I want to find somewhere super livable; aside from “livable” being subjective, it is often hard to find data even if you have specific criteria. The data at Walkscore is really interesting but I see two big problems with it.
- The data for an entire city favtors in part of the city I likely do not care about.
- The data for one neighborhood is likely too small for the “area” I’d visit most days.
What I really want is a dataset which looks at “networks” of neighborhoods. Specifically, I want the aggregate data for a neighborhood and all other neighborhoods which are within a specific distance. To me, a network is an area which I’d “call home” and like visit frequently. In my case, I consider anything within 3 miles of my neighborhood within my “network” since I can get there on foot, bike, or transit pretty easily.
Continue reading Creating Neighborhood Networks with Walkscore
I recently got a Nexus 6 (yesterday) and Android 5.1 came out today. Being impatient, I decided to flash Android 5.1 myself since it’s been a breeze in the past.
Turns out, I do not think the
flash-all.sh script actually works for the Nexus 6. Specifically, I kept getting the following error.
Continue reading Flashing Errors for Android 5.1 on Nexus 6
Raspberry Pi 2.0 means it’s time for Raspberry Pi 2.0 benchmarks!
I have run previous benchmarks on various Raspberry Pi units so when I got my Raspberry Pi 2.0, I was immediately curious how its CPU performance compared to my other units. Due to work, my time is limited at the moment but I wanted to get some initial benchmarks out of the door.
So, just how do the 1.0 and 2.0 models compare? Simply, the 2.0 model shows promise.
Continue reading Raspberry Pi 2.0 Benchmarks
Data is awesome and people generate a lot of data. It’s not surprising that a number of companies, such as Fitbit and Jawbone have moved into the space of capturing data from people. it seems that fitness is the first major (adopted) wave of “self quantification” which is being adopted.
Of course, bad data is not entirely useful data. To that end, I’ve wondered about the accuracy of some of these devices. While I cannot address their total accuracy, I have started to poke around whether they compute the same data for the same movements. Specifically, if I wore a Jawbone and Fitbit device for one week, would they have the same data? Fitbit vs Jawbone – one week of data – would I get the same data?
Turns out, no, not really.
Continue reading Fitbit vs Jawbone – one week of data
Recently, I have been fascinated with software defined radio (SDR.) There are a number of cheap
USB dongles which fall into the RTLSDR camp. I believe most of the USB dongles use Realtek (RTL) chipsets, hence the name.
RTLSDRs are interesting because they allow you to scan a wide frequency and cost maybe $20 on Amazon of eBay. After getting a few, however, I started to wonder how limiting the stock antenna might be. The stock antennas which come with most RTL-SDR USB dongles are meant to pick up digital TV in Europe and are pitifully small.
I wondered – would a proper antenna make a difference? The answer, in short, is yes.
Continue reading SDR Antenna Comparison
The other day I wanted to help get a set of Walk Score data for analysis and analytics (educational.) To their credit, Walk Score does have an API but getting a key turned out to be annoyingly difficult to me. Specifically, I used my GMail address but this website URL. Since the domain didn’t match, apparently they need to manually review my request.
Annoying. Rather than wait, it was just easier to programmatically pull Walk Score data with Python.
Continue reading Programmatically pull Walk Score data
Everything in this post is my own work, provided for educational purposes only, and anything in this post is my own viewpoint and does not reflect any viewpoint (or otherwise) of my employer.
Whew, I always love when I start posts with disclaimers!
I am a big fan of colors. Awhile back, an unnamed company released a headwear which has LEDs embedded in it. This is a pretty cool piece of tech because the LEDs are paired with an IR send and receive capability so the headwear can both change color in response to messages it receives and also influence the color of surrounding headwear. Needless to say, I was curious.
After a few pairs of the headwear (RIP, pieces of hat) and some late nights, I discovered the protocol and message construction for this particular pair of headwear. Using an Arduino with an IR LED, I was able to manipulate this headwear to change colors based on my whim. To make things easy, I decided to write a Python library to generate the messages which I could feed, in turn, to an Arduino project.
Oh yeah, this was about two years ago.
Since a lot of time has passed and it’s still pretty interesting, I have decided to release the library, known as the GlowColors lib, publicly. You can find the latest release in this GitHub repository.
A few notes on this library.
- You can change one or both ears. Changing just one, however, can be tricky because you have to set both and then unset one.
- It is possible to create a “show” with the headwear but that functionality is in a yet unreleased version of the library, sorry.
- This library is a lot of fun; please use it responsibly and do not cause trouble with it. I am also not responsible for what you do with it.