Modelling and Visualization of DQD for Fast Changing Urban Systems

The urban planning process is complex process due to: the several stakeholders involved, huge amount of data required, and short periods of time. The stakeholders includes architects, engineers, politicians, citizens, among others. As a result, this causes communication problems. The needed huge amount of data generate heterogeneity and data quality issues such as consistency, reliability, availability, or currency. Additionally, the urban planing process have to be performed in short periods of time in order to fulfil the citizens’ needs.

This work is focused on fast changing urban systems, these kind of urban systems changes their properties in short periods of time, from months to hours. Fast changing urban systems poses new challenges to the urban planners, governments, mayors, ministers, among others. Periods of time are shorter and data most of the times is missing or it comes from non conventional data sources. This situation deteriorates the data quality. Consequently, the governments can not wait for data quality improvements, the have to analyse the situation and take the decisions with the available data. On the other hand, the current tools that support the analysis lack of integrated environment for multiple users and representation of data quality problems.

Taking into account the mentioned problems, this research aims at supporting the urban planing process for fast changing urban systems by taking into account the data quality issues. The main challenges of this research are:

For the urban planning process

• Inclusion of data quality problems into the urban planning process: How to include the DQ in the process?, in which stages DQD should be included?

• Data modelling of data quality dimensions: How to represent the DQD?, How to include DQD in the existing data models?

• Generation of interactive visualisation to support the urban planing process while taking into account data quality issues: Which visualisation technique or graphical variables use? Which interactive technique select or adapt?

This research have been developed by three iterations, currently two iteration have been performed (Iteration 1 and Iteration 2). The two iterations are related to the analysis of the facilities system in Bogotá, Colombia. An application called VafusQ was developed to support the analysis tasks of the case study.

Iteration 1

The iteration 1 aims at analysing the deficit (lack of facilites) of the facilities system, as show in the figure bellow. The contributions of the iteration 1 are: the inclusion of data quality in the urban planning process, the design of a novel visualization technique to represent data quality, the implementation of an application to support the analysis of the facilities system, and a case study with experts assessing the usability and usefulness of the application.

VafusQ – Iteration 1
VafusQ – Iteration 1
Iteration 2

The iteration 2 supports the analysis and design of the facilities system in Bogotá. The main features of the iteration 2 are: a methodology to take the data quality problems (to represent them and simultaneously support the analysis process), an application as a use case of the methodology (analyzing the placement of new buildings for the facilities system in Bogotá), and a case study (to test the usefulness and effectiveness of the application).

VafusQ – Iteration 2
VafusQ – Iteration 2



John A. Triana, Dirk Zeckzer, Jose T. Hernandez, and Hans Hagen. VafusQ: A Visual Analytics Application with Data Quality Features to Support the Urban Planning Process. Workshop on Visualisation in Environmental Sciences (EnvirVis), 21-25 May. Cagliari-Italia. 2015. pp 49-53. Eurographics Association.

John A. Triana, Jose T. Hernandez, and Hans Hagen. Methodology to generate interactive visualisations with data quality features to support urban planning processes. Visual Computing Workshop IMAGINE 2015, 11-12 Jun. Bogotá-Colombia. 2015.

Alejandro Triana – Personal Web site