I use GIS to map and model different phenomena in data-rich and data-poor environments across space and time.

(human/animal-environment interactions and how places are connected)

It is an exciting time to be in the field of GIS! Technologies have evolved making the collection and communication of spatial data much easier. Although there has been an explosion in the availability of geospatial data, asking pertinent questions and sifting through streams of data to make sense of the world in which we live remains a challenge.

I use GIS, spatial analysis and geocomputational methods to address applied research questions to a wide variety of topics and analyze 'big' datasets. Much of my work concerns issues related to health geographics; in particular understanding the ecology of disease/health across space and time; using novel technologies and data sources to better understand perceptions within communities or during events as well as mobility and how places are connected and what this means in terms of the ecology of disease and human-environment interactions that takes place at different spatial-temporal scales. 

The fun part of using GIS is that it can be used in all disciplines to view where something is, explore patterns and relationships, model different outcomes and develop hypothesis. To do so may require the use of large datasets, data-driven and/or theoretical approaches.

In general I use GIS (and spatial analysis and visualizations) to better understand
  • The mechanisms influencing patterns across space and time?
  • At what scale should we map and model different phenomena?
  • What new data sources and technologies are useful? and
  • How can we effectively integrate these into our analyses?

Current projects include:

Twitter data: using Twitter data to understand mobility, perception and cyberbullying

Food Environments: understanding food environments and the role of availability vs accessibility of different food store types.

Vector-borne diseases: these include vectors that transmit malaria, dengue, zika, WNV and Lyme disease.


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 novel data streams by both citizens and public officials. I use these types of data in two ways:

MOBILITY: 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 human movement and connectivity between landscapes. However, collecting human movement data is inherently difficult. I use novel data sources to

  • look at activity space, movement patterns and connectivity between places at a local, regional and international scales and at different time intervals (hourly, daily, monthly, seasonal, etc).
  • Twitter data (see publications Blanford et al., 2015 and Where are they going? Using social media to understand human movement and disease transmission
  • News sources (see publications Tomaszewski, et al., 2011)
  • Open data sources such as Bike Share Data as a proxy for understanding movement patterns within urban areas.

PERCEPTION OF RISK: 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 an event. The data collected may include an individual’s reaction to a threat, their spatial displacement from the threat and their general perception of the level of danger the threat poses. So, how can we leverage social media as a vehicle to understand perceptions of risk and to possibly to stimulate appropriate citizen response to official advisories and warnings associated an event such as a  natural disaster. As a step towards addressing this question, we have been using social media data, specifically Twitter, to

  • understand people’s reactions leading up to, during and after an event using content-analysis and
  • assessing how effectively information is disseminated during an event by analyzing the public’s response to official NWS messages sent via Twitter (see publications Blanford et al, 2014 and Fall 2014 Geography Newsletter).
  • SensePlace3: forages place-time-attribute information from the Twitterverse to support crisis management (see publications).


To better understand the ecology of health/disease
By integrating biology, behaviour and environment
so that we can respond  (target interventions)
and recover (improve availability and access to treatment).

Factors influencing health and disease in the environment are complex and require an understanding of the

(i) ecology of the disease: Drawing from epidemiological theories where it is important to understand the the role of the agent, host and environment and how these interact in the environment across space and move through the landscape (mobility, connectivity and dispersion pathways);

(ii) the ability to respond: prevention of disease through the control or alteration of specific factors and 

(iii) the ability to recover: provision of timely diagnosis and treatment through the availability and accessibility to health care and healthy environments (nutrition and food).

Mosquito-vectored diseases - 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.

  • Investigate the spatial and temporal patterns of host-pathogen-environmental interactions across Pennsylvania and what this means in terms of disease dynamics.
  • Explore new ways of visualizing this large dataset and examine how environmental factors such as climate and habitat affects changes in transmission of the virus.
  • An interactive map showing WNV in Pennsylvania during 2003 Pennsylvania WNV visualize
  • Risk analysis of mosquito-vectored diseases. Assessing risk with imported cases to determine potential transmission zones. See Taber et al., 2017.
  • how disease (through host-pathogen-environment interactions) is affected with changes in temperature. See Blanford et al., 2013.
  • what temporal scale is necessary to model vector-borne diseases now and in the future using downscaled data.  See Blanford et al., 2013.

Access to health facilities and healthy food environments - Accessibility to health care can be complex and hinder the ability of populations to recover from disease in a timely manner.  One way of examining accessibility is through the examination of physical accessibility based on realistic walking and vehicular speeds and distances as examined in Niger in the following paper. See Blanford et al., 2012.

This work is being extended to examine accessibility to nutritious and healthy food environments in urban and rural environments.

Food Deserts - Access to healthy foods (e.g. fresh fruit and vegetables; diverse food choices) are important for maintaining healthy diets that include 5 A DAY portions of fruit and vegetables.  Disparities in access to diverse and healthy foods have been identified.  Reasons for this can be based on physical access (e.g. accessibility), economics (e.g. affordability) and availability (e.g. distribution of diverse food stores; diverse foods including fresh fruit and vegetables).

Vector-borne diseases
 Malaria Maps
host-pathogen interactions
 Dengue risk in PA

 Mobility Access to healthcare
 Twitter data and mobility
 Movement in Niger