Explaining demand patterns during COVID-19 using opportunistic data: a case study of the city of Munich

Abstract

Background The COVID-19 pandemic is a new phenomenon and has affected the population’s lifestyle in many ways, such as panic buying (the so-called “hamster shopping”), adoption of home-office, and decline in retail shopping. For transportation planners and operators, it is interesting to analyze the spatial factors’ role in the demand patterns at a POI (Point of Interest) during the COVID-19 lockdown viz-a-viz before lockdown. Data and Methods This study illustrates a use-case of the POI visitation rate or popularity data and other publicly available data to analyze demand patterns and spatial factors during a highly dynamic and disruptive event like COVID-19. We develop regression models to analyze the correlation of the spatial and non-spatial attributes with the POI popularity before and during COVID-19 lockdown in Munich by using lockdown (treatment) as a dummy variable, with main and interaction effects. Results In our case-study for Munich, we find consistent behavior of features like stop distance and day-of-the-week in explaining the popularity. The parking area is found to be correlated only in the non-linear models. Interactions of lockdown with POI type, stop-distance, and day-of-the-week are found to be strongly significant. The results might not be transferable to other cities due to the presence of different city-specific factors. Conclusion The findings from our case-study provide evidence of the impact of the restrictions on POIs and show the significant correlation of POI-type and stop distance with POI popularity. These results suggest local and temporal variability in the impact due to the restrictions, which can impact how cities adapt their transport services to the distinct demand and resulting mobility patterns during future disruptive events.

Six lessons from first year COVID-19 restrictions: what can we do better in the future?

“…Perception of safety and security were also consistently found significant in influencing people in choosing the destinations and time of their travel [8,18]. For example, to avoid too much interactions with strangers, many elderly consciously avoid congestion in shops and services and only do their activities on off-peak periods.…”

“…Whilst there have been a lot of studies on the impacts of COVID-19 restrictions on transport and mobility, there have not been much a reflection on how we can do better if another pandemic strike in the future, and this is the objective of this editorial article. Based on the evidences drawn upon 11 papers from select European countries, which investigated wide range issues, from the readiness of port-state control inspection procedure [1], the impacts of COVID-19 restrictions to the work and travel patterns of civil servants in Sweden [14], the impacts of inter personal distance towards the sustainability operation of public transport in Italy [12], the willingness to pay to use the public transport and shared services given the COVID-19 circumstances in Spain [5], and the impacts of COVID-19 restrictions to travel and spatial movement patterns and trade-off behaviours of urban and rural residents in Germany [4, [16][17][18], Greece [26], Italy, India and Sweden [2, 8], there are common learned lessons that can serve as a guidelines on how we can deal with the disruptions better in the future.…”

The Impact of SARS-COVID-19 Outbreak on European Cities Urban Mobility

“…In (Rahman et al, 2020), the authors exploited crowdsourced data from Google to analyze the different impacts of the pandemic in 88 countries. Recent studies exploited the popularity of Point of interest (POIs) to quantify the mobility patterns of a city, the information on POIs can be extracted from different sources, the authors of (Mahajan et al, 2021) used data from Google popular times, while in (Roy and Kar, 2020) the dataset of POIs is taken from SafeGraph Places data. While these studies analyzed the general mobility of citizens our approach aims at investigating more in deep the different modes of transport.…”

Risk Assessment and Prediction of COVID-19 Based on Epidemiological Data From Spatiotemporal Geography

“…Spatiotemporal geographic epidemiological data, including cellular signaling data (Xiao et al, 2019) , (Zhan et al, 2021), population flow data (He et al, 2020;Zhang and Yuan, 2021), and urban point of interest (POI) data, etc. (He et al, 2021;Mahajan et al, 2021). In a word, these spatiotemporal geographic data can represent the characteristics of epidemic risk in urban space, providing a new research perspective and solution to problems related to epidemic risks in relation to urban geography (Bachir et al, 2019;Sharifi and Khavarian-Garmsir, 2020).…”