Proposal Review

[LaurieLBaker]

Proposal Review

Reviewer: Professor Baker Date: 03/14/2022

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:

• Are squirrels seen more frequently at night or during the day
• What is the most commonly observed squirrel behavior?
• Does squirrel behavior change depending on time of day or time of year?
• What areas of NYC observe the highest number of squirrels?
• What is the squirrel to human ratio of NYC? Of different neighborhoods?

Is the general research question clear? If it is not clear, what questions do you have?

• The research question is clear. One thing you will want to establish is what was the "sampling effort" like for the dataset, i.e. how often were people observing in different areas. This will give you a baseline and can allow you to compare across different regions better.
• The day/night question could be tricky, depending on whether it is harder to observe squirrels at night.
• I really like the ideas of looking at different squirrel behaviors and how they change overtime. You might also look at the connectivity of the park depending on how many of the same squirrels you see.
• It may be more challenging to look at elements related to the population unless you have foot traffic in different parts of Central Park.

Section 2 - Data:

• [X] Is the data in the /data folder?
• [X] Does the README include the dimensions and codebook for the data set?
• [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):
• See data readme.

  Section 3 - Data analysis plan:

• [ ] 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.
• For this question you might look at a comparison of different areas of the park.
• You could also look at what factors influence specific squirrel behavior e.g. do the number of kuks, quaas at different times of the year. You could see if this varies depending on the type of squirrel, age, and location.

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

• Yes, I think you can do some nice analyses.

• [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.)

• [ ] Do they include what results from these specific statistical methods that are needed to support their hypothesized answer?

• I think that you could compare across different geographic areas and look at whether there are differences in sightings/behavior. You can also look at changes over time or what things influence different behavior.

Reflections

1. What was something you found interesting about the project?

   • Citizen science projects are really cool! It will be interesting to learn more about squirrels, but I'm also excited to see what the variability is in the data.

2. What ideas/feedback do you have for other things they may explore?

   • I think you could try to get some data about tree cover and/or habitat information that might be important for squirrels in different parts of Central Park. It would be interesting to know how much they are fed by humans!
   • You will also want to think about what things are missing from the dataset and pros and cons of citizen science projects would be interesting to include in your analysis.

3. 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?

   • I think some time series plots and also some maps would be interesting to look at for the data.
   • You may also want to look at barplots/stacked bar plots.

4. Any additional feedback you'd like to give the other group:

   • I'm looking forward to learning more about squirrels!

[LaurieLBaker]

@SadieKriegler, @lmorisse9, @miagates (see project feedback above). Well done!