Powering space-time environmental health analysis

Fuel innovation, accelerate exposure analysis, improve visualizations, and make informed spatial predictions.
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Introducing Vesta

Vesta is a powerful tool that visualizes and detects patterns in the health environment so you can target interventions, assess policy effectiveness, and control disease. Users have unlimited access to advanced features such as cartographic and statistical brushing, geographical joinpoint regression, missing values, and more.

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Uncover Missing Data

Being able to handle missing data is an integral part of good data analysis practice. Vesta is now the only GIS system that can handle missing data in visualizations and calculations. Many statistical analysis packages of course handle missing data, but other GIS systems do not. 

Health-Environment Geostatistics

Why Users Choose Vesta

Easy Setup

All you need is an internet connection and Windows 10 or later versions to install Vesta and get started. 

User-Friendly Design

Vesta is designed to be accessible to non-expert users as well as professional geostatisticians.

Cost Effective

Users get access to all of Vesta’s innovative, top-of-the-line features in one annual subscription fee. No add-ons or additional fees, ever.

Admin Support

Our team is available for support throughout all stages of your project. Vesta is also equipped with extensive documentation and video tutorials to ensure that users succeed.

Explore Geographical Joinpoint Regression

Analyzing temporal trends outside a spatial framework is unsatisfactory because significant variation even within a single State is not accounted for. Vesta is the only software that allows exploring how characteristics of time series vary geographically.

Spatial Epidemiology

Looking for more geostatistical analysis power?

SpaceStat
Visualize, analyze, model, and explore spatiotemporal data.

ClusterSeer
Detect and analyze event clusters.

BoundarySeer
Detect and analyze geographic boundaries.

Our Customers

“I’m using SpaceStat to analyze the spatial structure of estuarine seagrass beds in Atlantic Canada for purposes of conservation and management and I’ve been able to do the type of analysis that I was after. The variogram modeling capability and LISA functions have especially been very useful.”

Jeff Barrell, PhD Student
Dept. of Oceanography
Dalhousie University

United States Department of Agriculture: Animal and Plant Health Inspection Service
Curtin University
Nigata University
Michigan Department of Health and Human Services
Centers for Disease Control
University of Birmingham
University of Michigan
University of Guelph
Royal Veterinary College University of London
London School of Hygiene and Tropical Medicine
Columbia University of the City of New York
Harvard School of Public Health
The University of Georgia
University of Cambridge
The University of Tennessee