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3 Information systems Module

You have seen that data, indicators and indices form an interlinked information system. While these 4
elements are all related, developing them involves specific tasks. This section will provide an overview
of some of the key conceptual issues and methods in developing data for use in indicators and

indices.

The section reviews the types of data, including quantitative and qualitative data, and also looks at Abu Dhabi, UAE

the specific issues related to spatial and non-spatial data. The section also considers what is involved
in monitoring, construction and use of databases to store and analyze data. One of the prominent
examples reviewed at the end of the section is the GEO Data Portal, a global database maintained by

UNEP that is used in the production of global and sub-global GEO assessments.

3.1 Data

Data provide you with useful information that can be processed into a more readily accessible form for
use by policy-makers and the public. Data can be linked to important societal issues when placed in the
context of a relevant issue. For example:

? data on the number of patients with respiratory disease can provide information on the impact of

air pollution;
? the number of cars in urban centres can help provide estimates on the magnitude of air quality
problems;

? data on the quantity or quality of natural habitat can help assess, among others, the availability of
species for traditional resource users such as trappers or hunters; and
? the composition of solid waste can clearly indicate some emerging issues, such as the problems

associated with electronic waste in China and India.

3.1.1 Types of data

Environmental monitoring typically involves “hard” science, although there are also an increasing number
of examples of non-expert (community, youth) involvement. Quantitative indicators and data, usually

based on statistics or remote sensing and presented numerically in tables, graphs and maps, serve as the
main foundation of environmental assessment and subsequent decision making by policy-makers, civil
society and the public at large. Quantitative data is often complemented by qualitative data to capture
attributes that cannot be easily measured.

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