Spatial patterns of mosquitoes in the Netherlands: Finding mosquito hotspots based on crowdsourced data

Organisator Laboratory of Geo-information Science and Remote Sensing

wo 8 april 2015 09:00 tot 09:30

Locatie Gaia, building number 101
Droevendaalsesteeg 3
6708 PB Wageningen
+31 317 48 16 00
Zaal/kamer 1

By Linda Klein (The Netherlands)


Spatial patterns of mosquitoes in the Netherlands are largely unknown. Knowledge on these patterns is desired, since the risk of transmission of viruses transmitted by these mosquitoes, such as West Nile Virus, in Europe is increasing. The data that became available from the Muggenradar project, a project in which mosquitoes were collected in a crowdsourced way during January and February, and August and September 2014, can be used to analyse the spatial patterns of mosquitoes in the Netherlands. The main objective of this study was to find mosquito hotspots in the Netherlands in both winter and summer, based on crowdsourced data. It was found that more mosquito reports were located in urban areas. Four environmental factors were therefore tested for their relationship with mosquito presence in two urban study areas: Amsterdam and Rotterdam. The tested factors were proximity to water, proximity to deciduous forest and trees, building construction year, and population density. Although significance was found between the mosquito reports and proximity to water, proximity to deciduous forest, and population density in the 2014 January and February data, these patterns were not consistent. Hotspot maps created out of the found relationships did not result in accurate maps. The inconsistence in the results could be explained by the low number of Muggenradar mosquito reports, especially in the 2014 August and September data. Another possible explanation is the fact that more environmental factors that were not tested are predictive for mosquito presence. All reported mosquitoes were collected indoors. Local factors, like small water bodies on private properties, could therefore possibly be more predictive than the tested environmental factors.

Keywords: Culicidae; Spatial ecology; geographic information systems; GIS; crowd sourcing; citizen science.