Geographical cluster

Last updated

A geographical cluster is a localized anomaly, usually an excess of something given the distribution or variation of something else. [1] Often it is considered as an incidence rate that is unusual in that there is more of some variable than might be expected. Examples would include: a local excess disease rate, a crime hot spot, areas of high unemployment, accident blackspots, unusually high positive residuals from a model, high concentrations of flora or fauna, areas with high levels of creative activity, [2] physical features or events like earthquake epicenters etc... [ citation needed ]

Identifying these extreme regions may be useful in that there could be implicit geographical associations with other variables that can be identified and would be of interest. Pattern detection via the identification of such geographical clusters is a very simple and generic form of geographical analysis that has many applications in many different contexts. The emphasis is on localized clustering or patterning because this may well contain the most useful information. [ citation needed ]

A geographical cluster is different from a high concentration as it is generally second order, involving the factoring in of the distribution of something else. [ citation needed ]

Geographical cluster detection

Identifying geographical clusters can be an important stage in a geographical analysis. Mapping the locations of unusual concentrations may help identify causes of these. Some techniques include the Geographical Analysis Machine and Besag and Newell's cluster detection method. [3]

Related Research Articles

<span class="mw-page-title-main">Economic geography</span> Subfield of human geography and economics

Economic geography is the subfield of human geography that studies economic activity and factors affecting it. It can also be considered a subfield or method in economics. There are four branches of economic geography.

Unsupervised learning is a paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data.

<span class="mw-page-title-main">Cluster analysis</span> Grouping a set of objects by similarity

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning.

In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression. In both cases, the input consists of the k closest training examples in a data set. The output depends on whether k-NN is used for classification or regression:

Spatial ecology studies the ultimate distributional or spatial unit occupied by a species. In a particular habitat shared by several species, each of the species is usually confined to its own microhabitat or spatial niche because two species in the same general territory cannot usually occupy the same ecological niche for any significant length of time.

<span class="mw-page-title-main">Spatial analysis</span> Formal techniques which study entities using their topological, geometric, or geographic properties

Spatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is geospatial analysis, the technique applied to structures at the human scale, most notably in the analysis of geographic data. It may also be applied to genomics, as in transcriptomics data.

Machine olfaction is the automated simulation of the sense of smell. An emerging application in modern engineering, it involves the use of robots or other automated systems to analyze air-borne chemicals. Such an apparatus is often called an electronic nose or e-nose. The development of machine olfaction is complicated by the fact that e-nose devices to date have responded to a limited number of chemicals, whereas odors are produced by unique sets of odorant compounds. The technology, though still in the early stages of development, promises many applications, such as: quality control in food processing, detection and diagnosis in medicine, detection of drugs, explosives and other dangerous or illegal substances, disaster response, and environmental monitoring.

Spatial epidemiology is a subfield of epidemiology focused on the study of the spatial distribution of health outcomes; it is closely related to health geography.

Exposure assessment is a branch of environmental science and occupational hygiene that focuses on the processes that take place at the interface between the environment containing the contaminant of interest and the organism being considered. These are the final steps in the path to release an environmental contaminant, through transport to its effect in a biological system. It tries to measure how much of a contaminant can be absorbed by an exposed target organism, in what form, at what rate and how much of the absorbed amount is actually available to produce a biological effect. Although the same general concepts apply to other organisms, the overwhelming majority of applications of exposure assessment are concerned with human health, making it an important tool in public health.

<span class="mw-page-title-main">GeoDa</span> Free geovisualization and analysis software

GeoDa is a free software package that conducts spatial data analysis, geovisualization, spatial autocorrelation and spatial modeling.

Computer audition (CA) or machine listening is the general field of study of algorithms and systems for audio interpretation by machines. Since the notion of what it means for a machine to "hear" is very broad and somewhat vague, computer audition attempts to bring together several disciplines that originally dealt with specific problems or had a concrete application in mind. The engineer Paris Smaragdis, interviewed in Technology Review, talks about these systems — "software that uses sound to locate people moving through rooms, monitor machinery for impending breakdowns, or activate traffic cameras to record accidents."

Pipeline leak detection is used to determine if and in some cases where a leak has occurred in systems which contain liquids and gases. Methods of detection include hydrostatic testing, tracer gas leak detection, infrared, and laser technology after pipeline erection and leak detection during service.

Determining the number of clusters in a data set, a quantity often labelled k as in the k-means algorithm, is a frequent problem in data clustering, and is a distinct issue from the process of actually solving the clustering problem.

Fraud represents a significant problem for governments and businesses and specialized analysis techniques for discovering fraud using them are required. Some of these methods include knowledge discovery in databases (KDD), data mining, machine learning and statistics. They offer applicable and successful solutions in different areas of electronic fraud crimes.

Virus quantification is counting or calculating the number of virus particles (virions) in a sample to determine the virus concentration. It is used in both research and development (R&D) in academic and commercial laboratories as well as in production situations where the quantity of virus at various steps is an important variable that must be monitored. For example, the production of virus-based vaccines, recombinant proteins using viral vectors, and viral antigens all require virus quantification to continually monitor and/or modify the process in order to optimize product quality and production yields and to respond to ever changing demands and applications. Other examples of specific instances where viruses need to be quantified include clone screening, multiplicity of infection (MOI) optimization, and adaptation of methods to cell culture.

A spatial distribution in statistics is the arrangement of a phenomenon across the Earth's surface and a graphical display of such an arrangement is an important tool in geographical and environmental statistics. A graphical display of a spatial distribution may summarize raw data directly or may reflect the outcome of a more sophisticated data analysis. Many different aspects of a phenomenon can be shown in a single graphical display by using a suitable choice of different colours to represent differences.

<span class="mw-page-title-main">CrimeStat</span>

CrimeStat is a crime mapping software program. CrimeStat is Windows-based program that conducts spatial and statistical analysis and is designed to interface with a geographic information system (GIS). The program is developed by Ned Levine & Associates under the direction of Ned Levine, with funding by the National Institute of Justice (NIJ), an agency of the United States Department of Justice. The program and manual are distributed for free by NIJ.

In statistics, the bootstrap error-adjusted single-sample technique is a non-parametric method that is intended to allow an assessment to be made of the validity of a single sample. It is based on estimating a probability distribution representing what can be expected from valid samples. This is done use a statistical method called bootstrapping, applied to previous samples that are known to be valid.

A Crime concentration is a spatial area to which high levels of crime incidents are attributed. A crime concentration can be the result of homogeneous or heterogeneous crime incidents. Hotspots are the result of various crimes occurring in relative proximity to each other within predefined human geopolitical or social boundaries. Crime concentrations are smaller units or set of crime targets within a hotspot. A single or a conjunction of crime concentrations within a study area can make up a crime hotspot.

References

  1. Ian Turton, Stan Openshaw (1998-02-25). "WayBackMachine capture of http://www.ccg.leeds.ac.uk/smart/gam/gam3.html". CCG GAM Web Pages. Archived from the original on October 7, 1999. Retrieved 2011-04-19.{{cite web}}: External link in |title= (help)CS1 maint: unfit URL (link)
  2. Borowiecki, Karol Jan; Dahl, Christian Møller (January 2021). "What makes an artist? The evolution and clustering of creative activity in the US since 1850" (PDF). Regional Science and Urban Economics. 86: 103614. doi:10.1016/j.regsciurbeco.2020.103614. S2CID   228879785.
  3. Fotheringham, A. Stewart; Zhan, F. Benjamin (September 3, 2010). "A Comparison Of Three Exploratory Methods for Cluster Detection in Spatial Point Patterns". Geographical Analysis. 28 (3): 200–218. doi:10.1111/j.1538-4632.1996.tb00931.x.