Presentations

The vision of the Land Gazette is to develop new communication tools between government and the public in order to provide electronic public notice about land and land use, and an information and feedback mechanism that improves public participation into the land management processes. After evaluating the potential impact of the Land Gazette, the author will discuss its future improvements and trends in order to achieve better public participation in land management.

A data processing method - the "GPS-based geometric tracking strategy" - is proposed which consists of a kinematic sequential least squares filter/smoother. This strategy requires only GPS measurements and freely-available related information. It is therefore computationally more efficient than the conventional tracking approach.

To test the performance of this strategy the filter/smoother has been realised in computer code and applied to simulated data to assess the precision of the LEO position estimates. Results indicate that sub-decimetre precision of the LEO position in each Cartesian component is achievable with only tens of minutes of processing time. The position noise attributable to each error source was also determined and shows that the greatest error source is the GPS receiver measurement noise.

The objective of this paper is to examine DNC documentation and present enough relevant information to provide the non-DIGEST expert with an understanding of he product. The report examines the DNC/DIGEST relationship, the VPF/DNC data model, as well as the implementation of the standard. In the final sections of the report, the DNC update problem is explained and an end-user solution using a corrections layer is recommended.

In this paper, the basic concepts, properties, and computing algorithms of wavelets are introduced and compared to traditional signal processing methods. As an example of a wavelet application, the de-noising (removing the random noise in a signal and the artifacts in an image) of ship-track artifacts in multibeam sidescan imagery is presented. This is almost impossible to achieve using traditional signal processing methods.

As a part of the effort towards the construction of the 'one centimetre geoid' for Canada, the effects of lateral topographical density variation on gravity and geoid were investigated in the area of the Canadian Rocky Mountains. Density values were estimated from the geological maps of Canada and the United States and bedrock density tables were compiled for the use in the Arcview GIS. The 5' by 5' mean and point effects were computed from height and density data on a 30'' by 60'' grid. The mean direct (topographical) density effect (DDE) on gravity ranges between -7.9 and 3.8 mGal (mean of 0.001 mGal), at the earth surface, and from -22.3 to 13.8 mGal (mean of 0.002 mGal), at the geoid. The secondary indirect (topographical) density effect (SIDE) on gravity varies between -11 and 7uGal. The primary indirect (topographical) density effect (PIDE) on geoid changes from -6.7 to 4.3cm (mean of 0.5 cm). The total topographical density effect on the geoid ranges between -12.9 and 6.4 cm (mean of 0.8 cm). Our results suggest that the effect of topographical density lateral variations is significant enough and ought to be taken into account for the determination of the one centimetre geoid.

This paper introduces an aggregation index, L, that can be used to analyze spatial clusters formed using one variable. The index is originally derived in a research to aggregate contiguous areas to form clusters of similar characteristics. It is later found to be very useful in the general study of spatial association (identifying clusters of similar and dissimilar values), as performed by other indicators such as the Moran's I, Moran scatterplot and Gi* statistic. This paper looks into this particular application of the index in identifying clusters of similar and dissimilar values. It shows the derivation of the index, its properties and advantages.

The index measures the homogeneity within a cluster and heterogeneity between the cluster and a reference (e.g., grand mean). Homogeneity is the sum of squared difference between the observations within the cluster and their mean. Heterogeneity is the sum of squared difference for each observation between the mean of observations in the cluster and the reference. The aggregation index, L, for a cluster is the ratio of its heterogeneity over its total (sum of homogeneity and heterogeneity, as defined above). It measures both homogeneity and heterogeneity of a cluster by one number that falls between zero (when the mean of observations of the cluster equals the reference) and one (when all observations within the cluster are the same). Comparisons are made between the aggregation index, L, and the local Moran's I, local G statistics and Moran scatterplot used to explore different nature of local spatial clusters.

This report identifies the need for integration of command and control systems with simulations. The possible approaches to implement this integration are examined in terms of their advantages and disadvantages. Examples of current research and development in the United States, the United Kingdom and Canada are reviewed. Finally, a project to integrate computer based simulations with automated command and control systems currently in service with the Canadian Army will be introduced.

The DTMs have gone through a series of stringent quality control checks to eliminate blunders and ensure the elevations of data points re blunder-free and all fall within specified accuracy tolerances. However, users of these DTM's have continued to express concern over perceived data quality based on the evidence of a regular "ridging" effect within many of the DTM files when viewed under certain conditions. Aware of similar phenomena found in DTMs produced by other organizations, Service New Brunswick (SNB) commissioned researchers in the Department of Geodesy and Geomatics Engineering at the University of New Brunswick to investigate the respective requirements and potential costs existing alternatives for batch processing of the DTMs to remove this ridging effect.

This paper presents the results of research examining a specific compensation approach: TIN Random Densification. This process is employed in removal of the ridging phenomena found in SNB's Enhanced Topographic Database DTM data. Further, the factors that cause ridging will be discussed from both "data limitation" and "structuring and visualization" perspectives. Finally, the respective strengths and weaknesses of the TIN Random Densification process will be presented.

CGDI, as a concept, has five main thrusts. The Framework Data component is critical for accessing, retrieving, maintaining, and geo-referencing the data. Through the framework, geospatial data should be integrated (e.g., horizontally and vertically) to form the national consistency, application development, and for more specific detailed data collection. Furthermore, specific tools and services are needed to complement data discovery and access (e.g., visualization and generalization).

This seminar paper gives a - very brief - explanation about the CGDI concept and the five thrusts, and explores the data integration issues as seen from the framework data component of the CGDI. The paper identifies the issues and challenges faced with the data alignment and integration throughout nation, at both federal and provincial level.