scispace - formally typeset
J

John R. B. Palmer

Researcher at Pompeu Fabra University

Publications -  37
Citations -  1116

John R. B. Palmer is an academic researcher from Pompeu Fabra University. The author has contributed to research in topics: Aedes albopictus & Citizen science. The author has an hindex of 14, co-authored 31 publications receiving 833 citations. Previous affiliations of John R. B. Palmer include Office of Population Research & Princeton University.

Papers
More filters
Journal ArticleDOI

New Approaches to Human Mobility: Using Mobile Phones for Demographic Research

TL;DR: A pilot study in which volunteers around the world were successfully recruited to share GPS and cellular tower information on their trajectories and respond to dynamic, location-based surveys using an open-source Android application illustrates the great potential of mobile phone methodology for moving spatial measures beyond residential census units and investigating a range of important social phenomena.
Journal ArticleDOI

Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa

TL;DR: A nuanced picture of changes in woody cover in Africa is presented, which challenges widely held views of a general and ongoing reduction of the woody vegetation in Africa.
Journal ArticleDOI

Direct Evidence of Adult Aedes albopictus Dispersal by Car.

TL;DR: The first sampling study confirming that adult tiger mosquitoes travel with humans in cars and enabling us to estimate the frequency of these events is reported, and the Bayesian model suggests that of the 6.5 million daily car trips in the Barcelona metropolitan area, between 13,000 and 71,500 facilitate tiger mosquito movement.
Journal ArticleDOI

Citizen science provides a reliable and scalable tool to track disease-carrying mosquitoes

TL;DR: It is reported that a scalable citizen science system can provide accurate early warning of the invasion process of the Asian tiger mosquito in Spain, with far more scalable coverage than that of traditional surveillance methods.
Journal ArticleDOI

Expectation-Maximization Binary Clustering for Behavioural Annotation.

TL;DR: This work introduces the Expectation-Maximization binary Clustering (EMbC), a general purpose, unsupervised approach to multivariate data clustering, and focuses on the suitability of the EMbC algorithm for behavioural annotation of movement data.