Research Tools

We can provide technical assistance in the following activities:

Geographic Information Systems (GIS)

GIS is a spatial data infrastructure designed to capture, store, manipulate, analyze, manage, and present all types of geographical data. GIS applications are tools that allow users to create interactive queries (user-created searches), analyze spatial information, edit data in maps, and present the results of all these operations. We use ESRI ArcView 10.1 Enterprise version.

Community Mapping


Mappler Mobile is a customizable smartphone-based community participatory mapping application  that enables community members to map amenities and concerns.  Photos and data can be uploaded directly from the Mappler Mobile smartphone application onto a web-based interactive map.


MapplerX is an interactive mapping interface that optimizes the visual experience of mapping data online. It provides an easy comparison of large data sets that can be accessed from any computer with internet connection.  Maps created on ArcMAp and MapWindow can be integrated and become interactive.  Data collected on Mappler Mobile can also be included.

Spatial Analysis (ESRI ArcView 10.1)

Spatial analysis extends traditional statistics to support the analysis of geographic data.  It provides techniques to describe the distribution of data in the geographic space (descriptive spatial statistics) , analyze the spatial patterns of the data (spatial pattern analysis), identify and measure spatial relationships (spatial regression), and create a surface from sampled data (spatial interpolation, usually categorized as geostatistics). It can be used to study on human health by describing the spatial position of humans within the geography in which they live, work, or and play. Common errors in spatial analysis are sometimes due to the mathematics of space, the particular ways data are presented spatially, or the tools which are available. Spatial dependency, for instance, leads to the spatial autocorrelation problem in statistics.  Like temporal autocorrelation, this violates standard statistical techniques that assume independence among observations.  For example, regression analyses that do not compensate for spatial dependency can have unstable parameter estimates and yield unreliable significance tests.

Multi-level analysis (STATA, SAS, SPSS)

Multilevel analysis are statistical models of parameters that vary at more than one level and often is often used when data are grouped or nested in more than one category (for example, states, countries, etc). Multilevel models can be used on data with many levels, although 2-level models are the most common. The units of analysis are usually individuals (at a lower level) who are nested within contextual/aggregate units (at a higher level). The dependent variable must be examined at the lowest level of analysis. While the lowest level of data in multilevel models is usually an individual, repeated measurements of individuals may also be examined. As such, multilevel models provide an alternative type of analysis for univariate or multivariate analysis of repeated measures.


Computational analysis is a combination of mathematics and science that involves analysis of numbers mainly using computers, mathematical algorithms, and formulas. Data are used to identify patterns and to predict, forecast, and simulate future trends such as the spread of disease and the effects of immunizations. Dr. Michael Langston uses the Kraken SuperComputer at Oak Ridge National Laboratory to conduct the computational analysis.

For further information contact Dr. Wansoo Im at wim @