Assessment for CASA0002 – Urban Simulation Deadline: 27th April 11am. Word Limit – 3,000 words The following assessment is designed to assess your understanding of the different urban modelling methodologies introduced in the course. The questions are based on the practicals/coursework developed during the course and posted in Moodle. Please answer ALL questions, allocating ~1000 words per question. Each question will be assessed over 100 points, and your total mark will be the average of the 3 marks for each question, all contributing equally. Carefully explain and illustrate your analysis with appropriate diagrams. The assessment should not exceed 3000 words in total. (References, numerical tables and data plots do not count towards the total number of words.) Q1. Networks: Through this question you will critically assess how to identify the relevant measures to investigate the resilience of London’s underground as a network, and the limitations of the approach. For this question, you will need to use the material for the practical given on the 2nd lecture on Networks (week 5), i.e. the code and the network for London’s underground. Procedure: Select 3 different centrality measures. For each measure, remove at least 10 nodes following two different strategies: sequentially (one after the other), and all at the same time. Analysis: Find 2 different measures to evaluate the impact of the node removal on the network, and find a way to report these results in the form of a plot instead of a table. Discussion: critically assess your results following the comments given below. Structure and marking scheme. Present your work in 3 sections: Section 1: Measures. Max 33 points Introduce briefly the 3 centrality measures (3 points), and the 2 measures to evaluate the impact (10 points per measure). Explain whether these two measures to evaluate the impact are specific to the London underground or generic? i.e. would these also be useful to evaluate the resilience of any other network? (5 points per measure) Section 2: Analysis. Max 12 points Present the results of the analysis in the form of concise plots clearly labelled (2 points), and compared the 2 different strategies (‘sequential’ vs ‘at the same time’ removal) considering the results for all the centrality measures (5 points). Do the two different measures to evaluate the impact give the same results? Justify if findings were expected. (5 points) Section 3: Discussion. Max 50 points Critically discuss which centrality measures are more relevant for the specific network (10 points), and the limitations of the different measures selected for the evaluation of the node removal (15 points). Explain whether different centrality measures would be expected for different networks, and comment on the limitations of the approach to evaluate the “resilience” of London’s underground (15 points). Explain how this analysis can be used, or how could it be used if improved, to advice TFL (Transport for London) on how to make the underground more resilient (10 points). Good use of literature and presentation of work: Max 5 points Q2. Spatial Interaction Models: This question tests your knowledge on the different models introduced in the lectures and the role of the parameters, in addition to your working ability to use the models. Complete each of the sections below. Section 1: Max 45 points In the event of a refugee crisis given by many people fleeing their country at war, which spatial interaction model would you use to model the flow of people into other countries? Clearly explain the model of choice and the data that you would need for the model (15 points). Explain the limitations of the model, and whether these could be avoided with other modelling techniques such as networks or ABM (15 points). If each country put a restriction on the number of refugees they could allow in, would you use a different model? (15 points) Section 2: Max 55 points Explore the following scenarios using the code given to you during the practicals. Select a model to estimate the flows between boroughs according to salaries. Scenario A: assume that there is a decrease in salaries in the City of London of 30% after Brexit. Scenario B: assume that there is a significant increase in the cost of transport. Using the same model for both scenarios (unconstrained, singly-constrained or doubly-constrained) (10 points), select 2 values for the parameter in the cost function reflecting scenario B (10 points). Using plots, comment on the change in flows for the different boroughs given the 3 different situations. Which scenario would have more impact? Explain and justify your answers using the results of the analysis (25 points). Q3. ABM & CA: The purpose of this question is to demonstrate your understanding of agent-based modelling (ABM) and the analysis of such models. You will need to make use of the model presented in Lecture 9. The following sections include examples drawn from the Wolf-Sheep predator/prey model from Lecture 8. CA vs. ABM: Define and compare ABM and cellular automata (CA). ABM Description: Select 3 different combinations of parameters. Run the model with each of those parameter settings, and describe the difference between the behaviour of the model. Find a way to present the behaviours statistically, as a plot or table. ABM Analysis: Explain how the processes which make up the model produce the behaviours you saw in the previous section. Explain what the figures in the previous section mean, in terms of the functioning of the model. ABM Evaluation: Discuss what these findings imply about the overall system being described. Tip: Many combinations of parameters might produce results which are very similar to one another. You will likely want to experiment with the possible combinations of parameters before choosing which ones to write about in this section. Structure and marking scheme. Present your work in 4 sections: section 1: CA vs. ABM. Max 15 points Provide a short introduction defining both agent-based modelling (ABM) and cellular automata (CA), and comparing/differentiating these categories of model. Explain what is meant by either modelling technique (5 points). How they are similar or different (5 points each)? Please note that you should cite the literature to justify your claims (5 points). Please do not exceed 250 words on this section . section 2: ABM Description. Max 35 points Choose at least three distinct settings of the parameters of the model and run the model in each of these configurations. Describe the differences in model behaviour when the parameters are changed (10 points). You should quantify these changes in model behaviour (10 points). Where is it similar, between different parameter combinations? Where does it differ? This is a good place to identify steady states, tipping points, etc (15 points). For example: number-of-sheep=30, 300, 900, all other values default. Plot the average number of wolves alive at time step 1000. section 3: ABM Analysis. Max 30 points Explain how the interactions of the model give rise to these differences (10 points). Does this happen consistently, and if not why not (7 points)? How do these changes depend on other features/assumptions (7 points)? Note that in order to describe the behaviour of a set of parameters, you will need to run each parameter combination multiple times. Make sure to include a brief justification of the number of runs you have chosen to use (6 points for appropriate number of runs and justification). For example: “In most cases, the growth of the wolf population is so rapid that the sheep population cannot reproduce quickly enough to feed them all. As the wolves consequently die off, the sheep population is able to recover from the few survivor sheep, and ultimately to overwhelm the simulation. In a few cases, the wolves manage to eat all of the sheep, and again die off, leaving a completely empty environment”. section 4: ABM Evaluation. Max 20 points What do these fin
dings mean about the system you are studying (10 points)? Why should a researcher take away from your analysis (10 points)? Do you think this is a useful model? For example: A discussion of the resilience of the sheep population and how initial population levels contribute to the long-term survival of the sheep community. E.g. “The introduction of a predator population of greater size invariably wipes out the prey population, whereas a population of predators smaller than the prey population may die off due to rapid population shifts”.
