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NN-SAR: A Neural Network Approach for Spatial AutoRegression

NN-SAR: A Neural Network Approach for Spatial AutoRegression
NN-SAR: A Neural Network Approach for Spatial AutoRegression
Geographic phenomena such as weather, pollution, pollen are increasingly monitored through deployments of wide-area networked sensors. However, the coverage of these sensors is limited to key densely populated regions. A standard approach to inferring missing spatial and temporal values is to use regression. In this paper, we present a new approach, NN-SAR, to inferring spatiotemporal values from existing deployed sensors. We model this inference problem as that of learning a spatial representation of the underlying phenomena from the existing data and use deep learning based auto-encoder approach. Classical auto-encoders learn on image or singular time series data without taking “spatial” similarities into account. We present a novel mechanism for encoding the spatially distributed sensor readings as “images” and apply the auto-encoder with convolutional layers to learn an efficient representation of the data, which can then be used to infer missing sensor data. Preliminary results indicate that the performance of our approach is far superior to the state-of-the-art Spatial Auto-Regressive (SAR) models by 20% on average.
783-789
IEEE
Dewan, Pranita
8e0cb372-4bdd-4585-9a12-0292dc77ebe9
Ganti, Raghu
2a43a38b-8bad-466a-b877-9b55a4c2bc80
Srivatsa, Mudhakar
7866037c-a17f-4718-9d03-75b18d1251ca
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Dewan, Pranita
8e0cb372-4bdd-4585-9a12-0292dc77ebe9
Ganti, Raghu
2a43a38b-8bad-466a-b877-9b55a4c2bc80
Srivatsa, Mudhakar
7866037c-a17f-4718-9d03-75b18d1251ca
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b

Dewan, Pranita, Ganti, Raghu, Srivatsa, Mudhakar and Stein, Sebastian (2019) NN-SAR: A Neural Network Approach for Spatial AutoRegression. In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE. pp. 783-789 . (doi:10.1109/PERCOMW.2019.8730574).

Record type: Conference or Workshop Item (Paper)

Abstract

Geographic phenomena such as weather, pollution, pollen are increasingly monitored through deployments of wide-area networked sensors. However, the coverage of these sensors is limited to key densely populated regions. A standard approach to inferring missing spatial and temporal values is to use regression. In this paper, we present a new approach, NN-SAR, to inferring spatiotemporal values from existing deployed sensors. We model this inference problem as that of learning a spatial representation of the underlying phenomena from the existing data and use deep learning based auto-encoder approach. Classical auto-encoders learn on image or singular time series data without taking “spatial” similarities into account. We present a novel mechanism for encoding the spatially distributed sensor readings as “images” and apply the auto-encoder with convolutional layers to learn an efficient representation of the data, which can then be used to infer missing sensor data. Preliminary results indicate that the performance of our approach is far superior to the state-of-the-art Spatial Auto-Regressive (SAR) models by 20% on average.

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Published date: 11 March 2019

Identifiers

Local EPrints ID: 446413
URI: http://eprints.soton.ac.uk/id/eprint/446413
PURE UUID: 16ea8af2-832b-4c67-9e41-c927a815b2f2
ORCID for Sebastian Stein: ORCID iD orcid.org/0000-0003-2858-8857

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Date deposited: 08 Feb 2021 17:31
Last modified: 18 Feb 2021 17:11

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Contributors

Author: Pranita Dewan
Author: Raghu Ganti
Author: Mudhakar Srivatsa
Author: Sebastian Stein ORCID iD

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