The city of Houston makes a file available on the web every week containing a summary of the past week’s building permits. I found this file a bit difficult to digest - it needed a map, it needed search and filtering. So I wrote some code to automatically read the file in each week, merge it with the previous weeks files, and then upload that to the web where I have an application to display it.
We like to walk. When the weather cooperates, we can easily get in 5 or more miles in a day just walking around the neighborhood. We walk to the bank, to the grocery store, the hardware store, or just around the ’hood. There are two huge irritants on our walks. The terrible drivers who refuse to yield right-of-way to a pedestrian, and the abysmal quality of the sidewalks. This report will look at the sidewalks.
Having bought solar panels myself a couple of years ago, and realizing that the city permit database could be used to find most installations, I decided that it would be interesting to look at the recent history and a few other facets of residential solar panel installations. The first step is to download the structural permit data as a CSV file from the city open data website.
Introduction TXDoT has available, online, detailed data regarding traffic collisions throughout the state. The data itself must be queried and downloaded manually as CSV files, but that is not too bad. I downloaded the data for Harris county from 2010 to 2018. Database is documented at https://www.txdot.gov/inside-txdot/division/traffic/data-access.html Access is from https://cris.txdot.gov/secure/Share Log on and download one year at a time. The zip files will require the login password to open them.
Introduction Houston is one of the worst places in the country for allergies. Since there is reasonably good data available, I thought I should analyze the pollen and mold data with an eye towards prediction - both short and mid range time scales. As with any project like this, step one is reading in and cleaning up the raw data. The data is available online as artisanal spreadsheets at https://www.
Introduction In late 2017 I did an analysis of crime data in my neighborhood (The Heights) using the online Houston Police Department data. This was so interesting that I foolishly decided to expand the effort to cover the whole city. After all, how hard could it be to go from analyzing one police beat with about 13,000 records, to analyzing 109 beats, with a corresponding increase in volume? This effort is still ongoing in fits and starts today, but I thought it would be useful to start documenting the journey now before the pain fades away.
Introduction I have been struggling with geocoding for about a year now, and have begun to learn far more than I wanted about the ugly details of the tools available for free. In particular I have been using Google and the US Census Bureau for geocoding. They each have their own strengths and weaknesses, so I thought it would be appropriate to share what I have learned. I would call Google promiscuous - they will try very hard to return a location to you, even if it is all wrong.