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London Data Commission - Data Sharing Pilots
London Data Commission - Data Sharing Pilots
The London Data Commission has explored practical data-led solutions across four ‘first wave’ pilots, which closely map to London First’s business priorities for London. Each pilot is seeking to explore a specific question.
Identifying drivers of digital inclusion to enable COVID-19 recovery in London
Identifying drivers of digital inclusion to enable COVID-19 recovery in London
The COVID-19 pandemic has crystalised the need for a detailed understanding of London’s Digital Inclusion challenges, to ultimately enable Digital Access for All as part of the Mayor’s Recovery Missions.
The Digital Skills and Capabilities of both citizens and charities in London varies across Boroughs. In addition, there are multiple interconnected hurdles to achieving full digital inclusion across access to devices, provision of training, motivation of individuals and connectivity. The Greater London Authority is seeking to build a detailed and granular picture of London’s Digital Skill’s Challenge.
Solution and approach
Run data sharing pilot to identify how Consumer and Charity Digital capability fair across London Boroughs and map to connectivity rates to help the GLA identify communities needing additional support.
The Output: Three layers of the digital inclusion map for London
Initial insights and next steps
Initial Borough level analysis indicates variation between Boroughs across all three measures of digital capability and access. As can be seen from the above maps, there is a strong positive correlation between rates of Broadband uptake by BT customers and the measure of Consumer Digital Capability from the Lloyds Consumer Digital Index.
Our next steps will be to:
Continue to work with the Greater London Authority to identify additional questions and scope the data requirements for the Mayor’s Digital Access for All Recovery Mission.
Identify additional data sources that will continue to inform and enable Digital “Inclusion for All, such as data providing information on skills needed for in demand jobs across London from Microsoft and LinkedIn.
Review the correlation analysis to understand drivers of causality.
Understand how we can iterate the analysis at more granular geographic levels (e.g. Ward).
Helping define London’s first Digital Neighbourhoods
Smart districts and communities are becoming a common trend in cities around the world, but we know from recent history that while the intention may be positive, without a truly community centric and outcomes focus, these initiatives can become unhelpful and even detrimental to the local neighbourhood fabric and trust.
What does the London version of a smart district look like? How can we harness the opportunities provided to us by the rapid increase in data being generated and captured across our neighbourhoods to truly support the needs and objectives of our communities.
This pilot, focuses on working with place-based partner organisations across London to understand key local challenges and define how they as Digital Neighbourhood pioneers could work directly with their communities and use data to drive positive change.
Our work focused on four communities each looking to harness data to drive improvement across a particular aspect of their neighbourhood.
Harnessing the Queen Elizabeth Olympic Park mobility testbed environment to understand the neighbourhood objectives and impacts of new mobility services
Developing a data-led sustainability strategy in Mayfair that enables an ongoing understanding of the community impacts of various carbon zero innovations and initiatives
Working with the existing and new community in Brent Cross South to develop a data driven approach to understanding social value objectives and delivering social value benefits to everyone
Using occupancy, activity and mobility data to help Midtown Business Improvement District drive sustainable and responsible economic recovery post-COVID in their community
All four Digital Neighbourhood proposals are guided by three key principles:
An outcomes driven focus on a specific neighbourhood theme
Community centric from beginning to end
Scalable and replicable in other London neighbourhoods
Over the coming months all four Digital Neighbourhood proposals will be discussed in detail with the GLA to understand how they can be supported, developed and ultimately delivered working in partnership between the public and private sectors.
Supporting plans for EV charging infrastructure
To reduce London’s impact on climate change and improve air quality for those who live and work here, London aims to be a zero carbon city by 2050.
Increasing the operation of private and commercially-owned electric vehicles (EVs) is a central part of London’s Zero Carbon strategy.
Despite ongoing efforts, the uptake of EVs is lagging — a key barrier is a lack of trust in charging infrastructure roll-out.
Developers and charging point operators need richer datasets to understand demand and optimise supply to EV customers.
A data-sharing pilot to demonstrate the impact of insights from public-private data-sharing on unlocking EV charging market constraints.
Over 2,000 publicly-owned parcels of land in London match the suggested land size and likely power capacity requirements for charging hubs; these were evaluated based on proximity to popular traffic routes and remoteness from existing charging points
There is a concentration of the optimal locations alongside the Central London borders; this is reflective of land availability, sufficient power capacity and current traffic patterns.
Demonstrated through data sharing that charging infrastructure barriers to EV adoption could be overcome.
Enhanced data transparency to motivate investment in EV charging infrastructure.
Clearly identified additional data to further enhance EV charging location analysis.
Established potential EV charger locations based on correlation with demand, existing infrastructure and land availability.
Shared the list of locations and methodology with the GLA to benefit from the insights and continue the analysis
The list is not conclusive and subject to further considerations by individual boroughs and is subject to PSMA license terms : http://bit.ly/OaJ4Vs
Location optimisation is a process of analysis; taking into consideration a jigsaw of data points that impact suitability of locations for EV charging hubs across Greater London. Initial planning balanced carefully between the pilot’s objectives and respective data requirements on one hand, and data availability on the other. If additional private data points were shared with the pilot, they could have enriched this analysis.
A three-step approach was employed to identify suitable locations for public charging hubs for the future fleet of electric Light Goods Vehicles (LGVs). An interactive map of London was designed to visualise each data set as a map and overlay the maps to identify the areas of cross-over. A list of public land parcels sorted from most to least suitable based on multi-factor analysis was produced. Further analysis will be required to validate the list (see Analysis Limitations below).
Identified suitable public land parcels for small, medium and large EV charging hubs
Analysed sutable locations across three influencing factors:
Power capacity availability
Proximity to popular traffic routes
Remoteness from existing charger
Produced a multi-factor weighted metric for each location to sort from most to least suitable
The pilot made use of source data from both public (open) and private sources. The datasets used, a description of their usage and a link to the datasets are described in the table below.
Public (open) data sources licensed under the Open Government Licence v3.0
Traffic Counts (LGVs)
last updated in 2018
Department for Transport
Understanding traffic flows and extrapolating demand for charging electric vehicles
Private data source, subject to Data Sharing Agreement
London Power Networks PLC’s Distribution Network
Identifying locations of power substations, their total capacity and implied available capacity
A number of limitations have been identified and can be classified as falling within two broad categories, namely, Data and Analysis. Examples of these limitations include:
Traffic Counts: It indicates relative busyness of London streets at count points; however it does not reflect driving pattern (e.g. traffic direction, timing profile) and is dependent on unevent density of the count points network. Also, traffic count is not directly translatable into EV charging demand — other factors should be considered as well (e.g. journey starting point, millage, battery capacity, EV growth rate). PotentialNext Step: Estimate demand for charging capacity based on EV growth rate and charging profile, broken down by driver/fleet category
National Charge Point Registry: Dated data source (updated in 2012) reflective of about 50% of the current EV charger count. PotentialNext Step: Leverage up-to-date private dataset from ZapMap
Public Land Parcels: Limited to public land parcels and accuracy of suggested utilisation. PotentialNext Step: Get access to private land data and relative T&Cs for using this land for EV charging hubs
Pedestrian Zone: Limited georgaphically to the City of London and only one limiing factor (pedestrian). PotentialNext Step: Extend coverage to the Greater London and include other limiting factors (e.g. Cycling routes)
Distribution Network: Implicit power capacity availability, without commited capacity and re-inforcement plans.PotentialNext Step: Get accurate data for available power capacity, headroom and re-inforcement plans to (incl. projected timeline and respective cost)
Optimal Number of EV Chargers: The pilot focused on searching suitable geographical locations for EV charging hubs and did not aim to estimate optimal number of chargers (and their type) at any given point of time. Potential Next Step: Extend analysis to include quantitative assessment of charging demand to inform optimal number of EV chargers and their type (charging speed).
Driving and Charging Pattern: Better understanding of driving and charging pattern would be required to estimate EV charging demand. Potential Next Step: Incorporate driving and charging statistics to enable accurate demand estimate
Local Analysis: Better understanding of Councils’ planning strategies would be required to exclude land parcels considered for alternative utilisation as well as adjust scoring based on neighbouring local facilities (e.g. schools). Potential Next Step: Engage Councils to explore local planning strategies
Financial Analysis: The pilot did not include analysis of costs related to sourcing of land and power capacity (including capacity upgrade projection) as well as estimation of capacity utilisation and revenue forecast. Potential Next Step: Extend analysis to an investment case
One of the pilot’s objective was to proceed with data-sharing and understand the challenges of bringing together public and private datasets. The key lessons learned during the pilot include:
Data Sharing Template can be challenging to align across multiple parties. Having a Data Sharing Agreement template ahead of a pilot can help to save time and streamline planning process.
Accuracy and completeness of open datasets continues to be a challenge. For example, the National Charge Point Registry is a rich source of data but has challenges in being kept up-to-date. Private datasets would be crucial to progress with analysis of shared data.
Alignment of corporate strategies with the pilot’s objectives is the key criteria for selecting companies to share data for the pilot. It will secure alignment across multiple participants, long term motivation and sufficient resource allocation
Supporting London’s response and recovery during COVID-19
COVID-19 emerged as a threat to London in 2020 and the Commission quickly pivoted our work towards supporting the city in dealing with this challenge.
Defining objectives and question setting
Reliable data provides the foundation for targeted action which is critical in flattening the spread of the virus. The ability to heatmap movement around the city can inform public agencies how people are responding as restrictions are eased.
Working with the Mayor’s London Strategic Coordinating Group (SCG), the Alan Turing Institute and Public Health England, London’s Chief Digital Officer, Theo Blackwell and his team defined ambitious ways of using data to guide the city response. A major project was launched that aimed to heat-map movement around the city using a range of data indicators.
Live data set to monitor the capital’s busyness using a range of anonymised and aggregated data.
Data insight that allows more nuanced planning, targeted communications and, as we move into the recovery, a better understanding of the extent to which London is returning to normal.
The London Data Commission identified a number of private sector data sets which could add to our understanding of the city’s ‘busyness’, and in July brought together data owners from a wide range of sectors with the GLA and the Alan Turing Institute for a special briefing to explore how they might contribute their organisation’s data to the project.
In addition to looking to contribute data sets, Microsoft has provided the Azure AI and cloud infrastructure and services to support the project.
The GLA and Alan Turing Institute are working to a delivery time-line than runs beyond that of the London Data Commission, but London First intends to continue to support the project by bring together data owners with the public sector and provide guidance on how business might use these new live data sets to support their own recovery and business planning.
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