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Risk Management/Spatial risk management

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Spatial Risk Management is based on geospatial analysis, which is taught e.g. in Graduate Programmes[1]. The term Risk adds to the methods of statistical analysis and numerical analysis a special focus on geographical aspects of public/environmental health risks or potential damage to infrastructure and services. Spatial aspects are referring to risk factors and available resource for risk mitigation.

Spatial Decision Support System

Spatial risk analysis would typically employ software capable of rendering

  • maps showing the spatial distribution of risks and
  • maps visualising spatial allocation of resources.

The software that support spatial decisions is a Spatial Decision Support System (SDSS).

The applyication of analytical methods to terrestrial or geographic datasets evaluate, how the modelled risk is covered with the available resources. Management of resources according risk and reallocation of resources may be necessary according to results of the risk maps.

GRASS-GIS is an Open Source geographic information system, that was originally designed as Geographic Resource Analysis Support System and evolved into full featured GIS in the field of geomatics.[2][3][4].

Learning Activities

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  • (Collaborative Mapping) Explain how collaborative mapping can support the spatial risk management of a community.

Learning Resources

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Geographical information system usage

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Geographic information systems (GIS), which is a large domain that provides a variety of capabilities designed to capture, store, manipulate, analyze, manage, and present all types of geographical data, and utilizes geospatial analysis in a variety of contexts, operations and applications on spatial risks and spatial allocation of resources.

Basic applications

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Geospatial analysis, using GIS, was developed for problems in the environmental and life sciences, in particular ecology, geology and epidemiology. It has extended to almost all industries including defense, intelligence, utilities, Natural Resources (i.e. Oil and Gas, Forestry ... etc.), social sciences, medicine and Public Safety (i.e. emergency management and criminology), disaster risk reduction and management (DRRM), and climate change adaptation (CCA). Spatial statistics typically result primarily from observation rather than experimentation.

Basic operations

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Vector-based GIS is typically related to operations such as map overlay (combining two or more maps or map layers according to predefined rules), simple buffering (identifying regions of a map within a specified distance of one or more features, such as towns, roads or rivers) and similar basic operations. This reflects (and is reflected in) the use of the term spatial analysis within the Open Geospatial Consortium (OGC)) “simple feature specifications”. For raster-based GIS, widely used in the environmental sciences and remote sensing, this typically means a range of actions applied to the grid cells of one or more maps (or images) often involving filtering and/or algebraic operations (map algebra). These techniques involve processing one or more raster layers according to simple rules resulting in a new map layer, for example replacing each cell value with some combination of its neighbours’ values, or computing the sum or difference of specific attribute values for each grid cell in two matching raster datasets. Descriptive statistics, such as cell counts, means, variances, maxima, minima, cumulative values, frequencies and a number of other measures and distance computations are also often included in this generic term spatial analysis. Spatial analysis includes a large variety of statistical techniques (descriptive, exploratory, and explanatory statistics) that apply to data that vary spatially and which can vary over time. Some more advanced statistical techniques include Getis-ord Gi* or Anselin Local Moran's I which are used to determine clustering patterns of spatially referenced data.

Advanced operations

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Geospatial analysis goes beyond 2D and 3D mapping operations and spatial statistics. It includes:

  • Surface analysis —in particular analysing the properties of physical surfaces, such as gradient, aspect and visibility, and analysing surface-like data “fields”;
  • Network analysis — examining the properties of natural and man-made networks in order to understand the behaviour of flows within and around such networks; and locational analysis. GIS-based network analysis may be used to address a wide range of practical problems such as route selection and facility location (core topics in the field of operations research, and problems involving flows such as those found in hydrology and transportation research. In many instances location problems relate to networks and as such are addressed with tools designed for this purpose, but in others existing networks may have little or no relevance or may be impractical to incorporate within the modeling process. Problems that are not specifically network constrained, such as new road or pipeline routing, regional warehouse location, mobile phone mast positioning or the selection of rural community health care sites, may be effectively analysed (at least initially) without reference to existing physical networks. Locational analysis "in the plane" is also applicable where suitable network datasets are not available, or are too large or expensive to be utilised, or where the location algorithm is very complex or involves the examination or simulation of a very large number of alternative configurations.
  • Geovisualization — the creation and manipulation of images, maps, diagrams, charts, 3D views and their associated tabular datasets. GIS packages increasingly provide a range of such tools, providing static or rotating views, draping images over 2.5D surface representations, providing animations and fly-throughs, dynamic linking and brushing and spatio-temporal visualisations. This latter class of tools is the least developed, reflecting in part the limited range of suitable compatible datasets and the limited set of analytical methods available, although this picture is changing rapidly. All these facilities augment the core tools utilised in spatial analysis throughout the analytical process (exploration of data, identification of patterns and relationships, construction of models, and communication of results)

Mobile Geospatial Computing

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Traditionally geospatial computing has been performed primarily on personal computers (PCs) or servers. Due to the increasing capabilities of mobile devices, however, geospatial computing in mobile devices is a fast-growing trend.[6] The portable nature of these devices, as well as the presence of useful sensors, such as Global Navigation Satellite System (GNSS) receivers and barometric pressure sensors, make them useful for capturing and processing geospatial information in the field. In addition to the local processing of geospatial information on mobile devices, another growing trend is cloud-based geospatial computing. In this architecture, data can be collected in the field using mobile devices and then transmitted to cloud-based servers for further processing and ultimate storage. In a similar manner, geospatial information can be made available to connected mobile devices via the cloud, allowing access to vast databases of geospatial information anywhere where a wireless data connection is available.

See also

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References

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  1. ↑ "Graduate Program in Spatial Analysis". Ryerson University. Ryerson University. Retrieved 17 December 2015.
  2. ↑ geospatial. Collins English Dictionary - Complete & Unabridged 11th Edition. Retrieved 5tth August 2012 from CollinsDictionary.com website: http://www.collinsdictionary.com/dictionary/english/geospatial
  3. ↑ Dictionary.com's 21st Century Lexicon Copyright © 2003-2010 Dictionary.com, LLC http://dictionary.reference.com/browse/geospatial
  4. ↑ The geospatial web – blending physical and virtual spaces., Arno Scharl in receiver magazine, Autumn 2008
  5. ↑ La Isla Foundation - Public Health Research on CKDu epidemic, La Isla Foundation, P. O. Box 816, Ada, MI 49301, USA 347.585.7465
  6. ↑ Chen, Ruizhi; Guinness, Robert E. (2014). Geospatial Computing in Mobile Devices (1st ed.). Norwood, MA: Artech House. pp. 228. ISBN 978-1-60807-565-2. http://www.artechhouse.com/International/Books/Geospatial-Computing-in-Mobile-Devices-2159.aspx. Retrieved 1 July 2014. 
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