Challenges and Prospects of Uncertainties in Spatial Big Data Analytics

References

• Adams, B., and K. Janowicz. 2012. On the geo-indicativeness of non-georeferenced text. In Proceedings of the Sixth International Conference on Web and Social Media, ed. J. Breslin, J. G. Shanahan, N. Ellison, and Z. Tufekci, 375–78. Palo Alto, CA: AAAI Press.
   Google Scholar
• Chen, B., H. Yuan, Q. Li, D. Wang, S.-L. Shaw, H. Chen, and W. H. K. Lam. 2017. Measuring place-based accessibility under travel time uncertainty. International Journal of Geographical Information Science 31 (4):783–804.
   Web of Science ®Google Scholar
• Chen, J., and A. Zipf. 2017. DeepVGI: Deep learning with volunteered geographic information. In The 26th International World Wide Web Conference (WWW'17) Companion, ed. R. Barrett, R. Cummings, E. Agichtein, E. Gabrilovich, 771–72. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee.
   Google Scholar
• Cheng, T., and M. Adepeju. 2014. Modifiable temporal unit problem (MTUP) and its effect on space–time cluster detection. PLoS ONE 9 (6):e100465.
   PubMed Web of Science ®Google Scholar
• Crandall, D. J., Y. Li, S. Lee, and D. P. Huttenlocher. 2015. Recognizing landmarks in large-scale social image collections. In Large-scale visual geo-localization, ed. A. R. Zamir, A. Hakeem, L. Van Gool, M. Shah, and R. Szeliski, 121–44. Cham, Switzerland: Springer.
   Google Scholar
• Fan, J., F. Han, and H. Liu. 2014. Challenges of big data analysis. National Science Review 1 (2):293–314.
   PubMed Web of Science ®Google Scholar
• Fekete, E. 2017. Foursquare in the city of fountains: Using Kansas City as a case study for combining demographic and social media data. In Thinking big data in geography: New regimes, new research, ed. J. Thatcher, J. Eckert, and A. Shears, 165–88. Lincoln: University of Nebraska Press.
   Google Scholar
• Gal, Y., and Z. Ghahramani. 2016. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. Proceedings of the 33rd International Conference on Machine Learning 48:1050–59.
   Google Scholar
• Gao, S., K. Janowicz, and H. Couclelis. 2017. Extracting urban functional regions from points of interest and human activities on location-based social networks. Transactions in GIS 21 (3):446–67.
   Web of Science ®Google Scholar
• Gerhardt, B., K. Griffin, and R. Klemann. 2012. Unlocking value in the fragmented world of big data analytics. Cisco Internet Business Solutions Group. Accessed August 4, 2017. http://www.cisco.com/web/about/ac79/docs/sp/Information-Infomediaries.pdf.
   Google Scholar
• Han, J., D. Zhang, G. Cheng, L. Guo, and J. Ren. 2015. Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Transactions on Geoscience and Remote Sensing 53 (6):3325–37.
   Web of Science ®Google Scholar
• Hollenstein, L., and R. Purves. 2014. Exploring place through user-generated content: Using Flickr tags to describe city cores. Journal of Spatial Information Science 1:21–48.
   Google Scholar
• International Business Machines. 2016. Extracting business value from the 4 V's of big data. Accessed August 4, 2017. http://www.ibmbigdatahub.com/infographic/extracting-business-value-4-vs-big-data.
   Google Scholar
• Kwan, M. P. 2016. Algorithmic geographies: Big data, algorithmic uncertainty, and the production of geographic knowledge. Annals of the American Association of Geographers 106 (2):274–82.
   Web of Science ®Google Scholar
• Li, S., S. Dragicevic, F. A. Castro, M. Sester, S. Winter, A. Coltekin, C. Pettit, et al. 2016. Geospatial big data handling theory and methods: A review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing 115:119–33.
   Web of Science ®Google Scholar
• Longley, P. A., and M. Adnan. 2016. Geo-temporal Twitter demographics. International Journal of Geographical Information Science 30 (2):369–89.
   Web of Science ®Google Scholar
• Miller, H. J., and M. F. Goodchild 2015. Data-driven geography. GeoJournal 80:449–61.
   Web of Science ®Google Scholar
• Openshaw, S. 1983. The modifiable areal unit problem. Norwich, UK: Geo Books.
   Google Scholar
• OpenStreetMap Wiki. 2017. Vandalism. Accessed August 7, 2017. http://wiki.openstreetmap.org/wiki/Vandalism.
   Google Scholar
• Rösler, R., and T. Liebig. 2013. Using data from location based social networks for urban activity clustering. In Geographic information science at the heart of Europe, ed. D. Vandenbroucke, B. Boucher, and J. Crompvoets, 55–72. New York: Springer.
   Google Scholar
• Senaratne, H., A. Mobasheri, A. L. Ali, C. Capineri, and M. Haklay. 2017. A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science 31 (1):139–67.
   Web of Science ®Google Scholar
• Shi, W. 2010. Principle of modeling uncertainties in spatial data and spatial analyses. London and New York: Taylor & Francis Group/CRC.
   Google Scholar
• Thatcher, J. 2014. Living on fumes: Digital footprints, data fumes, and the limitations of spatial big data. International Journal of Communication 8:1765–83.
   Web of Science ®Google Scholar
• Tu, W., J. Cao, Y. Yue, S. L. Shaw, M. Zhou, Z. Wang, X. Chang, Y. Xu, and Q. Li. 2017. Coupling mobile phone and social media data: A new approach to understanding urban functions and diurnal patterns. International Journal of Geographical Information Science 31 (12):2331–58.
   Web of Science ®Google Scholar
• Weyand, T., I. Kostrikov, and J. Philbin. 2016. PlaNet—Photo geolocation with convolutional neural networks. In Computer Vision—ECCV 2016, ed. B. Leibe, J. Matas, N. Sebe, and M. Welling, 37–55. Cham, Switzerland: Springer.
   Google Scholar
• Zhao, Z., S. L. Shaw, Y. Xu, F. Lu, J. Chen, and L. Yin. 2016. Understanding the bias of call detail records in human mobility research. International Journal of Geographical Information Science 30 (9):1738–62.
   Web of Science ®Google Scholar
• Zhou, M., Y. Yue, Q. Li, and D. Wang. 2017. Portraying temporal dynamics of urban spatial divisions with mobile phone positioning data: A complex network approach. ISPRS International Journal of Geo-Information 5 (12):240.
   Web of Science ®Google Scholar
• Zhou, X., and L. Zhang. 2016. Crowdsourcing functions of the living city from Twitter and Foursquare data. Cartography and Geographic Information Science 43 (5):393–404.
   Web of Science ®Google Scholar
• Zielstra, D., and H. H. Hochmair. 2013. Positional accuracy analysis of Flickr and Panoramio images for selected world regions. Journal of Spatial Science 58 (2):251–73.
   Web of Science ®Google Scholar