### 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 language | English |
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Title of host publication | 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 |

Editors | Li Xiong, Roberto Tamassia, Kashani Farnoush Banaei, Ralf Hartmut Guting, Erik Hoel |

Publisher | Association for Computing Machinery |

Pages | 129-138 |

Number of pages | 10 |

ISBN (Electronic) | 9781450358897 |

DOIs | |

Publication status | Published - 6 Nov 2018 |

Event | 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 - Seattle, United States Duration: 6 Nov 2018 → 9 Nov 2018 |

### Other

Other | 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 |
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Country | United States |

City | Seattle |

Period | 6/11/18 → 9/11/18 |

### Fingerprint

### 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

*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

**TurboReg : A framework for scaling up spatial logistic regression models.** / Sabek, Ibrahim; Musleh, Mashaal; Mokbel, Mohamed.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018.*Association for Computing Machinery, pp. 129-138, 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018, Seattle, United States, 6/11/18. https://doi.org/10.1145/3274895.3274987

}

TY - GEN

T1 - TurboReg

T2 - A framework for scaling up spatial logistic regression models

AU - Sabek, Ibrahim

AU - Musleh, Mashaal

AU - Mokbel, Mohamed

PY - 2018/11/6

Y1 - 2018/11/6

N2 - 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.

AB - 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.

KW - Autologistic models

KW - Factor graph

KW - First-order logic

KW - Markov logic networks

KW - Spatial regression

UR - http://www.scopus.com/inward/record.url?scp=85058653614&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058653614&partnerID=8YFLogxK

U2 - 10.1145/3274895.3274987

DO - 10.1145/3274895.3274987

M3 - Conference contribution

SP - 129

EP - 138

BT - 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018

A2 - Xiong, Li

A2 - Tamassia, Roberto

A2 - Banaei, Kashani Farnoush

A2 - Guting, Ralf Hartmut

A2 - Hoel, Erik

PB - Association for Computing Machinery

ER -