Next Generation Spatial Infrastructures Program Update

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1 Towards Spatial Knowledge Infrastructure Our research program has set itself the task of defining what the Next Generation SDI will be and Dr Phil Collier (CRCSI Research Director) introduced some of the key elements at the CRCSI Annual Conference A white paper is currently under internal review which is available for comment before the end of the year. The premise of the paper is that within the next five years, new technology and growing user demands will render current approaches to SDIs inadequate. Existing unidirectional flows of predefined data products from traditional creators and custodians will fail to meet the growing demands for spatial products, analytics and knowledge Expert Non-expert Description Prediction Manual/bespoke Automated/integrated Post-analysis Real-time Authoritative Diverse 2D/Static 3D/4D/Dynamic Corporate systems Mobile devices Narrow Broad Supplier push Consumer pull Quality descriptors Warrantability We are predicting that the growing availability of new and diverse data sources (including those from the Internet of Things, mobile devices, 3D and 4D data, and humans) will disrupt consumer dependence on authoritative data and traditional data suppliers. Collaborative analysis tools and improved supply chain automation will provide users with the capability to confidently and efficiently access the information they need, when they need it, supporting myriad applications and improved decision making.

2 The spatial Knowledge Infrastructure (ski) moves the agenda from more traditional SDI concept, to creating, sharing, curating, delivering and using knowledge (not just data or information) in support of the emerging digital economy and the rise of spatially-aware and equipped citizens in its transition from data to knowledge and from a Suppler Push to a User Pull perspective. New Project Reducing consumer uncertainty in spatial data products through linked vocabularies We recently saw the approval of a new project for Program 3. Tom Spencer from PSMA is the project lead and Alistair Barros from QUT is the lead researcher. The first kick off meeting has occurred and we are finalising the partner deliverables. This project has been in the pipeline for quite some time and we are excited to have the team on board. Project Overview Problem Statement ISO 19157, Spatial Data Quality standard is one of the best frameworks available to convey quality to the user community. ISO defines and provides clarity to quality elements such as: completeness, consistency, positional accuracy, temporal accuracy, thematic accuracy and usability. This standard for publishers to define quality as part of their dataset. However, from a user perspective, conventional spatially technical metadata statements recorded in datasets cannot easily adjudge fitness-for-purpose. 1 In the Australian context, ANZLIC identified a key barrier to implementing an Australian Spatial Data Infrastructure (ASDI) is the spatial industry has to overcome the problem of incomplete knowledge about the availability and quality of existing spatially referenced data. 2 The producers of a dataset need to effectively communicate the quality of the data for a variety of reasons competitiveness, data warehousing, liability and litigation. 3 A gap has emerged from this, highlighting the lack of consumer understanding about how the technical quality descriptions for geospatial datasets can meet their purpose. (Fisher P et al, 2009). 4 This gap of understanding between publishers and consumers was also identified in the CRCSI research into Spatial Supply Chains (Arnold, L. 2016). 5 The lack of understanding results in significant consumer time and money (eg. NBN Co and GNAF) to invest in modifying products to meet a purpose (Browning T.R., 2002). 6 In addition, publishers spend continued time and effort to work around and eliminate the potential misuse of their data by consumers. 1 Mohamed Ghouse Z and Duckham M 2007 Storing and querying spatially varying data quality information using an integrated spatial RDBMS. In Proceedings of the Fifth International Symposium on Spatial Data Quality, Enschede, The Netherlands 2 ANZLIC, 2003, Implementing the Australian Spatial Data Infrastructure, Action Plan Tech.rep, Australia New Zealand Land Information Council (ANZLIC) 3 Mohamed-Ghouse, Z. S., Duckham, M., Hunter, G., Modeling spatial variation in data quality using linear referencing. In: Caetano, M., Painho, M. (Eds.), 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Lisbon, Portugal. pp Fisher P et al., (2009) Spatial Data Quality from Process to Decisions 5 Arnold, L., 2016, Spatial Data Supply Chain and End User Frameworks: Towards an Ontology for Value Creation 6 Browning T.R., (2002) On Customer Value and Improvement In Product Development Processes Systems Engineering, Vol 6, No. 1, 2003

3 Research questions What semantic ontology, or vocabulary, can best capture the language of consumers in describing data quality? How can data publishing processes be improved to better address the question of fitnessfor-purpose based on a consumer vocabulary? What approaches are needed to continually manage and validate a consumer vocabulary in communicating fitness-for-purpose? How can consumer vocabulary be leveraged through spatial data query and retrieval in response to consumer needs? Project Objectives To demonstrate through a semantic framework that linking publisher dataset attributes (mainly technical) to a consumer vocabulary will make it easier for traditional and non-traditional spatial consumers to discover and access data that meets their needs. To do this, the project intends to: Profile consumers to understand how they identify quality within spatial datasets to build a vocabulary based on consumer terminology Discover and link the consumer vocabulary to the publisher s vocabulary under a semantic framework Track the processes that link the publisher s datasets to the consumer vocabulary so it may be utilised by the project partners Evaluate the consumer vocabulary in maximising the fitness-for-purpose of spatial data queries by implementing the vocabulary with a project partner. Approach Profile Consumers: Working through partner organisations, the project will qualify consumer understanding of quality and determine how geospatial data is fit for their purpose. This will be conducted through a online survey and targeted interviews using a cross section of industries identifying use cases of determining understanding. The outputs of this survey will also be made available to partners as high-level requirements so they can reference this when determining the value of their geospatial data. Generate Vocabularies: Based on the profiling of consumers, generate a vocabulary (ontology) of interrelated terms and meanings that consumers use to define quality of geospatial data in meeting their desired purpose. The project will also work with partner datasets and products to generate a corresponding publisher vocabulary so that the technical quality elements can be linked to the consumer vocabulary. Define Linkages: Identify the appropriate linkages based on the publisher and consumer vocabularies through a semantic Resource Description Framework and then document this for non-online use. Validate: Working with project partners, implement the consumer vocabulary and monitor the effectiveness of translating publisher technical data descriptions (metadata and/or data attributes) to the consumer need based on fitness-for-purpose. Implementation will be in the form of documentation to assist PSMA to converse with their consumers in matching available geospatial data to consumer-based purposes, and with government partners to implement vocabularies online through their data portals.

4 Impact Through a consumer focused approach to communicating spatial data, publishers will have a tool to significantly improve the effectiveness which datasets are matched and used beyond spatial domains and applications. As part of this, the vocabulary will be available for non-gis industries to determine how spatial data will be fit for the end user purpose. The project outputs will be useable by CRCSI partners such as PSMA to communicate directly on how their data can be fit-for-purpose and by government online through their data portals. The vocabulary in natural language will provide confidence for non-gis users to use spatial data in a business context. For producers of spatial data, the vocabulary will determine the fitness-forpurpose of a dataset and how the data produced is best consumed, which in turn will improve the data product itself. Outcomes (Deliverables) A profile of spatial data consumer types, intended uses of spatial data and how these consumers define quality in determining if data is fit for their purpose. This output may be used by ICSM for further reference to SIDA Baseline Survey report. 7 Validated semantic vocabularies (ontologies) for publishers and consumers: Generated rules and documentation to link a publisher vocabulary to a consumer s vocabulary, which enables the linkage between technical quality elements to consumer quality terminology. Implemented proof of concept (PSMA for manual use and online through Landgate data.wa.gov.au) to validate the vocabulary s usefulness in informing consumers how geospatial datasets are fit-forpurpose. Documentation (including models) for publishers to utilise, will inform how their data can be enhanced to link to a consumer vocabulary. Project Highlight Automated Transactions Methods for spatial data processing and integration have been researched and developed over the past few years, however little work has considered the automation of the decision-making process where spatial data is an input to the approval process. Many of our partners are looking for more sustainable ways in which to manage spatial transactions with their customers. Our first Proof of Concept is based on Latha Varadharajulu s research and the Landgate Case Study that is focusing on the Automation of Road Name Approvals between local government, developers and Landgate. Landgate administers all official naming actions for Western Australia under the authority of the Minister for Lands. The relevant local government authority generally submits all naming proposals for ratification by Landgate. All new proposals must satisfy government policies and standards. The current process has an online submission form, but for the most part the process is paper-based and requires significant human involvement. Current methods often require negotiation between the parties involved (ie. local government and Landgate). While there are specific rules applying to new road name approvals, there are grey areas within policy that are often challenged and can only be resolved by an experienced negotiator. A request for a new road name may be transferred back- 7 SIDA Survey Baseline (2014) pg. 30,

5 and-forth until an outcome is achieved that is satisfactory to both parties. Outcomes may be different depending on the expertise of the negotiator/approver. Automation is needed to reduce the manual overhead by extracting expert knowledge for road name approvals to create a standard set of rules. The notion is to create a self-service online mechanism for developers to submit new road names for approval, underpinned by a complex rulebase and querying process. Complexity comes from the affect of such changes. New land development results in a change to the surrounding road network. This impacts property street addressing and administrative boundaries. The case study uses the Landgate geographic road names database, called GEONOMA, to process the road name proposal. The current online submission process has the following issues that complicate the approval process: The online form is only used to test whether new road names are allowable based on a set of road names that have been reserved for use. If a proposed name is a reserved road name then the request will fail. There is no opportunity to contest the decision. A maximum of 10 names per application is allowed; meaning separate applications are required for larger subdivisions. It is not possible to conduct cross-reference checks against other submissions and therefore the process is open to error. The current system does not consider the spatial extent of roads. Figure 1 shows a schematic submitted for road name approvals that does not represent the actual proposed location of roads. Roads don t actually meet up; they are stylised with continuous and dashed lines with arrows etc. Manual editing and digitising is therefore necessary to extract the full topology of the proposed road network complete with coordinates of junctions. The current system does not permit checks on phonetics which is an issue for similar sounding names (eg. Bailey, Baylee, Bayley, Baylea). Similar or like names (eg. Whyte and White) are not allowable under policy guidelines as can cause confusion for applications such as emergency services dispatch. Similarly, the same road name or a similar sounding road name is not permitted within an unacceptably close proximity. Where an extension to an existing road occurs or where a road type (eg. cul-de-sac, highway) changes, the current system is unable to return an extension to a road name or change to road suffix, respectively. Approach Figure 1 (below) shows the different phases in the land transaction process from knowledge acquisition to final feedback. Data is extracted from the various databases in formats such as html, json, csv and xml and converted to RDF. Ontologies in OWL are created from database schema and models in the interactive GUI based Protégé environment. Rules are generated in SWRL by an expert. Once the system has been developed, the data, ontologies and rules can be used in the runtime environment Jena with a rule engine by a developer to process road changes. One of the objectives of the Semantic Web is to evolve into a universal medium for information, data and knowledge exchange, rather than just being a source for information. To attain this, it uses the well-known http protocol and technologies (Shadbolt et al., 2006) (MILLARD, 2010), such as URIs (Universal Resource Identifiers), RDF (Resource Description Framework) and ontologies with reasoning and rules.

6 FIGURE 1: DATA INTEGRATION/REASONING ARCHITECTURE Any feedback on this first edition of Next Generation Spatial Infrastructures Program Update, please let me know, along with more information about this work. Kylie Armstrong and the Program 3 Team e. karmstrong@crcsi.com.au