Distance and Local Competition in Mobile Geofencing

2020 ◽  
Vol 31 (4) ◽  
pp. 1421-1442
Author(s):  
Yi-Jen (Ian) Ho ◽  
Sanjeev Dewan ◽  
Yi-Chun (Chad) Ho
Keyword(s):  
Mobile Apps ◽  
Consumer Welfare ◽  
Location Based Services ◽  
Data Set ◽  
New Era ◽  
Local Competition ◽  
Two Factors ◽  
Surrounding Areas ◽  
Rich Data ◽  
The Impact

The growing ubiquity of GPS-enabled smartphones has ushered in a new era of location-based services and online-to-offline commerce. Geofencing is one instance of this broader phenomenon, and it is being widely adopted in the context of retail, restaurant, entertainment, and other local services. By targeting users on mobile apps while they are in the vicinity of physical establishments, there is potential for higher levels of engagement and consumption of the products or services on offer. However, the level of consumer interest is likely to depend on distance from the establishment and local competition in the surrounding areas. This study examines the impact of these two factors on consumer response to geofence advertising at different points in the purchase funnel, namely the click stage and conversion phase. Analyzing a rich data set from one of the leading location-based marketing agencies and using a sophisticated Bayesian empirical methodology, we find that having one more competitor in the consumer’s vicinity reduces click-through rate by about 1%, and a 1-mile increase in distance is associated with a 17.6% reduction in conversion rate. These and other results suggest that accounting for distance and local competition in data-analytic mobile targeting would increase both the return on advertising spend and consumer welfare.

Automatic Deep Learning-Based Consolidation/Collapse Classification in Lung Ultrasound Images for COVID-19 Induced Pneumonia

2022 ◽  
Author(s):  
Nabeel Durrani ◽  
Damjan Vukovic ◽  
Maria Antico ◽  
Jeroen van der Burgt ◽  
Ruud JG van van Sloun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Class Imbalance ◽  
Ultrasound Images ◽  
Pleural Effusions ◽  
Data Set ◽  
Label Noise ◽  
Set Size ◽  
Video Frames ◽  
Two Factors ◽  
The Impact

<div>Our automated deep learning-based approach identifies consolidation/collapse in LUS images to aid in the diagnosis of late stages of COVID-19 induced pneumonia, where consolidation/collapse is one of the possible associated pathologies. A common challenge in training such models is that annotating each frame of an ultrasound video requires high labelling effort. This effort in practice becomes prohibitive for large ultrasound datasets. To understand the impact of various degrees of labelling precision, we compare labelling strategies to train fully supervised models (frame-based method, higher labelling effort) and inaccurately supervised models (video-based methods, lower labelling effort), both of which yield binary predictions for LUS videos on a frame-by-frame level. We moreover introduce a novel sampled quaternary method which randomly samples only 10% of the LUS video frames and subsequently assigns (ordinal) categorical labels to all frames in the video based on the fraction of positively annotated samples. This method outperformed the inaccurately supervised video-based method of our previous work on pleural effusions. More surprisingly, this method outperformed the supervised frame-based approach with respect to metrics such as precision-recall area under curve (PR-AUC) and F1 score that are suitable for the class imbalance scenario of our dataset despite being a form of inaccurate learning. This may be due to the combination of a significantly smaller data set size compared to our previous work and the higher complexity of consolidation/collapse compared to pleural effusion, two factors which contribute to label noise and overfitting; specifically, we argue that our video-based method is more robust with respect to label noise and mitigates overfitting in a manner similar to label smoothing. Using clinical expert feedback, separate criteria were developed to exclude data from the training and test sets respectively for our ten-fold cross validation results, which resulted in a PR-AUC score of 73% and an accuracy of 89%. While the efficacy of our classifier using the sampled quaternary method must be verified on a larger consolidation/collapse dataset, when considering the complexity of the pathology, our proposed classifier using the sampled quaternary video-based method is clinically comparable with trained experts and improves over the video-based method of our previous work on pleural effusions.</div>

How Price Dispersion Changes When Upgrades Are Introduced: Theory and Empirical Evidence from the Airline Industry

2019 ◽  
Vol 65 (8) ◽  
pp. 3835-3852 ◽  
Author(s):  
Yao Cui ◽  
A. Yeşim Orhun ◽  
Izak Duenyas
Keyword(s):  
Operations Management ◽  
Airline Industry ◽  
Price Dispersion ◽  
Consumer Welfare ◽  
Data Set ◽  
Differentiation Strategy ◽  
Valuation Effect ◽  
Price Distribution ◽  
The One ◽  
The Impact

This paper studies the effect of introducing a new vertical differentiation strategy, paying for an upgrade to a premium product after purchasing the base product, on the price dispersion of the base product arising from existing price discrimination strategies. In particular, we examine how a major U.S. airline’s price dispersion in the coach cabin changes after introducing the option to upgrade to a new type of premium economy seating within the coach cabin. We first provide a theoretical analysis that highlights two competing pressures that the new premium economy seating upgrades created on coach class prices. On the one hand, the airline benefits from lowering its prices because by allowing more customers to purchase in the first place, it increases the probability of selling upgrades (admission effect). On the other hand, for some customers, the value of flying with the airline increases because of the upgrade availability, therefore the airline may find it optimal to increase its prices (valuation effect). In the second part of the paper, we conduct an empirical investigation of the impact of upgrade introduction on coach class prices, based on a proprietary transaction-level data set from a major U.S. airline company. The empirical analysis tests the main predictions of our theoretical model and examines further nuances. The results show that the introduction of the premium economy seating upgrades is associated with an increase in the price dispersion and revenues in the coach class, the admission effect is stronger than the valuation effect on the low end of the price distribution, and the opposite is true on the high end of the price distribution. Finally, we discuss implications of our results for firm revenues and consumer welfare. This paper was accepted by Serguei Netessine, operations management.

Tropospheric layering of ozone in regions of urbanized complex and/or coastal terrain: a review

2000 ◽  
Vol 24 (3) ◽  
pp. 329-354 ◽  
Author(s):  
I. G. McKendry ◽  
J. Lundgren
Keyword(s):  
Boundary Layer ◽  
Global Climate ◽  
Research Priorities ◽  
Lower Troposphere ◽  
Ground Level ◽  
Tropospheric Chemistry ◽  
Future Research ◽  
Data Set ◽  
Rich Data ◽  
The Impact

Exchange of pollutants between the atmospheric boundary layer and free troposphere is an important (yet often neglected) process that tends to produce distinct layers of pollution in the lower troposphere. These layers represent a potential sink for pollutants from the boundary layer, have the potential to be mixed to ground and likely influence tropospheric chemistry and the global climate system. Factors influencing the vertical distribution of ozone in the troposphere are outlined as a prelude to a more specific discussion of elevated layers and myriad meteorological processes responsible for their development. Evidence from a range of geographical settings suggests that these phenomena are ubiquitous. A rich data set from the Lower Fraser Valley, British Columbia, is used to provide an inventory of layer structures and to highlight their diverse origins and histories. Approaches used to assess the impact of down-mixing of pollutants from elevated layers on ground-level concentrations of ozone are outlined and future research priorities recommended.

scholarly journals The regional significance of university locations in Lower Saxony

2020 ◽  
Vol 78 (4) ◽  
pp. 337-359
Author(s):  
Britta Stöver
Keyword(s):  
Spatial Econometrics ◽  
Supply Side ◽  
Lower Saxony ◽  
Data Set ◽  
Capital Theory ◽  
Demand And Supply ◽  
Panel Regressions ◽  
Spillover Theory ◽  
Rich Data ◽  
The Impact

AbstractUniversities are important economic actors and make a considerable impact on the demand and supply side of their local economies. The aim of this paper is to quantify, compare and classify the different economic demand-and supply-side contributions of the university locations within Lower Saxony (Germany) using a combination of multiplier analysis and spatial econometrics on a NUTS 3 level. In comparison to numerous other studies, this paper does not focus on the economic impact of individual cases or a selected university location but gives a complete picture of the importance and significance of all university locations within Lower Saxony. The income-induced direct and indirect demand effects are estimated using a rich data set of higher education statistics in combination with an income and employment multiplier derived from a regional input-output table. The supply-side effects, i.e. the impact of the education and research outcomes, are estimated with the help of spatial panel regressions, a model derived from human capital theory and knowledge spillover theory. The estimation results give a complete and reproducible impression of the importance and significance of the different university locations, offering the opportunity for comparisons and classifications.

scholarly journals Scotland: The Roman Presence

2012 ◽  
Author(s):  
Fraser Hunter ◽  
Martin Carruthers
Keyword(s):  
Aerial Survey ◽  
Data Set ◽  
Development Control ◽  
The Core ◽  
Roman World ◽  
Theoretical Perspectives ◽  
Integrated Approaches ◽  
The Rich ◽  
Rich Data ◽  
The Impact

The main recommendations of the panel report can be summarised under five key headings:  Scotland in the Roman world: Research into Roman Scotland requires an appreciation of the wider frontier and Empire-wide perspectives, and Scottish projects must be integrated into these wider, international debates. The rich data set and chronological control that Scotland has to offer can be used to inform broader understandings of the impact of Rome.  Changing worlds: Roman Scotland’s rich data set should be employed to contribute to wider theoretical perspectives on topics such as identity and ethnicity, and how these changed over time. What was the experience of daily life for the various peoples in Roman Scotland and how did interactions between incomers and local communities develop and change over the period in question, and, indeed, at and after its end?  Frontier Life: Questions still remain regarding the disposition and chronology of forts and forces, as well as the logistics of sustaining and supplying an army of conquest and occupation. Sites must be viewed as part of a wider, interlocking set of landscapes, and the study of movement over land and by sea incorporated within this. The Antonine Wall provides a continuing focus of research which would benefit from more comparison with frontier structures and regimes in other areas.  Multiple landscapes: Roman sites need to be seen in a broader landscape context, ‘looking beyond the fort’ and explored as nested and interlocking landscapes. This will allow exploration of frontier life and the changing worlds of the Roman period. To do justice to this resource requires two elements: o Development-control archaeology should look as standard at the hinterland of forts (up to c.1 km from the ‘core’), as sensitive areas and worthy of evaluation; examples such as Inveresk show the density of activity around such nodes. The interiors of camps should be extensively excavated as standard. o Integrated approaches to military landscapes are required, bringing in where appropriate topographical and aerial survey, LIDAR, geophysics, the use of stray and metal-detected finds, as well as fieldwalking and ultimately, excavation.  The Legacy of Rome: How did the longer term influence of the Romans, and their legacy, influence the formation, nature and organisation of the Pictish and other emergent kingdoms?

scholarly journals Understanding vehicular routing behavior with location-based service data

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Yanyan Xu ◽  
Riccardo Di Clemente ◽  
Marta C. González
Keyword(s):  
Travel Time ◽  
Large Scale ◽  
Census Data ◽  
The United States ◽  
Location Based Services ◽  
Smartphone Applications ◽  
United States Census ◽  
Data Set ◽  
High Resolution Data ◽  
The Impact

AbstractProperly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. Nowadays, there is not a standard and large scale data set collected over long periods that allows us to characterize these. In this work we thoroughly examine the use of data from smartphone applications, also referred to as location-based services (LBS) data, to extract and understand the vehicular route choice behavior. Taking the Dallas-Fort Worth metroplex as an example, we first extract the vehicular trips with simple rules and reconstruct the origin-destination matrix by coupling the extracted vehicular trips of the active LBS users and the United States census data. We then present a method to derive the commonly used routes by individuals from the LBS traces with varying sample rate intervals. We further inspect the relation between the number of routes and the trip characteristics, including the departure time, trip length and travel time. Specifically, we consider the travel time index and buffer index for the LBS users taking different number of routes. Empirical results demonstrate that during the peak hours, travelers tend to reduce the impact of traffic congestion by taking alternative routes. Overall, the proposed data analysis framework is cost-effective to treat sparse data generated from the use of smartphones to inform routing behavior. The potential in practice is to inform demand management strategies, by targeting individual users while generating large scale estimates of congestion mitigation.

Automatic Deep Learning-Based Consolidation/Collapse Classification in Lung Ultrasound Images for COVID-19 Induced Pneumonia

2022 ◽  
Author(s):  
Nabeel Durrani ◽  
Damjan Vukovic ◽  
Maria Antico ◽  
Jeroen van der Burgt ◽  
Ruud JG van van Sloun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Class Imbalance ◽  
Ultrasound Images ◽  
Pleural Effusions ◽  
Data Set ◽  
Label Noise ◽  
Set Size ◽  
Video Frames ◽  
Two Factors ◽  
The Impact

<div>Our automated deep learning-based approach identifies consolidation/collapse in LUS images to aid in the diagnosis of late stages of COVID-19 induced pneumonia, where consolidation/collapse is one of the possible associated pathologies. A common challenge in training such models is that annotating each frame of an ultrasound video requires high labelling effort. This effort in practice becomes prohibitive for large ultrasound datasets. To understand the impact of various degrees of labelling precision, we compare labelling strategies to train fully supervised models (frame-based method, higher labelling effort) and inaccurately supervised models (video-based methods, lower labelling effort), both of which yield binary predictions for LUS videos on a frame-by-frame level. We moreover introduce a novel sampled quaternary method which randomly samples only 10% of the LUS video frames and subsequently assigns (ordinal) categorical labels to all frames in the video based on the fraction of positively annotated samples. This method outperformed the inaccurately supervised video-based method of our previous work on pleural effusions. More surprisingly, this method outperformed the supervised frame-based approach with respect to metrics such as precision-recall area under curve (PR-AUC) and F1 score that are suitable for the class imbalance scenario of our dataset despite being a form of inaccurate learning. This may be due to the combination of a significantly smaller data set size compared to our previous work and the higher complexity of consolidation/collapse compared to pleural effusion, two factors which contribute to label noise and overfitting; specifically, we argue that our video-based method is more robust with respect to label noise and mitigates overfitting in a manner similar to label smoothing. Using clinical expert feedback, separate criteria were developed to exclude data from the training and test sets respectively for our ten-fold cross validation results, which resulted in a PR-AUC score of 73% and an accuracy of 89%. While the efficacy of our classifier using the sampled quaternary method must be verified on a larger consolidation/collapse dataset, when considering the complexity of the pathology, our proposed classifier using the sampled quaternary video-based method is clinically comparable with trained experts and improves over the video-based method of our previous work on pleural effusions.</div>

scholarly journals Birthplace diversity and economic growth: evidence from the US states in the Post-World War II period

2019 ◽  
Author(s):  
Frédéric Docquier ◽  
Riccardo Turati ◽  
Jérôme Valette ◽  
Chrysovalantis Vasilakis
Keyword(s):  
Economic Growth ◽  
World War Ii ◽  
College Education ◽  
Large Set ◽  
Market Outcomes ◽  
Data Set ◽  
Us States ◽  
The Usa ◽  
Rich Data ◽  
The Impact

Abstract This paper empirically investigates the impact of birthplace diversity on economic growth. We use panel data on US states over the 1960–2010 period. This rich data set allows us to better deal with endogeneity issues and to conduct a large set of robustness checks. Our results suggest that diversity among college-educated immigrants positively affects economic growth. We provide converging evidence pointing at the existence of skill complementarities between workers trained in different countries. These synergies result in better labor market outcomes for native workers and in higher productivity in the R&D sector. The gains from diversity are maximized when immigrants originate from economically or culturally distant countries (but not both), and when they acquired part of their secondary education abroad and their college education in the USA. Overall, a 10% increase in high-skilled diversity raises GDP per capita by about 6%. On the contrary, low-skilled diversity has insignificant effects.

scholarly journals State Health Insurance Regulations and the Price of High-Deductible Policies

2008 ◽  
Vol 11 (2) ◽  
Author(s):  
Amanda E. Kowalski ◽  
William J. Congdon ◽  
Mark H. Showalter
Keyword(s):  
Health Insurance ◽  
Family Policy ◽  
State Level ◽  
Demographic Characteristics ◽  
State Health ◽  
Data Set ◽  
Insurance Policies ◽  
State Health Insurance ◽  
Rich Data ◽  
The Impact

This study examines the impact of state health insurance regulations on the price of high-deductible family and individual polices in the nongroup market. We use a unique and rich data set on actual insurance policies sold through a large Internet health insurance distributor to examine the impact of various regulations on policy prices, controlling for policy characteristics, demographic characteristics of the purchasers, and state-level demographics. We also use data from a single major insurance firm that provided offer prices for a family policy from a set of randomly selected zip codes. Both datasets suggest a strong statistical relationship between regulation and insurance prices.

scholarly journals The impact of the COVID-19 pandemic on the poor: Insights from the Hrishipara diaries

2021 ◽  
Author(s):  
Risto Rönkkö ◽  
Stuart Rutherford ◽  
Kunal Sen
Keyword(s):  
Rural Setting ◽  
Negative Effects ◽  
Data Set ◽  
Household Expenditures ◽  
Daily Diaries ◽  
The Poor ◽  
Food Expenditures ◽  
Time Basis ◽  
Rich Data ◽  
The Impact

In this paper, we examine the economic impact of the COVID-19 pandemic on the livelihoods of the poor. We use an unusually rich data set from a ‘financial diaries’ study known as the Hrishipara Daily Diaries Project. The data set tracks the economic and financial transactions of 60 individuals and their families in a semi-rural setting in Bangladesh on a real-time basis from October 2019 to September 2020. We document individual diarists’ behavioural responses to COVID-19, which reveal the varied experiences of the poor during the pandemic. We find that the pandemic and associated government lockdowns had significant negative effects on the livelihoods of the poor in our study, with financial inflows and outflows, incomes, and household expenditures below pre-pandemic levels during the pandemic period. To cope with the pandemic, households drew down on their cash reserves at home, as well as cutting down on non-food expenditures to protect their spending on food.

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