A consumer research panel is a representative collection of individuals whose consumption habits are continually monitored by a marketing research company. It is designed to study the behaviour rather than the attitudes of consumers in the fast moving consumer goods (FMCG) sector. The analysed and interpreted reports help the retailer and manufacturing clients to better understand their markets and the changing dynamics within markets which are largely overseen by retail studies. It is shown that consumer research panels have a very high per panellist cost due to the nature of the data collection methodologies currently used being either very labour-intensive with the in-home interviewer visits, or due to the cost of the technology needed when using an in-home audit terminal to scan the barcode of items. The ubiquitous use of the mobile phone begs the question whether this technology, already in the hands of people, could be used as a data collection device. In 2007, Robert Adelmann demonstrated that it is possible to recognise linear barcodes using a mobile phone equipped with a camera and a barcode recognition algorithm. Three requirements are set for a mobile phone to be used as a data collection device in a consumer panel and are indicated by various studies researching each, focused on Gauteng, South Africa. The technology is shown to be quick and accurate enough to be used in everyday barcode scanning albeit not very prevalent among the responding sample. The data communication infrastructure needed to communicate the audited data was found to be 100% present although few of the respondents displayed their interest in joining a research panel based on mobile phones. It is, therefore, recommended that a phone-based consumer panel be used to supplement existing consumer panels to extend in hard-to-reach demographics like the upper-LSM households, and that the privacy and usage concerns raised by the respondents be addressed. It is also recommended that similar studies be conducted in developed economies where required mobile phone technology could be more prevalent. / Thesis (M.B.A.)--North-West University, Vaal Triangle Campus, 2010.

A consumer research panel is a representative collection of individuals whose consumption habits are continually monitored by a marketing research company. It is designed to study the behaviour rather than the attitudes of consumers in the fast moving consumer goods (FMCG) sector. The analysed and interpreted reports help the retailer and manufacturing clients to better understand their markets and the changing dynamics within markets which are largely overseen by retail studies. It is shown that consumer research panels have a very high per panellist cost due to the nature of the data collection methodologies currently used being either very labour-intensive with the in-home interviewer visits, or due to the cost of the technology needed when using an in-home audit terminal to scan the barcode of items. The ubiquitous use of the mobile phone begs the question whether this technology, already in the hands of people, could be used as a data collection device. In 2007, Robert Adelmann demonstrated that it is possible to recognise linear barcodes using a mobile phone equipped with a camera and a barcode recognition algorithm. Three requirements are set for a mobile phone to be used as a data collection device in a consumer panel and are indicated by various studies researching each, focused on Gauteng, South Africa. The technology is shown to be quick and accurate enough to be used in everyday barcode scanning albeit not very prevalent among the responding sample. The data communication infrastructure needed to communicate the audited data was found to be 100% present although few of the respondents displayed their interest in joining a research panel based on mobile phones. It is, therefore, recommended that a phone-based consumer panel be used to supplement existing consumer panels to extend in hard-to-reach demographics like the upper-LSM households, and that the privacy and usage concerns raised by the respondents be addressed. It is also recommended that similar studies be conducted in developed economies where required mobile phone technology could be more prevalent. / Thesis (M.B.A.)--North-West University, Vaal Triangle Campus, 2010.

Recent years have seen a huge increase in the amount of data collected from multiple sources: mobile phones are ubiquitous, social networks are widely used, cities are more and more connected and the mobility of people and goods has risen to a global scale. The Big Data Era has opened the doors to new kinds of studies that were unthinkable with previous qualitative methods: human behavior can now be analyzed with a fine-grained resolution, patterns of mobility and behavior can be extracted from the incredible amount of data collected every day. Modern large cities are becoming more and more interconnected and this phenomenon leads to an increasing communication and activities’ synchronization. Due to the amount of data available or for anonymization reasons, it is often necessary to aggregate data spatially and temporally. A natural representation of clustered mobility data is the temporal network representation. In this thesis we focus on these two aspects of spatial distance in human mobility: (i) we study the synchronization of 76 Italian cities, using mobile phone data, showing that both distance between cities and city size determine the synchronization in communication rhythms. Moreover, we show that the effect of the distance in synchronization decreases when the size of the city increases; (ii) we investigate how clustering continuous spatio-temporal data affects spatio-temporal network measures for real-life and synthetic datasets and analyze how spatio-temporal networks’ measures vary at different aggregation levels.

The present paper examines possible changes in temporal urban diversity caused by the COVID-19 pandemic in Stockholm and Uppsala metropolitan areas. In addition to general changes in diversity, potential differences of diversity levels at locations with varying socioeconomic characteristics are examined. The diversity levels are calculated based on mobile phone data and defined by the inflow and distribution of individuals to locations. The time frame involves eight study dates and extends from January to April 2020. The paper reaches the following conclusions. (1) Diversity levels display a general decline during the pandemic, with one exception - Easter Holidays. (2) Individuals residing in areas with high proportions of highly educated individuals or visible minorities experience a decrease in diversity whereas the opposite is true for areas with high proportions of low-income earners or senior citizens (3) The increase in diversity in the two last mentioned areas, which are located in remote parts of the metropolitan area, coincide with decreasing levels of diversity in the central parts of the metropolitan area. It is possible that changes in diversity levels in these areas can be explained by changes in general behavioural trends, e.g. incentives to avoid crowded city center areas.

No description available.

Die Covid-19 Pandemie hat gezeigt, wie stark die Ausbreitung von Infektionskrankheiten von der Dynamik der menschlichen Mobilität bestimmt wird. Gleichzeitig eröffnet die anhaltende Explosion an verfügbaren Mobilitätsdaten im 21. Jahrhundert einen viel genaueren Blick auf die menschliche Mobilität. In dieser Arbeit untersuchen wir verschiedene Ansätze, wie moderne Mobilitätsdaten zusammen mit Modellierung ein tieferes Verständnis des Zusammenspiels von menschlicher Mobilität und der Ausbreitung von Infektionskrankheiten ermöglichen. Wir verwenden Mobilitätsdaten um zu zeigen, dass landesweite Mobilitätsmuster während der Covid-19 Pandemie in Deutschland komplexe strukturelle Veränderungen durchlaufen haben. Wir stellen einen räumlich heterogenen Rückgang der Mobilität während Lockdown-Phasen fest. Vor allem beobachten wir, dass ein deutlicher Rückgang der Fernreisen während der Pandemie zu einem lokaleren Netzwerk und einer Abschwächung des “Small-World”-Effekts führt. Wir zeigen, dass diese strukturellen Veränderungen einen erheblichen Einfluss auf die Ausbreitungsdynamik von Epidemien haben, indem sie die epidemische Kurve abflachen und die Ausbreitung in geografisch weit entfernte Regionen verzögern. Des Weiteren entwickeln wir eine neue Methode zur Bestimmung des Ausbruchsursprungs anhand von hochaufgelösten geografischen Bewegungsdaten. Abschließend untersuchen wir, wie repräsentativ Mobilitätsdatensätze für das tatsächliche Reiseverhalten einer Bevölkerung sind. Wir identifizieren verschieden Arten von Verzerrungen, zeigen ihre Spuren in empirischen Datensätzen, und entwickeln einen mathematischen Rahmen um diese Verzerrungen abzuschwächen. Wir hoffen, dass unsere Studien in dieser Arbeit sich als hilfreiche Bausteine erweisen für ein einheitliches Verständnis von menschlicher Mobilität und der Dynamik von Infektionskrankheiten. / The Covid-19 pandemic demonstrated how strongly infectious disease spread is driven by the dynamics of human mobility. At the same time, the ongoing explosion of available mobility data in the 21st century opens up a much finer view of human mobility. In this thesis, we investigate several ways in which modern mobility data sources and modeling enable a deeper understanding of the interplay of human mobility and infectious disease spread. We use large-scale mobility data captured from mobile phones to show that country-wide mobility patterns undergo complex structural changes during the Covid-19 pandemic in Germany. Most prominently, we observe that a distinct reduction in long-distance travel during the pandemic leads to a more local, clustered network and a moderation of the “small-world” effect. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by “flattening” the epidemic curve and delaying the spread to geographically distant regions. Further, we show that high-resolution mobility data can be used for early outbreak detection. We develop a novel method to determine outbreak origins from geolocated movement data of individuals affected by the outbreak. We also present several practical applications that have been developed based on the above research. To further explore the question of applicability, we examine how representative mobility datasets are of the actual travel behavior of a population. We develop a mathematical framework to mitigate these biases, and use it to show that biases can severely impact outcomes of dynamic processes such as epidemic simulations, where biased data incorrectly estimates the severity and speed of disease transmission. We hope that our studies in this thesis will prove as helpful building blocks to assemble the emerging, unified understanding of mobility and infectious disease dynamics.