research-article

StreetNet: preference learning with convolutional neural network on urban crime perception

Authors:

Chang-Tien LuAuthors Info & Claims

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 269 - 278

https://doi.org/10.1145/3274895.3274975

Published: 06 November 2018 Publication History

Abstract

One can infer from the broken window theory that the perception of a city street's safety level relies significantly on the visual appearance of the street. Previous works have addressed the feasibility of using computer vision algorithms to classify urban scenes. Most of the existing urban perception predictions focus on binary outcomes such as safe or dangerous, wealthy or poor. However, binary predictions are not representative and cannot provide informative inferences such as the potential crime types in certain areas. In this paper, we explore the connection between urban perception and crime inferences. We propose a convolutional neural network (CNN) - StreetNet to learn crime rankings from street view images. The learning process is formulated on the basis of preference learning and label ranking settings. We design a street view images retrieval algorithm to improve the representation of urban perception. A data-driven, spatiotemporal algorithm is proposed to find unbiased label mappings between the street view images and the crime ranking records. Extensive evaluations conducted on images from different cities and comparisons with baselines demonstrate the effectiveness of our proposed method.

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Cited By

Mokbel MSakr MXiong LZüfle AAlmeida JAnderson TAref WAndrienko GAndrienko NCao YChawla SCheng RChrysanthis PFei XGhinita GGraser AGunopulos DJensen CKim JKim KKröger PKrumm JLauer JMagdy ANascimento MRavada SRenz MSacharidis DSalim FSarwat MSchoemans MShahabi CSpeckmann BTanin ETeng XTheodoridis YTorp KTrajcevski Gvan Kreveld MWenk CWerner MWong RWu SXu JYoussef MZeinalipour DZhang MZimányi E(2024)Mobility Data Science: Perspectives and ChallengesACM Transactions on Spatial Algorithms and Systems10.1145/365215810:2(1-35)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3652158
Alpherts TGhebreab SHsu YVan Noord N(2024)Perceptive Visual Urban Analytics is Not (Yet) Suitable for MunicipalitiesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658976(1341-1354)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658976
Yang SKrenz KQiu WLi W(2023)The Role of Subjective Perceptions and Objective Measurements of the Urban Environment in Explaining House Prices in Greater London: A Multi-Scale Urban Morphology AnalysisISPRS International Journal of Geo-Information10.3390/ijgi1206024912:6(249)Online publication date: 19-Jun-2023
https://doi.org/10.3390/ijgi12060249
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Index Terms

StreetNet: preference learning with convolutional neural network on urban crime perception
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Perception
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Learning to rank
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems
      1. Geographic information systems

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cover image ACM Conferences

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2018

655 pages

ISBN:9781450358897

DOI:10.1145/3274895

General Chairs:
Farnoush Banaei-Kashani
University of Colorado, Denver
,
Erik Hoel
Esri
,
Program Chairs:
Ralf Hartmut Güting
FernUniversität in Hagen, Germany
,
Roberto Tamassia
Brown University
,
Li Xiong
Emory University

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Published: 06 November 2018

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SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6 - 9, 2018

Washington, Seattle

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SIGSPATIAL '18 Paper Acceptance Rate 30 of 150 submissions, 20%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

Mokbel MSakr MXiong LZüfle AAlmeida JAnderson TAref WAndrienko GAndrienko NCao YChawla SCheng RChrysanthis PFei XGhinita GGraser AGunopulos DJensen CKim JKim KKröger PKrumm JLauer JMagdy ANascimento MRavada SRenz MSacharidis DSalim FSarwat MSchoemans MShahabi CSpeckmann BTanin ETeng XTheodoridis YTorp KTrajcevski Gvan Kreveld MWenk CWerner MWong RWu SXu JYoussef MZeinalipour DZhang MZimányi E(2024)Mobility Data Science: Perspectives and ChallengesACM Transactions on Spatial Algorithms and Systems10.1145/365215810:2(1-35)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3652158
Alpherts TGhebreab SHsu YVan Noord N(2024)Perceptive Visual Urban Analytics is Not (Yet) Suitable for MunicipalitiesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658976(1341-1354)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658976
Yang SKrenz KQiu WLi W(2023)The Role of Subjective Perceptions and Objective Measurements of the Urban Environment in Explaining House Prices in Greater London: A Multi-Scale Urban Morphology AnalysisISPRS International Journal of Geo-Information10.3390/ijgi1206024912:6(249)Online publication date: 19-Jun-2023
https://doi.org/10.3390/ijgi12060249
Das DChakraborty SMitra B(2023)DriCon: On-device Just-in-Time Context Characterization for Unexpected Driving Events2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PERCOM56429.2023.10099058(12-21)Online publication date: 13-Mar-2023
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Payandeh ABaghaei KFayyazsanavi PRamezani SChen ZRahimi S(2023)Deep Representation Learning: Fundamentals, Technologies, Applications, and Open ChallengesIEEE Access10.1109/ACCESS.2023.333519611(137621-137659)Online publication date: 2023
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Jenga KCatal CKar G(2023)Machine learning in crime predictionJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-023-04530-y14:3(2887-2913)Online publication date: 2-Feb-2023
https://doi.org/10.1007/s12652-023-04530-y
Mansha SRehman AAbdullah SKamiran FYin HAl Hasan MXiong L(2022)Locality Aware Temporal FMs for Crime PredictionProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557657(4324-4328)Online publication date: 17-Oct-2022
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Smith THaines K(2022)An Exploratory Study on Murders in the Chaos of COVID-19: An Analysis of Changes in Murder Rates and Patterns in Trinidad and TobagoInternational Criminology10.1007/s43576-022-00075-w2:4(332-346)Online publication date: 21-Nov-2022
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Cinnamon JJahiu L(2021)Panoramic Street-Level Imagery in Data-Driven Urban Research: A Comprehensive Global Review of Applications, Techniques, and Practical ConsiderationsISPRS International Journal of Geo-Information10.3390/ijgi1007047110:7(471)Online publication date: 9-Jul-2021
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