Data Input and Editing
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Transcript Data Input and Editing
GI Systems and Science
February 6, 2012
Points to Cover
A concept of data stream
Data encoding
Database Management System (DBMS)
Data editing
Finding and correcting errors
Concept of Data Stream
Formulating
research
question
Collecting
data
Creating
data model
Entering
data into a
GIS
The process of data encoding and editing is also
known as ‘data stream’
Concept of Data Stream
Figure 4.4
Source: Heywood et al., 2011
Figure 5.1
Source: Heywood et al., 2011
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
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
Source type
Data source
Applicable encoding methods
Resulting data type
Analogue
data
Tabular data
Keyboard entry
Attribute data
Text scanning
Geocoding
Vector data
Geolocating
Map data
Manual digitizing
Vector data
Automatic digitizing
Aerial
photographs
Field survey data
Scanning
Raster data
Manual digitizing
Vector data
Automatic digitizing
Scanning
Raster data
Keyboard entry
Vector data
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
Source type
Data source
Applicable encoding methods
Digital data
Tabular data
Digital data transfer which may
include
Map data
Aerial photographs
Field survey data
Data conversion
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
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
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
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
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
Correcting errors 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
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
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
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
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