I use GIS to
map and model different phenomena in data-rich and data-poor environments across space and time.
work currently centers on three main themes that include health geographics, mobility and novel data
NOVEL DATA SOURCES - TWITTER
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
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
people’s reactions leading up to, during and after an event and
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).
SensePlace2: I am also
involved with SensePlace2 which forages place-time-attribute information
from the Twitterverse that can support crisis management (see publications).
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).
The 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 malaria in Europe as well as chikungunya in Europe, the
Caribbean with local transmission reported in Florida. An integral part of defining how diseases are spread comes
from understanding movement.
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) and health/disease. 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.
Twitter Data: I use Twitter data to understand human mobility in different regions of the world.
See publications to learn more about this work.
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.
Kwiatkowski (MGIS 2014) GIS analysis of GPS commercial trucking movements:
improving the identification of destinations
Perrine (MGIS 2014) International Food Imports: Identification of
Vulnerabilities and Risks
Mosquito distributions in Pennsylvania
Working with the DEP
PA to investigate the spatial and temporal distribution of important
mosquito vectors of disease. These include vectors related to the transmission
of West Nile Virus (WNV); dengue and chikungunya and malaria.
West Nile Virus (WNV)
the spatial and temporal patterns of host-pathogen-environmental interactions
across Pennsylvania and what this means in terms of disease dynamics.
new ways of visualizing this large dataset and examine how environmental
factors such as temperature and rainfall affects changes in transmission of the
- An interactive map showing WNV in Pennsylvania during 2003 Pennsylvania WNV visualize
Brady (MGIS in progress) has been identifying spatially dynamic variables
affecting the distribution of West Nile Virus in Pennsylvania.
Aedes albopictus, an
invasive species that is a highly competent vector of dengue, chikungunya, WNV
and La Crosse virus is prevalent throughout PA.
- Eric Taber (Master
Student - graduated 2015) is analyzing the spatial and temporal distribution
of Ae. albopictus throughout
Pennsylvania and the implications for disease risk.
Malaria in Africa
During 2013, an estimated 198 million cases of malaria were
reported worldwide with 90% of all deaths occurring in Africa (World Health, 2014).
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.
Tompkins (MGIS 2014) Social and spatial clustering of personal relationships:
Understanding the impact of behavioural risks on sexually transmitted disease
transmission (in collaboration with Ann Jolly
(U of Ottawa))
Swinson-Williams (MGIS 2014) Lyme disease in Texas? Enhancing prevention
through the identification of areas of risk
Vlasak (MGIS 2015) Spatial and temporal analysis of rabid wild
terrestrial animals along the Colorado Front Range
Warne (MGIS 2015) Spatial analysis of pertussis outbreaks and herd
immunity in the USA
Tracking Turtles in Colombia
Working with CIMAD analyzing spatial movement of turtles and improving
conservation in Colombia.
- Rosemary Alles
(MGIS 2015) Exploring Open Source Geospatial technologies in the context
of monitoring and analyzing the behavior of highly vulnerable migratory
species: Boundless Geo and African elephants as a case study
Struthers (MGIS 2015) Assessment of climate, land use, and projected
population changes to sky island species within Saguaro National Park, Arizona