Proposal Review

Reviewer: Professor Baker Date: March 9, 2022

Section 1 - Introduction:

The introduction should introduce your general research question and your data (where it came from, how it was collected, what are the cases, what are the variables, etc.).

What is their general research question: How does increasing global temperatures measured by city predict the number of natural disasters in the same area? Is the general research question clear? If it is not clear, what questions do you have?

My main question is are there other environmental variables in addition to global temperatures that you would also want to include? For example, I could see rainfall, land coverage type, and some other variables being important depending on what the natural disaster is. It might be worth spending a little time reviewing the literature to see what factors are important for the different disasters. I see that in the wildfire dataset they have a vegetation code and some variables including the wind, humidity and precipitation that could be interesting. Remoteness might also relate to how quickly the fire was dealt with.
This question is a great one for considering multiple variables and also interaction terms! Which we will learn about in the coming weeks.
I think something interesting to consider would be where humans factor in to this process. Some of these will be "natural disasters" in the purest sense and others will be "man-made disasters" especially with something like the fires.
There's a lot of disasters here. It would be worth picking your favorites and then if there is time looking at the others.

Section 2 - Data:

[X] Is the data in the /data folder?
[ ] Does the README include the dimensions and codebook for the data set? (-0.25, you'll want to add the dimensions of the datasets, so we know how many observations there are).
[X] Does the proposal include the output of glimpse() or skim() of the data frame.

Data suitability:

[X] Does the dataset have at least 50 observations and between 10 to 20 variables (exceptions can be made).
[X] Does the data set include a mix of categorical variables, discrete numerical variables, and continuous numerical variables.
[X] What variables does the data include (list below):
- There might be some interesting things to look at in terms of the disaster duration (start and end time) and also geographic extent (designated_area).
- Fire causes might be interesting to look at as well and what effect that has on the duration of the disaster?

Section 3 - Data analysis plan:

[X] Does the proposal include outcome (response, Y) and predictor (explanatory, X) variables they will use to answer your question? And/or the comparison groups they will use, if applicable.
Yes! Although there are lots more variables that you can include in your model! It's a very cool topic!

Do the outcome and predictor variables and/or comparison groups make sense in the context of the question? Why or Why not?

See above.

[X] Does the proposal include some very preliminary exploratory data analysis, including some summary statistics and visualizations, along with some explanation on how they help you learn more about your data. (They can add to these later as they work on their project.)
[X] Does the proposal include the statistical method(s) that they believe will be useful in answering your question(s). (They can update these later as they work on their project.)
You might also consider a random forest to identify what variables are most important.
[X] Do they include what results from these specific statistical methods that are needed to support their hypothesized answer?

Reflections

What was something you found interesting about the project?
- This topic is really interesting. I think there is a lot of neat things to explore and it will work really well with interaction terms in the model.
What ideas/feedback do you have for other things they may explore?
- See above. I think that the models will be different depending on the disaster and what is important. You will be able to approach it both from a what is known about disaster dynamics standpoint and also could do some exploration with some other techniques.
What kinds of plots should they consider to complete the project goal to create some kind of compelling visualization(s) of this data in R?
- An animation of areas affected and where and how this has shifted over time would be really cool.
- I see there being a lot of neat things you could do with leaflet mapping and the ray shader package.
- Some background statistics looking at time series and how things have shifted overtime (maybe by U.S. state? could be really interesting).
Any additional feedback you'd like to give the other group:
- An alternative title for your project could be "Don't move here and here's why!"

DCS-210 / w2022-project-team-ace