The geography of Twitter topics in London

Social media data are increasingly perceived as alternative sources to public attitude surveys because of the volume of available data that are time-stamped and (sometimes) precisely located. Such data can be mined to provide planners, marketers and researchers with useful information about activities and opinions across time and space. However, in their raw form, textual data are still difficult to analyse coherently and Twitter streams pose particular interpretive challenges because they are restricted to just 140 characters. This paper explores the use of an unsupervised learning algorithm to classify geo-tagged Tweets from Inner London recorded during typical weekdays throughout 2013 into a small number of groups, following extensive text cleaning techniques. Our classification identifies 20 distinctive and interpretive topic groupings, which represent key types of Tweets, from describing activities or informal conversations between users, to the use of check-in applets. Our motivation is to use the classification to demonstrate how the nature of the content posted on Twitter varies according to the characteristics of places and users. Topics and attitudes expressed through Tweets are found to vary substantially across Inner London, and by time of day. Some observed variations in behaviour on Twitter can be attributed to the inferred demographic and socio-economic characteristics of users, but place and local activities can also exert a considerable influence. Overall, the classification was found to provide a valuable framework for investigating the content and coverage of Twitter usage across Inner London

Deriving age and gender from forenames for consumer analytics

This paper explores the age and gender distributions of the bearers of British forenames and identifies key trends in British naming conventions. Age and gender characteristics are known to greatly influence consumption behaviour, and so extracting and using names to indicate these characteristics from consumer datasets is of clear value to the retail and marketing industries. Data representing over 17 million individuals sourced from birth certificates and market data have been modelled to estimate the total age and gender distributions of 32,000 unique forenames in Britain. When aggregated into five year age bands for each gender, the data reveal distinctive age profiles for different names, which are largely a product of the rise and decline in popularity of different baby names over the past 90 years. The names database produced can be used to infer the expected age and gender structures of many consumer datasets, as well as to anticipate key characteristics of consumers at the level of the individual

The Impact of Local Demographics on Retail Centre Health in England and Wales

Spatial Interaction Models are a widely accepted means of linking retail centres to their local customer catchments. However, without real consumer data to validate the findings, such models remain as mere estimations based on geographic population and store data. Using a spatial interaction model which accounts for both the residential and local workplace populations, the following study seeks to evaluate the extent of which demographic characteristics of estimated retail catchments are an effective predictor of retail health for retail centres in England and Wales. Overall, this case study explores the value of demographics from estimated centre catchments to retail planners

Youth Spending and Geodemographics: A Review of Research into Adolescent Consumers

Our research seeks to undertake an exploratory analysis of adolescents’ consumer data. Our primary aim is to understand how variations in youth spending and earnings vary by demographics, and also by their neighbourhood characteristics. As the vast majority of consumer datasets focus on adults, little is known about how adolescents interact with the retail market as they age. The eventual aim is that the findings from our future research can be used to guide the production of youth geodemographic datasets using data pooled from a range of sources

Representing Population Dynamics from Administrative and Consumer Registers

This research attempts to derive representative metrics of household dynamics and migration by analysing changes between two annual composite registers of the UK population. Through appropriate data cleaning and linkage techniques, it is possible to match addresses and record changes in their size and composition over a two year period. The paper also demonstrates that it is feasible to approximate migration trends by filtering and matching records of household units and individuals whom are not recorded at the same address in both datasets

Improved targeted outdoor advertising based on geotagged social media data

With as many as 4 million passenger journeys within the London Underground system every weekday, the advertisement spaces across the stations hold considerable potential. However, the planning of specific advertisements across time and space is difficult to optimize as little is known about passers-by. Therefore, in order to generate detailed and quantifiable spatio-temporal information which is particular to each station area, we have explored local social media data. This research demonstrates how local interests can be mined from geotagged Tweets by using Latent Dirichlet Allocation, an unsupervised topic modelling method. The relative popularity of each of the key topics is then explored spatially and temporally between the station areas. Overall, this research demonstrates the value of using Geographical Information System and text-mining techniques to generate valuable spatio-temporal information on popular interests from Twitter data

Using linked consumer and administrative data to model demographic changes in London’s city fringe

This unique book demonstrates the utility of big data approaches in human geography and planning. Offering a carefully curated selection of case studies, it reveals how researchers are accessing big data, what this data looks like and how such data can offer new and important insights and knowledge

Using linked consumer registers to estimate residential moves in the United Kingdom

This paper argues that frequently updated data on the nature of residential moves and the circumstances of movers in the United Kingdom are insufficient for many research purposes. Accordingly, we develop previous research reported in this Journal to re-purpose consumer and administrative data in order to develop annual estimates of residential mobility between all UK neighbourhoods. We use a unique digital corpus of linked individual and household-level consumer registers compiled by the UK Consumer Data Research Centre, comprising over 143 million unique address records pertaining to the entire UK adult population over the period 1997–2016. We describe how records pertaining to individuals vacating a property can be assigned to their most probable residential destination, based on novel methods of matching names, assessing household composition, and using information on the date and probable distance of residential moves. We believe that the results of this analysis contribute highly granular, frequently updated estimates of residential moves that can be used to chart population-wide outcomes of residential mobility and migration behaviour, as well as the socio-spatial characteristics of the sedentary population

Consumer Registers as Spatial Data Infrastructure and their Use in Migration and Residential Mobility Research

This chapter outlines efforts to devise modelled estimates of population change at a small-area level using annual registers that blend consumer and voter registration data. Names and addresses of individuals are routinely collected by governments and commercial organisations. However, there have been few attempts by academics to pool the data in order to track population changes despite the registers representing the majority of the adult population. Therefore, the possibility of linking databases for chronological pairs of years could provide a unique insight into population dynamics on an annual basis. Aligned with consumer data analytics, this information could reveal important statistics about the United Kingdom’s changing social structure and how it varies geographically – with far more frequent refresh than available from comprehensive government sources such as the Census of Population. Comprehensive models of Consumer Registers as Spatial Data Infrastructure and their Use in Migration and Residential Mobility Research Guy Lansley and Wen Li migration at a household level would give us the opportunity to develop an understanding of social mobility and asset accumulation through linkage to other geographic datasets. In this chapter, we present work on the 2013 and 2014 Consumer Registers produced by CACI Ltd (London, UK). The registers comprise the public version of the Electoral Register (sometimes termed the ‘edited register’) and are supplemented by a range of unattributed consumer data sources. Together, these population databases provide near complete coverage of the adult population at the individual level and are consolidated on an annual basis. However, the data only contain information on adult individuals’ names and postal addresses and lack any demographic variables. In addition, due to the nature of their data collection and amalgamation, the consumer data are of unknown provenance. We have therefore developed novel data-linkage techniques in order to assess the completeness of the population recorded prior to modelling apparent trends from these pooled data. Set in the context of harnessing information on population dynamics from data linkage between two registers, this study has three broad aims. First, to devise an appropriate technique to match addresses. Second, to estimate household dynamics by linking names at matched addresses. And finally, to estimate migration by modelling the movements of those that have left and joined addresses – specifically between 2013 and 2014. We will explore the feasibility of this model as a means of representing migration and social mobility