12.10 Exercises

• What is the total distance of cycleways that would be constructed if all the routes presented in Figure 12.6 were to be constructed?
  • Bonus: find two ways of arriving at the same answer.
• What proportion of trips represented in the desire_lines are accounted for in the route_cycleway object?
  • Bonus: what proportion of trips cross the proposed routes?
  • Advanced: write code that would increase this proportion.
• The analysis presented in this chapter is designed for teaching how geocomputation methods can be applied to transport research. If you were to do this ‘for real’ for local government or a transport consultancy, what top 3 things would you do differently?
• Clearly, the routes identified in Figure 12.6 only provide part of the picture. How would you extend the analysis to incorporate more trips that could potentially be cycled?
• Imagine that you want to extend the scenario by creating key areas (not routes) for investment in place-based cycling policies such as car-free zones, cycle parking points and reduced car parking strategy. How could raster data assist with this work?
  • Bonus: develop a raster layer that divides the Bristol region into 100 cells (10 by 10) and provide a metric related to transport policy, such as number of people trips that pass through each cell by walking or the average speed limit of roads, from the bristol_ways dataset (the approach taken in Chapter 13).

References

Miller, Harvey J. 2004. “Tobler’s First Law and Spatial Analysis.” Annals of the Association of American Geographers 94 (2).

Rodrigue, Jean-Paul, Claude Comtois, and Brian Slack. 2013. The Geography of Transport Systems. Third. London, New York: Routledge.

Hollander, Yaron. 2016. Transport Modelling for a Complete Beginner. CTthink!

Horni, Andreas, Kai Nagel, and Kay W. Axhausen. 2016. The Multi-Agent Transport Simulation MATSim. Ubiquity Press.

Thiele, J. 2014. “R Marries NetLogo: Introduction to the RNetLogo Package.” Journal of Statistical Software 58 (2): 1–41.

Lovelace, Robin, and Morgane Dumont. 2016. Spatial Microsimulation with R. CRC Press.

Bristol City Council. 2015. “Deprivation in Bristol 2015.” Bristol City Council.

Office for National Statistics. 2014. “Workplace Zones: A New Geography for Workplace Statistics - Datasets.” https://data.gov.uk/dataset/workplace-zones-a-new-geography-for-workplace-statistics3.

Coombes, M. G., A. E. Green, and S. Openshaw. 1986. “An Efficient Algorithm to Generate Official Statistical Reporting Areas: The Case of the 1984 Travel-to-Work Areas Revision in Britain.” The Journal of the Operational Research Society 37 (10): 943. https://doi.org/10.2307/2582282.

Moreno-Monroy, Ana I., Robin Lovelace, and Frederico R. Ramos. 2017. “Public Transport and School Location Impacts on Educational Inequalities: Insights from São Paulo.” Journal of Transport Geography, September. https://doi.org/10.1016/j.jtrangeo.2017.08.012.

Lovelace, Robin, Anna Goodman, Rachel Aldred, Nikolai Berkoff, Ali Abbas, and James Woodcock. 2017. “The Propensity to Cycle Tool: An Open Source Online System for Sustainable Transport Planning.” Journal of Transport and Land Use 10 (1). https://doi.org/10.5198/jtlu.2016.862.

Tallon, Andrew R. 2007. “Bristol.” Cities 24 (1): 74–88. https://doi.org/10.1016/j.cities.2006.10.004.

Hickman, Robin, Olu Ashiru, and David Banister. 2011. “Transitions to Low Carbon Transport Futures: Strategic Conversations from London and Delhi.” Journal of Transport Geography, Special section on Alternative Travel futures, 19 (6): 1553–62. https://doi.org/10.1016/j.jtrangeo.2011.03.013.

• osmdata and nabor must also be installed, although these packages do not need to be attached.↩

• In cases where the first match does not provide the right name, the country or region should be specified, for example Bristol Tennessee for a Bristol located in America.↩

• the _if affix requires a TRUE/FALSE question to be asked of the variables, in this case ‘is it numeric?’ and only variables returning true are summarized.↩

• It would also be important to check that IDs match in the opposite direction on real data.This could be done by changing the order of the IDs in the summary() command — summary(bristol_zones$geo_code %in% zones_attr$geo_code) — or by using setdiff() as follows: setdiff(bristol_zones$geo_code, zones_attr$geo_code).↩

• od2line() works by matching the IDs in the first two columns of the bristolod object to the zone_code ID column in the geographic zones_od object.Note that the operation emits a warning because od2line() works by allocating the start and end points of each origin-destination pair to the _centroid of its zone of origin and destination.For real-world use one would use centroid values generated from projected data or, preferably, use population-weighted centroids (Lovelace et al. 2017).↩

• The location of these connectors should be chosen carefully because they can lead to over-estimates of traffic volumes in their immediate surroundings (Jafari et al. 2015).↩