Despite conceptual and technology advancements in cartography over the decades, choropleth map design and classification fail to address a fundamental issue: estimates that are statistically indifferent may be assigned to different classes on maps or vice versa. Recently, the class separability concept was introduced as a map classification criterion to evaluate the likelihood that estimates in two classes are statistical different. Unfortunately, choropleth maps created according to the separability criterion usually have highly unbalanced classes. To produce reasonably separable but more balanced classes, we propose a heuristic classification approach to consider not just the class separability criterion but also other classification criteria such as evenness and intra-class variability. A geovisual-analytic package was developed to support the heuristic mapping process to evaluate the trade-off between relevant criteria and to select the most preferable classification. Class break values can be adjusted to improve the performance of a classification.

Climate studies involve petabytes of spatiotemporal datasets that are produced and archived at distributed computing resources. Scientists need an intuitive and convenient tool to explore the distributed spatiotemporal data. Geovisual analytical tools have the potential to provide such an intuitive and convenient method for scientists to access climate data, discover the relationships between various climate parameters, and communicate the results across different research communities. However, implementing a geovisual analytical tool for complex climate data in a distributed environment poses several challenges. This paper reports our research and development of a web-based geovisual analytical system to support the analysis of climate data generated by climate model. Using the ModelE developed by the NASA Goddard Institute for Space Studies (GISS) as an example, we demonstrate that the system is able to (1) manage large volume datasets over the Internet; (2) visualize 2D/3D/4D spatiotemporal data; (3) broker various spatiotemporal statistical analyses for climate research; and (4) support interactive data analysis and knowledge discovery. This research also provides an example for managing, disseminating, and analyzing Big Data in the 21st century.

Earth Observation (EO) data are critical for many Geographic Information System (GIS)-based decision support systems to provide factual information. However, it is challenging for GIS to understand traditional EO data formats (e.g., Hierarchical Data Format (HDF)) given the different contents and formats in the two domains. To address this gap between EO data and GIS, the barriers and strategies of integrating various types of EO data with GIS are explored, especially with the popular Geospatial Data Abstraction Library (GDAL) that is used by many GISs to access EO data. The research investigates four key technical aspects: (i) designing a generic plug-in framework for consuming different types of EO data; (ii) implementing the framework to fix the errors in GIS when using GDAL to understand EO data; and (iii) developing extension for commercial and open source GIS (i.e., ArcGIS and QGIS) to demonstrate the usability of the proposed framework and its implementation in GDAL. A series of EO data products collected from NASA’s Atmospheric Scientific Data Center (ASDC) are used in the tests and the results prove the proposed framework is efficient to solve different problems in interpreting EO data without compromising their original content.