andrew wheeler

Once that is all done, you have it set up to query both API’s to get the images and then get the labels. And the number of constraints will be the number of customers plus an additional small number. I find Wilkinson’s grammer of graphics a more useful way to overall think about the components of a graph, although Bertin is more encyclopedic in coverage of different types of graphs and maps in the wild. Myron Ebell, an energy and environmental expert at the conservative-leaning Competitive Enterprise Institute (CEI), agreed with Wheeler’s assessment and emphasized that the Biden-Harris platform is more about eliminating oil and natural gas altogether, which, in turn, includes fracking.

So here is a pivot table of the sum of the scores across the 300 outside hotspot (column 0) and 300 inside (column 1) images. The Google Vision labels are a bit superficial to really use for many theory crim applications I am afraid, but is an interesting exploratory data analysis to check them out. (1999). So for network analysis we have examples of looking at use-of-force networks (Oullet et al., 2019b), and for survival analysis I would be interested in a time to solve example dataset. Most of the critiques of this book are that it is mostly just a collection of Tufte’s opinions about creating minimalist, dense, scientific graphs.

Though economic forces have also been at play reducing coal’s dominance as a primary source of U.S. power generation, those forces were no doubt been accelerated by the Obama administration’s regulatory regime. But for now, here is a small python snippet to help you build interaction variables between two sets of numpy arrays/dataframes. The simulations above could easily be amended to do that, via doing simulations of the Poisson distribution instead of dice rolls, or assigning weights to particular outcomes. https://andrewpwheeler.com/2020/10/17/simulating-runs-of-events/. https://andrewpwheeler.com/2020/10/01/a-linear-programming-example-for-turf-analysis-in-python/. The p_j are the estimates that an individual will comply with coming into the call-in. No one thinks, hey, I shouldn’t commit this robbery because I need to cast my ballot this fall. I would start here if you are interested in designing cognitive experiments to test certain graphs/maps. What happens if we go down one, and only select 28 people? In terms of predictive applications, I think using the streetview imagery is not likely to improve crime forecasts, that it is really only worthwhile for EDA or theory testing.

The legislation is also explicitly endorsed on Biden’s campaign website, who also tried to back away from his support of it during the first presidential debate. I end up picking the same 5 items that the XLStat program picked in their spreadsheet as well.

It is too late to participate, but they will be displaying the results this Sunday.

pic.twitter.com/iWcsU2Yam8, — Abigail Marone (@abigailmarone) August 31, 2020. Even for power users of SPSS, much of the things Wilkinson talks about are not implemented in SPSS’s GGRAPH language, so they are mostly just on paper. Also note you only need to do the network calculations once and then can cache them (and I could have made these network computations go faster if I parallelized the lookup).

Second constraint is we can only call in so many people, here k. The model solves very fast, so you can generate results for various k until you get the reach you want to in the end. I initially invested in it as he had a chapter on a critique of powerpoint presentations, which is very straightforward and provides practical advice on what not to do. The expected times person A would be reached then is additive in the probabilities, 0.4 + 0.4 = 0.8. Khorshidi, S., Carter, J., Mohler, G., & Tita, G. (2019). So previously I have shown how to automate the process of downloading google street view imagery (for individual addresses & running down a street). Here is a graph I made of selecting 20 individuals. (You could do the model the other way, minimize S_i while constraining the minimized acceptable reach, e.g. Shout out #3: My workplace, HMS, is involved in a data sharing collaborative called the Digital Health DRC. So you will get less variation on the X axis, and more variation on the Y axis. You just calculate the run length encoded version of the data, and see if any of the lengths are equal to or greater than 5. So for a test we can see if I get the same minimal dominating set as Borgotti did for his algorithm here, where const is just everybody complies 100% of the time. The network dynamics of criminal group persistence.

An official website of the United States government. Stephen Few’s books deserve a mention here as well, such as Show me the numbers. While most argue simply that individuals voting rights should be restored after an individuals imprisonment has ended, I don’t believe they should ever be stripped to begin with.

Raudenbush, S. W., & Sampson, R. J. This is similar in spirit to Tufte’s minimalist style, but gives practical advice on slides, writing, and presentations. So I am going to pretend this restaurant survey is maximizing the reach for Hoss’s buffet options. (I can afford the few bucks for the domain, and I use dropbox to back up my files anyway, so hosting extra materials is not a big deal.)

Here is an example of calling my function to select the individuals for a call-in based on the non-compliance estimates. These were clearly larger than what would be considered best practice in identifying hot spots (they were more like entire neighborhoods). So I edited a function I found from Stackoverflow to accomplish the rle.

You may think this is a bit far-fetched to my usual posts related to criminal justice, but it is very much related to the work I did on identifying optimal gang members to deliver the message in a Focused Deterrence initiative. So I have some from my work and a few other random examples I have come across on the internet. https://andrewpwheeler.com/2020/10/20/incorporating-treatment-non-compliance-into-call-ins/.

Ouellet, M., Hashimi, S., Gravel, J., & Papachristos, A. V. (2019b). While my list is not in rank order, I am putting Cairo’s book first for a reason. The TURF problem I did the other day gave me a bit of inspiration on how to tackle that treatment non-compliance problem though.

Hopefully I will have some time in the near future to write up some more data science posts. Here I have posted the python code and data used in the analysis, below I go through the steps in formulating different linear programs to tackle this problem. Redrawing hot spots of crime in Dallas, Texas. Nick Cox has a review of this book, and for this one he notes that the audience for this book is hard to pin down.

Wheeler, A. P., & Steenbeek, W. (2020). Red means I selected that person, pink means they are reached at least some, and the size of the reach is proportion to the node. For example here is one I went through the motions to get to (in the end) validate different survival prediction methods. I believe I have read all of Tufte’s other books as well, but this is the only one that made much of an impression on me (some of his others go beyond graphs, and talk about UI design).

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