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Improving data consistency and facilitating opportunities for cross-domain urban research is important to understanding patterns of urban development and modelling urban growth for a sustainable future. Researchers and policy analysts are confronted with a number of technical challenges when comparing data in the water and energy industries both within and across these sectors. These include variability in data formats, spatio-temporal granularity, access methods and differing semantic definitions. These hinder attempts to access, analyse, interpret, compare and combine the respective water and energy datasets. The AURIN Lens 6/2 project has developed the Water and Energy Consumption and Supply (WESC) data protocol for encoding WESC datasets and tools for consolidated access via standardised geospatial web services. In addition, a number of data agreements across Australian utilities and peak body organisations have developed in order to establish a thematic data hub, called the WESC data hub. The WESC data hub currently hosts relevant datasets and uses the WESC data protocol to provide access to them via the AURIN data portal. In developing the WESC data hub, a number of tools and techniques have been developed to streamline and automate the data extract-transform-load and web service deployment process for each data provider as well as set up continuous testing frameworks to provide quality assurance and performance monitoring of the WESC data hub. In this paper, we will present the technical challenges in delivering the WESC data hub across multiple datasets and data providers, as well as the tools and methodologies used to rapidly deliver data services and its testing framework namely, Docker, Linux virtual containers, Python and TeamCity. Previously, such deployments required considerable expertise in systems administration, configuration management, and web application development, were time-consuming and not easily repeatable. However, the tools and techniques presented in this paper provides a generic pattern for rapidly developing and deploying standardised geospatial web services and is thus applicable more broadly for other domains and data. The methodology used provides software infrastructure resilience as the data services can be re-deployed easily using Docker. Continuous build and testing tools, such as TeamCity, provide quality assurance as data services are deployed and re-deployed to ensure data integrity as well as performance monitoring which allows the systems to be fine-tuned and made more efficient.

Tools for enabling rapid deployment of water and energy consumption and supply data services