Making Positive Impacts with Environmental Intelligence

By Lillian Pierson

Elva is a shining example of how environmental intelligence technologies can be used to make a positive impact. This free, open-source platform facilitates cause mapping and data visualization reporting for election monitoring, human rights violations, environmental degradation, and disaster risk in developing nations.

In one of its more recent projects, Elva has been working with Internews, an international nonprofit devoted to fostering independent media and access to information in an effort to map crisis-level environmental issues in one of the most impoverished, underdeveloped nations of the world, the Central African Republic. As part of these efforts, local human rights reporters and humanitarian organizations are using Elva to monitor, map, and report information derived from environmental data on natural disasters, infrastructure, water, sanitation, hygiene, and human health. The purpose of Elva’s involvement on this project is to facilitate real-time humanitarian-data analysis and visualization to support the decision making of international humanitarian-relief experts and community leaders.

With respect to data science technologies and methodologies, Elva implements

  • Autofeeds for data collection: The data that’s mapped, visualized, and reported through the Elva platform is actually created by citizen activists on the ground who use SMS and smartphones to report environmental conditions by way of reports or surveys. The reporting system is built so that all reports come in with the correct structure, are collected by service-provider servers, and then are pushed over to the Elva database.
  • Non-relational database technologies: Elva uses a non-relational NoSQL database infrastructure to store survey data submitted by smartphone and SMS, as well as other sources of structured, unstructured, and semistructured data.
  • Open data: OpenStreetMap powers the map data that the Elva platform uses. Open data resources are data resources that have been made publicly available for use, reuse, modification, and sharing with others.
  • Inference from mathematical and statistical models: Elva’s data analysis methods aren’t overly complex, but that’s perfect for producing fast, real-time analytics for humanitarian decision support. Elva depends mostly on time series analysis, linear regression, and simple mathematical inference.
  • Data visualization: Elva produces data visualizations directly from reported data and also from inferential analyses. These are interactive JavaScript visualizations built from the Highcharts API.
  • Location-based predictions: Such predictions are based on simple inference and not on advanced spatial statistics. Elva staff can infer locations of high risk based on historical time series reported in the region.