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# GIS DATA QUALITY CONSIDERATIONS FOR ENVIRONMENTAL MANAGEMENT

Ahiadu, H. O.,

Department of Geography and Meteorology, Nnamdi Azikiwe University, Awka Nigeria.

Onojeghuo, A. O., Idhoko, K. E., and Eze, F. C.,

Department of Surveying and Geoinformatics, Nnamdi Azikiwe University, Awka Nigeria.

## Abstract

The problems and challenges presented to man in an effective management of the environment are enormous. This is as a result of the fact that the environment presents varied values to its various stakeholders. This therefore requires multiple input elements that reflect different concepts and perceptions. GIS's capability to handle such complex situations is not in doubt. However, GIS data quality consideration is vital. The paper thus highlighted the significance of GIS data quality for an effective environmental management. Emphasis was particularly placed on error types and sources of error that may reduce data quality and good result. Adopting good practice in terms of data capture and analysis; documentation and the incorporation of lineage information among others are the suggested solutions.

**Key words:** Data quality, Errors, Environmental management, Digitizing, Geographical information systems, Spatial data.

## 1.0 Introduction

The management of the environment has always presented man with many problems and challenges largely because of the complex nature of the Environment and of the complex interactions and relationships within it. Also the management of the environment like any other type of management is value - laden and there is usually more than one way of conceptualizing and expressing the value of the environment and its resources. The result is that the management of the environment normally requires that we deal with several elements and several perspectives reflecting the different concepts and perceptions held by different societies, belief systems, interests or disciplines. Geographical information systems (GIS) have been designed for such complex situations where several inputs and issues have to be considered before decisions can be made. A Geographic Information System (GIS) is a computer system for the acquisition, storage, processing,

analysis, and retrieval of geospatial data (Kang-Tuenge 2006). A GIS can help decision-makers use spatial information more fully than manual methods allow, but sometimes data quality issues cause concern about using GIS-generated outputs. Making environmental decisions without adequate consideration to data quality may lead to erroneous decisions, erode public confidence, or cause an agency to incur liability.

The issue of data quality encompasses all aspects of spatial data processing. A brief definition of data quality, proposed by Chrisman (1984), is that it is a measure of the fitness for use of data for a particular task. Data quality is important because it affects how reliable GIS-generated information is in the decision-making process. Too often, the availability of relatively inexpensive digital data overshadows data
quality concerns; people frequently use digital data because they are available, not because they have the necessary accuracy.

The computing saying "garbage in, garbage out" recognizes that if you put poor quality data into your GIS, you will output poor quality results. This paper therefore aims at creating more awareness for environmental decision-makers about data quality issues in the use of GIS for solving environmental problems.

Data quality is thus associated with a variety of interrelated concepts, (e.g. accuracy, error, precision, resolution). The paper discusses both inherent (source) error and the error that GIS operations introduce (operational error); and went further to highlight some error management strategies for coping with error and error reduction.

## 2.0 Data Quality Concepts and Importance

Data quality is a major issue for GIS generated maps much more so than it is for paper maps. Partly, because a GIS can perform operations on spatial data that would be nearly impossible without a GIS due to scale, complexity, and generalization issues (Openshaw, 1989).

There is no generally accepted single simple definition for data quality. For this paper, it can roughly mean how "good" the data are for a given purpose. People usually think of data quality in terms of error, but the term is broader and encompasses the six components, as we shall see later.

Error can mean the difference between the observed values and the "true" value. The "true" value of a variable is usually said to be unknown and unknowable (Griffin, 1994), but for this paper, "true" could be the value one would obtain from field measurements. Imperfect equipment or observers and environmental effects cause spatial error. Thapa and Bossler (1992) outlined three categories of spatial error, viz:

* Gross errors and blunders (people or equipment)

* Systematic errors (which induce bias)

* Random errors (due to imperfect instruments and observers).

Griffin (1994) divides spatial error into two different components: accuracy and precision. Accuracy means how close a value is to the "true" value or a known standard (absence of bias). Precision can have two definitions: it can be a measure of dispersion (standard deviation) of observations about a mean, or it can refer to the number of decimal digits used to represent a value (Burrough, 1986; Aronoff, 1989; Jones, 1998).

Environmental data are not accurate or inaccurate. Instead, data accuracy exists on a continuum, ranging from low to high accuracy. Although people strive for accurate (error-free) data, obtaining 100-percent accurate data is impractical. Robinson and Frank (1985) provided some reasons why 100-percent data accuracy is impracticable. They include:

* Vague definition of objects to be measured.

* Variable nature of some phenomena.

* Imprecise classification schemes.

* Inherently imprecise measurements.

* Occurrence of gross errors of non-statistical nature during measurement.

* Attributes encoded on an ordinal scale (high, medium, low) are approximate.

* Data represent past state of reality.

Users of geographic data should strive for data that are only as accurate as they need. A variety of factors could of course determine need:

* Intended use of the data.

* Budget constraints.

* Time constraints.

* Data storage considerations.

* Potential liability.

## 2.1 Components of Data Quality

According to the National Committee for Digital Cartographic Data Standards (America), six components of digital cartographic data qualities could be identified. These are discussed as follows:

### 2.1.1 Lineage

Due to the fact that uses and users of data change, it has become very necessary to include documentation when
disseminating spatial data. Data lineage, also known as metadata or a data dictionary is data about data. Metadata consists of information about the source data such as:

* Date of collection.

* Short definition.

* Data type, field length and format.

* Control points used.

* Collection method, field notes, and maps.

* Data processing steps.

* Assessment of the reliability of source data.

* Data quality reports.

Access to this information can help GIS personnel and an environmental manager determine if the data are appropriate for their use, thereby reducing risks associated with using the wrong data or using data inappropriately.

## 2.1.2 Positional Accuracy

This, frequently referred to as horizontal error, is how close a location on a map is to its "true" ground position. Features may be located inaccurately on maps for many reasons. Lunetta et al (1991) identified the following reasons:

* Poor field work.

* Distortion of the original paper map (temperature, humidity).

* Poor conversion from raster to vector or vector to raster data.

* Variation in the time of collection of data layers.

* Natural variability in data (tides, vegetation, soil)

* Human-induced changes (altering reservoir water levels)

* Movement of features (due to scale of the map and printing constraints) so the map-reader can easily discern them.

* Combining maps with different scales, projection and coordinate systems.

* Different national horizontal datum in source material.

* Different minimum mapping units.

The importance of positional accuracy depends on the

intended use of the data. In an urban area for instance, a positional error of 1 foot on a tax map may be unacceptable because 1 foot may be worth millions of naira. Somers (1994) suggested that positional accuracy of 10 to 20 feet might be sufficient for environmental analysis. The decision maker must however determine the needed positional accuracy.

## 2.1.3 Attribute Accuracy

Attribute accuracy refers to how well the description of a characteristic of spatial data matches what actually exists on the ground. For some spatial data, the location does not change over time, but the value of the attribute does (e.g., the location of an enumeration area does not change but the population within the area changes).

## 2.1.4 Logical Consistency

This focuses on flaws in logical relationships among data elements. For example, a vector GIS should label all polygons with only one label per polygon, and all polygons should be closed. Logical inconsistency can also occur by collecting data layers at different time or from different scale maps with different positional accuracies. For example, the edge of a lake on the hydrology data layer should coincide with the edge of land in the land use data layer. If data on the lake were collected during wet year rather than a dry year, the lake's volume would be higher than normal, affecting its location on the map. If land use data for the same area were collected during a dry year, the boundary of the lake on the two layers would not be the same.

Logical inconsistencies usually do not appear until the two maps are overlaid and the boundaries do not coincide. All maps are therefore adjusted so that the feature on each data layer lines up with the same feature on the base map.

## 2.1.5 Completeness

Completeness focuses on the adequacy of data collection procedures. Robinson and Frank (1985) reported two kinds of uncertainty associated with collecting spatial data that can lead to error. One is the inability to measure or predict the exact characteristic or event with certainty. Examples of this are blunders in data collection or measurement error. The other kind of uncertainty is associated with concepts that are inherently ambiguous (e.g. problem of defining
boundary between two land uses).

Although it is known that spatial data are variable, classification systems generally ignore the second type of uncertainty. Everett and Simonett (1976) thus reasoned that mapping spatial data is a function of how humans aggregate and disaggregate data either in space, categories, quantities, or time; spatial data seldom exist in nature the way maps depict them.

Data are therefore seldom complete because analysts use classification rules to indicate how homogenous an area must be before it is classified a particular way (e.g., more than 50 percent, more than 75 percent). Another decision to be made is where to draw the boundary between two different areas; it is not always clear where a forest ends and a rural development begins.

### 2.1.6 Temporal Accuracy

Collecting data at different times introduces error because the variable may have changed since data collection. The effect of time, referred to as the date of the source data has daily, weekly, seasonal, or annual cycles that are important to consider. For example, obtaining land use data from remotely sensed imagery in the dry season for Northern Nigeria produces a very different land use map than data analysts obtain during the wet-growing season.

Demographic and land use information characteristically changes quickly in rapidly urbanizing area. Data collected at several times can thus produce logical inconsistency between data layers, forcing the analyst to adjust the location of features to coincide with the base map. Another problem with collecting data at different times as noted by Burrough (1986) is that data may be collected using different standards, which may not be apparent to the user.

## 3.0 Sources of Errors in GIS

Having the terminology to describe flaws in data is a good first step to providing quality GIS. Awareness of the sources of problems comes next. Spatial and attribute errors can occur at any stage in a GIS project. Sources of errors in a GIS are therefore discussed in this section of the paper.

### 3.1 Errors arising from our understanding and modeling of reality

The different ways in which people perceive reality can have effects on how they model the world using GIS. Our perception of reality influences our definition of reality and in turn our use of spatial data. This can create real errors and often gives rise to inconsistencies between data collected by different surveyors, maps drawn by different cartographers, and databases created by different GIS users.

### 3.2 Errors in source data for GIS

Our models of reality in GIS are built from a variety of data sources including survey data, remotely sensed and map data. All sources of spatial and attribute data for GIS are likely to include errors. Some examples of data are examined below to identify possible errors.

**Survey data** can contain errors due to mistakes made by people operating equipment or recording observations, or due to technical problems with equipment. For example, GPS errors in spatial data might occur if there were technical problems with the receiver. Errors in attribute data could occur if features were recorded incorrectly by the operator. Attribute errors would occur if the characteristics of the respondents were wrongly allocated, or incorrectly noted.

**Remotely sensed, and aerial photography data** could have spatial errors if they were spatially referenced wrongly, and mistakes in classification and interpretation would create attribute errors.

### 3.3 Errors in data encoding

The process of encoding/inputting spatial and attribute data can introduce error. The major sources of input error are manual entry of attribute features and scanning or digitizing spatial features. Manual entry errors include incomplete entry of attribute data, entering the wrong attribute data, or entering the right attribute data at the wrong location. Digitizing errors originate from equipment, personnel, or the source material.
Digitizing errors, such as under and overshoot of lines, and polygons that are not closed, can introduce error. As illustrated in figure 1 below, the various major errors during digitizing are illustrated in the ArcView GIS package. Although GIS software can “snap” lines together that really do not connect, depending on the tolerance selected, this can result in the movement of both lines, which can decrease the accuracy of the resultant map.

<figure><img src="image_2.png" /><figcaption>Fig. 1. Representation of Common Digital Errors</figcaption></figure>

## 3.4 Types of Digitizing Errors

### 3.4.1 Personnel Errors

* Changes in the origin

* Incorrect registration of the map on the digitizing table

* Creation of over-and undershoots

* Creation of polygons that are not closed

* Incomplete spatial data when data are not entered

* Duplication of spatial data when lines are digitized twice

* Line-following error (inability to trace map lines perfectly with the cursor)

* Line-sampling error (selection of points used to represent the map)

* Physiological error (involuntary muscle spasms)

### 3.4.2 Equipment Errors

* Digitizing table (center has higher positional accuracy than the edges)

* Resolution of the digitizer

* Differential accuracy depending on cursor orientation

### 3.4.3 Errors in Source Material

* Distortion because source maps have not been scale-corrected

* Distortion due to changes in temperature and humidity

* Necessity of digitizing sharp boundary lines when they are actually gradual transitions

* Width of map boundaries (0.4mm) digitized with a 0.02mm accuracy digitizer.

## 3.5 Analysis/Manipulation Errors

GIS analysis/manipulation functions, designed to transform or combine data sets, also can introduce errors. These errors originate from the measurement scale used or during data conversion (vector to raster and raster to vector), map overlay, generalization, converting small-scale to large-scale maps, and other analysis functions as discussed below.

## 3.6 Measurement Scale

Four measurement scales can depict spatial data: nominal, ordinal, interval, or ratio scales. A name or letter describes nominal data (e.g. land use type). Ordinal or ranked data have an order to them such as low, medium, and high. Interval data have a known distance between the intervals such as 0, 1 to 5, 6 to 9, more than 9. Ratio data are similar to interval data except ratio data have a meaningful zero (e.g. temperature on the Kelvin scale).

Often during GIS operations, analysts convert interval or ratio data into nominal data (e.g. low slope is 0 to 3 percent, medium slope is 4 to 10 percent), resulting in a loss of information. Analysts should preserve the original slope values in the GIS in case the user later wants to modify the classification scheme. Robinson and Frank (1985) however described the tradeoff between information content and the meaning that can be derived from it, which partly helps explain why interval data are frequently converted to nominal data. They identify a continuum progressing from nominal data, that is highly subjective, has low information
content, and high meaning (low slope means something to the average user) to ratio data that has low subjectivity, high information content and low meaning (a slope of 7 percent may not mean much to the average user).

## 3.7 Data Conversion

Errors can occur in converting a vector map to a raster map or a raster map to a vector map. For instance, remotely sensed data are collected using a raster-based system. Using a vector GIS, however, requires conversion from raster to vector data. The size of the error depends on the conversion algorithm, complexity of features, and grid cell size and orientation (Lunetta, et al, 1991).

A line on a vector map converted to a raster map has lower accuracy in the raster representation because vector data structures store data more accurately than raster ones (Griffin, 1994). When polygons in a vector GIS are converted to a raster GIS, the coding rule usually used assigns the value that covers the largest area within the cell of a categorical map to the entire cell. Displayed in figure 2 below is an example of points, lines and polygon in vector format converted to raster form using the ArcTool Box module of the ArcGIS Desktop Application. For example, when placing a grid over a vector map with an urban land polygon adjacent to an agricultural polygon, the cell placement can include part of both polygons. If the resultant cell comprises 51 percent urban and 49 percent agricultural land, the cell is assigned 100 percent urban. Converting a numerical map between raster and vector systems requires spatial interpolation procedures. GIS software packages use different interpolation methods that can produce different output even when using the same input data (this is an inherent error source).

<figure><img src="image_3.png" /><figcaption>Fig.2. Vector – Raster Conversion of Points, Lines and Polygons</figcaption></figure>

## 3.8 Map Overlay

Map overlay, used extensively in planning and natural resource management, is the combining of two or more data layers to create new information. In a vector GIS, slivers or spurious polygons can result from overlaying two data layers to produce a new map. When combining the data layers, lines do not coincide, resulting in the creation of a new polygon or sliver (an error) that did not exist on either layer (see fig. 3).
<figure><img src="image_4.png" /><figcaption>Fig.3. Silver Example</figcaption></figure>

According to Newcomer and Szajgin (1984), the number of map layers, accuracy of each map layer, and the coincidence of errors at the same position from several map layers all determine the accuracy of the map overlay procedures.

## 3.9 Transforming Small-Scale Maps to Large-Scale Maps

Data mapped at a small scale are subject to different accuracy standards than data mapped at a large scale. According to Connin (1994), problems with accuracy arise when positions are reported to decimal parts of a foot or meter, but the method of data capture may cause the positional error to be as much as hundreds of feet or meter. Yet when converting the data from small-to-large-scale, the data appear to have the accuracy of the large-scale map. Converting small-scale maps to large-scale maps is therefore advisable only if the analyst fully understands the effect of this procedure on map quality.

### 3.10 Output Errors

A variety of errors are associated with data output:

* Output devices create error.

* Paper shrinks and swells.

* Line implies certainty that may not exist because boundaries are gradual.

* A cell or polygon implies homogeneity.

* Scale can be modified to imply higher accuracy than exists in the source data.

* Precision can be modified to imply higher precision than exists in the source data.

Depiction of symbols and colours may not follow convention.

From the preceding discussions it should be clear that all GIS databases would contain error. In addition, further errors will be introduced during manipulation and analysis of data. Therefore, it is inevitable that all GIS output, whether in the form of a paper map or a digital database, will contain inaccuracies. The extent of these inaccuracies will depend on the care and attention paid during the construction, manipulation and analysis of the databases.

### 4.0 Managing GIS Errors

We must accept that no matter how careful we are in the preparation of our data and how cautious we are in our choice of analysis, errors will find their way into the GIS database (Heywood, Cornelius and Carver, 1999).

One major aspect of a GIS is data acquisition, which deals with the precision of geospatially acquired data. To manage these errors we must track and document them. Data quality parameters (accuracy, precision, resolution, completeness, consistency, etc.) provide a useful checklist of quality indicators. Documenting these for each data layer in a GIS, and for any new layers produced by analysis, will help subsequent users of the GIS to keep track of data quality.

## 5.0 Conclusion

No doubt, GIS is a powerful tool for analyzing spatial data. Everyone who uses GIS-generated output, however, must be aware of source errors and operational errors introduced during data input, storage, analysis/ manipulation, and output. Error in GIS is a 'fact of life" but not an intractable problem. Adopting good practice in terms of data capture and analysis should, in most instances, be sufficient to ensure errors are kept to a minimum. Increased awareness of the sources and magnitude of error can help environmental decision makers determine if data are appropriate for their use. Conscientious documentation and the incorporation of lineage information and quality statements on GIS output will help to ensure that end-users of GIS products are aware of their limitations.
## REFERENCES

Aronoff, S. (1989): Geographic information systems: A management perspective. Ottawa, Ontario: WDL Publications.

Burrough, P.A. (1986): Principles of geographical information systems for land resources assessment. New York, NY: Oxford University Press.

Chrisman, N.R. (1984): The role of quality information in the long term functioning of a geographic information system. *Cartographica*. **21**(2): 79-87.

Connin, R. (1994): The trouble with data. *Professional Surveyors*, **14**(1): 24-25.

Everett, J., and Simonett, D.S. (1976): Principles, concepts and philosophical problems in remote sensing. In: Lintz, J., and Simonett, D.S., Eds. *Remote sensing of the environment*. Reading, MA: Addison-Wesley.

Griffin, C. B. (1994): Data quality issues affecting environmental problem-solving. National Conference on Environmental Problem-solving with Geographic Information Systems. Cincinnati, Ohio; September 21-23.

Heywood, I., Cornelius, S., and Carver, S. (1999): An introduction to geographical information systems. New York: Pearson Education Inc.

Jones, C.B. (1998): Geographical information systems and computer cartography. England: Longman.

Kang-Tsuang Chang (2006): Introduction to Geographic Information Systems, 3rd Edition MacGraw Hill Higher Education, New York.

Lunetta, R.S., Congalton, R.C., Fenstermaker, L.K. Jenson, J.R., McGwire, K.C. and Thinney, L.R. (1991): Remote sensing and geographic information system data integration: Error sources and research issues. *Photogrammetric Eng. and Remote Sensing*. **57**(6): 677-688.

Newcomer, J.A., and Szajgin, J. (1984): Accumulation of thematic map errors in digital overlay analysis. *Amer Cartographer*. **11**(1): 58-62.

Openshaw, S. (1989): Learning to live with errors in spatial databases. In: Goodchild, M., and Gopal, S. Eds.

The accuracy of spatial databases. Bristol, PA: Taylor and Francis. pp 263-276.

Robinson, V.B., and Frank, A.U. (1985): About different kinds of uncertainty in collections of spatial data. In: *Proceedings of digital representations of spatial knowledge*. Washington, DC. Auto-Carto 7. Falls Church, VA: ASPRS and ACSM. pp 440-449.

Somers, R. (1994): Is a GIS department the answer? *Geo. Info. Sys*. **4**(6): 29-32.

Thapa, K., and Bossler, J. (1992): Accuracy of spatial data used in geographic information systems. *Photogrammetric Eng. and Remote Sensing*. **58**(6): 835-841.