Function Modeling Improves the Efficiency of Spatial Modeling Using Big Data from Remote Sensing

John Hogland,Nathaniel Anderson

doi:10.3390/bdcc1010003

Abstract

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.

Highlights

Spatial modeling has become an integral component of geographic information systems (GISs) and remote sensing
We introduce a new GIS spatial modeling framework called function modeling (FM) and highlight some of the advantages of processing big data spatial models using lazy evaluation techniques
To more fully explore the use of FM to analyze data and create predictive surfaces in real applications, we evaluated the efficiencies associated with two case studies: (1) a recent study of the Uncompahgre National Forest (UNF) in the US state of Colorado [6,29] and (2) a study focused on the forests of the Montana State Department of Natural Resources and Conservation (DNRC) [unpublished data]

Summary

Introduction

Spatial modeling has become an integral component of geographic information systems (GISs) and remote sensing. A number of challenges place significant limitations on producing final outputs in this manner, including learning additional software, implementing predictive model outputs, managing large data sets, and handling the long processing time and large storage space requirements associated with this work flow [10,11]. These challenges have intensified over the past decade because large, fine-resolution remote sensing data sets, such as meter and sub-meter imagery and Lidar, have become widely available and less expensive to procure, but the tools to use such data efficiently and effectively have not always kept pace, especially in the desktop environment. The coding library facilitates a wide range of big data spatial, statistical, and machine learning type analyses, including FM

NET Libraries

Case Studies

Results