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Workshops and excursions

Research behind Axwoman 6.0: An ArcGIS Extension for Urban Morphological and Streets Related Studies by Prof. Bin Jiang (University of Gävle, SW)

• Axwoman 6.0 integrates installer and functionality of both Axwoman 5.0 and AxialGen 1.0 at the new GIS platform ArcGIS 10. It is free for academic purposes, and downloadable from this website: https://sites.google.com/site/axwoman60/. This workshop aims to introduce the research behind the software tool, in particular, some key concepts and techniques such as axial lines, named streets, natural streets, and how to automatically generate the axial lines. It is important to note that the auto-generated axial lines are based on the notion of walkability or drivability rather than that of visibility on which the axial line was initially defined. To attend the workshop, you must have run a series of tutorials and learned some basics of Axwoman 6.0; both the tutorials and related papers are available at the Axwoman website. Feel free to contact me should you have had any questions about installation and the related papers.
• Keywords: Accessibility, traffic flow, topological analysis, space syntax, street networks
• References:
  • Jiang B., Claramunt C. and Batty M. (1999), Geometric accessibility and geographic information: extending desktop GIS to space syntax, Computers, Environment and Urban Systems, 23, 127 – 146.
  • Jiang B. and Claramunt C. (2002), Integration of space syntax into GIS: new perspectives for urban morphology, Transactions in GIS, 6(3), 295-309.
  • Jiang B. and Claramunt C. (2004), Topological analysis of urban street networks, Environment and Planning B: Planning and Design, 31, 151-162.
  • Jiang B. (2007), A topological pattern of urban street networks, Physica A: Statistical Mechanics and its Applications, 384, 647 - 655.
  • Jiang B., Zhao S., and Yin J. (2008), Self-organized natural roads for predicting traffic flow: a sensitivity study, Journal of Statistical Mechanics: Theory and Experiment, July, P07008.
  • Jiang B. and Liu X. (2010), Automatic generation of the axial lines of urban environments to capture what we perceive, International Journal of Geographical Information Science, 24(4), 545–558.
  • Liu X. and Jiang B. (2012), Defining and generating axial lines from street center lines for better understanding of urban morphologies, International Journal of Geographical Information Science, 26(8), 1521-1532.
• Free of charge.

Spatio-Temporal Analytics of Network Data (space-time clustering and forecasting) by Dr. James Haworth (University College London, GB)

• Traffic congestion, crime and epidemics are all emergent phenomena that detract from citizen well-being in big cities today. They each also impose huge economic and social costs. Prevention, early detection and strategic mitigation are all critical policy interventions, yet each requires understanding of the potentially huge number of factors that may contribute to their emergence, as well as the paths in space and time along which they co-evolve. In the past, identifying the emergence of these phenomena was made difficult by lack of detailed data, and only became possible with the innovation of cheap tagging technologies, GPS, sensor-webs, location-aware devices and field-work flow management tools for assembling massive detailed real-time spatially referenced datasets. These datasets shape enormous spatio-temporal networks (STNs) that evolve in structure and in states, reflecting the emergence and dynamics of real world phenomena in space and time. The spatio-temporal dependence, nonlinearity and heterogeneity of network data present two fundamental challenges to modelling the complexity of networks: (1) to model dependency in both space and time seamlessly and simultaneously, (2) to fully accommodate the topology and geometry of the networks. This presentation will report the progress made in addressing these challenges through innovative combination of machine learning methods with advanced statistical approaches, drawing upon concepts from network complexity and data mining. It will illustrate the procedures used to integrate spatio-temporal prediction, pattern detection, simulation, and visualization for analysing traffic, crime, and social media in Central London.
• Free of charge.

Application of satellite InSAR for monitoring highway deformations (Milan Lazecky)

• Satellite SAR interferometry (InSAR) is a valuable remote sensing technique for very sensitive monitoring of deformations of terrain or constructions such as buildings, dams or highways. The main unique advantage of the technique is its ability to evaluate temporal movements of a target in the precision better than 1 mm/year (in appropriate conditions). This workshop will demonstrate InSAR abilities by an example of monitoring deformations of D1 highway over Ostrava-Svinov. Attendees will perform a guided processing and interpretation of TerraSAR-X data using specialized open-source tools.
• Free of charge.

Floating Car Data. Workshop on how can it be utilized? (Doc. Ing. Pavel Hrubes, Ph.D. – Czech Technical University in Prague)

• Floating Car Data are relatively new source of spatial data, arise as a byproduct of services related to the monitoring of vehicle fleets or car protecting systems. Their use is often mentioned in preserving privacy.
• The workshop will introduce a number of existing possibilities of using floating car data and ask participants to submit further ideological concepts in terms of their specialization and professional opportunities.
• Free of charge.

Call for workshop

Submission of workshops proposals are welcome - contact us at gisostrava@vsb.cz

Sightseeing tour Ostrava (i.e. industrial heritage and architecture, new city centre)

• 0 EUR

Ostrava-Mošnov Airport (Training Centre, Boeing Repair Centre)

• 0 EUR

National Transport Information and Control Centre

• This centre is central and operational office providing 24/7 of collecting, processing, publishing and distributing information about the current traffic situation in the Czech Republic.
• 0 EUR

Contact us: gisostrava@vsb.cz | © 2013-2014 Institute of geoinformatics