Data4Sustain - integration of data to facilitate renewable energy technology use within urban environments

The Data4Sustain feasibility study integrate environmental data into a prototype webGIS decision-support tool for renewable energy technology selection in urban development and redevelopment projects. Each data source will be processed in a way that adds value. Customers of the webGIS platform comprise house builders, developers and their advisors, local authorities, community groups and businesses. At least one dataset will be crowdsourced, as proof-of-concept to showcase its value and practicality. When Data4Sustain is market ready, it is intended that it will be available via professional subscription. A free low resolution product will also be made available for the general public. Data4Sustain will be designed to be modular and capable of including new data sources as they arise. Outputs of this feasibility stage will include an evaluation of the availability, limitations and applicability of each dataset measured against cost, usability, relevance, potential for added value; a prototype built for local authority use to evaluate how well it works; and a refined business model for bringing the product to market.

The Data4Sustain project will provide significant impacts to the sustainable energy community which are described below. The impacts will be delivered through the creation of an innovative big data platform that is capable of storing and processing amalgamated location based data sets both open/crowd sourced and those owned by the BGS. The platform will enable the visualisation of complex above and below ground spatio-temporal datasets that will produce a novel and substantial decision making tool for operators in the sustainable / renewable markets. The impacts will be wide ranging influencing local councils, planning authorities, developers as some specific examples, with wider societal benefits.

Furthermore, advances in location-based data collection technologies and the rapid reduction in their costs are providing a vast and continuously increasing amount of data about the movement of people, energy, goods and transportation systems across the globe. However, the explosive growth of geospatially-referenced data has far outpaced the capability of the technology to analyse and transform the data into insightful information for discovering spatio-temporal patterns in massive movement data. This general lack of effective and easy to use geovisualisation analytics tools, available as web or cloud based client driven applications therefore creates an adverse impact on the return on the investment made to collect and manage these data.

The outcomes of this project will be of interest to a range of stakeholders across a variety of sectors including: business, academia, government (including local and regional authorities), and citizens' organisations. A number of these stakeholders are already engaged directly thorough the networks accessed by LQM/NGI/BGS/NEP as demonstrated by the commitment to the project by Derby City Council.

The major impact to the sustainable energy communities will be the demonstration of the feasibility and value of new data visualisation technology for interpreting and gaining insights from 'big' data sets outlined above. Good visualisation of data leads to a better understanding of situations, increased and enhanced clarity and thereby underpins the propensity for better and faster decision making. It enables the users to gain a more complete and easily interpretable understanding of scenarios.

The presence of a new renewable feasibility assessment decision-support tool and the associated improvements in decision-making will, following commercialisation, lead to reductions in carbon emissions and increased revenue for organisations and households from the use of the technologies. Overall, these benefits culminate in moving society toward sustainable development of urban areas, with clear social, environmental and economic gains for present and future generations, enhanced by the latest in data science and information technologies.