@inproceedings{1c77f7f331b34203818fc1bb590b2276,
title = "The impact of community safety on house ranking",
abstract = "It is well recognized that community safety which affects people's right to live without fear of crime has considerable impacts on housing investments. Housing investors can make more informed decisions if they are fully aware of safety related factors. To this end, we develop a safety-aware house ranking method by incorporating community safety into house assessment. Specifically, we first propose a novel framework to infer community safety level by mining community crime evidences from rich spatio-temporal historical crime data. Then we develop a ranking model which fuses multiply community safety features to rank house value based on the degree of community safety. Finally, we conduct a comprehensive evaluation of the proposed method with real-world crime and house data. The experimental results show that the proposed method substantially outperforms the baseline methods for house ranking.",
keywords = "Community safety, House ranking, Spatio-temporal",
author = "Zijun Yao and Yanjie Fu and Bin Liu and Hui Xiong",
note = "Publisher Copyright: Copyright {\textcopyright} by SIAM.; 16th SIAM International Conference on Data Mining 2016, SDM 2016 ; Conference date: 05-05-2016 Through 07-05-2016",
year = "2016",
doi = "10.1137/1.9781611974348.52",
language = "English (US)",
series = "16th SIAM International Conference on Data Mining 2016, SDM 2016",
publisher = "Society for Industrial and Applied Mathematics Publications",
pages = "459--467",
editor = "Venkatasubramanian, \{Sanjay Chawla\} and Wagner Meira",
booktitle = "16th SIAM International Conference on Data Mining 2016, SDM 2016",
}