https://socictopen.socict.org/files/original/cbc2492094ecfc390a8fcdd0b5cff70c.pdf 42324ebdfb32e992661119efda9f13c5 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Explaining demand patterns during COVID-19 using opportunistic data: a case study of the city of Munich Creator An entity primarily responsible for making the resource Vishal Mahajan, Guido Cantelmo, Constantinos Antoniou Description An account of the resource 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. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource covid-19, machine learning, demand patterns, POIs, Spatial-temporal, Crowdsensed data Identifier An unambiguous reference to the resource within a given context 10.1186/s12544-021-00485-3 Source A related resource from which the described resource is derived European Transport Research Review Publisher An entity responsible for making the resource available SpringerOpen Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Transportation engineering, Transportation and communications