The basic characteristics of GIS data quality and the common causes of errors

1 Basic concepts of data quality.

1 1 Accuracy

1 2 Precision

1 3 Spatial resolution

1 4 scale

1 5 error

1 6 Uncertainty

2 Spatial data quality issues**.

2 1 The instability of the existence of spatial phenomena themselves.

2 2 Expression of spatial phenomena.

2 3 Errors in spatial data processing.

2 4 Errors in the use of spatial data.

Data Processing Process Error**.

Data collection Field measurement error: instrument error, recording error.

Remote sensing data error: radiometric and geometric correction error, information extraction error.

Map data error: original data error, coordinate conversion, cartography synthesis and printing.

Data Input Digitization Error: Instrument Error, Operation Error.

Conversion errors of different system formats: raster-vector conversion, triangulation-contour conversion.

Data storage Numerical accuracy is insufficient.

Spatial accuracy is insufficient: each grid point is too large, and the minimum mapping unit of the map is too large.

The data processing classification interval is not reasonable.

Error propagation caused by multi-layer data superposition: interpolation error and multi-source data comprehensive analysis error.

Errors caused by scale bars that are too small.

Data Output: Error caused by an inaccurate output device.

Errors caused by instability of the output medium.

Data Usage: Misunderstanding of the information contained in the data.

Improper use of data information.

3 Spatial data quality control.

Data quality control is a complex process, and in order to control data quality, we should start from all the processes and links of data quality generation and diffusion, and use certain methods to reduce errors. Common methods for spatial data quality control are:

3 1 Traditional manual methods.

The manual method of quality control is mainly to compare the digitized data with the data source, the inspection of the graphic part includes the visual method, drawing on the transparent diagram and the overlay comparison with the original map, and the inspection of the attribute part adopts the comparison with the original attribute one by one or other comparison methods.

3 2 Metadata Method.

The metadata of the dataset contains a lot of information about the quality of the data, through which the data quality can be checked, and at the same time, the metadata also records the changes in the quality during the data processing, and the status and changes of the data quality can be understood by tracking the metadata.

3 3 Geography Correlation Law.

The quality of the spatial data is analyzed by the correlation of the geographic features and features of the spatial data. Therefore, when superimposing the data of the two layers of rivers and contours, if the location of the rivers is not on the convex connection line of the contour line, it means that one of the two layers of data must have quality problems, and if it is not certain which layer of data has problems, they can be further analyzed by superimposing them with other data layers with reliable quality. Therefore, a knowledge base of correlation between geographic feature elements can be established for the correlation analysis of geographic feature elements between various spatial data layers.

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Answer]: b, c, d, e

Any practical GIS has data quality problems, and there are five main types of data quality problems or data errors: Position accuracy. It is inevitable that there will be errors in the surveyors, and in many cases, it is impossible to increase a lot of workload and invest a lot of equipment in order to improve accuracy; Attribute precision.

Errors often occur due to negligence in the process of investigation, registration, classification, and coding of attribute data. Logical consistency. In the process from investigation to input into the computer, there are often problems such as poor data classification, ambiguous data definitions, multiple interpretations or multiple definitions, which will bring trouble to the application. Completeness. It is also a common data quality problem that the basic data cannot be fully covered in geographical space, different historical data cannot be synchronized in time, and the content of the survey is deficient. The human factor.

For some profit or confidentiality reasons, it is necessary to artificially create defects and increase errors.

To evaluate the data quality of GIS, please analyze the data error from data source acquisition to data compression**.

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To evaluate GIS data quality in this way, please analyze the data error from data source collection to data compression**: The main indicators of GIS data quality include data accuracy, data accuracy, data integrity, data consistency, data timeliness, etc. From the data source collection to the data compression process, there are many data errors, mainly including the following aspects:

1.Human error: During data collection, human factors may lead to data errors.

For example, the operator is improper, negligent, or does not take measurements according to regulations. Such errors can be avoided with rigorous training and supervision. 2.

Instrument error: During data acquisition, there may also be errors in the instrument itself. For example, the calibration of equipment is inaccurate, and the accuracy of the equipment is reduced due to the aging of the equipment collapse.

Such errors can be solved by timely maintenance and replacement of equipment. 3.Atmospheric Impacts:

In the process of data collection, the influence of atmosphere is also an important factor that leads to data error. For example, the effect of atmospheric turbulence on radar measurements, the effect of atmospheric disturbances on camera imaging, etc. Such errors can be corrected by building a weather forecast model or by performing a later correction of the data.

4.Data conversion error: In the application process of GIS, the data may have to go through multiple transformations, for example, after obtaining data from GPS, it needs to be converted by software for coordinate conversion, projection transformation and other operations before further use.

Errors can also occur in this process. Such errors can be addressed by selecting the appropriate conversion algorithm and minimizing the number of conversions. 5.

Data compression error: In the process of GIS data transmission and storage, it is often necessary to compress the data. Errors can also occur during data compression.

For example, for boundaries of high-precision data, compression may reduce the accuracy of the boundary. This error can be avoided by selecting the appropriate compression algorithm and setting the appropriate compression parameters. In order to ensure the quality of GIS data, it is necessary to comprehensively consider the errors in the processing process, and take corresponding measures to correct them.

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The process of quality control of data in GIS typically involves the following steps:1Data Acquisition:

Obtain data from reliable sources, such as those provided by institutions, professional organizations, or certification bodies. Ensure that your data is reliable and accurate. 2.

Data inspection: The data is examined to identify potential errors and inconsistencies. These errors include null values, duplicate values, parameter exceptions, and more.

Adopt review and testing tools such as ArcGIS Data Reviewer as part of this process. 3.Data Cleansing:

Corrects errors found and removes duplicate or unnecessary data. You can use ArcGIS tools, such as Feature Layer and Attribute Table, to remove or modify incorrect attribute values. 4.

Data verification: through statistical analysis, spatial analysis, etc., to evaluate whether the quality of the data meets the specified standards. 5.

Documentation: Document each step and the changes made and create metadata for the data to ensure that it can be understood and used by other users. In summary, data quality control in GIS requires multiple considerations, with the ultimate goal of ensuring a trusted GIS database.

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The Department of Geographic Information System (GIS) is an emerging discipline integrating computer science, informatics, geography and other sciences.

It is a spatial information system that uses the theories of systems engineering and information science to scientifically manage and comprehensively analyze geographic data with spatial connotation with the support of computer software and hardware, so as to provide information required for planning, management, decision-making and research. GIS is defined as a computer system that collects, stores, processes, analyzes, retrieves, and displays spatial data. It is a discipline and a technology for the related processing of spatial data.

At present, GIS has a wide range of applications in all walks of life, such as in the land industry, planning industry and so on. As long as it is related to spatial information, GIS can be used. Laughing hunger.

Extended Materials. 1. Characteristics 1, the public concept of the big concept of the hidden positioning of the foundation; 2. Have the ability to collect, manage, analyze and output a variety of geospatial information; 3. The system is driven by analysis models, with strong spatial comprehensive analysis and dynamic capabilities, and can generate high-level geographic information; 4. For the purpose of geographical research and geographic decision-making, it is a human-computer interactive spatial decision support system.

While GIS technology has made progress in the field of environmental resources, it is undeniable that there are still many problems in the application of GIS, which are mainly manifested in:

Data** and data quality are difficult to guarantee (data** is extensive, but the data quality is not high). Resource and environmental issues involve various disciplines such as soil science, environmental science and geography, and their influencing factors are complex, requiring a large amount of data and high quality. However, due to the limitations of instruments and equipment, as well as manpower and material resources, many data are difficult to obtain.

And the existing data is often due to the fact that the data is not.

1. It is difficult to ensure the quality of land resources and ecological environment data due to different data formats and different ages, especially the different data formats, which make it difficult to share data in various regions, which seriously affects the application of GIS. At the same time, the most basic feature of GIS is that each data item has spatial coordinates, while the traditional manual collection and field survey data have poor spatial positioning ability, and often replace the surface with points, which inevitably brings various errors. Therefore, data accuracy and data accuracy have always been a "bottleneck" for GIS technology to truly solve resource and environmental problems.

The application level is low, and the resource and environmental management geographic information system is still at the level of simple resource browsing and query, mapping and simple analysis, while the professional application system in the real sense of rational allocation of resources and environment and decision support is still very lacking;

The function of GIS has not been fully utilized, and the limitations of managers' awareness level, basic data, and model methods have made the spatial analysis function of GIS not effective in resource and environmental management.

Standards and norms are not uniform.

First, the degree of data sharing is low, due to the strong professionalism of resource and environmental management, there are great differences in technical standards, data exchange standards, metadata standards and other aspects in the process of establishing the corresponding GIS, which makes it difficult to share between different information systems;

The degree of integration is low, many resource and environmental management GIS functions are relatively single, the system structure is poorly developed, and the integrated application with global positioning system and remote sensing information is not realized, which is difficult to meet the needs of the development of modern resource and environmental management in the direction of integration and integration.

(1) Metal protrusion defects: mainly formed by spikes on high-voltage conductors;

2) Free metal particle defects: the shape is powdery, flake or large-size solid particles, etc., which can jump or displace under the action of electric field force;

3) Insulator surface defects: caused by damage caused by partial discharge, metal particles or water vapor in insulating gas;

4) Air gap defects: mainly include the air gap inside the insulator and the air gap defect at the interface between the insulator and the high-voltage conductor.