Application scenario

You work in a data science team that tries to model the road accidents in an area to improve the prediction for rescue services demand. For your convenience, you are provided with the following data links, but you are encouraged to include other relevant data for your analyses.

1. The road accidents data set, attached to the assignment:

Dataset

1. The daily temperature and precipitation data for the region accessible through the NOAA data APIs.

https://www.ncdc.noaa.gov/cdo-web/webservices/v2

Of particular relevance is the "Global Historical Climatology Network - Daily" data:

https://www.ncdc.noaa.gov/ghcn-daily-description

https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt

Task 1: Source weather data (10 points)

From Assignment 1, you have processed data for the road accidents of different types in a given region of Victoria. We still need to find local weather data from the same period. You are encouraged to find weather data online. Besides the NOAA data, you may also use data from the Bureau of Meteorology historical weather observations and statistics. (The NOAA Climate Data might be easier to process, also a full list of weather stations is provided here: https://www.ncei.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt )

Answer the following questions:

1. Which data source do you plan to use? Justify your decision. (4 points)
2. From the data source identified, download daily temperature and precipitation data for the region during the relevant time period. (Hint: If you download data from NOAA https://www.ncdc.noaa.gov/cdo-web/, you need to request an NOAA web service token for accessing the data.) (2 points)
3. Answer the following questions:

• How many rows are in the data? (2 points)
• What time period does the data cover? (2 points)

Task 2: Model planning (10 points)

Careful planning is essential for a successful modelling effort. Please answer the following planning questions.

1. Model planning:

• • How will the final model be used? (1 point)
  • How will it be relevant to the emergency services demand?(1 point)
  • Who are the potential users of your model? (1 point)

2. Relationship and data:

• • What relationship do you plan to model or what do you want to predict? (1 pont)
  • What is the response variable? (1 point)
  • What are the predictor variables? (1 point)
  • Will the variables in your model be routinely collected and made available soon enough for prediction? (1 point)
  • As you are likely to build your model on historical data, will the data in the future have similar characteristics? (1 point)

3. What statistical method(s) will be applied to generate the model? Why? (2 points)

Task 3: Model the number of road traffic accidents (30 points)

We will start with simple models and gradually make them more complex and improve them. We will focus on the road traffic accident variable(s) that you defined in Assignment 1. Let's denote it Y.

Randomly pick a region from the road traffic accidents data.

1. Which region do you pick? (1 point)
2. Fit a linear model for Y usingdate as the predictor variable. Plot the fitted values and the residuals. Assess the model fit. Is a linear function sufficient for modelling the trend of Y? Support your conclusion with plots. (4 points)
3. As we are not interested in the trend itself, relax the linearity assumption by fitting a generalised additive model (GAM). Assess the model fit. Do you see patterns in the residuals indicating insufficient model fit? (5 points)
4. Augment the model to incorporate the weekly variations. (5 points)
5. Compare the models using the Akaike information criterion (AIC). Report the best-fitted model through coefficient estimates and/or plots. (5 points)
6. Analyse the residuals. Do you see any remaining correlation patterns among the residuals? (4 points)
7. What data type is your day-of-the-week variable? (3 points)Does the data type of this variable affect the model fit? (3 points)

Task 4 Heatwaves, precipitation and road traffic accidents (30 points)

The connection between weather and the road traffic accidents is widely reported. In this task, you will try to measure the heatwave and assess its impact on the road accident statistics.

Task 4.1: Measuring heatwave (8 points)

1. John Nairn and Robert Fawcett from the Australian Bureau of Meteorology have proposed a measure for the heatwave, called the excess heat factor (EHF). Read the following article to understand the definition of the EHF. (3 points)

https://dx.doi.org/10.3390%2Fijerph120100227

1. Use the NOAA data to calculate the daily EHF values for the area you chose during the relevant time period. Plot the daily EHF values. (5 points)

Task 4.2: Models with EHF (7 points)

Use the EHF as an additional predictor to augment the model(s) that you fitted before. Report the estimated effect of the EHF on the road accident numbers. (3 points)

Does the extra predictor improve the model fit? (1 point)

What conclusions can you draw? (3 points)

Task 4.3: Research question - extra weather features (15 points)

Is EHF a good predictor for road traffic accidents? Can you think of extra weather features that may be more predictive of road traffic accident numbers? (5 points)

Try incorporating your feature into the model and see if it improves the model fit. Use AIC to prove your point. (10 points)

Task 5: Reflection (20 points)

In the form of a short report answer the following questions:

1. We used some historical data to fit regression models. What are the limitations of such data, if any? (5 points)
2. Regression models can be used for 1) understanding a process, or 2) making predictions. In this assignment, do we have reasons to choose one objective over the other? (5 points) How would the decision affect our models? (5 points)
3. Overall, have your analyses answered the questions that you set out to answer? (5 points)