ESRC UCL, Bloomsbury, East London Doctoral Training Partnership Co-Funded Phd Studentships

ESRC UCL, Bloomsbury, East London Doctoral Training Partnership Co-funded PhD studentships at the Consumer Data Research Centre (CDRC)

The Consumer Data Research Centre have three co-funded PhD studentships in quantitative social science based in UCL’s Department of Geography. The awards will be administered through the UBEL Doctoral Training Partnership.Projects are availableworking with Boots, the Local Data Company (LDC) and Transport for West Midlands, commencing September 2017.
These awardsare open to applicants with backgrounds in quantitative social science and related disciplines, such as geography, social statistics, political science, economics, applied mathematics, planning or sociology. Students will be expected to work with consumer data as part of an exciting multidisciplinary research centre.

The studentships will cover

• Full payment of tuition fees per year – for either three years (Ph.D. only) or 1+3 years (including a preparatory year’s Masters study in a quantitative social science course).
• Annual maintenance stipend full-time: £16,296 for 2016/7. (An enhanced stipend of £3,000 per annum for Ph.D. only available for an exceptional Advanced Quantitative Methods candidate for the Transport for West Midlands project.)

If you are interested in applying, please:

1. Ascertain your eligibility to hold an ESRC studentship here.Note that full awards are intended for students ordinarily resident in the UK although, exceptionally, overseas students with strong backgrounds in advanced quantitative methods may be eligible.

2. Ascertain your research training foundation. If you hold or expect to obtain a relevant MSc with methods training meeting the 2015 ESRC Postgraduate Training Guidelines, you may apply for a +3 studentship. If you do not, you will need to take one of the related MSc courses at University College London, examples detailed here. If following this route we will discuss with you the most appropriate course to apply for.

3. Please apply to the Consumer Data Research Centre’s Project Manager, Sarah Sheppard () by12th February 2017. Please send:

• Max 500 word covering email summarising your interest in pursuing a particular co-funded PhD studentship with the CDRC.

• Academic CV including marks awarded to date plus details of 2 referees
Please note that only strong candidates (at least 2.1/Merit with elements of first/distinction level) will be considered.

Details of the three projects appear below.

CO-FUNDED PROJECTS
1. Understanding the new digital retail landscape

Industry Partner: Boots plc
First Supervisor: Professor Paul Longley
Second Supervisor: Dr James Cheshire
Industry Supervisor: Martin Squires

Like other major business-to-customer organisations, Boots Plc assembles vast amounts of customer data, much of them from its Advantage Card programme. The data are used to achieve a greater understanding of consumer purchasing habits, but are also of more general interest in investigating the activity patterns that characterize households living in different circumstances in different parts of the country. In this respect, the data offer the prospect of supplementing official data sources such as the Census of Population. The data could present a unique opportunity to create small area level indicators on a range of variables which have previously not been available in the public domain. Consumer data have already been found to be useful indicators of local and regional population growth, lifestyle choices and vulnerability to adverse social conditions.

However, the provenance of retail data remains largely unknown. Although a major player in British retailing, Boots data inevitably have only partial market coverage and the source and operation of resultant bias is not fully understood. This project will begin by creating metadata for a large assemblage of retail data and addressing challenges to effective concatenation and congflation (Goodchild and Longley 1999) with framework sources such as the 2011 Census.

This analysis will extend the experiences of the ESRC Consumer Data Research Centre (CDRC) in adapting the use of such data to applications of wider concern. Given the research centre’s broader agenda of facilitating the future sustainability of UK research using consumer data, the research will consider how the data could be appropriate for population analytics and what challenges need to be addressed and overcome. ‘Omni-retailing’, today occurs at the confluence of the digital and offline economies, and encompasses a number of hybrid forms such as click-and-collect. This research will develop to seek a better understanding of the new retail economy through spatial analysis of Advantage Card data triangulated with a range of administrative and conventional survey sources.

A common theme amongst consumer datasets is that they are increasing in size, update speed and complexity. Big datasets are increasingly difficult to store and analyse efficiently. The data vary in structure considerably, from cleanly structured transaction data to complex unstructured customer feedback responses. However, many of the data share two components. Firstly a temporal element, be it the occurrence of a transaction or the joining of a register. Secondly, a geographic component, often the location of where transactions take place or where consumers live. Whilst pre-existing data on consumer behaviour are very limited, often based on surveys and never on a sufficient scale to devise local measures, consumer data could fill the data void and even provide insights into wider activities which are not available from other sources on a large scale.

This research is important because academics have hitherto gained rather little exposure to use of retail data, which account for an increasing real share of all of the data that are collected about citizens today. Consumer data have the potential to be of considerable use beyond the retail industry. It is likely that the student will evaluate data pertaining to online, in store and click and collect sales in relation to the CDRC ‘Areas and activities’ Service Linked Research Project, with its focus upon identifying population dynamics at a range of spatial and temporal scales.

The work will also be related to research within the CDRC that has sought to estimate the e-resilience of British town centres by modelling the local population’s engagement with the Internet (Singleton et al, 2016). Local indicators of Internet engagement have been built from linking demographic statistics, surveys and internet connectivity data. The research student will seek to understand the implications of Internet use for retailing purposes. By considering data from retailers with both online and offline offerings, it is possible to understand how the population negotiates spending between channels. Crucially, this research also offers insight into why these patterns will vary between households and also retail centres. The importance of the Internet to everyday activities is profound and access to the Internet is now considered a legal right within the UK. However, open data are yet to catch up and there is insufficient geographic information on Internet usage and behaviour.

Consumer data could present exclusive insights into how neighborhoods engage with online channels. Finally, the work will also link to the current Office for National Statistics Data Science agenda for refining and extending the range of retail relevant official statistics.

Goodchild M F, Longley P A (1999) The future of GIS and spatial analysis. In P A Longley, M F Goodchild, D J Maguire, D W Rhind (eds) Geographical Information Systems: Principles, Techniques, Management and Applications New York, Wiley: 567-580.

Singleton A D, Dolega L, Riddlesden D, Longley P A (2016) Measuring the spatial vulnerability of retail centres to online consumption through a framework of e-resilience. Geoforum 69: 5-1

2. A scale-based analysis of retail store location profitability

Industry Partner: Local Data Company Ltd
First Supervisor: Professor Paul Longley
Second Supervisor: Dr James Cheshire
Industry Supervisor: Matthew Hopkinson

Conventional geographic models of store location and profitability seek to understand profitability in terms of the attractiveness of the store (typically measured in terms of floorspace, but also sometimes micro-site characteristics such as fascia, uses of adjacent retail units, and aspects of micro site location), its catchment area (measured as straight line or road distance, sometimes adjusted for travel time considerations), the demographic characteristics of the catchment area (in terms of the spending power of those resident within it) and the locations of competitor stores. This basic approach has been developed across a range of scales from the local to the regional, and was particularly successful in the era of out of centre retail development in the 1980s and 1990s.

However, the basic tenets of the approach have come under increasing strain in the New Millennium, particularly from the reintroduction of convenience stores in urban locations, and the use of these and other stores for ‘omni retailing’ arising from the innovation of online shopping and hybrid forms of retailing such as click-and-collect. In these changed circumstances many of the strategic investments of past developments are now thought of as sunk costs, while new investments need to be more cognizant of the changes to the retail landscape that have taken place – not least the persistence of high retail unit vacancy rates in many parts of the country, with adverse consequences for local economic health.

The mainstay of Local Data Company’s business is a detailed and continuously updated inventory of the composition of every retail centre in the UK, carried out by a team of fieldworkers that records the tenancies of every retail unit, or whether the unit is vacant. These data can be used to create summary measures of retail centre health, measured for example in terms of vacancy rates, market niche of retail players and special considerations such as incidence of charity shops (which are exempt from the property rates system). A version of these data has been acquired by the ESRC CDRC, and can be accessed for research purposes as an ESRC dataset.

The ongoing ESRC-funded collaboration between CDRC and LDC is investigating ways of supplementing data on retail occupation with micro site measures of footfall, measured using WiFi sensors. The resulting data are processed by CDRC and are building into a major ESRC ‘Big Data’ assemblage.

This research will investigate the factors affecting store location profitability in the changed environment of UK retailing, using ESRC data resources not previously available to academic research. Relevant considerations to be addressed at a range of scales will include: (a) retail centre composition and health; (b) footfall; (c) the health of competitor retail centres; (d) the dynamics of demand and supply interactions at local and regional levels; (e) the effects of local business rates and apparent inconsistencies in their calibration; and (f) the national picture.

The research will bring strong focus to the profitability and hence sustainability of retail units. A novel aspect of this collaboration is that LDC’s reach into the retail industry will be used to recruit retailers to provide retail sales data in order to inform decision-making with respect to store network expansion or rationalization decisions with respect to comparable locations.

This work, in turn, will begin to address wider investment decisions in an omni retailing environment. One particular aspect will be the innovation of so-called ‘theatre stores’ which retailers such as John Lewis or manufacturers such as Nike are prepared to fund (sometimes from marketing budgets) to develop market share and support online sales. Such initiatives stand in stark contrast to the view of retail units as sunk costs that may not be recouped in the changed retail system.

As such, the student will be required to develop a wide-ranging understanding of what drives retail profitability, rather than how conventional distance and residence based retail models are specified, estimated and tested. The placements will be organized in order to allow the student to become familiarized with the LDC data dashboard system, as well as the LDC store inventory and footfall data series.

Singleton A D, Dolega L, Riddlesden D, Longley P A (2016) Measuring the spatial vulnerability of retail centres to online consumption through a framework of e-resilience. Geoforum 69: 5-18

3. New perspectives on daily urban mobility: Harnessing the potential of Smart Card Travel data

Industry Partner: Transport for West Midlands
First Supervisor: Professor Paul Longley
Second Supervisor: Dr Jens Kandt
Industry Supervisor: Chris Lane

Smart Travel Cards (STCs) are introduced in many cities in the UK and elsewhere. The data form a novel resource routinely collected by transport authorities, complementing the wide range of travel data already held by those organisations. STCs record all electronic ticket transactions in public transport and in this way trace daily mobility in unprecedented detail for a large proportion of both local residents and visitors. In combination with other data held in the public and private domains, the data offer potential to understand, in new ways, the dynamics of daily, urban mobility for the benefit of transport organisations as well as important issues of wider concern in social science and policy. In particular, the data complement traditional, often costly data sources (Travel-To-Work component of the UK Census, travel diaries in household travel surveys) by offering a nearly real-time temporal resolution, geographical detail and wide population coverage. In assembling the data provided by TfWM (based on an existing formal data sharing agreement), the research will contribute to pressing social science and policy questions of achieving sustainable and inclusive mobility in the context of social inequalities and lifestyle choices (Kandt et al 2015). The research is also envisioned to deliver a framework to harness STC data for social science research more generally and thereby develop a blueprint for other cities that have introduced or will introduce STCs. The research also contributes directly to the objectives of TfWM of using their vast databases to support their mandate of delivering sustainable and inclusive mobility.

While the STC data are particularly interesting because they deliver key variables of travel demand (e.g. trip generation, frequency, distance, time of day, dwelling time), a number of heuristics will have to be developed in order to derive useful indicators of travel, ascertain the coverage of the data, understand the extent and operation of bias and link the indicators to wider social domains, such as exclusion or health and well-being. Travel demand is a derived demand and may be understood as traces of all sorts of daily activities, which aggregated across population groups, neighbourhoods or regions may well add up to a detailed, multi-level understanding of urban dynamics. Current work on using TSC data is still in its infancy, especially in relation to efforts to extend analytics beyond operational questions of the transport sector. Within the research (and in accordance to existing interests at TfWM), it will need to be established, how many residents are represented in routinely collected datasets. This question is related to bias in Big Data and hence relates to a common concern in Big Data Analytics. Specific methods of turning the data into information include strategies and techniques to (1) generate daily mobility profiles from travel transactions, (2) link trips to daily activities, e.g. by inferring trip destinations and purposes and (3) infer demographics from mobility profiles. As part of these undertakings, the student will need to make use of the implicit geographic information available in the data through data linkage of STC-recorded boardings and GPS vehicle tracking data. The research thus links to the long-established geodemographics with geo-spatial research undertaken by Paul Longley’s team (e.g. Longley et al 2016, Longley et al 2015). The resulting expertise from these steps will enable the student to both develop research frameworks of Big Data Analytics in social science and address challenges in the transport sector. The successful PhD will open academia, government and private companies as potential sectors for employment.

Kandt, J., Rode, P., Hoffmann, C., Graff, A., Smith, D. (2015.). Gauging interventions for sustainable travel: A comparative study of travel attitudes in Berlin and London, Transportation Research Part A: Policy and Practice, (80), 35-48, doi:10.1016/j.tra.2015.07.008.

Longley, P.A., Gale, C.G., Singleton, A., Bates, A.G. (2016). Creating the 2011 area classification for output areas (2011 OAC). Journal of Spatial Information Science, (12), 1-27. doi:10.5311/JOSIS.2016.12.232.

Longley, P.A., Goodchild M.F., Maguire, D.J., Rhind, D.W. (2015). Geographic Information Science and Systems 4th Edition. Wiley