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Mining Points-of-Interest for Explaining Urban Phenomena: A Scalable Variational Inference Approach

Authors:

Christof Naumzik,

Patrick Zoechbauer,

Stefan FeuerriegelAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 2342 - 2353

https://doi.org/10.1145/3366423.3380298

Published: 20 April 2020 Publication History

Abstract

Points-of-interest (POIs; i.e., restaurants, bars, landmarks, and other entities) are common in web-mined data: they greatly explain the spatial distributions of urban phenomena. The conventional modeling approach relies upon feature engineering, yet it ignores the spatial structure among POIs. In order to overcome this shortcoming, the present paper proposes a novel spatial model for explaining spatial distributions based on web-mined POIs. Our key contributions are: (1) We present a rigorous yet highly interpretable formalization in order to model the influence of POIs on a given outcome variable. Specifically, we accommodate the spatial distributions of both the outcome and POIs. In our case, this modeled by the sum of latent Gaussian processes. (2) In contrast to previous literature, our model infers the influence of POIs without feature engineering, instead we model the influence of POIs via distance-weighted kernel functions with fully learnable parameterizations. (3) We propose a scalable learning algorithm based on sparse variational approximation. For this purpose, we derive a tailored evidence lower bound (ELBO) and, for appropriate likelihoods, we even show that an analytical expression can be obtained. This allows fast and accurate computation of the ELBO. Finally, the value of our approach for web mining is demonstrated in two real-world case studies. Our findings provide substantial improvements over state-of-the-art baselines with regard to both predictive and, in particular, explanatory performance. Altogether, this yields a novel spatial model for leveraging web-mined POIs. Within the context of location-based social networks, it promises an extensive range of new insights and use cases.

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Cited By

Cellmer RBełej MTrojanek R(2024)Housing prices and points of interest in three Polish citiesJournal of Housing and the Built Environment10.1007/s10901-024-10124-739:3(1509-1540)Online publication date: 15-May-2024
https://doi.org/10.1007/s10901-024-10124-7
Hummler PNaumzik CFeuerriegel S(2022)Web Mining to Inform Locations of Charging Stations for Electric VehiclesCompanion Proceedings of the Web Conference 202210.1145/3487553.3524264(166-170)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524264
Tschernutter DFeuerriegel S(2021)A Latent Customer Flow Model for Interpretable Predictions of Check-In Counts2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671946(529-539)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671946

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Mining Points-of-Interest for Explaining Urban Phenomena: A Scalable Variational Inference Approach

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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Published: 20 April 2020

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Cited By

Cellmer RBełej MTrojanek R(2024)Housing prices and points of interest in three Polish citiesJournal of Housing and the Built Environment10.1007/s10901-024-10124-739:3(1509-1540)Online publication date: 15-May-2024
https://doi.org/10.1007/s10901-024-10124-7
Hummler PNaumzik CFeuerriegel S(2022)Web Mining to Inform Locations of Charging Stations for Electric VehiclesCompanion Proceedings of the Web Conference 202210.1145/3487553.3524264(166-170)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524264
Tschernutter DFeuerriegel S(2021)A Latent Customer Flow Model for Interpretable Predictions of Check-In Counts2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671946(529-539)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671946

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