Vector-based mapping is emerging as a preferred format in Location-based
Services(LBS), because it can deliver an up-to-date and interactive map visualization.
The Progressive Transmission(PT) technique has been developed to
enable the ecient transmission of vector data over the internet by delivering
various incremental levels of detail(LoD). However, it is still challenging to apply
this technique in a mobile context due to many inherent limitations of mobile
devices, such as small screen size, slow processors and limited memory. Taking
account of these limitations, PT has been extended by developing a framework of
ecient data management for the visualization of spatial data on mobile devices.
A data generalization framework is proposed and implemented in a software
application. This application can signicantly reduce the volume of data for
transmission and enable quick access to a simplied version of data while preserving
appropriate visualization quality. Using volunteered geographic information
as a case-study, the framework shows
exibility in delivering up-to-date spatial
information from dynamic data sources.
Three models of PT are designed and implemented to transmit the additional
LoD renements: a full scale PT as an inverse of generalisation, a viewdependent
PT, and a heuristic optimised view-dependent PT. These models are
evaluated with user trials and application examples. The heuristic optimised
view-dependent PT has shown a signicant enhancement over the traditional PT
in terms of bandwidth-saving and smoothness of transitions.
A parallel data management strategy associated with three corresponding
algorithms has been developed to handle LoD spatial data on mobile clients.
This strategy enables the map rendering to be performed in parallel with a process
which retrieves the data for the next map location the user will require. A viewdependent
approach has been integrated to monitor the volume of each LoD for
visible area. The demonstration of a
exible rendering style shows its potential
use in visualizing dynamic geoprocessed data. Future work may extend this
to integrate topological constraints and semantic constraints for enhancing the
vector map visualization
