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Research Articles

A method to evaluate task-specific importance of spatio-temporal units based on explainable artificial intelligence

ORCID Icon, , , , & ORCID Icon
Pages 2002-2025 | Received 22 Oct 2019, Accepted 30 Jul 2020, Published online: 18 Aug 2020

References

  • Allam, Z. and Dhunny, Z.A., 2019. On big data, artificial intelligence and smart cities. Cities, 89, 80–91.
  • An, L., et al., 2015. Space-time analysis: concepts, quantitative methods, and future directions. Annals of the Association of American Geographers, 105 (5), 891–914.
  • Anselin, L., 1995. Local indicators of spatial association—LISA. Geographical Analysis, 27 (2), 93–115.
  • Bach, S., et al., 2015. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE, 10 (7), e0130140.
  • Birant, D. and Kut, A., 2007. ST-DBSCAN: an algorithm for clustering spatial–temporal data. Data and Knowledge Engineering, 60 (1), 208–221.
  • Breiman, L., 2001. Random forests. Machine Learning, 45 (1), 5–32.
  • Cheng, T., Haworth, J., and Wang, J., 2012. Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems, 14 (4), 389–413.
  • De Lathauwer, L., 1997. Signal processing based on multilinear algebra. Leuven: Katholieke Universiteit Leuven.
  • Erhan, D., et al. (2009). Visualizing higher-layer features of a deep network. University of Montreal, 1–13.
  • Gahegan, M., 2020. Fourth paradigm GIScience? Prospects for automated discovery and explanation from data. International Journal of Geographical Information Science, 34 (1), 1–21.
  • Gao, S., 2015. Spatio-temporal analytics for exploring human mobility patterns and urban dynamics in the mobile age. Spatial Cognition and Computation, 15 (2), 86–114.
  • Hägerstraand, T., 1970. What about people in regional science? Papers in Regional Science, 24 (1), 7–24.
  • Hinton, G.E., Osindero, S., and Teh, Y.W., 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18 (7), 1527–1554.
  • Hinton, G.E. and Salakhutdinov, R.R., 2006. Reducing the dimensionality of data with neural networks. Science, 313 (5786), 504–507.
  • Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural Computation, 9 (8), 1735–1780.
  • Homma, T. and Saltelli, A., 1996. Importance measures in global sensitivity analysis of nonlinear models. Reliability Engineering and System Safety, 52 (1), 1–17.
  • Huang, B., Zhao, B., and Song, Y., 2018. Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sensing of Environment, 214, 73–86.
  • Janowicz, K., et al., 2020. GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34 (4), 625–636.
  • Lapuschkin, S., et al., 2016. The LRP toolbox for artificial neural networks. The Journal of Machine Learning Research, 17 (1), 3938–3942.
  • Lapuschkin, S., et al., 2019. Unmasking Clever Hans predictors and assessing what machines really learn. Nature Communications, 10 (1), 1–8.
  • LeCun, Y., Bengio, Y., and Hinton, G., 2015. Deep learning. Nature, 521 (7553), 436–444.
  • Li, S., et al. 2016. Geospatial big data handling theory and methods: A review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 119–133.
  • Li, W. and Hsu, C.Y., 2020. Automated terrain feature identification from remote sensing imagery: A deep learning approach. International Journal of Geographical Information Science, 34 (4), 637–660.
  • Liu, X., et al., 2016. Incorporating spatial interaction patterns in classifying and understanding urban land use. International Journal of Geographical Information Science, 30 (2), 334–350.
  • Liu, X., et al., 2018. Characterizing mixed-use buildings based on multi-source big data. International Journal of Geographical Information Science, 32 (4), 738–756.
  • Liu, Y., et al., 2012. Urban land uses and traffic ‘source-sink areas’: evidence from GPS-enabled taxi data in Shanghai. Landscape and Urban Planning, 106 (1), 73–87.
  • Liu, Y., et al., 2015. Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers, 105 (3), 512–530.
  • Ma, X., et al., 2015a. Large-scale transportation network congestion evolution prediction using deep learning theory. PLoS ONE, 10 (3), e0119044.
  • Ma, Y., et al. 2015b. Remote sensing big data computing: challenges and opportunities. Future Generation Computer Systems, 51, 47–60.
  • Montavon, G., Samek, W., and Müller, K.R., 2018. Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73, 1–15.
  • Openshaw, S., and Taylor, P. J. (1979). A million or so correlation coefficients: three experiments on the modifiable areal unit problem. In N. Wrigley (Ed.), Statistical applications in the spatial sciences. 127–144. London: Pion.
  • Pedregosa, F., et al. 2011. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
  • Pei, T., et al., 2014. A new insight into land use classification based on aggregated mobile phone data. International Journal of Geographical Information Science, 28 (9), 1988–2007.
  • Protas, É., et al., 2018. Visualization methods for image transformation convolutional neural networks. IEEE Transactions on Neural Networks and Learning Systems, 30 (7), 2231–2243.
  • Ratti, C., et al., 2006. Mobile landscapes: using location data from cell phones for urban analysis. Environment and Planning B: Planning and Design, 33 (5), 727–748.
  • Reichstein, M., et al., 2019. Deep learning and process understanding for data-driven Earth system science. Nature, 566 (7743), 195–204.
  • Saltelli, A., 2002. Sensitivity analysis for importance assessment. Risk Analysis, 22 (3), 579–590.
  • Samek, W., et al., 2017. Evaluating the visualization of what a deep neural network has learned. IEEE Transactions on Neural Networks and Learning Systems, 28 (11), 2660–2673.
  • Samek, W., et al., 2019. Explainable AI: interpreting, explaining and visualizing deep learning. Cham: Springer.
  • Schmidhuber, J., 2015. Deep learning in neural networks: an overview. Neural Networks, 61, 85–117.
  • Seabold, S. and Perktold, J., 2010. Statsmodels: econometric and statistical modeling with Python. In: Proceedings of the 9th Python in Science Conference, Austin, TX, USA, 57–61.
  • Selvaraju, R.R., et al., 2017. Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 618–626.
  • Shi, L., Gangopadhyay, A., and Janeja, V.P., 2015. STenSr: spatio-temporal tensor streams for anomaly detection and pattern discovery. Knowledge and Information Systems, 43 (2), 333–353.
  • Silver, D., et al., 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529 (7587), 484–489.
  • Silverman, B. W., 1986. Density estimation for statistics and data analysis, London: Chapman and Hall.
  • Tobler, W.R., 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46 (sup1), 234–240.
  • Wu, L., et al., 2020. A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data. International Journal of Digital Earth, 13 (6), 708–726.
  • Xie, S., Hu, H., and Wu, Y., 2019. Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition. Pattern Recognition, 92, 177–191.
  • Yu, H., et al., 2017. Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors, 17 (7), 1501.
  • Zang, D., et al., 2018. Long-term traffic speed prediction based on multiscale spatio-temporal feature learning network. IEEE Transactions on Intelligent Transportation Systems, 20 (10), 3700–3709.
  • Zeiler, M.D. and Fergus, R., 2014. Visualizing and understanding convolutional networks. In: European Conference on Computer Vision, Zurich, Switzerland, 818–833.
  • Zhang, F., Du, B., and Zhang, L., 2016. Scene classification via a gradient boosting random convolutional network framework. IEEE Transactions on Geoscience and Remote Sensing, 54 (3), 1793–1802.
  • Zhang, J., et al., 2018a. Top-down neural attention by excitation backprop. International Journal of Computer Vision, 126 (10), 1084–1102.
  • Zhang, J., et al. 2018b. Predicting citywide crowd flows using deep spatio-temporal residual networks. Artificial Intelligence, 259, 147–166.
  • Zheng, Y., et al., 2011. Urban computing with taxicabs. In: Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China, 89–98.
  • Zhou, B., et al., 2016. Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2921–2929.
  • Zhou, H., et al., 2015a. Spatio-temporal tensor completion for imputing missing internet traffic data. In: 2015 IEEE 34th International Performance Computing and Communications Conference (IPCCC), Nanjing, China, 1–7.
  • Zhou, Y., et al. 2015b. Functionally critical locations in an urban transportation network: identification and space-time analysis using taxi trajectories. Computers, Environment and Urban Systems, 52, 34–47.
  • Zhu, D., et al., 2020. Understanding place characteristics in geographic contexts through graph convolutional neural networks. Annals of the American Association of Geographers, 110 (2), 408–420.

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