Logical accuracy in gis

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There is no such thing as the perfect GIS data. It is a fact in any science, and cartography is no exception. However, the imperfection of data and its effects on GIS analysis had not been considered in great detail until recent years. In the last decade, GIS specialists started to accept that error, inaccuracy, and imprecision can affect the quality of many types of GIS projects, in the sense that errors that are not accounted for can turn the analysis in a GIS project to a useless exercise.

logical accuracy in gis

Understanding error inherent in GIS data is critical to ensuring that any spatial analysis performed using those datasets meets a minimum threshold for accuracy.

The power of GIS resides in its ability to use many types of data related to the same geographical area to perform the analysis, integrating different datasets within a single system.

But when a new dataset is brought to the GIS, the software imports not only the data, but also the error that the data contains. The first action to take care of the problem of error is being aware of it and understanding the limitations of the data being used.

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In order to really understand the relevance of accuracy and precision, we should start getting the difference between both terms:. Accuracy can be defined as the degree or closeness to which the information on a map matches the values in the real world. Therefore, when we refer to accuracy, we are talking about quality of data and about number of errors contained in a certain dataset. In GIS data, accuracy can be referred to a geographic position, but it can be referred also to attribute, or conceptual accuracy.

Precision refers how exact is the description of data. Precise data may be inaccurate, because it may be exactly described but inaccurately gathered. Maybe the surveyor made a mistake, or the data was recorded wrongly into the database. In the series of images above, the concept of precision versus accuracy is visualized.

The crosshair of each image represents the true value of the entity and the red dots represent the measure values. Understanding both accuracy and precision is important for assessing the usability of a GIS dataset. When a dataset is inaccurate but highly precise, corrective measures can be taken to adjust the dataset to make it more accurate. Some sources of error in GIS data are very obvious, whereas others are more difficult to notice.

GIS software can make the users to think that their data is accurate and precise to a degree that is not quite real.

Scalefor example, is an inherent error in cartography; depending on the scale used, we will be able to represent different type of data, in a different quantity and with a different quality. Cartographers should always adapt the scale of work to the level of detail needed in their projects. The age of data may be another obvious source of error. When data sources are too old, some, or a big part, of the information base may have changed.

GIS users should always be mindful when using old data and the lack of currency to that data before using it for contemporary analysis. There are some types of errors created when formatting data for processing. Other sources of error may not be so obvious, some of them originated at the moment of initial measurements, even from the moment of capturing the data cause by users.

Quite often we can identify quantitative and qualitative errors. A common mistake consists on label errors. For instance, an agricultural land may be incorrectly marked as a marsh, and this would cause an error that the map user may not notice because he may not be familiar with the area in question.

Quantitative errors may occur also when using instrument that have not been properly calibrated creating subsequent errors hard to identify in the field, but that will cause your project to lose accuracy and reliability. We also have to pay attention to what has been defined as positional accuracywhichis dependent on the type of data.

Earth Imaging - Episode V - Positional Accuracy

Cartographers can accurately locate certain features like roads, boundary lines, etc. Other features, like climate, for instance lack defined boundaries in nature and, therefore, are subject to subjective interpretation.

Topological errors occur often during the digitizing process.This guide clearly illustrates where to put the information associated with each FGDC metadata element. This topic is split into sections that correspond with the standard. Production rules for each metadata section are provided in tables. Screen captures following each table show where to put an element's content in the ArcGIS metadata editor. Additional tables contain the production rules for each compound metadata element.

If information is required to explain what to type or how ArcGIS handles an element's content, it will be provided in a paragraph that immediately precedes the associated screen capture.

Separate sections in this topic illustrate how to provide their content. In the seven main metadata sections, if an element references a reusable section, the screen capture will only show how to add that section, for example, a contact. Refer to the reusable section's detailed instructions for more information.

The exception is the citation that describes the item itself, where some content is handled differently than for other resources that may be cited in the metadata.

Section zero in the CSDGM provides production rules for the seven metadata sections themselves, indicating which sections are mandatory. This section is presented first in the topic below. It provides links that can quickly take you to the detailed information for each metadata section further down in this topic. Some information, such as an item's detailed spatial reference properties, can only be added to an item's metadata by the metadata synchronization process. To include this information in an item's metadata, automatic updates must be turned on in the Options dialog box.

The only way to change the properties of an item that are recorded in its metadata is to change the item's data or settings in ArcGIS. The next time the item's metadata is synchronized after the item has been updated, its current properties will be updated accordingly in its metadata. The screen captures may illustrate where to find the item's properties in the metadata display, so you can check that this information has been recorded by the synchronization process. In ArcGIS metadata, all dates must be valid dates that consist of a year, a month, and a day.

To enter this information in the Description tabclick the calendar control and click the appropriate date. Times in ArcGIS metadata must be valid, and must include hours, minutes, and seconds. To enter this information, click the portion of the time you want to change and type in the appropriate value.

The up and down arrows can only be used to change the hour. For more details about using the calendar and time controls, see the Time Period Information section. If an item's existing FGDC metadata contains a date that is only a year, when this metadata is upgraded or imported into ArcGIS the date is converted to the first of January in that year. If the original date consists of a year and a month, this date is converted to the first day of that month in the specified year when the metadata is imported or upgraded.

If the original value provided is a string, such as Springthis information can't be converted to a date; the value won't be imported or upgraded. Similarly, if the original date is invalid, for example, because the date didn't adhere to the FGDC yyyymmdd format, the date won't be imported or upgraded.

When describing an item's time period, provide a date range that runs from the first day to the last day of the appropriate year or month instead of providing a partial date.

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Instead of specifying spring in a given year, specify a range of months during which the data was collected or the map was created. If the original metadata includes only a partial time, zero seconds, minutes, or hours will be added as appropriate. If the original time provided is a string, such as 2 a. Times can't be provided in ArcGIS metadata without an accompanying date. In FGDC metadata, some elements that typically require dates or times may allow text, such as unknownunpublished materialor now.

This information will be upgraded or imported to ArcGIS metadata. However, at present, imprecise dates and times such as these can't be edited in the Description tab.The error, accuracy, and precision of the GIS data we use in projects are often overlooked when we download data from various government, open source, and commercial sources.

Metadata is data about data. We'll explore these topics in more detail in this lesson. This lesson will take us one week to complete. Specific directions for the assignments below can be found in this lesson.

What is positional accuracy assessment?

If you have any questions, please post them to our Questions? I will check that discussion forum daily to respond. While you are there, feel free to post your own responses if you, too, are able to help out a classmate. Errors can be injected at many points in a GIS analysis, and one of the largest sources of error is the data collected.

Each time a new dataset is used in a GIS analysis, new error possibilities are also introduced. One of the feature benefits of GIS is the ability to use information from many sources, so the need to have an understanding of the quality of the data is extremely important. Accuracy in GIS is the degree to which information on a map matches real-world values.

It is an issue that pertains both to the quality of the data collected and the number of errors contained in a dataset or a map. One everyday example of this sort of error would be if an online advertisement showed a sweater of a certain color and pattern, yet when you received it, the color was slightly off. Precision refers to the level of measurement and exactness of description in a GIS database. Map precision is similar to decimal precision.

Precise location data may measure position to a fraction of a unit meters, feet, inches, etc. Precision attribute information may specify the characteristics of features in great detail. As an example of precision, say you try on two pairs of shoes of the same size but different colors.

One pair fits as you would expect, but the other pair is too short. Do you suspect a quality issue with the shoes or do you buy the shoes that fit? Would you do the same when selecting GIS data for a project?In the fields of engineering, industry and statistics, accuracy is the degree of closeness of a measured or calculated quantity to its actual true value. Precisionalso called reproducibility or repeatability, the degree to which further measurements or calculations show the same or similar results.

The results of calculations or a measurement can be accurate but not precise, precise but not accurate, neither, or both. For example, if an experiment contains a systematic errorthen increasing the sample size will generally produce more precise results, but they will not necessarily be more accurate. A measurement system or computational method is called valid if it is both accurate and precise. The related terms are bias non- random or directed effects caused by a factor or factors unrelated by the independent variable and error random variabilityrespectively.

Accuracy is the degree of veracity while precision is the degree of reproducibility. In this analogy, repeated measurements are compared to arrows that are shot at a target. Accuracy describes the closeness of arrows to the bullseye at the target center. Arrows that strike closer to the bullseye are considered more accurate. The closer a system's measurements to the accepted value, the more accurate the system is considered to be.

To continue the analogy, if a large number of arrows are shot, precision would be the size of the arrow cluster. When only one arrow is shot, precision is the size of the cluster one would expect if this were repeated many times under the same conditions. When all arrows are grouped tightly together, the cluster is considered precise since they all struck close to the same spot, if not necessarily near the bullseye. The measurements are precise, though not necessarily accurate.

However, it is not possible to reliably achieve accuracy in individual measurements without precision—if the arrows are not grouped close to one another, they cannot all be close to the bullseye.

Lesson 7 - Understanding GIS Error, Accuracy, and Precision, and Metadata

Their average position might be an accurate estimation of the bullseye, but the individual arrows are inaccurate. See also circular error probable for application of precision to the science of ballistics. Ideally a measurement device is both accurate and precise, with measurements all close to and tightly clustered around the known value.

The accuracy and precision of a measurement process is usually established by repeatedly measuring some traceable reference standard. Such standards are defined in the International System of Units and maintained by national standards organizations such as the National Institute of Standards and Technology. In some literature, precision is defined as the reciprocal of variancewhile many others still confuse precision with the confidence interval.

The interval defined by the standard deviation is the If enough measurements have been made to accurately estimate the standard deviation of the process, and if the measurement process produces normally distributed errors, then it is likely that This also applies when measurements are repeated and averaged.

logical accuracy in gis

In that case, the term standard error is properly applied: the precision of the average is equal to the known standard deviation of the process divided by the square root of the number of measurements averaged. Further, the central limit theorem shows that the probability distribution of the averaged measurements will be closer to a normal distribution than that of individual measurements.The ability to obtain precise information is nothing new.

With great patience and skill, mapmakers and land surveyors have long been able to create information with an impressive level of accuracy. However, today the ability to determine and view locations with submeter accuracy is now in the hands of millions of people. Commonly available high-resolution digital terrain and aerial imagery, coupled with GPS-enabled handheld devices, powerful computers, and Web technology, is changing the quality, utility, and expectations of GIS to serve society on a grand scale.

This accuracy and precision revolution has raised the bar for GIS quite high. This pervasive capability will be the driver for the next iteration of GIS and the professionals who operate them.

When I say there is a "revolution" going on in GIS, I am referring to the change in the fundamental accuracy and precision kernel of commonly used geographic data brought about by new technologies previously mentioned.

For many ArcGIS users, this kernel used to be about 10 meters or 40 feet at a scale ofWith today's technologies and those in the futureGIS will be using data with 1-meter and submeter accuracy and precision. There are probably GIS departments—in a large city or metro area—where this standard is already in place. But as lidar, GPS, and high-resolution imagery begin to proliferate standard sources for "ground" locations, GIS professionals will begin to feel the consequences in three areas: data quality, analytic methods, and hardware and software.

As we try to integrate highly resolved data into existing GIS, the errors in legacy data will become more apparent. The expectation is that data is as accurate and precise as possible, so new geometry must be developed either through editing or by capturing new data.

We will need to be more careful about documentation and mindful of appropriately mixing data in databases. The four figures accompanying this article illustrate the problems GIS professionals might encounter as they integrate more accurate data into GIS operations. For these illustrations, I used recently acquired lidar elevation data. Figure 1 illustrates a typical base dataset displayed atscale.

Hillshade and contours have been derived from the U. Geological Survey National Elevation Dataset. The hydrography came from the U. Geological Survey National Hydrography Dataset. Roads were taken from BLM internal files.

logical accuracy in gis

The standards of accuracy and precision of this data is typical of levels of the data used by natural resource management agencies such as the BLM and Forest Service. Most of the data used in these databases was originally derived from U. Geological Survey ,scale topographic maps or from existing paper maps of lesser quality. Only in recent years has data been developed using GPS or heads-up digitizing from large-scale imagery or photography.

Until recently, I considered the quality of this data pretty good since at commonly used scales ranging fromtoI could not readily detect any flaws. Figure 2 shows hillshade and hydrography displayed atscale, which is the intended scale of the data. The problem occurs when, because this is the highest resolution in the GIS, this same data is used for scales larger thanNote how hydrography matches the terrain hillshade in most areas.

A red square surrounds the magnified areas in Figures 3 and 4 that show where flaws in the data become painfully apparent.

Figure 4 uses a hillshade of the bare earth lidar returns from 1-meter lidar data. In this figure, one can see how poorly the hydrography matches the terrain at 1-meter resolution.Let's go into more detail about error, accuracy, and precision.

The following information is taken, with permission, from The Geographer's Craft :. Until quite recently, people involved in developing and using GIS paid little attention to the problems caused by error, inaccuracy, and imprecision in spatial datasets.

Certainly, there was an awareness that all data suffers from inaccuracy and imprecision, but the effects on GIS problems and solutions was not considered in great detail. Major introductions to the field such as C.

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This situation has changed substantially in recent years. It is now generally recognized that error, inaccuracy, and imprecision can "make or break" many types of GIS projects. That is, errors left unchecked can make the results of a GIS analysis almost worthless. GIS gain much of their power from being able to collate and cross-reference many types of data by location. They are particularly useful because they can integrate many discrete datasets within a single system.

Unfortunately, every time a new dataset is imported, the GIS also inherits its errors.

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These may combine and mix with the errors already in the database in unpredictable ways. One of the first thorough discussions of the problems and sources of error appeared in P. Now, the issue is addressed in many introductory texts on GIS.

The key point is that even though error can disrupt GIS analyses, there are ways to keep error to a minimum through careful planning and methods for estimating its effects on GIS solutions.

Logical consistency

Awareness of the problem of errors has also had the useful benefit of making GIS practitioners more sensitive to potential limitations of GIS to reach impossibly accurate and precise solutions. It is important to distinguish from the start a difference between accuracy and precision :. Accuracy is an issue pertaining to the quality of data and the number of errors contained in a dataset or map.

In discussing a GIS database, it is possible to consider horizontal and vertical accuracy with respect to geographic position, as well as attribute, conceptual, and logical accuracy.

logical accuracy in gis

Precise locational data may measure position to a fraction of a unit. Precise attribute information may specify the characteristics of features in great detail. It is important to realize, however, that precise data — no matter how carefully measured — may be inaccurate.

Surveyors may make mistakes or data may be entered into the database incorrectly. High precision does not indicate high accuracy nor does high accuracy imply high precision. But high accuracy and high precision are both expensive. Be aware also that GIS practitioners are not always consistent in their use of these terms. Sometimes the terms are used almost interchangeably and this should be guarded against. Positional error is often of great concern in GIS, but error can actually affect many different characteristics of the information stored in a database.Positional accuracy is the quantifiable value that represents the positional difference between two geospatial layers or between a geospatial layer and reality.

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An example of this is the comparison of the location of roads in a feature class versus their location in a TIFF image. The Positional Accuracy Assessment tool PAAT allows you to compare two items to assess a data layer's accuracy in relation to a reference layer of known or unknown accuracy. To assess positional accuracy, two layers are required: the layer whose accuracy you want to evaluate and another layer that can be used as a point of reference. The uncertainty is defined as the circular error CE for two-dimensional features and linear error LE for three-dimensional features.

The confidence level for the feature class or raster being evaluated can be at the 90, 95, or 99 percent level when you use the PAAT. You can use z-enabled data for one or both of the layers in a PAAT session.

Depending on the layers you are using for evaluation and reference, respectively, the workflow below might not be followed exclusively:. Arc GIS Desktop. Available with Data Reviewer license.

The PAAT workflow.


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