to current GIS projects. Past collection standards may be unknown, non-existent, or not currently acceptable Much of the information base may have subsequently changed through erosion, deposition, and other geographic processes Reliance on old data may unknowingly skew, bias, or negate results |
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Obvious sources of data - areal cover |
Data on a given area may be completely lacking, or only partial levels of information may be available for use in a GIS project.
Example, lack of remote sensing data in certain parts of the world due to almost continuous cloud cover Example, vegetation or soils maps may be incomplete at borders and transition zones and fail to accurately portray reality |
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Obvious sources of error - map scale |
Detail in maps is determined by its scale. Scale restricts type, quantity, and quality of data. Have to match the appropriate scale to the level of detail required in the project. Enlarging a small scale map does not increase its overall accuracy or detail |
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Base map detail- scale decisions |
Selection of map scale and center control the geographic context of the map |
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Base map detail - generalization |
How much detail and what features do you need on your base map. Beginners misconceptions - reducing the amount of information is unscientific. More detail = better map Too much detail muddles maps and makes them difficult to interpret |
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Obvious sources of error |
The number of observations within an area is a guide to data reliability and should be known by the map user. An insufficient number of observations may not provide the level of resolution required to adequately perform spatial analysis. Example, if the contour line interval on a map is 40ft, resolution below this level is not accurately possible Example, if you are trying to map habitat/ behaviour of a particular animal species, and you input have 2 data points in BC, your analysis may be challenging, and your ability to generalize will be undermined |
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Obvious sources of error - relevance |
Often the desired data regarding a site or area may not exist and the surrogate data may have to be used instead
A valid relationship must exist between the surrogate and the phenomena it is used to study
But... error may still creep in coz the phenomena is not being measured daily. Example, habitat studies of the golden cheeked warblers in the hill country of Virginia. Costly to inventory these habitats through direct field observation. The warblers prefer to live in stands of old growth cedar juniperus ashei. These stands can be identified from aerial photographs
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Problems with relevance error |
The density of juniperus ashei can be used as surrogate measure to the density of warbler habitat. But some areas of cedar may be uninhabited or inhibiting due to very high tree density. These areas will be missed when aerial photographs are used to tabulate habitats. Can estimate human pop by water usage, etc |
