Copying or distributing in print or electronic forms without written permission
of Idea Group Inc. is prohibited.
The preceding differences can be controlled algorithmically and are currently handled
by commercial software and interoperability standards. However, there remains
other sources of distortion that cannot be controlled totally when dealing with multiple
sources of data: diversity of measurement instruments, inherent imprecision
of the measurement methods and tools used, data acquisition specifications that
evolve over the years, limitations of the human interpretation of measured phenomena,
independent data update policies, conflicting priorities over data quality,
and so forth. The overall results are spatial data integration problems that cannot
be avoided. Such problems happen, for example, when one integrates updates to an
existing dataset (e.g., original maps can have been made from aerial photographs
but updates may be coming from field surveys required by municipal bylaws).
They also happen when one integrates data from two adjacent maps made by two
municipalities. They also take place when integrating different data collected independently
for different purposes such as land use maps and utilities maps. They
also occur when using real-time GPS vehicle tracking over a road map made from
satellite imagery. Many more examples could be presented to explain why spatial
Figure 2.
Pages:
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558