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The integration of spatial datasets for network analysis operations
Sanphet Chunithipaisan1, Philip James2, David Parker3 Department of Geomatics, University of Newcastle upon Tyne, Newcastle, UK, NE1 7 RU 1 PhD Student, Sanphet.Chunithipaisan@ncl.ac.uk, 2 Lecturer, Philip.James@ncl.ac.uk, 3 Professor, David.Parker@ncl.ac.uk,
One of the most important current challenges for Geographic Information Systems (GIS) is the generation of corporate geo-spatial resources whose full potential can only be realised by making them accessible to a large number of applications and end-users [25]. In the field of facilities management, such as gas, electricity, water, telecommunications and transportation companies, spatial network GIS could provide a useful graphical interface and geographical database for the management of network assets and flows [11]. Utility networks typically impact on many people over vast areas and are generally managed by government departments, large organizations or companies. There is often little collaboration between the organisations despite similarities of interest and in some cases new legal requirements to share data with other utilities to minimise the impact of repairs and new build on both the public and the environment [24]. Thus, there is a growing need both to share the basic network information and in some cases to integrate data sets to carry out more complex network analysis operations.
In the real world, objects are connected to each other: thus an optical cable is connected to a multiplexer that in turn is connected to copper cables connecting into our homes to provide cable TV, telephony and internet access. Using GIS in support of network utility management typically involves many types of features that may have connectivity to each other. Several GIS vendors have developed GIS software whose potential functions can provide for network management and analyses [7], but each system has a proprietary format to deal with the connectivity between geometry or features. Topology in GIS is generally defined as the spatial relationship between such connecting or adjacent features [1,5], and is an essential prerequisite for many spatial operations such as network analysis [23]. There are, in general, three advantages of incorporating topology in GIS databases: data management, data correction and spatial analysis [13]. Topology structures provide an automated way to handle digitising and editing errors, and enable advanced spatial analyses such as adjacency, connectivity and containment [2]. In some systems this relationship is assumed (by the user) whereas in others it makes up part of the structure of the geometry [1,22]. In some systems topology can be built where all arcs intersect or touch [4] and in others rules defining connectivity between feature types can be used to build topology [6]. The latter approach is typically used in the major GIS especially for network utility applications. For example, ArcInfo has a “rule base” to specify the connectivity of the features whereas GE Smallworld has a “manifold” to describe how features connect. In addition, the network may be a directional network depending on the application in question. Each GIS designed for network utility applications has different alternatives to manage the issue of a directional network. Some systems have a feature that is part of the data structure such as a “Turn” to deal with directional links in the network [4], whereas in some systems, directional links in the network can be specified in the application by through code [7]. The ability to reuse existing data is a benefit that new applications should be able to take advantage of [21]. This is often not possible because of problems with data integration due to proprietary data formats. Attempts have been made to integrate formats using standards such as GML [16] and tools such as FME [18]. There is however a particular problem in network GIS in that topology is not exchanged in general import/export (other than that assumed by the geometry). Some systems do not support topology or network analysis functions at all, and yet these non-topological datasets still contain valuable data. In order for network analysis to be carried out the current options are to import data into the tool of choice, coercing data into the required format. If further changes are made to the original dataset then the process needs to be repeated. However, data conversion across systems is not straightforward and is similarly time consuming. Whilst systems exist that handle network topological issues in a structured and efficient manner, these tend to be high cost systems and it is not usually possible, nor desirable to upgrade from an existing system to one that manages topology. Likewise, a one off import of all data into such a system to carry out basic network analysis functions is not a practical solution. Furthermore, data concerning the same feature type may be maintained in different systems. To distinguish the duplicated features when converting to the new database or importing to the new network analysis application is a difficult process. Even at the semantic level, inconsistencies in definition cause problems: for example, a feature, such as a road, may be labelled differently (e.g. as a street) in a different system. This paper reports on an on-going research project entitled “The development of generic, topology-aware spatial datasets and models”. This research has been undertaken to address and solve those problems mentioned above. The research framework comprises three main parts: The first stage is to design a model to incorporate data from various systems and to model attributes, geometry and topology so as to be able to carry out network analysis. Several models were developed to describe real world features and connectivity of features, including the defining rules of connectivity between features. The second stage of the research is to design analytical tools and other tools to manage the data. Several tools are being developed to test the conceptual model designed from the first stage and to support network topology and network analyses. This stage also includes investigation into mechanisms for data integration and dealing with data redundancy. The final stage is to develop an application that can be served across public and private networks to carry out network analysis “on-the-fly". This paper focuses on the first stage where the concepts underpinning the conceptual data model are introduced. A limited implementation of the application for the purpose of testing the data model is also introduced. Paper Structure Firstly this paper identifies the research overview and motivation. The overarching concept of the research is then introduced followed by the methodology used. The data model and structures are then discussed. There is an analysis of the implementation thus far and finally some conclusions are drawn on the suitability of the current data model and avenues for further development are presented. Vision Most major GIS support relational databases in some form, and often it underpins the data structure. Data import/export from and to a relational database is relatively straightforward using built-in functionality, or using macros or scripts to connect to a relational database. Furthermore, ODBC [15] or similar tools for connecting to external data sources are available for most platforms. The widespread support of SQL within relational databases also provides a structured and common interface for addition/update and deletion. Distribution of applications and data via the World Wide Web and associated technologies is clearly becoming a major trend [12]. Many web GIS applications have been developed over the last few years [8,9], but these applications typically provide only basic GIS functions. The vision of the research is to enable a “web” based application that allows the transferral, where appropriate, from proprietary datasets, whether topological or not, into a generic relational database. Topology is then created and rules defined to allow network analyses to take place over all the required data. Where appropriate data can remain in an existing format, but topology added by setting a semantic schema for the names of features. Figure 1 gives an overview of the process.  Figure 1 The vision of the research
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