In recent years the integration of spatial data coming from different sources has become a crucial issue for many geographical applications, especially in the process of building and maintaining a Spatial Data Infrastructure (SDI). In such context new methodologies are necessary in order to acquire and update spatial datasets by collecting new measurements from different sources. The traditional approach implemented in GIS systems for updating spatial data does not usually consider the accuracy of these data, but just replaces the old geometries with the new ones. The application of such approach in the case of an SDI, where continuous and incremental updates occur, will lead very soon to an inconsistent spatial dataset with respect to spatial relations and relative distances among objects. This paper addresses such problem and proposes a framework for representing multi-accuracy spatial databases, based on a statistical representation of the objects geometry, together with a method for the incremental and consistent update of the objects, that applies a customized version of the Kalman filter. Moreover, the framework considers also the spatial relations among objects, since they represent a particular kind of observation that could be derived from geometries or be observed independently in the real world. Spatial relations among objects need also to be compared in spatial data integration and we show that they are necessary in order to obtain a correct result in merging objects geometries.

A framework for integrating multi-accuracy spatial data in geographical applications

Many spatial datasets in use today have been manually digitised from hard-copy maps. These datasets are typically of relatively low positional accuracy compared to the latest positioning technologies, such as those based on Global Navigation Satellite Systems (GNSS). Discrepancies between the legacy dataset and GNSS positioning solutions are becoming increasingly evident, a problem that is compounded by the growing trend to underlay such datasets with high resolution imagery. As a result, many data providers are seeking methods to update their spatial datasets.In the example of the Victorian land parcel map base, survey-accurate data could be used to improve the positional accuracy of the existing cadastral dataset. However, since the higher accuracy data is only available in localised pockets, its integration into the map base will require rigorous methods that also enable the resultant spatial variation in the quality of the upgraded dataset to be communicated. Adjustment software based on the m...

Positional accuracy improvement: lessons learned from regional Victoria, Australia

When spatial datasets are overlaid, corresponding features do not always coincide. This may be a result of the datasets having differing quality characteristics, being captured at different scales or perhaps being in different projections or datums. Data integration methods have been developed to bring such datasets into alignment. Although these methods attempt to maintain topological relationships within each dataset, spatial relationships between features in different datasets are generally not considered. The preservation of inter-dataset topology is a research area of considerable current interest. This research addresses the preservation of topology within a data integration process. It describes the functional models established to represent a number of spatial relationships as observation equations. These are used to provide additional information concerning the relative positions of features. Since many topological relationships are best modelled as inequalities, an algorithm is developed to accommodate such relationships. The method, based on least squares with inequalities (LSI), is tested on simulated and real datasets. Results are presented to illustrate the optimal positioning solutions determined using all of the available information. In addition, updated quality parameters are provided at the level of the individual coordinate, enabling communication of local variation in the resultant quality of the integrated datasets.

Using Topological Relationships to Inform a Data Integration Process

With the rapid development of geospatial data capture technologies such as the Global Positioning System, more and higher accuracy data are now readily available to upgrade existing spatial datasets having lower accuracy using positional accuracy improvement (PAI) methods. Such methods may not achieve survey-accurate spatial datasets but can contribute to significant improvements in positional accuracy in a cost-effective manner. This article addresses a comparative study on PAI methods with applications to improve the spatial accuracy of the digital cadastral for Shanghai. Four critical issues are investigated: (1) the choice of improvement model in PAI adjustment; five PAI models are presented, namely the translation, scale and translation, similarity, affine, and second-order polynomial models; (2) the choice of estimation method in PAI adjustment; three estimation methods in PAI adjustment are proposed, namely the classical least squares (LS) adjustment, which assumes that only the observation vector contains error, the general least squares (GLS) adjustment, which regards both the ground and map coordinates of control points as observations with errors, and the total least squares (TLS) adjustment, which takes the errors in both the observation vector and the design matrix into account; (3) the impact of the configuration of ground control points (GCPs) on the result of PAI adjustment; 12 scenarios of GCP configurations are tested, including different numbers and distributions of GCPs; and (4) the deformation of geometric shape by the above-mentioned transformation models is presented in terms of area and perimeter. The empirical experiment results for six test blocks in Shanghai demonstrated the following. (1) The translation model hardly improves the positional accuracy because it accounts only for the shift error within digital datasets. The other four models (i.e., the scale and translation, similarity, affine, and second-order polynomial models) significantly improve the positional accuracy, which is assessed at checkpoints (CKPs) by calculating the difference between the updated coordinates transformed from the map coordinates and the surveyed coordinates. On the basis of the refined Akaike information criterion, the two best optimal transformation models for PAI are determined as the scale and translation and affine transformation models. (2) The weighted sum of square errors obtained using the GLS and TLS methods are much less than those obtained using the classical least squares method. The result indicates that both the GLS and TLS estimation methods can achieve greater reliability and accuracy in PAI adjustment. (3) The configuration of GCPs has a considerable effect on the result of PAI adjustment. Thus, an optimal configuration scheme of GCPs is determined to obtain the highest positional accuracy in the study area. (4) Compared with the deformations of geometric shapes caused by the transformation models, the scale and translation model is found to be the best model for the study area.

Positional accuracy improvement: a comparative study in Shanghai, China

This thesis presents the research undertaken to investigate how geospatial data handling techniques and technology can be potentially used to enhance the existing management of entire survey datasets from their captured stage to a GIS-ready state and the delivery of this to the user. Discovery of the current systems for managing survey data and information in the Survey and Mapping Department Malaysia (JUPEM) has been presented. In addition, the surveying practice and processes carried out have been examined, especially the different type of data and information existed within the raw data capture right through to the production of GIS-ready information. The current GIS technology and techniques for managing geospatial data have been inspected to gain an in-depth understanding of them. Geospatial object as an approach to model reality of the world has been discovered and used to model the raw, processed, the GIS-ready information. To implement the management, a prototype Database Management System (DBMS) has been implemented, and a trial data population and processing steps have been carried out. An enhancement of the management of the datasets from geospatial data capture to the GIS-ready infori-nation has been demonstrated. To deliver online the final product, demonstration of available methods were illustrated, and then contrasted. A range of datasets around Malaysian context were used in the research. The investigation revealed that raw, processed and GIS-ready information can be successfully modelled as object in an object-relational spatial database. Using inherent GIS tools, survey datasets management and processing steps within the same system are evidently achieved in a prototype implemented DBMS. An improved management showing the capability of 'drill-down search' and 'two-way traceability' to access and search spatial and non-spatial information in the system is effectively illustrated. Demonstration of the vendor specific and open source technology for the GIS-ready information delivery leads to the comparison between them. The thesis concludes by recognising that a management for raw captured data, processed set of data and GIS-ready information, and the delivery of this, within GIS environment is possible. The inherent GIS tools and DBMS have presented a single-view system for geospatial data management providing superior interfaces that are easy to learn and use, and users are able to specify and perform the desired tasks efficiently. Delivery of

From geospatial data capture to the delivery of GIS-ready information :improved management within a GIS environment

SUMMARY Common geographic information systems use spatial data with a point accuracy of about 0,5 down to 5 meter. The geometrical quality of the spatial base data is improved stepwise by integration of precise geodesic measurements as well as by air photos or existing field book measurements. The mounted spatial thematic data should profit from this accuracy improvement without loosing its internal geometrical quality. But the most GIS data models do not support such strategies, and update transformations lead to inconsistency because proximity fitting principles are neglected. The paper shows how the requirements concerning PAI can be provided using adjustment techniques. A data maintaining strategy is presented which regards point coordinates just as a view on redundant primary data. PAI updates are to be seen as the generation of a new view applying adjustment techniques. The necessity of a point topology in the GIS structure is pointed out.

Frank Gielsdorf

Papers

Positional Accuracy Improvement A necessary tool for updating and integration of GIS data