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