Deriving retail centre locations and catchments from geo-tagged Twitter data

This investigation offers an initial foray into the application of geo-tagged Twitter data for generating insights within two areas of retail geography: establishing retail centre locations and defining catchment areas. Retail related Tweets were identified and their spatial attributes examined with an adaptive kernel density estimation, revealing that retail related Twitter content can successfully locate areas of elevated retail activity, however, these are constrained by biases within the data. Methods must also account for the underlying geographic distribution of Tweets to detect these fluctuations. Additionally, geo-tagged Twitter data can be utilised to examine human mobility patterns in a retail centre context. The catchments constructed from the data highlight the importance of accessibility on flows between locations, which have implications for the likely commuting choices that may be involved in retail centre journey decision-making. These approaches demonstrate the potential applications for less conventional datasets, such as those derived from social media data, to previously under-researched areas

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

Calibrating spatial interaction models from GPS tracking data: an example of retail behaviour

Global Positioning System (GPS) technology has changed the world. We now depend on it for navigating vehicles, for route finding and we use it in our everyday lives to extract information about our locations and to track our movements. The latter use offers a potential alternative to more traditional sources of movement data through the construction of trip trajectories and, ultimately, the construction of origin-destination flow matrices. The advantage of being able to use GPS-derived movement data is that such data are potentially much richer than traditional sources of movement data both temporally and spatially. GPS-derived movement data potentially allow the calibration of spatial interaction models specific to very short time intervals, such as daily or even hourly, and for user-specified origins and destinations. Ultimately, it should be possible to calibrate continuously updated models in near real-time. However, the processing of GPS data into trajectories and then origin-destination flow matrices is not straightforward and is not well understood. This paper describes the process of transferring GPS tracking data into matrices that can be used to calibrate spatial interaction models. An example is given using retail behaviour in two towns in Scotland with an origin-constrained spatial interaction model calibrated for each day of the week and under different weather conditions (normal, rainy, windy). Although the study is small in terms of individuals and spatial context, it serves to demonstrate a future for spatial interaction modelling free from the tyranny of temporally static and spatially predefined data sets

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

Planificación urbana 4.0: datos geolocalizados de redes sociales para la intervención en la ciudad

There are many different approaches emerging from urban research which recognize the great potential of social media data for describing, diagnosing, predicting, and prescribing decision-making processes in the field of urban planning and urban regeneration. Numerous methods and sources have been widely explored to enrich our knowledge of cities. However, only few and/or scarcely disseminated urban agendas, planning instruments, projects and interventions have explicitly been based on the findings obtained from the analysis of geolocated social media data. In this sense, this research explores the possibility of integrating this layer of information into urban diagnoses to inform the formulation of urban planning documents and agendas in the Spanish context. A selection of planning instruments and their classification in the context of these two different kinds of intervening in the city are discussed, suggesting the possible inclusion of data from specific social media platforms in the specific areas of these regulations. This paper illustrates the practical application of these data as a main source for the development of diagnoses and intervention proposals by examining the experience of three specific case studies.En investigación urbana, se reconoce gran potencial a los datos de redes sociales para la descripción, el diagnóstico, la predicción y la toma de decisiones en el ámbito del planeamiento y la regeneración urbana. Se han explorado numerosos métodos y fuentes para actualizar el conocimiento de las ciudades. Sin embargo, son escasos y/o poco difundidos los instrumentos, incluyendo agendas, planes, proyectos y actuaciones en los que explícitamente se hayan tomado decisiones a partir de hallazgos obtenidos mediante el análisis de redes sociales geolocalizadas. En este sentido, este trabajo explora la posibilidad de incorporar esta capa de información a los diagnósticos urbanos para informar la redacción de Planes urbanísticos y las actuaciones en el contexto de las agendas urbanas en España. Se seleccionan algunos instrumentos, se clasifican según estas dos formas de intervención en la ciudad y se propone la posible incorporación de la información datos de redes sociales específicas a los alcances y competencias de dichas regulaciones. A partir de tres experiencias previas, se ejemplifica la aplicación práctica de estos datos como fuente principal para la realización de diagnósticos y propuestas de intervención

Urban Planning 4.0: geolocated data from social networks for urban intervention

En investigación urbana, se reconoce gran potencial a los datos de redes sociales para la descripción, el diagnóstico, la predicción y la toma de decisiones en el ámbito del planeamiento y la regeneración urbana. Se han explorado numerosos métodos y fuentes para actualizar el conocimiento de las ciudades. Sin embargo, son escasos y/o poco difundidos los instrumentos, incluyendo agendas, planes, proyectos y actuaciones en los que explícitamente se hayan tomado decisiones a partir de hallazgos obtenidos mediante el análisis de redes sociales geolocalizadas. En este sentido, este trabajo explora la posibilidad de incorporar esta capa de información a los diagnósticos urbanos para informar la redacción de Planes urbanísticos y las actuaciones en el contexto de las agendas urbanas en España. Se seleccionan algunos instrumentos, se clasifican según estas dos formas de intervención en la ciudad y se propone la posible incorporación de la información datos de redes sociales específicas a los alcances y competencias de dichas regulaciones. A partir de tres experiencias previas, se ejemplifica la aplicación práctica de estos datos como fuente principal para la realización de diagnósticos y propuestas de intervención.There are many different approaches emerging from urban research which recognize the great potential of social media data for describing, diagnosing, predicting, and prescribing decision-making processes in the field of urban planning and urban regeneration. Numerous methods and sources have been widely explored to enrich our knowledge of cities. However, only few and/or scarcely disseminated urban agendas, planning instruments, projects and interventions have explicitly been based on the findings obtained from the analysis of geolocated social media data. In this sense, this research explores the possibility of integrating this layer of information into urban diagnoses to inform the formulation of urban planning documents and agendas in the Spanish context. A selection of planning instruments and their classification in the context of these two different kinds of intervening in the city are discussed, suggesting the possible inclusion of data from specific social media platforms in the specific areas of these regulations. This paper illustrates the practical application of these data as a main source for the development of diagnoses and intervention proposals by examining the experience of three specific case studies.Esta investigación se desarrolla en el contexto del proyecto Espacios públicos urbanos en transformación: diagnóstico y estrategias de resiliencia urbana a partir de las redes sociales geolocalizadas. Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana. Ref. no. GV/2021/177

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

Socio-spatial analysis of social media data

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