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gi systems and science february 6 2012 n.
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Data entry and editing PowerPoint Presentation
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Data entry and editing

Data entry and editing

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Data entry and editing

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  1. GI Systems and Science February 6, 2012 Data entry and editing

  2. Points to Cover • A concept of data stream • Data encoding • Database Management System (DBMS) • Data editing • Finding and correcting errors

  3. Concept of Data Stream • The process of data encoding and editing is also known as ‘data stream’

  4. Concept of Data Stream Figure 4.4 Source: Heywood et al., 2011 Figure 5.1 Source: Heywood et al., 2011

  5. Concept of Data Stream • Specific steps of this process and methods used will depend on: • Source of spatial data • Analogue data • Digital data • Type, format, scale or resolution of spatial data • The need for and importance of universal data standards

  6. Data Encoding • Data encoding is the process of getting data into the computer • Various methods of data input exist depending on data source, project requirements and available resources

  7. Data Encoding • Data encoding is the process of getting data into the computer • All data in analogue format need to be converted to digital form • Digital data do not need to be encoded but most often than not require to be converted into a proper format

  8. Data Encoding • Manual digitizing • Most common method of encoding spatial features from paper maps and hard copy aerial photos • Box 5.1, page 138: Using a manual digitizing table • Key step • Registration of a map using control points Figure 5.2 Source: Heywood et al., 2011

  9. Data Encoding • Manual digitizing • Two modes of digitizing • Point mode • Stream mode • The accuracy of data generated by this method depends on many factors, including ‘hand-wobble’ • Quite time consuming and expensive • ArcGIS (ArcInfo version) has ‘on-screen’ digitizing capabilities • Consult Editing and data compilation section of ArcGIS Help files

  10. Data Encoding • Scanning • One of the automatic digitizing methods • Produces raster data • Useful way to create background images used in on-screen digitizing • Box 5.3, page 143: Using a scanner • ArcGIS does not have scanning capabilities Figure 5.7 Source: Heywood et al., 2011

  11. Data Encoding • Electronic data transfer • Includes downloading data from GPS and survey and monitoring equipment • This data may require geolocation • Most often than not includes data conversion to a format understood by your GIS • Check ArcGIS Help files to find what data format are supported • Finding spatial data on-line • Box 5.8 on page 154 of the text • U of R Library

  12. Data Editing (Cleaning) • Once entered, data almost always needs to be corrected and manipulated to ensure that their structure is consistent with your GIS requirements or capabilities • Issues that may have to be addressed at this stage of the GIS project • Correctingerrors in the data • The re-projecting of data from different sources to a common projection • The generalization of complex data to provide a simpler dataset • The matching and joining adjacent map sheets once the data are in digital form

  13. Data Editing (Cleaning) • Finding and correcting errors • Errors in input data may derive from three main sources • In the source data • Introduced during encoding • Propagated during data transfer and conversion • Ways to check for errors in attribute data • Checking for outliers • Checking internal consistency • Constructing trend surfaces • Box 5.9 on page 156 of the text

  14. Data Editing (Cleaning) • Finding and correcting errors • Possible errors in spatial data • Vary depending the data model and method of data encoding • Possible errors in vector data • Created in the process of digitizing • ArcGIS (ArcInfo version) has a suite of editing tools for removal of errors in vector data • Possible errors in raster data • Missing entities • Noise • Usually corrected by filtering

  15. Data Editing (Cleaning) • Re-projection and transformation • Data derived from maps drawn on different projections will need to be converted to a common projection system before they can be combined or analyzed • Data derived from different sources referenced using different co-ordinate systems need to be transformed to a common coordinate system • Project tool in ArcGIS

  16. Data Editing (Cleaning) • Generalization • Data derived from larger-scale maps should be generalized to be compatible with the data derived from the smaller-scale maps • Vector data • Weeding out superfluous points from lines so that the general shape of lines is preserved • Raster data • Aggregation of cells with the same attribute values • Filtering • Reflection Box on page 171