Environmental statistics

Last updated

Environment statistics is the application of statistical methods to environmental science. It covers procedures for dealing with questions concerning the natural environment in its undisturbed state, the interaction of humanity with the environment, and urban environments. The field of environmental statistics has seen rapid growth in the past few decades as a response to increasing concern over the environment in the public, organizational, and governmental sectors.

Contents

The United Nations' Framework for the Development of Environment Statistics (FDES) defines the scope of environment statistics as follows: [1] The scope of environment statistics covers biophysical aspects of the environment and those aspects of the socioeconomic system that directly influence and interact with the environment. The scope of environment, social and economic statistics overlap. It is not easy – or necessary – to draw a clear line dividing these areas. Social and economic statistics that describe processes or activities with a direct impact on, or direct interaction with, the environment are used widely in environment statistics. They are within the scope of the FDES.

Uses

Statistical analysis is essential to the field of environmental sciences, allowing researchers to gain an understanding of environmental issues through researching and developing potential solutions to the issues they study. The applications of statistical methods to environmental sciences are numerous and varied. Environmental statistics are used in many fields including; health and safety organizations, standard bodies, research institutes, water and river authorities, meteorological organizations, fisheries, protection agencies, and in risk, pollution, regulation and control concerns. [2]

Environmental statistics is especially pertinent and widely used in the academic, governmental, regulatory, technological, and consulting industries. [2]

Specific applications of statistical analysis within the field of environmental science include earthquake risk analysis, environmental policymaking, ecological sampling planning, environmental forensics. [2]

Within the scope of environmental statistics, there are two main categories of their uses. [2]

Types of studies covered in environmental statistics include: [3]

Sources

Sources of data for environmental statistics are varied and include surveys related to human populations and the environment, records from agencies managing environmental resources, maps and images, equipment used to examine the environment, and research studies around the world. A primary component of the data is direct observation, although most environmental statistics use a variety of sources. [3]

Methods

Methods of statistical analysis in environmental sciences are as numerous as its applications. While there is a basis for the methods used in other fields, many of these methods must be adapted to suit the needs or limitations of data in environmental science. Linear regression models, generalized linear models, and non-linear models are some methods of statistical analysis that are widely used within environmental science to study relationships between variables. [2]

See also

Related Research Articles

Biostatistics is a branch of statistics that applies statistical methods to a wide range of topics in biology. It encompasses the design of biological experiments, the collection and analysis of data from those experiments and the interpretation of the results.

Econometrics is an application of statistical methods to economic data in order to give empirical content to economic relationships. More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference." An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships." Jan Tinbergen is one of the two founding fathers of econometrics. The other, Ragnar Frisch, also coined the term in the sense in which it is used today.

<span class="mw-page-title-main">Statistics</span> Study of the collection, analysis, interpretation, and presentation of data

Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments.

<span class="mw-page-title-main">Statistical inference</span> Process of using data analysis

Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population.

The following outline is provided as an overview of and topical guide to statistics:

Geostatistics is a branch of statistics focusing on spatial or spatiotemporal datasets. Developed originally to predict probability distributions of ore grades for mining operations, it is currently applied in diverse disciplines including petroleum geology, hydrogeology, hydrology, meteorology, oceanography, geochemistry, geometallurgy, geography, forestry, environmental control, landscape ecology, soil science, and agriculture. Geostatistics is applied in varied branches of geography, particularly those involving the spread of diseases (epidemiology), the practice of commerce and military planning (logistics), and the development of efficient spatial networks. Geostatistical algorithms are incorporated in many places, including geographic information systems (GIS).

Social statistics is the use of statistical measurement systems to study human behavior in a social environment. This can be accomplished through polling a group of people, evaluating a subset of data obtained about a group of people, or by observation and statistical analysis of a set of data that relates to people and their behaviors.

<span class="mw-page-title-main">Quantitative research</span> All procedures for the numerical representation of empirical facts

Quantitative research is a research strategy that focuses on quantifying the collection and analysis of data. It is formed from a deductive approach where emphasis is placed on the testing of theory, shaped by empiricist and positivist philosophies.

Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system can be divided and allocated to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; ideally, uncertainty and sensitivity analysis should be run in tandem.

<span class="mw-page-title-main">Mathematical statistics</span> Branch of statistics

Mathematical statistics is the application of probability theory, a branch of mathematics, to statistics, as opposed to techniques for collecting statistical data. Specific mathematical techniques which are used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure theory.

<span class="mw-page-title-main">C. R. Rao</span> Indian-American mathematician (1920–2023)

Calyampudi Radhakrishna Rao was an Indian-American mathematician and statistician. He was professor emeritus at Pennsylvania State University and Research Professor at the University at Buffalo. Rao was honoured by numerous colloquia, honorary degrees, and festschrifts and was awarded the US National Medal of Science in 2002. The American Statistical Association has described him as "a living legend” whose work has influenced not just statistics, but has had far reaching implications for fields as varied as economics, genetics, anthropology, geology, national planning, demography, biometry, and medicine." The Times of India listed Rao as one of the top 10 Indian scientists of all time.

The United Nations Statistics Division (UNSD), formerly the United Nations Statistical Office, serves under the United Nations Department of Economic and Social Affairs (DESA) as the central mechanism within the Secretariat of the United Nations to supply the statistical needs and coordinating activities of the global statistical system. The Division is overseen by the United Nations Statistical Commission, established in 1947, as the apex entity of the global statistical system and highest decision making body for coordinating international statistical activities. It brings together the Chief Statisticians from member states from around the world.

Statistics, in the modern sense of the word, began evolving in the 18th century in response to the novel needs of industrializing sovereign states.

<span class="mw-page-title-main">Official statistics</span> Statistics published by government agencies

Official statistics are statistics published by government agencies or other public bodies such as international organizations as a public good. They provide quantitative or qualitative information on all major areas of citizens' lives, such as economic and social development, living conditions, health, education, and the environment.

<span class="mw-page-title-main">David A. Freedman</span>

David Amiel Freedman was Professor of Statistics at the University of California, Berkeley. He was a distinguished mathematical statistician whose wide-ranging research included the analysis of martingale inequalities, Markov processes, de Finetti's theorem, consistency of Bayes estimators, sampling, the bootstrap, and procedures for testing and evaluating models. He published extensively on methods for causal inference and the behavior of standard statistical models under non-standard conditions – for example, how regression models behave when fitted to data from randomized experiments. Freedman also wrote widely on the application—and misapplication—of statistics in the social sciences, including epidemiology, public policy, and law.

Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The study of why things occur is called etiology, and can be described using the language of scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning.

Sudipto Banerjee is an Indian-American statistician best known for his work on Bayesian hierarchical modeling and inference for spatial data analysis. He is Professor of Biostatistics and Senior Associate Dean in the School of Public Health at the University of California, Los Angeles. He served as the Chair of the Department of Biostatistics at UCLA from 2014 through 2023. He served as the elected President of the International Society for Bayesian Analysis in 2022.

Alan Enoch Gelfand is an American statistician, and is currently the James B. Duke Professor of Statistics and Decision Sciences at Duke University. Gelfand’s research includes substantial contributions to the fields of Bayesian statistics, spatial statistics and hierarchical modeling.

References

  1. ↑ "FRAMEWORK FOR THE DEVELOPMENT OF ENVIRONMENT STATISTICS (FDES 2013)" (PDF). Retrieved 2024-05-07.
  2. 1 2 3 4 5 Rong, Yue (2011-09-01). "Environmental Statistics". Environmental Forensics. 12 (3): 189–190. doi:10.1080/15275922.2011.599263. ISSN   1527-5922.
  3. 1 2 Manly B.F.J. (2001) Statistics for Environmental Science and Management, Chapman & Hall/CRC. ISBN   1-58488-029-5
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.