Using Energy Metering Data to Support Official Statistics: A Feasibility Study Final Report to the Office for National Statistics

The Office for National Statistics (ONS) is the UK’s largest independent producer of official statistics and is the recognised national statistical institute for the UK. It is responsible for collecting and publishing statistics related to the economy, population and society at national, regional and local levels. It also conducts the census in England and Wales every ten years. ONS also plays a leading role in national and international good practice in the production of official statistics. To maintain and further its expertise, the ONS conducts and commissions research covering key topics relating to official statistics and encompassing key emerging conditions. One emerging change relates to the new data sources becoming available through the growth of technologies such as the Internet. These data sources might have a role in official statistics in a number of ways such as helping to validate or improve official estimates, providing more timely information on trends or reducing costs and response burden through the diminishing need to collect data through normal survey processes. One new data source of interest to statistical organisations around the world is the high frequency electricity data recorded by domestic smart meters. Such data may help with understanding energy use and expenditure as well as various features such as occupancy status or household size which may be inferred from the profile of energy use over time. All constituent countries of the UK have programs to roll out smart meters to domestic dwellings by 2020, so that information on an almost universal coverage of dwellings may be available from this date. Energy trials using smart-type meter devices have led to the availability of data on smaller numbers of dwellings for current research and ONS has commissioned the University of Southampton to use some of these trial datasets to test the feasibility of using this data to identify features of households which may have relevance for official statistics. Specifically, this research focuses on the potential of using smart-type meter data to identify household characteristics such as the presence of retired occupants. A second objective is the development of a method to determine occupancy status. It must be emphasised that the principal interest for ONS is the development of methods to derive estimates for groups of households so as to monitor broad trends whilst ensuring no disclosure of personal information. As a first step towards this aim, it is necessary to conduct research at the individual household level as within this paper. ONS recognises that smart meter data poses major questions around ethics, privacy and the safeguarding of personal information. ONS has already sought advice from privacy groups on this research and been given approval so as to demonstrate more fully the benefits of using this data. Future use of this data in a production setting will involve extensive engagement with all stakeholders to ensure that the appropriate levels of security are in place to satisfy the strict controls demanded under the code of practice for official statistics (UK Statistics Authority 2009). The University of Southampton is continuing this research under an ESRC funded project (http://www.energy.soton.ac.uk/category/research/energy-behaviour/census-2022/). Additionally, ONS is conducting internal research using smart-type meter data through its Big Data project and regular updates are published at http://www.ons.gov.uk/ons/guidemethod/development-programmes/the-ons-big-data-project/index.htm

Exploring small area demand for grocery retailers in tourist areas

This paper uses data from a major loyalty card scheme to draw insights about the characteristics of grocery expenditure by tourists. The authors explore the volume, value and composition of storebased visitor expenditure using consumer data from the loyalty card scheme. They focus on grocery spending at selected stores in Cornwall, a popular tourist destination in South West England. The loyalty card data provide a valuable source rarely available for academic investigations. The authors are able to analyse visitor spend by socio-economic and geodemographic characteristics, drawing a range of comparisons with residential demand from within the store catchment areas. They demonstrate that visitor grocery expenditure is complex and varies by store, destination and type of customer. The paper presents evidence to suggest that the current approaches used to estimate sales uplift and local-level economic impact from visitor demand are unable to account for the complexities of this form of expenditure. Based on these insights, the authors recommend that sophisticated modelling is employed to estimate the impact of visitor expenditure

Identifying seasonal variations in store‐level visitor grocery demand

Purpose: The purpose of this paper is to understand the contribution of visitor demand to the seasonal sales variations experienced at grocery retailers in Cornwall, South West England. Design/methodology/approach: Working collaboratively with a major UK retailer provides access to store trading information and customer data from a popular loyalty card scheme. The authors use spatial analysis to identify revenue originating from outside the store catchment, and explore the spatial and temporal nature of the visitor demand recorded in‐store. Findings: The paper demonstrates the significant degree of seasonality experienced around stores in terms of their revenue generated from out‐of‐catchment visitors, and highlights implications for store location planning. Most notably, visitor expenditure tends to demonstrate far more spatial and temporal clustering than residential demand. The authors argue that it is essential for retailers to ensure that their location planning makes full use of all available consumer data to understand the local nature of demand, including the impact of visitor expenditure. Research limitations/implications: The authors aim to use this insight to develop a spatial decision support system (SDSS) for use within site location planning in the retail sector. This would incorporate a spatial interaction model to estimate and account for variation in local demand generated by seasonal tourist visits. Originality/value: Customer level loyalty card data are rarely available for academic investigations and the authors are able to provide a unique insight into customer expenditure in tourist locations. There has been little exploration of seasonal tourist demand in store location planning, and this study addresses an identified academic and commercial need

Understanding Chinese tourist mobility and consumption-related behaviours in London using Sina Weibo check-ins

In this paper, we detail an individual-level analysis of under-exploited location-based social network (LBSN) data extracted from Sina Weibo, a comprehensive source for data-driven research focused on Chinese populations. The richness of the Sina Weibo data, coupled with high-quality venue and attraction information from Foursquare, enables us to track Chinese tourists visiting London and understand behaviours and mobility patterns revealed by their activities and venue-based ‘check-ins’. We use these check-ins to derive a series of indicators of mobility which reveal aggregate and individual-level behaviours associated with Chinese tourists in London, and which act as a tool to segment tourists based on those behaviours. Our data-driven tourist segmentation indicates that different groups of Chinese tourists have distinctive activity preferences and travel patterns. Our primary interest is in tourists’ consumption behaviours, and we reveal that tourists with similar activity preferences still exhibit individualised behaviours with regards to the nature and location of key consumption activities such as shopping and dining out. We aim to understand more about Chinese tourist shopping behaviours as a secondary activity associated with multi-purpose trips, demonstrating that these data could permit insights into tourist behaviours and mobility patterns which are not well captured by official tourism statistics, especially at a localised level. This analysis could be up-scaled to incorporate additional LBSN data sources and broader population subgroups in order to support data-driven urban analytics related to tourist mobilities and consumption behaviours

Electricity consumption and household characteristics: Implications for census-taking in a smart metered future

This paper assesses the feasibility of determining key household characteristics based on temporal load profiles of household electricity demand. It is known that household characteristics, behaviours and routines drive a number of features of household electricity loads in ways which are currently not fully understood. The roll out of domestic smart meters in the UK and elsewhere could enable better understanding through the collection of high temporal resolution electricity monitoring data at the household level. Such data affords tremendous potential to invert the established relationship between household characteristics and temporal load profiles. Rather than use household characteristics as a predictor of loads, observed electricity load profiles, or indicators based on them, could instead be used to impute household characteristics. These micro level imputed characteristics could then be aggregated at the small area level to produce ‘census-like’ small area indicators. This work briefly reviews the nature of current and future census taking in the UK before outlining the household characteristics that are to be found in the UK census and which are also known to influence electricity load profiles. It then presents descriptive analysis of two smart meter-like datasets of half-hourly domestic electricity consumption before reporting on the results from a multilevel modelling-based analysis of the same data. The work concludes that a number of household characteristics of the kind to be found in UK census-derived small area statistics may be predicted from particular load profile indicators. A discussion of the steps required to test and validate this approach and the wider implications for census taking is also provided

Applied spatial modelling for retail planning in tourist resorts

Purpose: The purpose of this paper is to demonstrate that applied spatial modelling can inform the planning, delivery and evaluation of retail services, offering improvements over traditional retail impact assessment (RIA), especially within localities which experience seasonal fluctuations in demand. Design/methodology/approach: The paper first describes a new theoretically informed tourist-based spatial interaction model (SIM) which has been custom-built and calibrated to capture the dynamics of the grocery sector in Cornwall, UK. It tests the power of the model to predict store performance for stores not used in the original calibration process, using client data for a new store development. The model is operationalised for the evaluation of various retail development schemes, demonstrating its contribution across a full suite of location decision making application areas. Findings: The paper demonstrates that this highly disaggregate modelling framework can provide considerable insight into the local economic and social impacts of new store developments, rarely addressed in the retail location modelling literature. Practical implications: Whilst SIMs have been widely used in retail location research by the private sector, the paper shows that such a model can have considerable value for public sector retail planning, a sector which seemed to have abandoned such models from the 1980s onwards, replacing them with often very limited and crude RIA. Originality/value: The ability to review the forecasting capabilities of a model (termed post-investment review) are very rare in academic research. This paper offers new evidence that SIMs can support the RIA process

Supermarket Store Locations as a Proxy for Neighbourhood Health, Wellbeing, and Wealth

The “Waitrose effect” captures the notion that the presence of stores operated by Waitrose, an upmarket UK grocer, increases the value of nearby real estate. This paper considers the broader relationship between Waitrose store locations and neighbourhood type by comparing the health and wealth of neighbourhoods with and without access to Waitrose stores in England. Whilst we do not seek to imply causality, we demonstrate better health, wellbeing, and wealth in neighbourhoods falling within a Waitrose store catchment. In those neighbourhoods, median home prices were almost 2.5 times higher (in urban neighbourhoods) compared to neighbourhoods served only by other major grocers, which formed our control groups. Neighbourhoods in Waitrose catchment areas fare better on indicators of health too. In urban neighbourhoods falling within a Waitrose store catchment (accounting for 98% of Waitrose catchment neighbourhoods), residents are more likely to self-report very good health than those in our largest control groups. The prevalence of mood and anxiety disorders is also significantly lower in those neighbourhoods than in the control groups. Our findings strongly suggest that the presence or absence of a specific retailer (in this case, Waitrose, a mature and well-established chain) could serve as a proxy for neighbourhood characteristics. This could supplement existing multivariate indicators of neighbourhood type. We recommend more research to identify the extent to which locations of a single retail chain—across a variety of sectors—can encode neighbourhood health, wellbeing, and wealth. If the patterns observed with Waitrose stores hold true for other retailers, then the mix of retail stores within a given locality could serve as a useful proxy for neighbourhood type, with the potential for the change in retail mix to highlight changes in neighbourhood characteristics or composition

Understanding Town Centre Performance in Wales: Using GIS to Develop a Tool for Benchmarking

Welsh Government policy establishes town centres as central places of community activity and local prosperity, recognising the positive impact towns have on the local economy and the well-being and cohesion felt amongst local communities. In light of this, recent declines in the usage of town centres are a major cause for concern. These have not been experienced uniformly across all towns, with some towns out-performing others. This paper applies principles outlined in Welsh Government’s Planning Policy Wales to develop a tool which classifies a sample of 71 towns and cities in Wales based on their centre and catchment characteristics. Catchment areas have been delineated using a Spatial Interaction Model to account for complex consumer behaviours and competition between centres. The tool identifies six distinct types of towns alongside key socio-economic catchment area characteristics. Once developed, we demonstrate our tool’s application by exploring variations in town centre performance between and within each town type. Case study examples exemplify how policymakers may use this tool to benchmark between towns, evaluating the suitability of a town’s retail offering based on its performance relative to the benchmark, guiding decisions relating to the types of businesses and uses a town should pursue to improve its appeal to its catchment community. In conclusion, several recommendations to policymakers are suggested

Evaluating the Geographical Accessibility and Equity of COVID-19 Vaccination Sites in England

We assess the geographical accessibility of COVID-19 vaccination sites—including mass vaccination centers and community-level provision—in England utilizing open data from NHS England and detailed routing data from HERE Technologies. We aim to uncover inequity in vaccination site accessibility, highlighting small-area inequality hidden by coverage figures released by the NHS. Vaccination site accessibility measures are constructed at a neighborhood level using indicators of journey time by private and public transport. We identify inequity in vaccination-site accessibility at the neighborhood level, driven by region of residence, mode of transport (specifically availability of private transport), rural-urban geography and the availability of GP-led services. We find little evidence that accessibility to COVID-19 vaccination sites is related to underlying area-based deprivation. We highlight the importance of GP-led provision in maintaining access to vaccination services at a local level and reflect on this in the context of phase 3 of the COVID-19 vaccination programme (booster jabs) and other mass vaccination programmes

Microdata selection for estimating household consumption-based emissions

To estimate household emissions from a consumption-perspective, national accounts are typically disaggregated to a sub-national level using household expenditure data. While limitations around using expenditure data are frequently discussed, differences in emission estimates generated from seemingly comparable expenditure microdata are not well-known. We compare UK neighbourhood greenhouse gas emission estimates derived from three such microdatasets: the Output Area Classification, the Living Costs and Food Survey, and a dataset produced by the credit reference agency TransUnion. Findings indicate moderate similarity between emission estimates from all datasets, even at detailed product and spatial levels; importantly, similarity increases for higher-emission products. Nevertheless, levels of similarity vary by products and geographies, highlighting the impact microdata selection can have on emission estimates. We focus our discussion on how uncertainty from microdata selection can be reduced in other UK and international contexts by selecting data based on the data generation process, the level of disaggregation needed, physical unit availability and research implications