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Intro to GIS

Lesson 2: Fundamentals

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Objectives

Describe how real-world features are represented by GIS data
Explain basic differences between the raster and vector data models
Define map scale and explain how source scale affects the use of map data
Recognize the types of shortcomings associated with GIS data
Correctly cite data sets on a map or in a report
Use ArcGIS Pro to explore data and view maps
TerrSet System Overview

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Types of map data

Source: USGS

Discrete objects exist in a defined location/space

Lookout tower (point).
Stream (line).
Snail habitat (polygon).

Continuous data exist everywhere

Elevation (raster).

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G I S data models

Source: Esri

Source: USGS

Source: Google Earth and TeleAtlas

The vector model is best for discrete data

state boundaries.
streams.
snail habitat.

The raster model is used for imagery and is best for continuous data

elevation.
aerial photography.

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Vector data model

The vector data model is best for discrete data

Features are stored map objects

Points, lines, polygons.

A feature class is a collection of similar features stored together, like states or rivers

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Attribute tables

Source: Esri

Features are linked to tables containing information about the spatial objects
The map object and the table data are connected by a unique integer

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Raster data model

Source: USGS

The raster model breaks map areas into small squares known as cells or pixels
A single numeric value is stored in each cell, such as elevation

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Map Layers

A layer refers to the visualization of something on a map.

Layers make maps more contextual and help you focus on specific aspects like assets, roads, and points of interest.

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Map scale

1 cm on map = 100,000 cm on ground

Map scale is the ratio of distance on the map to distance on the ground
It is dimensionless and can be expressed in any units: cm or inches or mm…

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Map scale is a measure of the size at which features in a map are represented. The scale is expressed as a fraction, or ratio, of the size of features represented by the map to the size of the actual objects on the ground. Because it is expressed as a ratio, it is valid for any unit of measure. For the common US Geological Survey topographic map, which has a scale of 1:24,000, one inch on the map represents 24,000 inches on the ground. The map scale can be used to determine the true size of any feature on the map, such as the width of a lake. Measure the lake with a ruler and then set up a proportion such that the map scale equals the measured width over the actual width (x). Then solve for x. Keep in mind that the actual width and the measured width will have the same units, which can be converted if necessary.

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Generalization

Source: Esri

Generalization is used to simplify map features for clear display

Small scale maps present less detailed versions of objects.
Large scale maps present more detailed versions.

Source scale is the scale at which data are captured

It impacts detail and accuracy of the data set.

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G I S data and scales

Source: Esri

G I S data may be presented at any scale
It is best not to display it at scales too different from the source scale
Larger scale maps have finer sampling distance (resolution)

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Impact of source scale

Source: Esri

Two different data sets of the same objects may not align, especially when the source scale is different, as for these Oregon counties from two different data sources

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GIS Accuracy and Precision

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?

The more accurate and precise the data, the higher cost to obtain and store it because it can be very difficult to obtain and will require larger data files. For example, a 1-meter-resolution aerial photograph will cost more to collect (increased equipment resolution) and cost more to store (greater pixel volume) than a 30-meter-resolution aerial photograph.

Highly precise data does not necessarily correlate to highly accurate data nor does highly accurate data imply high precision data. They are two separate and distinct measurements. Relative accuracy and precision, and the inherent error of both precision and accuracy of GIS data determine data quality.

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Some Basic Definitions

Accuracy is the degree to which information on a map or in a digital database matches true or accepted values.
Precision refers to the level of measurement and exactness of description in a GIS database.
Data quality refers to the relative accuracy and precision of a particular GIS database. These facts are often documented in data quality reports.
Error encompasses both the imprecision of data and its inaccuracies.

2.1 Accuracy is the degree to which information on a map or in a digital database matches true or accepted values. 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.

The level of accuracy required for particular applications varies greatly.

Highly accurate data can be very difficult and costly to produce and compile.

2.2 Precision refers to the level of measurement and exactness of description in a GIS database. 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.

The level of precision required for particular applications varies greatly. Engineering projects such as road and utility construction require very precise information measured to the millimeter or tenth of an inch. Demographic analyses of marketing or electoral trends can often make do with less, say to the closest zip code or precinct boundary.

Highly precise data can be very difficult and costly to collect. Carefully surveyed locations needed by utility companies to record the locations of pumps, wires, pipes and transformers cost $5-20 per point to collect.

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.

Two additional terms are used as well:

Data quality refers to the relative accuracy and precision of a particular GIS database. These facts are often documented in data quality reports.

Error encompasses both the imprecision of data and its inaccuracies.

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Data Quality

Data sets are rarely perfect
No absolute quality standard exists
Quality is defined as the fitness of a data set for a particular purpose
The same data set may be unsuitable for one use but adequate for another
A user has ethical and legal responsibility to determine whether a data set is sufficient for its intended use

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Types of Error

Positional accuracy and precision
Attribute accuracy and precision
Conceptual accuracy and precision
Logical accuracy and precision

The need for accuracy and precision will vary radically depending on the type of information coded and the level of measurement needed for a particular application.

Positional error is often of great concern in GIS, but error can actually affect many different characteristics of the information stored in a database.

3.1. Positional accuracy and precision

This applies to both horizontal and vertical positions.

Accuracy and precision are a function of the scale at which a map (paper or digital) was created. The mapping standards employed by the United States Geological Survey specify that:

"requirements for meeting horizontal accuracy as 90 percent of all measurable points must be within 1/30th of an inch for maps at a scale of 1:20,000 or larger, and 1/50th of an inch for maps at scales smaller than 1:20,000."

Accuracy Standards for Various Scale Maps

1:2,400 ± 6.67 feet

1:4,800 ± 13.33 feet

1:10,000 ± 27.78 feet

1:12,000 ± 33.33 feet

1:24,000 ± 40.00 feet

1:63,360 ± 105.60 feet

1:100,000 ± 166.67 feet

1:1,200 ± 3.33 feet

This means that when we see a point on a map we have its "probable" location within a certain area. The same applies to lines.

Beware of the dangers of false accuracy and false precision, that is reading locational information from map to levels of accuracy and precision beyond which they were created. This is a very great danger in computer systems that allow users to pan and zoom at will to an infinite number of scales. Accuracy and precision are tied to the original map scale and do not change even if the user zooms in and out. Zooming in and out can, however, mislead the user into believing – falsely – that the accuracy and precision have improved.

3.2. Attribute accuracy and precision

The non-spatial data linked to location may also be inaccurate or imprecise. Inaccuracies may result from mistakes of many sorts. Non-spatial data can also vary greatly in precision. Precise attribute information describes phenomena in great detail. For example, a precise description of a person living at a particular address might include gender, age, income, occupation, level of education, and many other characteristics. An imprecise description might include just income, or just gender.

3.3. Conceptual accuracy and precision

GIS depend upon the abstraction and classification of real-world phenomena. The users determines what amount of information is used and how it is classified into appropriate categories. Sometimes users may use inappropriate categories or misclassify information. For example, classifying cities by voting behavior would probably be an ineffective way to study fertility patterns. Failing to classify power lines by voltage would limit the effectiveness of a GIS designed to manage an electric utilities infrastructure. Even if the correct categories are employed, data may be misclassified. A study of drainage systems may involve classifying streams and rivers by "order," that is where a particular drainage channel fits within the overall tributary network. Individual channels may be misclassified if tributaries are miscounted. Yet, some studies might not require such a precise categorization of stream order at all. All they may need is the location and names of all stream and rivers, regardless of order.

3.4 Logical accuracy and precision

Information stored in a database can be employed illogically. For example, permission might be given to build a residential subdivision on a floodplain unless the user compares the proposed plan with floodplain maps. Then again, building may be possible on some portions of a floodplain but the user will not know unless variations in flood potential have also been recorded and are used in the comparison. The point is that information stored in a GIS database must be used and compared carefully if it is to yield useful results. GIS systems are typically unable to warn the user if inappropriate comparisons are being made or if data are being used incorrectly. Some rules for use can be incorporated in GIS designed as "expert systems," but developers still need to make sure that the rules employed match the characteristics of the real-world phenomena they are modeling.

Finally, It would be a mistake to believe that highly accurate and highly precision information is needed for every GIS application. The need for accuracy and precision will vary radically depending on the type of information coded and the level of measurement needed for a particular application. The user must determine what will work. Excessive accuracy and precision is not only costly but can cause considerable error in details.

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Sources of Inaccuracy and Imprecision

Burrough (1986) divides sources of error into three main categories:

Obvious sources of error.

Age of data
Areal Cover
Map Scale
Density of Observations
Relevance
Format/Accessibility/Cost

Errors resulting from natural variations or from original measurements.

Measurement Error/Biases

Errors arising through processing.

Numerical Errors

4. Sources of Inaccuracy and Imprecision

There are many sources of error that may affect the quality of a GIS dataset. Some are quite obvious, but others can be difficult to discern. Few of these will be automatically identified by the GIS itself. It is the user's responsibility to prevent them. Particular care should be devoted to checking for errors because GIS are quite capable of lulling the user into a false sense of accuracy and precision unwarranted by the data available. For example, smooth changes in boundaries, contour lines, and the stepped changes of chloropleth maps are "elegant misrepresentations" of reality. In fact, these features are often "vague, gradual, or fuzzy" (Burrough 1986). There is an inherent imprecision in cartography that begins with the projection process and its necessary distortion of some of the data (Koeln and others 1994), an imprecision that may continue throughout the GIS process. Recognition of error and importantly what level of error is tolerable and affordable must be acknowledged and accounted for by GIS users.

Burrough (1986) divides sources of error into three main categories:

Obvious sources of error.

Errors resulting from natural variations or from original measurements.

Errors arising through processing.

Generally errors of the first two types are easier to detect than those of the third because errors arising through processing can be quite subtle and may be difficult to identify. Burrough further divided these main groups into several subcategories.

4.1 Obvious Sources of Error

4.1.1. Age of data

Data sources may simply be to old to be useful or relevant to current GIS projects. Past collection standards may be unknown, non-existent, or not currently acceptable. For instance, John Wesley Powell's nineteenth century survey data of the Grand Canyon lacks the precision of data that can be developed and used today. Additionally, much of the information base may have subsequently changed through erosion, deposition, and other geomorphic processes. Despite the power of GIS, reliance on old data may unknowingly skew, bias, or negate results.

4.1.2. Areal Cover

Data on a give area may be completely lacking, or only partial levels of information may be available for use in a GIS project. For example, vegetation or soils maps may be incomplete at borders and transition zones and fail to accurately portray reality. Another example is the lack of remote sensing data in certain parts of the world due to almost continuous cloud cover. Uniform, accurate coverage may not be available, and the user must decide what level of generalization is necessary, or whether further collection of data is required.

4.1.3. Map Scale

The ability to show detail in a map is determined by its scale. A map with a scale of 1:1000 can illustrate much finer points of data than a smaller scale map of 1:250000. Scale restricts type, quantity, and quality of data (Star and Estes 1990). One must match the appropriate scale to the level of detail required in the project. Enlarging a small scale map does not increase its level of accuracy or detail.

4.1.4. Density of Observations

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 and determine the patterns GIS projects seek to resolve or define. A case in point, if the contour line interval on a map is 40 feet, resolution below this level is not accurately possible. Lines on a map are a generalization based on the interval of recorded data, thus the closer the sampling interval, the more accurate the portrayed data.

4.1.5. Relevance

Quite often the desired data regarding a site or area may not exist, and "surrogate" data may have to be used instead. A valid relationship must exist between the surrogate and the phenomenon it is used to study but, even then, error may creep in because the phenomenon is not being measured directly. A local example of the use of surrogate data are habitat studies of the golden-cheeked warblers in the Hill Country. It is very costly (and disturbing to the birds) to inventory these habitats through direct field observation. But the warblers prefer to live in stands of old growth cedar Juniperus ashei. These stands can be identified from aerial photographs. The density of Juniperus ashei can be used as surrogate measure of the density of warbler habitat. But, of course, some areas of cedar may uninhabited or inhibited to a very high density. These areas will be missed when aerial photographs are used to tabulate habitats.

Another example of surrogate data are electronic signals from remote sensing that are use to estimate vegetation cover, soil types, erosion susceptibility, and many other characteristics. The data is being obtained by an indirect method. Sensors on the satellite do not "see" trees, but only certain digital signatures typical of trees and vegetation. Sometimes these signatures are recorded by satellites even when trees and vegetation are not present (false positives) or not recorded when trees and vegetation are present (false negatives). Due to cost of gathering on site information, surrogate data is often substituted, and the user must understand variations may occur, and although assumptions may be valid, they may not necessarily be accurate.

4.1.6. Format

Methods of formatting digital information for transmission, storage, and processing may introduce error in the data. Conversion of scale, projection, changing from raster to vector format, and resolution size of pixels are examples of possible areas for format error. Expediency and cost often require data reformation to the "lowest common denominator" for transmission and use by multiple GIS. Multiple conversions from one format to another may create a ratchet effect similar to making copies of copies on a photo copy machine. Additionally, international standards for cartographic data transmission, storage and retrieval are not fully implemented.

4.1.7. Accessibility

Accessibility to data is not equal. What is open and readily available in one country may be restricted, classified, or unobtainable in another. Prior to the break-up of the former Soviet Union, a common highway map that is taken for granted in this country was considered classified information and unobtainable to most people. Military restrictions, inter-agency rivalry, privacy laws, and economic factors may restrict data availability or the level of accuracy in the data.

4.1.8. Cost

Extensive and reliable data is often quite expensive to obtain or convert. Initiating new collection of data may be too expensive for the benefits gained in a particular GIS project and project managers must balance their desire for accuracy the cost of the information. True accuracy is expensive and may be unaffordable.

4.2. Errors Resulting from Natural Variation or from Original Measurements

Although these error sources may not be as obvious, careful checking will reveal their influence on the project data.

4.2.1. Positional accuracy

Positional accuracy is a measurement of the variance of map features and the true position of the attribute (Antenucci and others 1991, p. 102). It is dependent on the type of data being used or observed. Mapmakers can accurately place well-defined objects and features such as roads, buildings, boundary lines, and discrete topographical units on maps and in digital systems, whereas less discrete boundaries such as vegetation or soil type may reflect the estimates of the cartographer. Climate, biomes, relief, soil type, drainage, and other features lack sharp boundaries in nature and are subject to interpretation. Faulty or biased field work, map digitizing errors and conversion, and scanning errors can all result in inaccurate maps for GIS projects.

4.2.2. Accuracy of content

Maps must be correct and free from bias. Qualitative accuracy refers to the correct labeling and presence of specific features. For example, a pine forest may be incorrectly labeled as a spruce forest, thereby introducing error that may not be known or noticeable to the map or data user. Certain features may be omitted from the map or spatial database through oversight, or by design.

Other errors in quantitative accuracy may occur from faulty instrument calibration used to measure specific features such as altitude, soil or water pH, or atmospheric gases. Mistakes made in the field or laboratory may be undetectable in the GIS project unless the user has conflicting or corroborating information available.

4.2.3. Sources of variation in data

Variations in data may be due to measurement error introduced by faulty observation, biased observers, or by miscalibrated or inappropriate equipment. For example, one can not expect sub-meter accuracy with a hand-held, non-differential GPS receiver. Likewise, an incorrectly calibrated dissolved oxygen meter would produce incorrect values of oxygen concentration in a stream.

There may also be a natural variation in data being collected, a variation that may not be detected during collection. As an example, salinity in Texas bays and estuaries varies during the year and is dependent upon freshwater influx and evaporation. If one was not aware of this natural variation, incorrect assumptions and decisions could be made, and significant error introduced into the GIS project. In any case, if the errors do not lead to unexpected results, their detection may be extremely difficult.

4.3. Errors Arising Through Processing

Processing errors are the most difficult to detect by GIS users and must be specifically looked for and require knowledge of the information and the systems used to process it. These are subtle errors that occur in several ways, and are therefore potentially more insidious, particularly because they can occur in multiple sets of data being manipulated in a GIS project.

4.3.1. Numerical Errors

Different computers may not have the same capability to perform complex mathematical operations and may produce significantly different results for the same problem. Burrough (1990) cites an example in number squaring that produced 1200% difference. Computer processing errors occur in rounding off operations and are subject to the inherent limits of number manipulation by the processor. Another source of error may from faulty processors, such as the recent mathematical problem identified in Intel's Pentium (tm) chip. In certain calculations, the chip would yield the wrong answer.

A major challenge is the accurate conversion of existing to maps to digital form (Muehrcke 1986). Because computers must manipulate data in a digital format, numerical errors in processing can lead to inaccurate results. In any case, numerical processing errors are extremely difficult to detect, and perhaps assume a sophistication not present in most GIS workers or project managers.

4.3.2. Errors in Topological Analysis

Logic errors may cause incorrect manipulation of data and topological analyses (Star and Estes 1990). One must recognize that data is not uniform and is subject to variation. Overlaying multiple layers of maps can result in problems such as Slivers, Overshoots, and Dangles. Variation in accuracy between different map layers may be obscured during processing leading to the creation of "virtual data which may be difficult to detect from real data" (Sample 1994).

4.3.3. Classification and Generalization Problems

For the human mind to comprehend vast amounts of data, it must be classified, and in some cases generalized, to be understandable. According to Burrough (1986, pp. 137), about seven divisions of data is ideal and may be retained in human short-term memory. Defining class intervals is another problem area. For instance, defining a cause of death in males between 18-25 years old would probably be significantly different in a class interval of 18-40 years old. Data is most accurately displayed and manipulated in small multiples. Defining a reasonable multiple and asking the question "compared to what" is critical (Tufte 1990, pp. 67-79). Classification and generalization of attributes used in GIS are subject to interpolation error and may introduce irregularities in the data that is hard to detect.

4.3.4. Digitizing and Geocoding Errors

Processing errors occur during other phases of data manipulation such as digitizing and geocoding, overlay and boundary intersections, and errors from rasterizing a vector map. Physiological errors of the operator by involuntary muscle contractions may result in spikes, switchbacks, polygonal knots, and loops. Errors associated with damaged source maps, operator error while digitizing, and bias can be checked by comparing original maps with digitized versions. Other errors are more elusive.

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Beware of False Precision and False Accuracy!

GIS users are not always aware of the difficult problems caused by error, inaccuracy, and imprecision. They often fall prey to False Precision and False Accuracy, that is they report their findings to a level of precision or accuracy that is impossible to achieve with their source materials. If locations on a GIS coverage are only measured within a hundred feet of their true position, it makes no sense to report predicted locations in a solution to a tenth of a foot. That is, just because computers can store numeric figures down many decimal places does not mean that all those decimal places are "significant." It is important for GIS solutions to be reported honestly, and only to the level of accuracy and precision they can support.

This means in practice that GIS solutions are often best reported as ranges or ranking, or presented within statistical confidence intervals.

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Geometric accuracy

Is it where it says it is?

Depends on the level of error in original source.
Additional errors may be incurred or propagated during processing.
Assessed by comparing the data to another data set with known high accuracy.

Which road is “correct”? What errors might occur in the locations of both?

Source: Google Earth and TeleAtlas

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Thematic accuracy

How accurate are the attributes?

One might ask questions about, for example, tree crown density.

How is it measured?
How accurate are the measurements?
What are the possible sources of error?

Source: Esri

Source: Google Earth and TeleAtlas

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Resolution

Resolution is the sampling interval of measurements during data collection

Spatial sampling resolution.

Distance between G P S points along a road.
Size of pixel for elevation or satellite image raster.

Thematic data resolution.

How fine was the measuring scale?
Were the data classified after measurement?

Temporal data resolution.

How frequently were data sampled?
Daily, monthly, every decade?

Source: Google Earth and TeleAtlas

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Precision

Precision has two meanings in science

It is NOT the same as accuracy!

Meaning One: the number of significant digits in a measurement

Example: a G P S unit reports locations to the nearest meter. The precision is about 1 meter.

Meaning Two: The statistical variability of a repeated measurement

Example: 20 G P S measurements at same spot have a standard deviation of 5 meters. The precision is about ±10 meters.

Source: Google Earth and TeleAtlas

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Logical consistency

Logical consistency assesses how well a data set represents the real-world relationships?

Are common boundaries identical.
Do roads actually connect?
Do voting district and county boundaries align?

Source: Esri

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Citing G I S data

Formats may vary for different agencies or companies
A good citation enables someone to find the source should they wish to obtain a copy
Citing data is a professional and ethical responsibility
Data set name (Year published) [source type]. Producer name, producer contact information. Resource U R L: [Date accessed].

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Citation examples

Black Hills R I S Vegetation Database (2008) [downloaded file]. Black Hills National Forest, Custer, S D. U R L: http://www.fs.usda.gov/main/blackhills/landmanagement/gis [August, 2010].
Esri™ Data and Maps (2012) [D V D]. Esri™, Inc., Redlands, C A.
National Hydrography Dataset (2015) [downloaded file]. United States Geological Survey on the National Map Viewer. U R L: http://viewer.nationalmap.gov/viewer/ [July 23, 2015].
U S A Topo Maps (2009) [map service]. Esri™ on ArcG I S Online. U R L: http://server.arcgisonline. com/ arcgis/services/U S A_Topo_Maps/MapServer [January 1, 2012].

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TerrSet

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Clark Labs is best known for development of pioneering geospatial software products. IDRISI was the first GIS software system specifically targeted at a microcomputer platform. Since its introduction in 1978, it has grown to become one of the most widely used raster systems of its type.

Projects are an organizing concept throughout TerrSet. Projects contain folders which in turn contain files. Projects are similar to the concept of a geodatabase in some other systems – a collection of geographic data for use in a particular project. TerrSet recognizes two kinds of folders – a working folder where all outputs are placed and resource folders from which files can only be read. Projects can contain only one working folder but an unlimited number of resource folders. Important data that will be used again and again without being changed should be placed in resource folders.

TerrSet Explorer allows you to manage both projects and the layers and compositions they contain. The Filters tab allows you to control what files are viewed while the Files tab shows the filtered results for all project folders. In Explorer, layers are shown as objects with their associated metadata in the lower panel. Explorer can also be used to display map layers (double click or rightclick) as well as many other options (right-click) such as adding the layer to the map window that currently has focus, deleting the layer, renaming it, etc

While TerrSet uses both raster and vector data layers, its focus is decidedly raster.

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Summary

A GIS is a spatially enabled database that uses map and attribute data to answer questions about where things are and how they are related.
Maps may represent discrete features, such as a road or a lake, or continuous measurements, such as elevation or temperature. The vector data model is ideal for discrete data, and the raster data model excels at storing continuous data.
Map scale is the ratio of the size of objects in the map to their size on the ground. The source scale of GIS data affects their accuracy and precision and the map scales for which they are suitable.

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Summary

Every GIS user has a responsibility to ensure that data are suitable for the proposed application and an obligation to cite the sources of data used.
Data quality is measured in terms of geometric accuracy, thematic accuracy, resolution, and precision.
Metadata store information about GIS data layers to help people use them properly.

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