1 - 4 of 4
ItemTracking the Impact of the Covid-19 Pandemic with the Use of High-Frequency Geo-Located Bank Transaction Data( 2022-01-04)Using geo-located transaction data from 2 million customers of ABN AMRO bank in the Netherlands, this paper distinguishes the economic effects of consumers responses to the Covid-19 pandemic from those attributable to non-pharmaceutical interventions (NPIs). We compare municipalities that experienced large Covid-19 outbreaks with municipalities that had few or no cases and ﬁnd that during the first Covid-19 wave the scale of the outbreak in a municipality has a strong negative effect on physical transactions by consumers in that municipality. This behavioral response function of consumers towards the virus is however not constant over time. During the second Covid-19 wave, the behavioural effect of consumers towards the virus has no real impact on consumption.
ItemThe effect of COVID-19 on customer traffic: A case study of Food and Beverage stores in Erie County, New York( 2022-01-04)The declaration of the COVID-19 pandemic and the resulting lockdowns brought focus on the importance of the retail sector for community well-being. The restrictive government policies that were put into place to curb the spread of COVID-19 added pressure on retailers to adapt to the subsequent changes in consumption. This research, using a case study of Erie County in the State of New York (NY), investigates these changes in visitation patterns for a commercial service sector that was deemed ‘essential’ - food and beverage. This study uses mobile location data to identify variations in shopping patterns for independent and chain stores. The study finds that by comparing the pre-pandemic to pandemic, there were changes to visitation patterns over time and between retail types. While the study highlights the potential to use mobile data to study shifts in consumption behaviours, the paper also reveals several challenges in using such data.
ItemBuilding GIS Platforms for Spatial Business: A Focus on the Science of Maximizing Location Intelligence Benefits Through Risk/Cost Management( 2022-01-04)An ensemble model for aggregating weighted risks and costs is tested in a Monte Carlo simulation with Tomlinson's 22 lower-order risk factors for GIS implementations. The basic assumption of the model is that practitioners incorrectly manipulate and transpose risk and cost factors contributing to less than optimum implementation results. Such examples include: (1) violation of Lusser's probability product law, (2) non-use of Galton's 50th percentile/median as the "wisdom of the crowd" estimate, (3) incorrect use of weighting (if at all), (4) dubious ranking of lower-order risk factor importance and (5) the inability to automatically predict a Bayesian posterior adjusted cost projection. The ensemble model corrects for these and other errors. Life data analysis and reliability functions from reliability engineering are built into the model for further enhancement of results.