Geospatial analysis, or just spatial analysis, is an approach to applying statistical analysis and other analytic techniques to data which has a geographical or spatial aspect . Such analysis would typically employ software capable of rendering maps processing spatial data, and applying analytical methods to terrestrial or geographic datasets, including the use of geographic information systems and geomatics.
Geographical information system usage
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.
Basic applications
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
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 specificationsâ€. 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
Geospatial analysis goes beyond 2D and 3D mapping operations and spatial statistics. It includes:
- Surface analysis â€"in particular analysing the properties of physical surfaces, such as gradient, aspect and visibility, and analysing surface-like data “fieldsâ€;
- Network analysis â€" 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 â€" 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
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. 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
- Geospatial intelligence
References
Further reading
- Awange, Joseph; Paláncz, Béla (2016). Geospatial Algebraic Computations, Theory and Applications, Third Edition. New York: Springer. ISBN 978-3319254630.Â
External links
- Rolta’s Geospatial Fusion™ Solution
- Cartography and Geographic Information Society - USA (CaGIS)
- Association of American Geographers (AAG), Spatial Analysis Special Interest Group
- Association for Geographic Information - UK (AGI)
- American Society for Photogrammetry & Remote Sensing (ASPRS)
- Cartography and Geographic Information Society
- Centre for Advanced Spatial Analysis (CASA)
- Centre for Spatially Integrated Social Science (CSISS)
- STARS Entry-Level Geospatial Certification
- Centre for Computational Geography
- DGI - European Geospatial Intelligence Conference
- European Commission Joint Research Centre (CEC/JRC) Geostatistics Unit
- EURO Working Group on Locational Analysis
- Free GIS Organization â€" portal and mailing list
- Geospatial Information & Technology Association
- Joint Board of Geospatial Information Societies (JB GIS)
- National Geospatial-Intelligence Agency (NGA) [1]
- Open Geospatial Consortium (OGC): [2]
- Open Source Geospatial Foundation: [3]
- Royal Geographical Society/Institute of British Geographers: GIS in Teaching
- Spatial Analysis Laboratory, University of Thessaly, Greece
- SPLINT: Spatial Literacy in Teaching
- Topologically Integrated Geographic Encoding and Referencing (TIGER): U.S. Census Bureau
- US Geospatial Intelligence Foundation [4]
- Urban and Regional Information Systems Association (URISA)
