Let’s take a look at the early voting data for Harris County Since I already have a bunch of data for Harris county precincts and zipcodes, why not make some use of it?
path <- "/home/ajackson/Dropbox/Rprojects/Voting/" BBM <- read_csv(paste0(path, "Cumulative_BBM_1120.csv"), col_types = "ccccccccccccccccccccccccccccccccccccccccc") BBM <- BBM %>% mutate(ActivityDate=mdy_hms(ActivityDate)) %>% mutate(ActivityDate=force_tz(ActivityDate, tzone = "US/Central")) %>% select(ElectionCode:ActivityDate) %>% mutate(Ballot_Type="Mail") EV <- list.files(path=path, pattern="Cumulative_EV_1120_1*", full.names=TRUE) %>% map_df(~read_csv(.
Harris County COVID-19 data I have a very nice (I hope) dataset consisting of number of positive COVID-19 cases per day in Harris county by zipcode. In this blog entry I would like to study this dataset and look at comparisons with various other data.
Initial look First off, let’s explore the data for issues, and for ideas about what might be interesting.
Covid tests in Texas The second entry to look at the testing in Texas. This time there is much more data to examine, but also a new and irritating problem. At some point, for some counties, antibody testing got mixed in with the PCR tests, so the numbers are not nearly as good as they should be. Last I read, about 10% of the tests are the wrong test, but I suspect this is not evenly distributed by county, but rather concentrated in a few.
Introduction Towards the end of May the state of Texas suddenly began adding the number of COVID-19 cases detected amongst prison inmates to the county totals for the counties in which the prisons resided. However, they have not indicated if they did this change on a single day, or if it may have taken place over several days, for different prisons. In this bit of work, I will try to ferret out what they did as best I can, so that I may best correct my own data.
Piecewise data fitting As the COVID-19 pandemic progresses, the simple exponential and logistic models no longer fit the data very well. As waves of infection and retrenchment occur, it seems likely that the best fits will be done piecewise. For this blog entry I will experiment with various schemes to see if I can get a reasonably good strategy for constrained fitting to the data.
As I have a well-structured dataset for all the counties in Texas, that is what I will use for the experiments.
Miscellaneous analyses related to the Covid-19 pandemic After reading the paper this morning about a county nearby (Houston county) with zero reported cases, I got curious. What does the distribution of test coverage look like, i.e., number of tests per capita? And also, what is the rate of tests that come back positive?
So let’s look at the data.
We can now grab an excel spreadsheet from the state that gives number of tests per county.
Prisons and their effects on the surrounding community It is becoming clear that prisons, meat-packing plants, and nursing homes all resemble cruise ships in terms of providing a fertile environment for rapid spread of the virus. Unlike cruise ships, none of these are isolated from their surrounding communities - staff enter and leave on a daily basis, potentially spreading the virus nearby.
There are a few counties that have “specialized” in hosting prisons, to the extent that a large fraction of the county population is inmates.
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.