Deriving Supply-side Variables to Extend Geodemographic Classification

The traditional proprietary geodemographic information systems that are on the market today use well-established methodologies. Demographic indicators are selected as a proxy for affluence and are then often linked to customer databases to derive a measure of the level of consumption expected from the different area typologies. However, these systems ignore fundamental relationships in the retail market by focusing upon demand characteristics in a ‘vacuum’ and ignore the supply side and consumer-supplier interaction. This paper argues that there may be considerable advantages to including supply-side indicators within geodemographic systems. Whilst the term ‘supply’ in this context might imply the number of consumer services already in an area, equally important for understanding demand are variables such as the supply of jobs and houses. We suggest that profiling an area in terms of its labour market characteristics gives a better insight into the income chain while the supply of houses could be argued to be a crucial factor in household formation that in turn will impact upon demographic structure. Using the regional example of Yorkshire and Humberside in northern England, we indicate how a suite of supply-side variables relating to the labour market can be assembled and used alongside a suite of demand variables to generate a new area classification. Spatial interaction models are calibrated to derive some of the variables that take into account zonal self-containment and catchment size

A new geodemographic classification of commuting flows for England and Wales

This paper aims to contribute to the area of geodemographic research through the development of a new and novel flow-based classification of commuting for England and Wales. In doing so, it applies an approach to the analysis of commuting in which origin-destination flow-data, collected as part of the 2011 census of England and Wales, are segmented into groups based on shared similarities across multiple demographic and socioeconomic attributes. K-means clustering was applied to 49 flow-based commuter variables for 513,892 interactions that captured 18.4 million of the 26.5 million workers recorded as part of the 2011 census of England and Wales. The final classification resulted in an upper-tier of nine ‘Supergroups’ which were subsequently partitioned to derive a lower-tier of 40 ‘Groups’. A nomenclature was developed and associated pen-portraits derived to provide basic signposting to the dominant characteristics of each cluster. Analysis of a selection of patterns underlying the nine-fold Supergroup configuration revealed a highly variegated structure of commuting in England and Wales. The classification has potentially wide-ranging descriptive and analytical applications within research and policy domains and the approach would be equally transferable to other countries and contexts where origin-destination data is disaggregated based on commuter characteristics

The applications of loyalty card data for social science

Large-scale consumer datasets have become increasingly abundant in recent years and many have turned their attention to harnessing these for insights within the social sciences. Whilst commercial organisations have been quick to recognise the benefits of these data as a source of competitive advantage, their emergence has been met with contention in research due to the epistemological, methodological and ethical challenges they present. These issues have seldom been addressed, primarily due to these data being hard to obtain outside of the commercial settings in which they are often generated. This thesis presents an exploration of a unique loyalty card dataset obtained from one of the most prominent UK high street retailers, and thus an opportunity to study the dynamics, potentialities and limitations when applying such data in a research context. The predominant aims of this work were to firstly, address issues of uncertainty surrounding novel consumer datasets by quantifying their inherent representation and data quality issues and secondly, to explore the extent to which we may enrich our current knowledge of spatiotemporal population processes through the analysis of consumer activity patterns. Our current understanding of such dynamics has been limited by the data-scarce era, yet loyalty card data provide individual level, georeferenced population data that are high in velocity. This provided a framework for understanding more detailed interactions between people and places, and what these might indicate for both consumption behaviours and wider societal phenomena. This work endeavoured to provide a substantive contribution to the integration of consumer datasets in social science research, by outlining pragmatic steps to ensure novel data sources can be fit for purpose, and to population geography research, by exploring the extent to which we may utilise spatiotemporal consumption activities to make broad inferences about the general population

Profiling the Dynamic Pattern of Bike-sharing Stations: a case study of Citi Bike in New York City

This research applies a hierarchical k-means clustering method on the TF-IDF weighted 2019 cycling transactions from the Citi Bike bike-sharing system operating in New York City, with the primary goal of investigating the spatiotemporal usage pattern of its docking points. With a particular focus on bike-sharing stations in Manhattan, we classify 504 stations into four main clusters featuring heterogeneous dynamic usages, including leisure-oriented, residentialoriented, workplace-oriented, and off-peak oriented. We interpret each cluster based on their salient characteristics and anticipate possible future directions of this work

You are how you travel: A multi-task learning framework for Geodemographic inference using transit smart card data

Geodemographics, providing the information of population's characteristics in the regions on a geographical basis, is of immense importance in urban studies, public policy-making, social research and business, among others. Such data, however, are difficult to collect from the public, which is usually done via census, with a low update frequency. In urban areas, with the increasing prevalence of public transit equipped with automated fare payment systems, researchers can collect massive transit smart card (SC) data from a large population. The SC data record human daily activities at an individual level with high spatial and temporal resolutions. It can reveal frequent activity areas (e.g., residential areas) and travel behaviours of passengers that are intimately intertwined with personal interests and characteristics. This provides new opportunities for geodemographic study. This paper seeks to develop a framework to infer travellers' demographics (such as age, income level and car ownership, et al.) and their residential areas for geodemographic mapping using SC data with a household survey. We first use a decision tree diagram to detect passengers' residential areas. We then represent each individual's spatio-temporal activity pattern derived from multi-week SC data as a 2D image. Leveraging this representation, a multi-task convolutional neural network (CNN) is employed to predict multiple demographics of individuals from the images. Combing the demographics and locations of their residence, geodemographic information is further obtained. The methodology is applied to a large-scale SC dataset provided by Transport for London. Results provide new insights in understanding the relationship between human activity patterns and demographics. To the best of our knowledge, this is the first attempt to infer geodemographics by using the SC data

Identifying and understanding road-constrained areas of interest (AOIs) through spatiotemporal taxi GPS data: A case study in New York City

Urban areas of interest (AOIs) represent areas within the urban environment featuring high levels of public interaction, with their understanding holding utility for a wide range of urban planning applications. Within this context, our study proposes a novel space-time analytical framework and implements it to the taxi GPS data for the extent of Manhattan, NYC to identify and describe 31 road-constrained AOIs in terms of their spatiotemporal distribution and contextual characteristics. Our analysis captures many important locations, including but not limited to primary transit hubs, famous cultural venues, open spaces, and some other tourist attractions, prominent landmarks, and commercial centres. Moreover, we respectively analyse these AOIs in terms of their dynamics and contexts by performing further clustering analysis, formulating five temporal clusters delineating the dynamic evolution of the AOIs and four contextual clusters representing their salient contextual characteristics

Designing a location model for face to face and on-line retailing for the UK grocery market

The vast and rapid expansion of internet usage has generated widespread online sales, making the UK one of the leading countries for e-commerce. Until now there has been no clear understanding or analysis of the spatial variations of online activities. Many studies have,however, examined the variance in online buying among different demographic groups usually based on limited survey information. These variations have often been explained by reference to two theories – efficiency theory and diffusion of innovations theory (Rogers, 1995). This lack of research to date is also manifest in the lack of consideration of online sales in traditional store location methodologies. The aim of this research is to establish a new model for site location which includes e-grocery shopping on the UK retail sector. Having explored the literature around the geography of e-commerce and the surveys of geodemographic usage, the thesis explores data unique to the academic sector- namely Sainsbury’s store revenue (for both physical and online channels) and customer data based on their loyalty card (interaction data). The analysis of these data sets establishedfour major trends in the relationship between online share and store provision with insights into the substitution of online and physical channels in areas with limited accessibility to physical grocery stores. Using this information, a new, revised SIM is built and calibrated to include estimates of revenue for both face to face and online stores. It is hoped this will provide an important addition to the existing kitbag of techniques available to retail store location planners

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Designing a location model for face to face and on-line retailing for the UK grocery market

The vast and rapid expansion of internet usage has generated widespread online sales, making the UK one of the leading countries for e-commerce. Until now there has been no clear understanding or analysis of the spatial variations of online activities. Many studies have,however, examined the variance in online buying among different demographic groups usually based on limited survey information. These variations have often been explained by reference to two theories – efficiency theory and diffusion of innovations theory (Rogers, 1995). This lack of research to date is also manifest in the lack of consideration of online sales in traditional store location methodologies. The aim of this research is to establish a new model for site location which includes e-grocery shopping on the UK retail sector. Having explored the literature around the geography of e-commerce and the surveys of geodemographic usage, the thesis explores data unique to the academic sector- namely Sainsbury’s store revenue (for both physical and online channels) and customer data based on their loyalty card (interaction data). The analysis of these data sets establishedfour major trends in the relationship between online share and store provision with insights into the substitution of online and physical channels in areas with limited accessibility to physical grocery stores. Using this information, a new, revised SIM is built and calibrated to include estimates of revenue for both face to face and online stores. It is hoped this will provide an important addition to the existing kitbag of techniques available to retail store location planners