Visual Analysis of Public Utility Service Problems in a Metropolis

Issues about city utility services reported by citizens can provide unprecedented insights into the various aspects of such services. Analysis of these issues can improve living quality through evidence-based decision making. However, these issues are complex, because of the involvement of spatial and temporal components, in addition to having multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. To analyze these issues, we propose a visual analytics process based on the main tasks of utility service management. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations for events. In addition, we provide a set of tools and interaction techniques to explore such issues. Our approach enables administrators to make more informed decisions.

[1]  David S. Ebert,et al.  A visual analytics process for maritime resource allocation and risk assessment , 2011, 2011 IEEE Conference on Visual Analytics Science and Technology (VAST).

[2]  Xiaoru Yuan,et al.  Visual Traffic Jam Analysis Based on Trajectory Data , 2013, IEEE Transactions on Visualization and Computer Graphics.

[3]  Xin Yao,et al.  Pipe failure prediction: A data mining method , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[4]  Hongan Wang,et al.  An approach to visual analysis for task flow management , 2013, Science China Information Sciences.

[5]  Tamara Munzner,et al.  A Nested Model for Visualization Design and Validation , 2009, IEEE Transactions on Visualization and Computer Graphics.

[6]  Gennady L. Andrienko,et al.  Spatio-temporal aggregation for visual analysis of movements , 2008, 2008 IEEE Symposium on Visual Analytics Science and Technology.

[7]  Barbara Endicott-Popovsky,et al.  Threats to municipal information systems posed by aging infrastructure , 2013, Int. J. Crit. Infrastructure Prot..

[8]  Andrzej Stepnowski,et al.  Geographical Information System for analysis of Critical Infrastructures and their hazards due to terrorism, man-originated catastrophes and natural disasters for the city of Gdansk , 2009, IF&GIS.

[9]  S. Loskovska,et al.  Information visualization from the public utilities databases of local municipality for municipalities managers , 2008, ITI 2008 - 30th International Conference on Information Technology Interfaces.

[10]  Dana J. Vanier.  Geographic information systems (GIS) as an integrated decision support tool for municipal infrastructure asset management , 2004 .

[11]  Gennady L. Andrienko,et al.  Spatial Generalization and Aggregation of Massive Movement Data , 2011, IEEE Transactions on Visualization and Computer Graphics.

[12]  Anu Pradhan,et al.  Infrastructure Management Information System Framework Requirements for Disasters , 2007 .

[13]  J. Adamowski.  Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks , 2008 .

[14]  Lizhen Liu,et al.  Notice of Retraction The research of city community safety management system , 2010 .

[15]  Venkat Venkatasubramanian,et al.  An optimization framework for cost effective design of refueling station infrastructure for alternative fuel vehicles , 2011, Comput. Chem. Eng..

[16]  Dulcy M. Abraham,et al.  Optimization Modeling for Sewer Network Management , 1998 .

[17]  Sancho Salcedo-Sanz,et al.  Near optimal citywide WiFi network deployment using a hybrid grouping genetic algorithm , 2011, Expert Syst. Appl..

[18]  Aya Hagishima,et al.  Total utility demand prediction system for dwellings based on stochastic processes of actual inhabitants , 2010 .

[19]  Jan Adamowski,et al.  Erratum to: Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy , 2012, Water Resources Management.

[20]  Siyuan Liu,et al.  Visual analysis of route diversity , 2011, 2011 IEEE Conference on Visual Analytics Science and Technology (VAST).

[21]  Jaime Lloret,et al.  Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks , 2013 .

[22]  Liz Varga,et al.  Future utility services’ (un)knowns framework: Knowledge existence and knowledge reach , 2013 .

[23]  David S. Ebert,et al.  Bristle Maps: A Multivariate Abstraction Technique for Geovisualization , 2013, IEEE Transactions on Visualization and Computer Graphics.

[24]  Zhigang Liu,et al.  WebGIS application based on real-time traffic flow network analysis , 2007, Geoinformatics.

[25]  George Grekousis,et al.  Analyzing High-Risk Emergency Areas with GIS and Neural Networks: The Case of Athens, Greece , 2014 .

[26]  Xiaohua Sun,et al.  Whisper: Tracing the Spatiotemporal Process of Information Diffusion in Real Time , 2012, IEEE Transactions on Visualization and Computer Graphics.

[27]  Harald Piringer,et al.  AlVis: Situation awareness in the surveillance of road tunnels , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[28]  N. Kunwar,et al.  Area-Load Based Pricing in DSM Through ANN and Heuristic Scheduling , 2013, IEEE Transactions on Smart Grid.

[29]  M. Sheelagh T. Carpendale,et al.  Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations , 2009, IEEE Transactions on Visualization and Computer Graphics.

[30]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[31]  Dominic Maratukulam,et al.  ANNSTLF-a neural-network-based electric load forecasting system , 1997, IEEE Trans. Neural Networks.

[32]  David S. Ebert,et al.  MarketAnalyzer: An Interactive Visual Analytics System for Analyzing Competitive Advantage Using Point of Sale Data , 2012, Comput. Graph. Forum.

[33]  Liping Fang,et al.  Community-based Decision Making in Japan , 2013 .

[34]  Ali Mostashari,et al.  Citizens as sensors: The cognitive city paradigm , 2011, 2011 8th International Conference & Expo on Emerging Technologies for a Smarter World.

[35]  J. Adamowski,et al.  Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy , 2012, Water Resources Management.

[36]  Ryan Hafen,et al.  Forecasting Hotspots—A Predictive Analytics Approach , 2011, IEEE Transactions on Visualization and Computer Graphics.

[37]  James H. Garrett,et al.  Detection of Patterns in Water Distribution Pipe Breakage Using Spatial Scan Statistics for Point Events in a Physical Network , 2011, J. Comput. Civ. Eng..

[38]  Lizhen Liu,et al.  Notice of RetractionThe research of city community safety management system , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[39]  Jason Dykes,et al.  An Extensible Framework for Provenance in Human Terrain Visual Analytics , 2013, IEEE Transactions on Visualization and Computer Graphics.

[40]  David S. Ebert,et al.  Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[41]  Cláudio T. Silva,et al.  Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips , 2013, IEEE Transactions on Visualization and Computer Graphics.

[42]  Victor Ostromoukhov,et al.  Fast hierarchical importance sampling with blue noise properties , 2004, ACM Trans. Graph..

[43]  Biao Chen,et al.  Using GIS and KDE analysis spatial distribution on public housing households: A case study , 2013, 2013 8th International Conference on Computer Science & Education.

[44]  B. Perera,et al.  A GIS based screening tool for locating and ranking of suitable stormwater harvesting sites in urban areas. , 2013, Journal of environmental management.