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). So I decided that I would create my own geocoder.

The first step was to download the address file from the city, and try to both understand what was in it, and do whatever cleanup might be necessary.

This first part is all about preparing the master file for the geocoding process. The actual geocoder will be covered in part 2.

I should note at this point that it appears that the locations in the file refer to either the approximate center of the building with that address, or to the center of the plat if there is no building. At least usually.

The actual code I used to explore and clean the data may be found at my github site.

Contents of the file

The file contains 3 different ID field, OBJECTID, ID, and ADDR_ID. OBJECTID is a unique value for each entry. ID is something else, and is not a unique identifier for each entry. ADDR_ID is almost unique to each entry, falling one short. For future work, I will retain and use OBJECTID as my key field.

TYPES is a field that is apparently somewhat free-form, since it has entries like “COMMERCIAL” as well as “Commercial”, “Residential” as well as “RES”. It could be very useful, except that it is only filled in for about 300,000 of the entries, so falling far short of being comprehensive.

STATUS appears to be mostly about the origin of the data, with the greatest number of records having a status of “COH” for “City of Houston”. Also popular is “CP”, which I suspect means “Center Point”. “ABD” seems clear enough - abandoned. The code “CNTY” clearly means “County”. The other, fairly rarely codes used are a mystery: UTA, RVSG, PRE, and OOR.

The STREET_NUM field is pretty good except that sometimes (11,977 records) the number has some extra stuff, probably designating an apartment, a duplex, or something similar like “A” or “1/2”, or “#3”. I made a decision to pull this stuff out of the numeric address and store it separately, since it is likely to cause problems later on and really of only marginal interest for geocoding.

There are 66 street numbers that are larger than 50,000. Spot checking determined that these are unlikely to be correct, so those records were dropped.

There is a FRACTION field that is probably meant to be used for the fractional and letter additions to the street number, but it also contains a few trash items like period, square bracket, and slash. Sometimes it matches the extra stuff that appeared in the number field, but not always.

The directional PREFIX field is pretty good, a few entries in lower case, so those can be easily corrected, and one entry of “C”, which is simply set to blank.

Street Names

Now it gets much more interesting. How do we find mistakes in the street names?

Look at low occurrence names.

  • names that occur only once
    • update names that are < 1060 ft from a 1 char change in 3 names in the zip
    • then 2 character changes < 300 feet with >= 5 names in zip
    • then 3 character changes < 300 feet with >= 9 names in zip
  • names that occur twice
    • update names that are < 500 ft from a 1 char change in 8 names in the zip

Because we will look for nearby names that are just one character different from our names that occur only once or twice, we also will have to exclude a few names that this would not work for.

  • Names of three letters or less
  • Names like “A STREET” or “B STREET”
  • Names like “14TH”, “15TH”, etc.
  • County Roads (CR 234, CR 235, etc.)

Also corrected a number of special cases, ST changed to SAINT, SQ changed to SQUARE to enforce some sort of consistency. In total, 1,719 street names were changed. Many of those were cases where the road type was repeated in the street name, e.g., BLACK FALCON RD instead of BLACK FALCON with a type of RD.

Street types

Compared street types to the standard USPS list, plus the Houston specific SPWY designation used only for Buffalo Speedway, and corrected them.

For street types of blank, looked at other streets with the same name in the zip code and if there were more than 9 addresses, replaced the blank with the appropriate type, if there was only one choice. I ignored types of CT, CIR, and PL since those were likely to get it wrong.

Changed 109 street type fields to blank, and changed from blank to a street type for 2,122 records.

Street Prefix

For directional prefixes, a similar strategy. 24 prefixes were set to blank, and 714 blank prefixes were updated to something else.


All the records have what appear to be well-behaved lat/long values. There are also X-Y coordinates, with some of those being bogus. There are 201 obviously bad Y coordinates. The bad Y coordinates were replaced by reprojecting the Lat-Longs.

Zip Codes

Checked the zip codes against the 2019 zip code polygon file. Looked at the data at the same address and looked for duplicate addresses that had unreasonably large distances between them.

Fourteen records mismatched so badly that they were deleted. Mostly from 77040 curiously enough.

Corrected the zip code for 34 records.

Prefix redux

Went back and looked at streets where within a zip, there were 5 times as many records with a prefix as there were with blank prefix. For each blank prefix, if there were 5 or more non-blank prefixes within 300 feet, then update it. There were 577 of those updates. Then looked at 10 or more neighbors within 500 feet, and that caught another 111 records.

Manual corrections

Finally there were a few manual corrections applied. Prominent among these was Martin Luther King Jr, which was mostly lacking the Jr.

Final processing

To simplify things a bit, I decided that housing complexes of various sorts that have the same street address (maybe only distinguished by unit numbers) will be combined to a single address location. So as the final step, all records with identical addresses had their locations averaged. These I call my geocoding locations. And then, finally, I converted that dataframe into a data table which will allow much faster searches to be made later on.


In total, there were 6,423 corrections of various sorts made, which out of 1.5 million records is amazingly small. This is a very clean dataset. Now I did take a pretty conservative approach, I know that there are still errors that I did not correct, probably another 6,000 or so, but that is still pretty good. Good enough, in my estimation.