Estimating Footfall From Passive Wi-Fi Signals: Case Study with Smart Street Sensor Project

Measuring the distribution and dynamics of the population at granular level both spatially and temporally is crucial for understanding the structure and function of the built environment. In this era of big data, there have been numerous attempts to undertake this using the preponderance of unstructured, passive and incidental digital data which are generated from day-to-day human activities. In attempts to collect, analyse and link these widely available datasets at a massive scale, it is easy to put the privacy of the study subjects at risk. This research looks at one such data source - Wi-Fi probe requests generated by mobile devices - in detail, and processes it into granular, long-term information on number of people on the retail high streets of the United Kingdom (UK). Though this is not the first study to use this data source, the thesis specifically targets and tackles the uncertainties introduced in recent years by the implementation of features designed to protect the privacy of the users of Wi-Fi enabled mobile devices. This research starts with the design and implementation of multiple experiments to examine Wi-Fi probe requests in detail, then later describes the development of a data collection methodology to collect multiple sets of probe requests at locations across London. The thesis also details the uses of these datasets, along with the massive dataset generated by the ‘Smart Street Sensor’ project, to devise novel data cleaning and processing methodologies which result in the generation of a high quality dataset which describes the volume of people on UK retail high streets with a granularity of 5 minute intervals since August 2015 across 1000 locations (approx.) in 115 towns. This thesis also describes the compilation of a bespoke ‘Medium data toolkit’ for processing Wi-Fi probe requests (or indeed any other data with a similar size and complexity). Finally, the thesis demonstrates the value and possible applications of such footfall information through a series of case studies. By successfully avoiding the use of any personally identifiable information, the research undertaken for this thesis also demonstrates that it is feasible to prioritise the privacy of users while still deriving detailed and meaningful insights from the data generated by the users

A Review Of Data Monetization: Strategic Use Of Big Data

In this data-rich big data age, industries are capable to collect data that could not be imagined before. Many industries today are now thinking of how to better use these data assets properly to generate value, either for internal or external purpose. Data monetization is adopted as one of strategies used to create additional stream of revenue from the discovery, capture, storage, analysis, dissemination, and use of that data. It gains in popularity among different industries. The three research questions of interest to this study are: (1) what does data monetization mean to business; (2) what are types of data monetization and industries currently use; (3) how to initiate a data monetization strategy. To address these questions, this study did a comprehensive review of prior research from academia as well as from industry. This study clarifies and defines the data monetization, presents the synthesis of use cases learned from other industries as well as provides guiding principles of how to start with data monetization. The contributions of this study are twofold. First, this paper contributes to industry communities that start to explore opportunities of creating value from their data assets but lack of directions and how to. Second, this study contributes to raise awareness of academic communities over the potential of big data monetization research and the opportunities in further discussing the converging information system and strategy domain

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

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

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

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

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available

WELCOME TO DIGITAL TRANSFORMATION ERA: FROM PROOF-OF-CONCEPT TO BIG DATA INSIGHTS CREATION

Digital transformation (DT) is no longer an optional strategic priority, but the direction for managers of traditional firms that their success is built in the pre-digital era. With all hype around DT opportunities, it is rather a highly complex challenge that affects many or all segments of a firm and more so at the early stages of DT. Firms at the early stage of DT face the challenge of choosing among a big variety of existing and emerging technologies on the market, neglecting technological uncertainty, navigating through the technological solutions ocean, and avoiding hype-driven decisions while being technology competence-less. With this respect, the phase preceding any adoption or rejection of a new DT initiative and aiming at the first meeting and proving feasibility and commercial opportunities becomes increasingly important. The thesis investigates three particular phenomena of the earliest Digital Transformation (DT) stage, that are seemingly well-known and intuitively clear but suffer from the lack of empirical and conceptual evidence base as well as theoretical ground on closer inspection, namely, proof-of-concept, data-driven decision-making, and Big Data insights creation. Focusing on the three aspects of the early stage of DT allows building a research agenda that consists of complementing each other parts. Three-essays research was run with three related objectives. Each objective is addressed by conducting independent research using comparative methods. The thesis applies the qualitative approach as the overarching, with the relative to the three essays methodologies, namely, qualitative case study, ethnography, and participatory observation. The thesis uses qualitative methods to derive main findings and quantitative methods based on novel computational techniques to add more nuances to the results. This allows a new empirical and conceptual perspective on the earliest stages of DT. The findings suggest that a) cognitive biases drive what I labeled as perceived technology potentiality, moreover, technology awareness develops step-wise as PoC is run moving from borrowed technology awareness to minimum acquired technology awareness and enhanced technology awareness. These findings were used to explain how PoC dynamic changes with time. Further, findings show how b) different types of traps (cognitive and data) drive managerial trust in data when data-driven decision-making is first used. The findings were taken as the ground to build the three traps zones notion, where the decisions and trust in data are driven by different combinations of traps. Finally, findings reveal that c) Big Data dimensions have their related sub-dimensions, differences and similarities of which led to the discovery of the two effects of Big Data dimensions, namely, Proliferation and Additive. These findings helped to explain how exactly Big Data dimensions participate in the Big Data insights creation and to build the conceptual matrix of Big Data insights creation. In this vein, the research contributes to the technology innovation literature by shedding light on the phenomena of the earliest stage of DT and by initiating the first comprehensive conversation on PoC, data-driven decision-making, and Big Data insights creation. Further, the research contributes to the existing literature on managerial cognition, decision-making, and Big Data usefulness. Finally, contributions to methods in the technology innovation field are drawn

Applications of new forms of data to demographics

At the outset, this thesis sets out to address limitations in conventional population data for the representation of stocks and flows of human populations. Until now, many of the data available for studying population behaviour have been static in nature, often collected on an infrequent basis or in an inconsistent manner. However, rapid expansion in the use of online technologies has led to the generation of a huge volume of data as a byproduct of individuals’ online activities. This thesis sets out to exploit just one of these new data channels: raw geographically referenced messages collected by the Twitter Online Social Network. The thesis develops a framework for the creation of functional population inventories from Twitter. Through the application of various data mining and heuristic techniques, individual Twitter users are attributed with key demographic markers including age, gender, ethnicity and place of residence. However, while these inventories possess the required data structure for analysis, little is understood about whom they represent and for what purposes they may be reliably employed. Thus a primary focus of this thesis is the assessment of Twitter-based population inventories at a range of spatial scales from the local to the global. More specifically, the assessment considers issues of age, gender, ethnicity, geographic distribution and surname composition. The value of such rich data is demonstrated in the final chapter in which a detailed analysis of the stocks and flows of peoples within the four largest London airports is undertaken. The analysis demonstrates both the extraction of conventional insight, such as passenger statistics and new insights such as footfall and sentiment. The thesis concludes with recommendations for the ways in which social media analysis may be used in demographics to supplement the analysis of populations using conventional sources of data