Afterword - Big Data and Geospatial Analysis

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Afterword - Big Data and Geospatial Analysis

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Perhaps one of the mostly hotly debated topics since the 4th edition of Geospatial Analysis was published has been the question of "GIS and Big Data". Much of the discussion has been about the data, where huge volumes of 2D and 3D spatial data can be stored, how they can be accessed, and whether it is possible to map and interpret massive datasets in an effective manner. Less attention has been paid to questions of analysis, although this has risen up the agenda recently. Examples include the use of density analysis to represent map request events, with Esri demonstrating that (given sufficient resources) they can process and analyze large numbers of datapoint events using kernel density techniques within a very short timeframe (under a minute); data filtering (to extract subsets of data that are of particular interest); and data mining (broader than simple filtering). For real-time data, sequential analysis has also been successfully applied; in this case the data is received as a stream and is used to build up a dynamic map or to cumulatively generate statistical values that may be mapped and/or used to trigger events or alarms. To this extent the analysis is similar to that conducted on smaller datasets, but with data and processing architectures that are specifically designed to cope with the data volumes involved and focusing on data exploration as a key mechanism for discovery.

Miller and Goodchild (2014) have argued that considerable care is required when working with Big Data - significant issues arise in terms of the data (the four Vs): the sheer Volume of data;  Velocity of data arrival and associated timestamps of the data;  the Variety of data available and the way in which this is selected (e.g. self selection); and the Validity of such data. A presentation by Prof Mike Goodchild of some of the key elements of the Big Data debate are provided in the Resources page of the website and can be accessed directly at: http://www.spatialanalysisonline.com/PPTS/BigData.pdf - this presentation should be viewed alongside the article by Miller and Goodchild, as the latter provides a fuller explanation of the main ideas covered.