Geospatial Visual Analytics for Supporting Decision Making for Underground Utility Integrated Interventions

Author:

Genger Tersoo K.11,Hammad Amin11

Affiliation:

1. Concordia Institute for Information Systems Engineering, Concordia Univ., Montreal, QC.

Publisher

American Society of Civil Engineers

Reference27 articles.

1. Andrienko, G., Andrienko, N., Fuchs, G., Rüping, S., Cordero, J. M., Scarlatti, D., Vouros, G. A., et al. (2020). “Visual analytics in the aviation and maritime domains”, Big Data Analytics for Time-Critical Mobility Forecasting: From Raw Data to Trajectory-Oriented Mobility Analytics in the Aviation and Maritime Domains, Springer International Publishing, pp. 59–84.

2. Social Media Visual Analytics;Chen S.;Computer Graphics Forum,2017

3. Esri. (2016). “How Band Collection Statistics works—ArcMap | Documentation” available at: https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-band-collection-statistics-works.htm (accessed 24 January 2022).

4. Ester, M., Kriegel, H. P., and Xu, X. (1995). “Knowledge discovery in large spatial databases: Focusing techniques for efficient class identification”, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 951, Springer Verlag, pp. 67–82.

5. Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips

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