Using machine learning to find optimal locations for food bank depots

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dc.contributor.authorMercado, Joseph
dc.date.accessioned2018-12-06T16:54:50Z
dc.date.available2018-12-06T16:54:50Z
dc.date.issued2018-12-06
dc.description.abstractAside from the main food bank location, the Calgary Food Bank also distributes food to clients through food distribution depots located throughout Calgary. The K-Means clustering machine learning algorithm was applied to Calgary Food Bank data to determine where future depots should be located in order to minimize the time clients spend travelling to depots. This would reduce carbon emissions and increase food accessibility in the city.en_US
dc.identifier.citationMercado, J. (2018). "Using machine learning to find optimal locations for food bank depots". 13th Annual Students' Union Undergraduate Research Symposium, December 6, 2018. University of Calgary, Calgary, AB.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/34899
dc.identifier.urihttp://hdl.handle.net/1880/109236
dc.language.isoenen_US
dc.publisher.departmentMathematics & Statisticsen_US
dc.publisher.facultyScienceen_US
dc.publisher.institutionUniversity of Calgaryen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0en_US
dc.subjectMachine Learningen_US
dc.subjectClusteringen_US
dc.subjectFood Banken_US
dc.subjectStatisticsen_US
dc.subjectFooden_US
dc.subjectOptimizationen_US
dc.subjectCalgaryen_US
dc.titleUsing machine learning to find optimal locations for food bank depotsen_US
dc.typeconference posteren_US
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