A Java-based tool for determining if spatial objects (polygons) require simplification before delivery to a mobile device using a Location-based Service (LBS) was developed. Visualisation of vector-based spatial data on mobile devices is constrained by: small screen display size; small data storage on the device; and potentially poor bandwidth connectivity. Our Java-based tool can download OpenStreetMap XML data in real-time and calculate a number of shape complexity measures for each object in the data. From these measures an overall complexity score is assigned to the data. If this complexity score is above a pre-defined threshold, specific to LBS, then the data is passed to a related software component which generalises (simplifies) the data. There are a number of advantages to this approach: the tool is completely web-based and runs free and open source software; all XML data processing is performed “on-the-fly” and the tool can be used for OpenStreetMap data anywhere in the world. We feel that this tool can become part of a very useful and efficient pre-processing step in the delivery of OpenStreetMap data to mobile devices accessing LBS

Development of a Model for Selective Progressive transmission ofDevelopment of a Model for Selective Progressive transmission of Geospatial Data based on Shape ComplexityGeospatial Data based on Shape Complexity

Fangli Ying, Peter Mooney, Padraig Corcoran and Adam C. Winstanley

Due to the limited bandwidth available to mobile devices transmitting large amounts of geographic data over the Internet to these devices is challenging. Such data is often high-resolution vector data and is far too detailed for most location-based services (LBS) user requirements. A less detailed version may be sent prior to the complete dataset to users’ mobile displays using a progressive transmission strategy. Progressive transmission is generally performed by transmitting a series of independent pre-computed representations of the original dataset at increasing levels of detail. The transitions between these are not necessarily smooth. These strategies do not consider the fact that certain features are more suitable candidates for transmission than others – for example the transmission of the most significant details first. To overcome these problems a model is proposed for selective progressive transmission which will provide a smoother transmission over increasing levels of detail. We define criteria for the comparison of similarity between the progressive states of the vector-data based on shape complexity of the polygon features. This allows us to develop a real-time strategy for the progressive transmission of vector data over the Internet to mobile devices.

Java Application Development

A model for progressive transmission of spatial data based on shape complexity has been proposed. The target area of implementation is in the delivery of spatial data to mobile devices accessing Location Based Services (LBS). As described in the example, our model aims to provide a smooth transmission between data levels of detail. The outcome of the user tests show the significant relationship between data reduction and usability of the multi-resolution representation of geospatial data. The empirical findings show the potential use of shape complexity to improve the user experience of levels of details for progressive transmission.

Research presented in this poster was funded by a Strategic Research Cluster Grant (07/SRC/I1168) by Science Foundation Ireland under the National Development Plan. The authors gratefully acknowledge this support.

What is Progressive Transmission?

Conclusions

A flowchart of components for this selective progressive transmission An example of user test

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Standard deviationMean Scores

Users were given 10 maps in five levels, a ten point rank scale was used in the questionnaire from (1) ‘‘strongly disagree with the representation’’ to (10) ‘‘strongly satisfied”. This broad scale allows users a wide range of possible answers to correctly express their opinion of their satisfaction to the maps. The two tables shown below are mean scores for these users’ votes as well as standard deviations of the differences of their opinions. Another diagram shows how users’ opinion changed over the progressive transmission in levels of detail for all test cases.

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