Spatial Analysis with R - ASSIGNMENTS
Although you are certainly welcome to confer with others - including me - during your research, your report should be essentially unique and different from anyone else's. Collaborative projects may be conducted only with my explicit prior approval. Read the University System of Maryland POLICY ON MISCONDUCT IN SCHOLARLY WORK.
Submissions (or a URL) should be emailed to
LDECOLA@COMCAST.NET (not to UMBC) by the due time listed below on the assignment.
Use the following file naming rule as it makes it easy to keep track of your work. Submission files are to be named as follows:
f = first letter of your first name,
lname = your last name,
n = assignment number, and
.ext = file extension (
.doc, .pdf, .ppt, etc.).
You are welcome to create an HTML document and post it on the web, sending me the URL.
- All submissions must show a title, your name, class name, and date.
- Any item you copy or refer to must be cited correctly (see the examples in the syllabus) but look at a style guide for details. Be sure to include the correct URL and exact references for any data you use.
- I rarely print documents and generally view MSWord documents in 'web layout' mode, so don't spend much time trying to format them. The font should be readable at normal size.
- Graphics should fill about 2/3 of the width of the screen; anything smaller is too small, anything larger is likely to bleed off the screen in some circumstances.
- If you submit a spreadsheet, the worksheets and columns should be labeled meaningfully.
- You're welcome to submit the data as well if possible so I can experiment with them.
- If you want to include a map, submit it as a graphics file (.gif, .jpg, etc.) in order to share your work.
A report writing guide from my website; please read and refer to this when writing!
- A few places with spatial data:
- Some of the R spatial datasets i'm aware of:
As always: please post questions, etc. on the Blackboard discussion forum!
Assignment #1: Stats and maps
Using the text and the code from our first two sessions as a guide, perform a statistical and visual analysis of the Georgia county data from the Brundsdon book. The report should be about 3 "pages" in length and cover the following topics:
- Select two variables from the dataframe. Tell what they measure.
Describe the data using standard statistical measures (
mean(), summary()), as well as visualizations (
hist(), qqnorm() etc.).
- Analyze possible 'relationships' between the two variables in several ways correlation, scatter diagram, regression.
- Map one of the variables using at least two binning techniques: interval (cuts() and quantile().
Include a legend either based on the above barplot (preferred) or the legend() function.
- Discuss the spatial pattern; spatial autocorrelation, trend, causality, etc. - this discussion may be impressionistic rather than based on formal methods.
Assignment #2: Point pattern analysis
Perform a point pattern analysis on one of the New Haven crime point datasets. Use the examples from our online session and the text. In addition to a narrative, include:
- a map of the incidents and a discussion of the 'raw' point pattern; show them with and without contextual polygon information.
- a couple of kernel density images and what each shows.
- a simulated random point pattern using the same number of events as the study object, comparing the pattern with the data.
- a point pattern statistic and discussion.
Assignment #3: Final report
This research will be presented either online or in class at Shady Grove on Nov 15
Develop an original spatial analysis project based on your professional or course work or some other interest. You're urged to share your ideas with me and others as you work on the project. Submit it by the usual deadline and be prepared to present it at our last meeting.
Submission and presentation
- The submission must be a digital report document (see my report writing guide).
- The presentation may be from the digital document, PowerPoint slide show, etc.
- For the presentation, be prepared to demo your analysis using R: i.e. the data should be on your computer or readible in class for further exploration.
- It's always interesting to look deeper into data you're already familiar with from prior work; browse your computer and old reports and see what you can come up with. Work from concurrent classes is also acceptable provided you check with me first.
- Do some searching using keywords of interest plus DATA, DATASET, DOWNLOAD, SPREADSHEET, etc...
- 2N eyes are better than 2, so please share ideas you find - especially on Blackboard!