Geocoding Part 2 Let’s take the address for the Art Car Museum and use that as our example address. The first address is correct, the next 5 have a flaw in one of the fields. # Address for the Art Car Museum Test_data <- tribble( ~ID, ~Street_num, ~Prefix, ~Street_name, ~Street_type, ~Zipcode, "1", "140", "" , "Heights", "BLVD", "77007", "2", "138", "" , "Heights", "BLVD", "77007", "3", "140", "W" , "Heights", "BLVD", "77007", "4", "140", "" , "Hieghts", "BLVD", "77007", "5", "140", "" , "Heights", "LN", "77007", "6", "140", "" , "Heights", "BLVD", "77070" ) Exact Matches The basic expected way to run the code is to first find all exact matches, and then use the additional tools to try to repair any failures that occurred.

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Geocoding Attaching a Lat-Long to a street address is not an easy task. I have tried a variety of freely available geocoders, and have found all of them to be lacking for various reasons. See one of my earliest posts on this blog for more details. Finally, I discovered that the city of Houston has made available a file from their GIS group that has most of the addresses and associated Lat-Longs for the city (a total of 1,480,215 records when I downloaded it).

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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.

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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? Setup 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.

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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.

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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.

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Solar Panels in Houston

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.

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Author's picture

Alan Jackson

Retired Geophysicist, geophysical consultant, budding data scientist, trouble-maker.

Consultant and Chief Bottle Washer

Houston and Seattle