Geospatial & Landscape Analysis

Geospatial analytical tools provide the foundation for comprehensive spatial assessments. Working with outputs from mathematical and statistical models Geographic Information Systems (GIS) can assist with trend and forensic analysis, as well as offering insight into problem causation and possible solutions. While not the only tool employed by our consultants, GIS is one of most powerful analytical tools for characterizing and studying spatial relationships through layering of data (e.g., soil chemistry, ecological relationships, geology, historical land use, time-series data, trend analysis). Visualization and analysis of the spatial relationships underlying site-specific data sets is often the key to understanding processes and effects at the site.

Services

Exponent provides a full range of GIS and spatial analytical capabilities to assist clients in reaching an increased level of understanding of their data and site. Whether evaluating the spatial orientation of data, tracking changes in a site through time, or preparing exhibits to further enhance technical opinions, visual data evaluation is critical to the success of any spatial assessment. Using GIS and relying on increasingly sophisticated source data such as, digital maps, photographs, high resolution satellite images, GPS field data, site specific data layers, and other emerging visual media, Exponent’s GIS analysts work closely with engineers, chemists, geologists, toxicologists, and biologists to add spatial context to complex problems.

Geospatial

Exponent’s professionals have expertise in most general aspects of spatial assessments. Listed below are examples of a few of our technical areas of expertise:


  • Geospatial analysis integrated with quantitative environmental, population, and statistical modeling in R, allowing complex models to both access spatial data, such as environmental variables, and create spatial model outputs.
  • Geostatistics used to understand the spatial distribution of environmental and health processes. For example, to identify and make statistical inference on clusters of disease, in an attempt to ascertain whether clusters are part of a natural random process or are extraordinary and require further investigation. These methods also have a host of applications in economic settings, such as housing markets and other consumer sales.
  • Spatial prediction that is capable of using previously collected data to infer the likely distribution of a particular item where limited measurements are available. This area has regularly used classical geostatistical tools such as Kriging and spline interpolation, but modern machine learning methods can be used to make use of multivariate data with potentially complex spatial structures.

Professionals