No class monday
Wed - History of Maps video
Data Types and the NSDI
Data Input
- the number 1 bottleneck in GIS applications. Often 80%+
of the project cost
- Need to automate data input process, but that causes problems.
Common problems/considerations include:
- map projections, scale, coordinate system
- raster/vector conversions
- paper distortions
- error corrections
- control points
- discrepancies across map sheets
- user fatigue and boredom
- Modes of data input include the keyboard, scanners, direct conversion
from other digital data, and voice input
Socio-Economic data
- Generally include: (may be aggregate or disaggregate)
- demographics
- housing
- migration
- transportation
- economics
- retail
- Sources of Socio-economic data
- field surveys
- government statistics
- government administrative records
- other stuff, including marketing info, mailing lists, etc.
- Issues in using socio-economic data
- cost
- documentation
- data quality
- data conversion
- aggregation
- accuracy of location
Environmental and Natural Resource Data
- Purposes of resource-based GIS are primarily
- Inventory tool
- better manage the marketing of the resource
- protect the resource from improper development
- model the complex interactions between phenomenae so that
predictions can be made
- Contents of Environmental databases include not only natural
information, but info regarding any human impacts on the area.
- General notes on resource data
- Comparatively static
- generally low spatial resolution
- typically raster
- Often uses satellite imagery.
- We then went over an example of all the data that might be required
for siting a waste incinerator.
National Spatial Data Infrastructure (NSDI)
- Concept not new
- more and more people requiring spatial data
- to maximise benefits, dataset must be accessable
- benefits: decisions only as good as the information upon which
they are based.
- provide government with reliable, consistent, timely data
- Provide community access to data
- minimise waste & duplication of datasets
- common standards allow maximum integration of datasets
- Current shortcomings and obstacles
- knowledge of data availability
- metadata
- access impeded or not possible
- inconsistent policies regarding data use
- fundamental datasets incomplete/out of date
- lack of data consistency across nation
- pricing
- duplication among agencies/agency competition
- national security considerations
- enforcing standards
- low levels of customer focus
- poor/inconsistent funding
Finally, we spent some time briefly discussing SDTS:
Spatial Data Transfer Standards.