Mapping tick dynamics and tick bite risk using data-driven approaches and volunteered observations

This deposit contains the materials used during the development of this PhD thesis. During this research, we applied machine learning methods to obtain new insights about tick dynamics and tick bite risk in the Netherlands. We combined volunteered data sources coming from two citizen science projects with a wide array of environmental variables (e.g. weather, remote sensing, official geodata) to devise models capable of predicting the risk of tick bite or daily tick activity at the national level. We hope that this research and the associated materials can be inspiring for future researchers.

Identifier
DOI https://doi.org/10.17026/dans-zre-tggd
PID https://nbn-resolving.org/urn:nbn:nl:ui:13-xa-gvr0
Source https://nbn-resolving.org/urn:nbn:nl:ui:13-xa-gvr0
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:131887
Provenance
Creator Garcia-Marti, I. (Faculty of Geo-Information Science and Earth Observartion ITC, Faculty of Twente)
Publisher Data Archiving and Networked Services (DANS)
Contributor Zurita-Milla, R
Publication Year 2019
Rights info:eu-repo/semantics/restrictedAccess; DANS License
OpenAccess false
Representation
Language English
Resource Type Dataset
Discipline Life Sciences;Medicine
Spatial Coverage (5.210 LON, 52.225 LAT); RD_New (EPSG:28992)