Hydrographic Data and GIS
83
Data Suitability
Data may be readily available, of excellent quality and compatible with the leading
makes of GIS, but it may still not be "fit for purpose". Data not only has to be
compatible with the target GIS, it also has to be compatible with the application with
which it is to be used.
Two main aspects are important in this context, content and structure.
For data to be fit for purpose it must contain all the elements needed for the
application.
For example, a dataset might contain detailed information on the
composition of sediments on the surface of the seabed, but it could not be used to
calculate the volume of sediment available for extraction as aggregates if the dataset
contained no information on the depths of the sediments. Similarly, most surveys
collected by national hydrographic offices are at scales that are too small for many
engineering applications. Large scale surveys are needed, for example, to plan coastal
defence schemes and to determine the most suitable location for submarine pipelines,
cables and outfalls.
Different applications also require data to be structured in different ways.
Most applications require geospatial data for one of three purposes: to create a map or
chart as a back-drop to other information, to monitor change or to model processes.
Maps and charts can be constructed from dumb raster or unstructured vector data if
users do not need to be able to de-select data or interact with any of the elements
shown on the map. Intelligent vector data will be required if users do need to do
either of these things. Similarly, for monitoring change, both raster and vector data
can be used. Changes to spatially continuous data, such as sea surface or ocean
temperatures, are most easily compared using raster or tessellated data. This applies
especially if remotely-sensed data is used. Vector data is more suitable for monitoring
changes to discrete features, such as the positions of coastlines, sandwaves, longshore
bars, offshore banks, etc. In addition to raster and vector data, gridded data is often
needed to model processes. For example, most computer models of tides, waves and
sediment transport require gridded bathymetric data as one of their main inputs.
These data sets can contain actual or interpolated depths and can be based on regular
or irregular grids that are rectangular, triangular or curvilinear in shape.
Way Ahead
Most users of GIS either cannot afford or are unwilling to pay a high price for data.
The UK Government has made it clear that any initiative aimed at providing coastal
zone mapping for use in GIS must be self-financing. This also applies to the supply of
any other data for which there is no endorsed defence or other national requirement.
If the OS and the HO are to satisfy the requirements of the UK Government and most
users, data must be cheap to buy and inexpensive to produce. At present it is not
possible to satisfy these requirements. The Coastal Zone Mapping Project showed that
although it is feasible to combine topographic and hydrographic data from the OS and
HO, this cannot be done easily or cheaply. Less complex datasets, such as digital
83
Data Suitability
Data may be readily available, of excellent quality and compatible with the leading
makes of GIS, but it may still not be "fit for purpose". Data not only has to be
compatible with the target GIS, it also has to be compatible with the application with
which it is to be used.
Two main aspects are important in this context, content and structure.
For data to be fit for purpose it must contain all the elements needed for the
application.
For example, a dataset might contain detailed information on the
composition of sediments on the surface of the seabed, but it could not be used to
calculate the volume of sediment available for extraction as aggregates if the dataset
contained no information on the depths of the sediments. Similarly, most surveys
collected by national hydrographic offices are at scales that are too small for many
engineering applications. Large scale surveys are needed, for example, to plan coastal
defence schemes and to determine the most suitable location for submarine pipelines,
cables and outfalls.
Different applications also require data to be structured in different ways.
Most applications require geospatial data for one of three purposes: to create a map or
chart as a back-drop to other information, to monitor change or to model processes.
Maps and charts can be constructed from dumb raster or unstructured vector data if
users do not need to be able to de-select data or interact with any of the elements
shown on the map. Intelligent vector data will be required if users do need to do
either of these things. Similarly, for monitoring change, both raster and vector data
can be used. Changes to spatially continuous data, such as sea surface or ocean
temperatures, are most easily compared using raster or tessellated data. This applies
especially if remotely-sensed data is used. Vector data is more suitable for monitoring
changes to discrete features, such as the positions of coastlines, sandwaves, longshore
bars, offshore banks, etc. In addition to raster and vector data, gridded data is often
needed to model processes. For example, most computer models of tides, waves and
sediment transport require gridded bathymetric data as one of their main inputs.
These data sets can contain actual or interpolated depths and can be based on regular
or irregular grids that are rectangular, triangular or curvilinear in shape.
Way Ahead
Most users of GIS either cannot afford or are unwilling to pay a high price for data.
The UK Government has made it clear that any initiative aimed at providing coastal
zone mapping for use in GIS must be self-financing. This also applies to the supply of
any other data for which there is no endorsed defence or other national requirement.
If the OS and the HO are to satisfy the requirements of the UK Government and most
users, data must be cheap to buy and inexpensive to produce. At present it is not
possible to satisfy these requirements. The Coastal Zone Mapping Project showed that
although it is feasible to combine topographic and hydrographic data from the OS and
HO, this cannot be done easily or cheaply. Less complex datasets, such as digital