I recently put together a small analysis of the Campus Travel Survey report's data in response to a request from David Greenwald, a Davis Vanguard reporter. He asked how many UC Davis students owned a car, so I provided him with a summary of how many students have access to a car (just under half, regardless of residential location). I noted that the figure most likely represented an over-estimate of the true number of cars owned by students, as students may be referring to shared cars when they responded to the survey.
The article, "Analysis: New Data Demonstrates Fewer Students Have Access to a Vehicle", then used the data to respond to comments about a proposed apartment complex in Davis. Greenwald shows that the reduced number of parking spots provided by the new complex - 1 spot per 3 residents - is in line with current patterns of car access among undergraduate and graduate students.
From my perspective, it is encouraging to see the city and developers provide sufficient parking but no more than is necessary - housing vacancy is at a ridiculously low 0.3%, so it makes less sense than ever to provide empty parking spaces where people could live. In a forthcoming paper written with Jamey Volker, we note that the city of Davis provides far too much residential parking - only about 1/3 of available spaces were used at peak hours (late evening and early morning) on the streets we surveyed. This shift toward reduced parking requirements is therefore a refreshing change. But arguably even more could be done to reduce parking provision at this apartment complex, with shared car programs, etc. to further reduce the necessary number of parking spaces and thereby increase the number of apartments built - something that the university is actively looking into in its updated Long Range Development Plan.
One of my first forays into the world of scientific research was working with Bret Beheim and Richard McElreath on an analysis of the prevalence and role of social learning in the ancient Chinese game of Go. So it has been enjoyable to watch to media blizzard over the occurence of a once-unthinkable: a computer beat the World Go Champion! Although chess has been conquered by computers for many years, it was thought that the substantially larger number of potential game states in Go (played on a 19x19 board) would allow for human experience and intuition to win out over brute-force approaches of computers. That proved not to be the case when the World Go Champion, Lee Sedol of South Korea, lost the best-of-three series against Google's AlphaGo in three straight losses.
As Richard has noted, perhaps one of the results from our paper about human Go masters applies to AlphaGo as well. We found that Go professionals attend to the popularity of a move as well as to its recent successes. AlphaGo was trained on a database of Go games, making it perhaps the most reliant of all on social learning.
And interestingly enough, our results indicate that Lee Sedol heavily relies on social, rather than individual, learning.
Together, this seems to indicate that neither Lee Sedol nor AlphaGo would be well-predicted by their past moves, and therefore would be harder to prepare against. Anecdotally, this contrasts with how Deep Blue was developed to play World Chess Champion Garry Kasparov. Deep Blue's creators actually programmed the computer to attack Kasparov's weaknesses, but when Kasparov played outside of his usual repertoire, he was able to stymie Deep Blue. It seems that Lee Sedol has discovered a similar weakness in AlphaGo, as he was able to win the fourth game in the series.
Although admittedly published in a news outlet expressly designed for UC Davis staff and faculty, not the New York Times, it's fun to see my "baby", the 2014-15 UC Davis Campus Travel Survey, get some media coverage in the UC Davis DATELINE.
The article highlights one of the more encouraging developments identified by the survey, which showed that awareness and use of UC Davis' travel demand management offerings was up across the board from previous years, and half of the programs had over 80% use/awareness:
This is my first blog post on my website, and fittingly it is about one of the most important things on my mind - my dissertation proposal.
I am currently in the process of writing my dissertation proposal, which (perhaps obviously) involves reading a lot of scientific publications, books, etc. To aid my retention of the texts I'm reading and to help out future-me when I come back to the texts and write a literature review, I have been putting together an annotated bibliography.
Because I'm picky when it comes to such things, I had a particular work flow in mind. The first option I considered, Microsoft Word, was dissatisfying. I could write a review in alphabetical order, but that would get lengthy and cumbersome to search using Word's tools. And as my annotated bibliography grows, it will become increasingly difficult to even find my annotations for a particular article. I wanted a tool that allows for separate entries for each paper I read and annotate, and allows easy searching.
In addition, some of the literature I am reading is relevant to one of my dissertation studies, some papers are relevant to all... so keeping separate documents for each study doesn't make a lot of sense. I wanted a tool that would allow me to include the same annotation in multiple "folders".
On top of all that, I wanted something that would work across my multiple devices. So what I've settled on for annotating the literature I read for my dissertation is Letterspace, which works on all Apple products (no Android or web applications, unfortunately). I have found it to be extremely useful thus far. To sort the same annotation under multiple projects/studies, I simply use the projects' hashtags. And to organize by author, I simply use the "@" symbol in front of their name, allowing me to later conduct quick searches by author. The project hashtags and the authors are all organized nicely in a sidebar.
Letterspace also does a stellar job with organizing notes. The bullet system is plain simple to use, and even better, there is a built in checklist option that allows you to check off items within a note. Really handy for when I'm reading an article and want to remind myself to look up another paper or look more deeply into a certain topic.
So far, Letterspace has treated me well. I would be interested to hear what other PhD students use for their annotated bibliographies in the comments section below.