Skip to main content

Fake Apple Store, Real Hysteria.

The NY Times website recently published a story about "The Rise of the Fake Apple Store".

Um, there are "fake" Apple Stores everywhere, including in the US. There is even a "fake" store up the street from my Dad's house in Erie, Pennsylvania.

The real story isn't "Asians are Slavishly Copying American Creativity", but "Local Entrepreneurs Meet Demand for Apple Retail Experience when Apple Doesn't".

Basically, even in places where Apple doesn't set up shop like Erie, PA, Kunming, and Seoul (which I know also has plenty of Apple Store-like stores) there is still a latent demand for well designed modern places to try and buy Apple products. Look-a-like stores are just filling this demand. Since (all the ones I've ever been to) sell actual Apple products what is the harm in this?

However, the comments on both the NY Times site and at Slate (where it is largely reprinted) have largely picked up the "Slavish Asians" reading and become kind of hysterical about Chinese counterfeiting.

The NY Times article, based largely on one blog post the reporter read, doesn't actually give any evidence that the products sold at the look-a-like stores, even in Kunming, are fake.

I just don't see how selling real Apple products at a store that looks like an Apple Store is a bad thing either for Apple or for consumers, especially when the consumers live in areas without Apple Stores.


Comments

Popular posts from this blog

Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame.I've found the various R methods for doing this hard to remember and usually need to look at old blogposts. Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command–slide–to the DataCombine R package (v0.1.5).Building on the shift function TszKin Julian posted on his blog, slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data.Note: your data needs to be in ascending time order with equally spaced time increments. For example 1995, 1996, 1997. ExamplesNot…

A Link Between topicmodels LDA and LDAvis

Carson Sievert and Kenny Shirley have put together the really nice LDAvis R package. It provides a Shiny-based interactive interface for exploring the output from Latent Dirichlet Allocation topic models. If you've never used it, I highly recommend checking out their XKCD example (this paper also has some nice background).LDAvis doesn't fit topic models, it just visualises the output. As such it is agnostic about what package you use to fit your LDA topic model. They have a useful example of how to use output from the lda package.I wanted to use LDAvis with output from the topicmodels package. It works really nicely with texts preprocessed using the tm package. The trick is extracting the information LDAvis requires from the model and placing it into a specifically structured JSON formatted object.To make the conversion from topicmodels output to LDAvis JSON input easier, I created a linking function called topicmodels_json_ldavis. The full function is below. To use it follow …

Showing results from Cox Proportional Hazard Models in R with simPH

Update 2 February 2014: A new version of simPH (Version 1.0) will soon be available for download from CRAN. It allows you to plot using points, ribbons, and (new) lines. See the updated package description paper for examples. Note that the ribbons argument will no longer work as in the examples below. Please use type = 'ribbons' (or 'points' or 'lines'). Effectively showing estimates and uncertainty from Cox Proportional Hazard (PH) models, especially for interactive and non-linear effects, can be challenging with currently available software. So, researchers often just simply display a results table. These are pretty useless for Cox PH models. It is difficult to decipher a simple linear variable’s estimated effect and basically impossible to understand time interactions, interactions between variables, and nonlinear effects without the reader further calculating quantities of interest for a variety of fitted values.So, I’ve been putting together the simPH R p…