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    Author(s): John HoglandNathaniel Anderson
    Date: 2017
    Source: Big Data and Cognitive Computing. 1(3): 1-14.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: Download Publication  (2.0 MB)


    Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have applied those models in a multiprocessor environment. Few, however, have recognized the inefficiencies associated with the underlying spatial modeling framework used to implement such analyses. In this paper, we identify a common inefficiency in processing spatial models and demonstrate a novel approach to address it using lazy evaluation techniques. Furthermore, we introduce a new coding library that integrates Accord.NET and ALGLIB numeric libraries and uses lazy evaluation to facilitate a wide range of spatial, statistical, and machine learning procedures within a new GIS modeling framework called function modeling. Results from simulations show a 64.3% reduction in processing time and an 84.4% reduction in storage space attributable to function modeling. In an applied case study, this translated to a reduction in processing time from 2247 h to 488 h and a reduction is storage space from 152 terabytes to 913 gigabytes.

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    Hogland, John; Anderson, Nathaniel. 2017. Function modeling improves the efficiency of spatial modeling using big data from remote sensing. Big Data and Cognitive Computing. 1(3): 1-14.


    function modeling, remote sensing, machine learning, geographic information system

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