Lesley Arnold

High Water Mark Determination Based on the Principle of Spatial Continuity of the Swash Probability

ABSTRACT Liu, X.; Xia, J. (C.); Blenkinsopp, C.; Arnold, L., and Wright, G., 2014. High water mark determination based on the principle of spatial continuity of the swash probability This study presents a model that determines the position of the high water mark (HWM) based on the spatial continuity of inundation probability due to swash for a range of HWM indicators. These indicators include mean high water (MHW), high water line (HWL), and a number of shoreline features, such as the vegetation line. HWM identifies the landward extent of the ocean and is required for cadastral boundary definition, land-use and infrastructure development along the foreshore ,and for planning associated with climate change adaptation. In this paper, shoreline indicators are extracted using an object-oriented image analysis (OOIA) approach. Ten-year hourly swash heights (shoreline excursion length) are fitted into a cumulative distribution function. The probability that swash will reach the various HWM indicators over a 10 y period is then estimated. The spatial continuity distances of the swash probability of HWM indicators are calculated using semivariogram models that measure similarity of swash probability. The spatial continuity distance is defined as the distance between the lower bound of sampling position (the most seaward HWM indicator) and the position where autocorrelation, or the similarity of swash probability of the various HWM indictors, approaches zero. The latter is considered as the HWM position in this study. This HWM determination method is evaluated at two study sites at different latitudes and with distinct coastal features.

Analysing Product-Specific Behaviour to Support Process Dependent Updates in a Dynamic Spatial Updating Model

A scale-independent database that allows derived maps to be dynamically updated from a centrally maintained data source is an appealing alternative to traditional map revision techniques, which by todays standards are costly and inefficient. This paper presents a dynamic spatial updating model that supports automated updating of non-standard maps in a scale-independent database-centric map production environment. Maps derived from the database are not separate data sets, but rather active views of the database. Each derived map is displayed in a unique way by implementing cartographic operations at the map level. While the operations applied require user involvement for strategic cartographic decisions, and algorithmic initiation and control, the technique allows geographic data to be processed cartographically without affecting the geometric integrity of the database. Each time a derived map is opened it retrieves the spatial data (and updates) from the database and applies the unique cartographic representation methods that persist on the individual derived maps. Database updates are automatically triggered to cartographic products, as process dependent updates, according to their individual product-specific behaviour. This paper investigates product-specific behaviour (product multiplicities) and the cartographic processing requirements to support dynamic spatial updating techniques in an object-oriented map publishing environment. These techniques are implemented in an off-the-shelf software environment using ArcGIS.

Semantic Web Technologies Automate Geospatial Data Conflation: Conflating Points of Interest Data for Emergency Response Services

Conflating multiple geospatial data sets into a single dataset is challenging. It requires resolving spatial and aspatial attribute conflicts between source data sets so the best value can be retained and duplicate features removed. Domain experts are able to conflate data using manual comparison techniques, but the task it is labour intensive when dealing with large data sets. This paper demonstrates how semantic technologies can be used to automate the geospatial data conflation process by showcasing how three Points of Interest (POI) data sets can be conflated into a single data set. First, an ontology is generated based on a multipurpose POI data model. Then the disparate source formats are transformed into the RDF format and linked to the designed POI Ontology during the conversion. When doing format transformations, SWRL rules take advantage of the relationships specified in the ontology to convert attribute data from different schemas to the same attribute granularity level. Finally, a chain of SWRL rules are used to replicate human logic and reasoning in the filtering process to find matched POIs and in the reasoning process to automatically make decisions where there is a conflict between attribute values. A conflated POI dataset reduces duplicates and improves the accuracy and confidence of POIs thus increasing the ability of emergency services agencies to respond quickly and correctly to emergency callouts where times are critical.

Automatic geospatial data conflation using semantic web technologies

A Spatial Data Supply Chain (SDSC) is a series of processes that convert raw spatial data (e.g. road locations) into useable products (e.g. road networks). Duplicate SDSCs are not uncommon across Australia. There exists data duplication between local government authorities, State/Territory government departments and commonwealth agencies. To improve the SDSC process, this research explores data conflation as a means of removing the need for duplicate data handling in SDSCs. The research is studying how Semantic Web Technologies can be used to automate the conflation process. The approach focuses on building ontologies for spatial datasets and creating relevant rules based on geometry, topology, and policies. By satisfying these rules through a computer reasoning process, relevant datasets can be intelligently linked and integrated. The conflated dataset can then be used as the single authoritative and trusted source of data that is fit for multiple purposes. In this way the data can be co-maintained and used by multiple organizations eliminating the need for siloed SDSCs.

Automating Government Spatial Transactions

The land development approval process between local authorities and government land and planning departments is manual, time consuming and resource intensive. For example, when new land subdivisions, new roads and road naming, and administrative boundary changes are requested, approval and changes to spatial datasets are needed. The land developer submits plans, usually on paper, and a number of employees use rules, constraints and policies to determine if such plans are acceptable. This paper presents an approach using Semantic Web and Artificial Intelligence techniques to automate the decision-making process in Australian jurisdictions. Feedback on the proposed plan is communicated to the land developer in real-time, thus reducing process handling time for both developer and the government agency. The Web Ontology Language is used to represent relationships between different entities in the spatial database schema. Rules on geometry, policy, naming conventions, standards and other aspects are obtained from government policy documents and subject-matter experts and described using the Semantic Web Rule Language. Then when the developer submits an application, the software checks the rules against the request for compliance. This paper describes the proposed approach and presents a case study that deals with new road proposals and road name approvals.

SWRL Rule Development to Automate Spatial Transactions in Government

The land development approval process between local councils and government planning authorities is time consuming and resource intensive because human decision-making is required to complete a transaction. This is particularly apparent when seeking approval for a new land subdivisions and administrative boundary changes that require changes to spatial datasets. This paper presents a methodology that automates the approval process by developing. Feedback on the transaction is communicated to the land developer in real-time, thus reducing process handling time for both developer and the government agency. This paper presents an approach for knowledge acquisition on rule development using Semantic Web and Artificial Intelligence to automate the spatial transaction process. The Web Ontology Language (OWL) is used to represent relationships between different entities in the spatial database schema. Rules that replicate human knowledge are extracted from government policy documents and subject-matter experts, and are defined in the form of Semantic Web Rule Language (SWRL) and based on geometry and attributes of database entities. The SWRL rules work with OWL-2 (spatial schema and vocabulary) ontologies to enable the automatic transactions to occur. These rules are implemented using an ontology and rule reasoner, which accesses the instances of data elements stored in the underlying spatial database. When the developer submits an application, the software checks the rules against the request for compliance with the relevant government policies and standards. This paper presents results for dealing with road proposals and road name approvals.

Provenance ontology model for land administration spatial data supply chains

Land Administration Spatial Data Supply Chains (SDSC) for state and territory jurisdictions in Australia require extensive investigation to address several contemporary issues and challenges that are hampering innovation and the use of spatial information across the land administration sector. The management of cadastral data involves multiple value and supply chains. Each has heterogeneous geo-processes, methods, models and workflows that combine to generate, modify and deliver spatial data. The integration and processing of multiple datasets gives rise to end user questions about trust, quality, fitness for purpose, currency and authoritativeness of the data. This is because datasets originate from various sources, and different geo-processes are executed to deliver the final product. Understanding how data is collected, processed, managed and disseminated provides knowledge about its history, believability and provenance. This in turn increases the usability of data. This paper explores methods to capture spatial data provenance and data flow lineage. The aim is to develop a spatial data provenance model for the land administration domain using a comprehensive ontology. In the GeoPROV-LM model under development, all business and technical phases are defined and an extensive ontology structure developed using a semantic approach at the data flow level.

Identification of onshore features for delineation of the land water interface and their spatial-temporal variation using high resolution imagery

High water mark (HWM) line is accepted as the most landward of the cadastral boundaries between water and land. Only limited research has been done to determine the horizontal location of HWM accurately because of the spatial and temporal variation of its location. In this study, different HWM indicators, such as vegetation lines and sudden change of slope (SCoS) are defined and realized using an object-oriented image analysis (OOIA) approach. An evaluation model is introduced to access the spatial and temporal variation of different HWM indictors based on their consistence and stability. The methodology and results of a case study in South Fremantle, Western Australia, are presented. The study shows that the height of Highest Tide Recorded (HTR) was the most stable and consistent indicator when positioning on the ground and as a consequence is suggested as an appropriate HWM location.

The West Australian Travellers Atlas

With the continued development of computing technology traditional map production techniques are no longer used in many large mapping organisations. This new technology has been embraced by the Cartographic Services Branch, Mapping and Survey Division, Department of Land Administration (DOLA) in Western Australian. All new map products are now being produced using computer assisted map production technology and maps originally produced by traditional means are progressively being converted to digital form. One of the most recent mapping projects undertaken by DOLA has been its contribution to the West Australian Travellers Atlas, a road guide of Western Australia, produced as a joint venture with Countryman, West Australian Newspapers. The atlas has been produced utilising Intergraph and Apple computing technology and a combination of software packages which include Microstation PC, Aldus FreeHand, QuarkXPress, Adobe Photoshop, Altsys Fontographer and INposition, an extension to QuarkXPress. This paper is...