I use GIS to map and model different phenomena in data-rich and data-poor environments. My work centers on three main themes that include disease, mobility and novel data sources.
ANALYZING AND MAPPING TWITTER DATA
Social media for crisis management
Social media and micro-blogging is increasingly being used during crisis events to provide live up-to date information as events evolve with information being disseminated using these novel data streams by both citizens and public officials.
SensePlace2: I am also involved with SensePlace2 which forages place-time-attribute information from the Twitterverse that can support crisis management (see publications).
Perception of risk during a crisis event: Of
particular interest is whether a person’s geographical location and the
relevant content of their message can be mined to answer critically important
questions about how a person perceives the risk associated with a
life-threatening weather event. The data collected may include an individual’s
reaction to the threat, their spatial displacement from the threat and their
general perception of the level of danger the threat poses. Therefore, how
to can we leverage social media as a vehicle through which to stimulate appropriate
citizen response to official advisories and warnings associated with natural
disasters. As a step towards addressing this question, we have been using social
media data, specifically Twitter, to
(1) understand people’s reactions leading
up to, during and after an event and
(2) assessing how effectively information is disseminated during an event by analyzing the public’s response to official NWS messages sent via Twitter (see publications and Fall 2014 Geography Newsletter).
Symbology: Investigating diversity and standardization of symbols across multiple agencies through the use of a repeatable process for expanding symbol sets to support new needs, and to develop new technology to support symbol sharing and dissemination. Further details can be found at Map Symbology(see publications).
emergence of new diseases and the re-emergence of old
diseases are an increasing challenge. Recent years have seen the swift
movement of West Nile virus (WNV) across the continental US; resurgence of
dengue in the Americas; outbreaks of chikungunya in Europe, the Caribbean with
local transmission reported in Florida. More recently, we have seen the spread
of Ebola in Western Africa with a small number of cases exported globally. An
integral part of defining how diseases are spread comes from understanding
movement and in particular those associated with humans.
Human movement is of
course multi-faceted occurring across local, regional,
national and international scales for many reasons ranging from work and economic
well-being, conflict to displacement caused by loss of livelihoods
and due to natural hazards (e.g. climate- and
weather-related events such
as flooding, drought and heat stress).
Despite its importance collecting human movement data is inherently difficult.
I am exploring the use of novel datasets to better capture human mobility and integrating these data into my current research to better understand disease pathways.
Bike share Data: Bike sharing systems have increased dramatically throughout the world and serve as a proxy for understanding movement patterns within urban areas. I am currently working with students to analyze the spatial and temporal biking patterns to better understand mobility throughout a year in an urban setting.
Malaria in Africa
Working with the Thomas Lab looking at how malaria will be affected with changes in temperature. In particular, I am modeling transmission potential of malaria using different climate resolutions to understand what temporal scale is necessary to model vector-borne diseases both currently and in the future using down scaled data. See publications to learn more about this work.
Mosquito distributions in Pennsylvania
West Nile Virus (WNV)
Tracking Turtles in Colombia
Working with CIMAD analyzing spatial movement of turtles and improving conservation in Colombia.