TurboReg: A framework for scaling up spatial logistic regression models

Ibrahim Sabek, Mashaal Musleh, Mohamed Mokbel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Predicting the presence or absence of spatial phenomena has been of great interest to scientists pursuing research in several applications including epidemic diseases detection, species occurrence prediction and earth observation. In this operation, a geographical space is divided by a two-dimensional grid, where the prediction (i.e, either 0 or 1) is performed at each cell in the grid. A common approach to solve this problem is to build spatial logistic regression models (a.k.a autologistic models) that estimate the prediction at any location based on a set of predictors (i.e., features) at this location and predictions from neighboring locations. Unfortunately, existing methods to build autologistic models are computationally expensive and do not scale up for large-scale grid data (e.g., fine-grained satellite images). This paper introduces TurboReg, a scalable framework to build autologistic models for predicting large-scale spatial phenomena. TurboReg considers both the accuracy and efficiency aspects when learning the regression model parameters. TurboReg is built on top of Markov Logic Network (MLN), a scalable statistical learning framework, where its internals and data structures are optimized to process spatial data. A set of experiments using large real and synthetic data show that TurboReg achieves at least three orders of magnitude performance gain over existing methods while preserving the model accuracy.

Original languageEnglish
Title of host publication26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
EditorsLi Xiong, Roberto Tamassia, Kashani Farnoush Banaei, Ralf Hartmut Guting, Erik Hoel
PublisherAssociation for Computing Machinery
Pages129-138
Number of pages10
ISBN (Electronic)9781450358897
DOIs
Publication statusPublished - 6 Nov 2018
Event26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 - Seattle, United States
Duration: 6 Nov 20189 Nov 2018

Other

Other26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
CountryUnited States
CitySeattle
Period6/11/189/11/18

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Keywords

  • Autologistic models
  • Factor graph
  • First-order logic
  • Markov logic networks
  • Spatial regression

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modelling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

Cite this

Sabek, I., Musleh, M., & Mokbel, M. (2018). TurboReg: A framework for scaling up spatial logistic regression models. In L. Xiong, R. Tamassia, K. F. Banaei, R. H. Guting, & E. Hoel (Eds.), 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 (pp. 129-138). Association for Computing Machinery. https://doi.org/10.1145/3274895.3274987