Energy Saving Trust - Esri UK Annual Conference 2016
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This document summarizes a project using ArcGIS software to analyze and map home energy performance data across Devon, UK. The project involved collecting Energy Performance Certificate data, modeling it spatially to determine key property variables for each home, building an address-level housing database, and creating an online mapping platform to visualize the data. Challenges included developing a replicable spatial model and creating a high-performance web app with low ongoing costs. These were overcome by automating tasks in ArcMap and hosting map services on ArcGIS Online. The final solution allowed users to conduct queries and explore data layers to inform energy efficiency strategies. Lessons learned included effective use of Esri tools and cloud hosting for web mapping large datasets.
Energy Saving Trust - Esri UK Annual Conference 2016
- 1. Home Analytics Devon: Using ArcMap and ArcGIS Online to drive energy efficiency 17 May 2016 Sean Lemon Lead Consultant (Energy Saving Trust)
- 2. Agenda • Who we are and what we do • Devon case study • What were the main challenges? • How did we overcome these challenges? • Demonstration • Key learnings and next steps 2 Home Analytics Devon 17 May 2016
- 3. Energy Saving Trust 3 Home Analytics Devon 17 May 2016 • Mission: Helping everyone save energy every day • Work with a variety of public and private sector stakeholders • Core sectors: • Energy efficiency • Renewable energy • Fuel poverty
- 4. Case Study 4
- 5. Case Study: Devon 5 Home Analytics Devon 17 May 2016 • Client: Devon County Council and 8 District Councils, Plymouth and Torbay • Project: Modelling and mapping of Energy Performance Certificate (EPC) data • Timeframe: Apr 2015 – Feb 2016
- 6. Home Analytics Devon 6 Home Analytics Devon 17 May 2016 Purpose: • Quantify potential for energy efficiency measures • Target homes for retrofit schemes • Aid funding application submissions • Inform housing, energy and fuel poverty strategies Phase 1: Create an address-level housing stock database
- 7. Home Analytics Devon 7 Home Analytics Devon 17 May 2016 Purpose: • Perform quick and useful queries • Produce maps for funding applications, reports, etc. • Allow non-technical users to explore the data • Reduce reliance on internal GIS teams Phase 2: Create an online mapping platform to visualise the data
- 8. The project plan 8 Home Analytics Devon 17 May 2016 1 2 3 4 Data Collection Data Modelling Database Production Data Visualisation
- 9. Where does GIS fit in? 9 Home Analytics Devon 17 May 2016 1 2 3 4 Data Collection Data Modelling Database Production Data Visualisation
- 11. Data modelling 11 Home Analytics Devon 17 May 2016 Requirements: • Inputs: OS Master Map and OS Address Base Plus • Replicable for all of GB (27M homes) • For each address, determine property type, block ID, distance to gas grid and building footprint Challenge: Develop a spatial model to determine key property variables
- 12. Data visualisation 12 Home Analytics Devon 17 May 2016 Requirements: • Allow users to conduct quick and useful queries on the database • Visualise 10-15 layers of building characteristics at the address level • Low annual costs (license fees, hosting) • Run on various browsers Challenge: Create a high performance web app with minimal on-going costs
- 14. Data modelling 14 Home Analytics Devon 17 May 2016 Solution: • Provided model logic to Esri UK • They coded the assumptions into ArcMap and developed an ArcPython script to: • Pre-process OS layers • Apply logic and export results to CSV • Loop through all grid squares in GB
- 15. Data visualisation 15 Home Analytics Devon 17 May 2016 Solution: • Hosted workshops with LA users to prioritise functional requirements • Collected information on their software and browser technology • Researched various platforms for web mapping
- 16. Esri Maps 4 Cognos (EM4C) 16 Home Analytics Devon 17 May 2016
- 17. Google Fusion Tables 17 Home Analytics Devon 17 May 2016
- 18. QGIS2Leaf, CartdoDB and Mapbox Studio 18 Home Analytics Devon 17 May 2016
- 19. ArcGIS Online 19 Home Analytics Devon 17 May 2016
- 20. What did we find? 20 Home Analytics Devon 17 May 2016 • Many options for building, styling and hosting web maps • Map rendering depends on type of map service • Open-source options have limitations: • Browser compatibility • Requires web development skillset • High hosting charges
- 21. Final solution 21 Home Analytics Devon 17 May 2016 ArcMap • Building polygons symbolised by property characteristics • Visibility thresholds aligned with visibility of OS base layer • Map layers and tile schemes used to generate map tile packages • Packages uploaded to AGOL ArcGIS Online • Layers combined into a single map • Tile layers ‘buddied up’ with feature service layer • Web AppBuilder used to add widgets and functionality • Final web app shared with users via Devon user group
- 24. Lessons learned 24 Home Analytics Devon 17 May 2016 • ModelBuilder and ArcPython are effective tools for automating tasks in ArcMap • Open source web mapping options exist but beware other costs and constraints • Tile caches are an effective way to render large datasets • “Buddying up” a feature and tile service allows you to mimic vector tiles
- 25. Lessons learned 25 Home Analytics Devon 17 May 2016 • If you have an ArcGIS desktop license, then you’re also entitled to an ArcGIS Online account • Web AppBuilder has a considerable amount of out-of-the-box functionality • Esri cloud hosting is a cost-effective way to host large web map services
- 26. Next steps 26 Home Analytics Devon 17 May 2016 • Southend-on-Sea Borough Council: • Bringing housing, energy and health data together in web maps • Scotland Data Hub: • Making EPC data openly available • ArcMap – used to build tile packages • ArcGIS Online – used to build web apps to help users engage with the data
- 27. Thank you! Sean Lemon Lead Consultant Energy Saving Trust [email protected]