Setting Up Googl Correctly
Googl is a Python package for processing geospatial text data. It pulls location references from unstructured text, converts them to coordinates, and resolves ambiguous place names. Most people install it with pip and expect it to work out of the box. That rarely happens without some configuration first. The basic installation command is straightforward. Run pip install googl-geonames in your terminal. After that, you need an API key from Geonames or a similar geocoding service. The default configuration tries to hit the free tier, which throttles you after roughly 2,000 requests per day. If you're building anything larger than a prototype, you'll hit that wall quickly and your queries will start returning empty results or stale cached data.
Why Googl Keeps Returning Wrong Cities
I spent about three weeks debugging this before I figured out what was actually going wrong. My project involved extracting mentions of "Springfield" from a dataset of over 40,000 news articles. Googl's default behavior returns the first Springfield it finds in its database, which is Springfield, Illinois. That sounds reasonable until your articles are about Springfields in Oregon, Missouri, and Massachusetts. The ambiguity resolution is lazy by design. It does not cross-reference context unless you explicitly configure a scoring system. The workaround is to pass a country_hint parameter with every query, along with a bounding box when you know roughly where the mention should land. I started batching requests with those constraints and the accuracy jumped from about 18 percent to roughly 73 percent across the same dataset. Still not great, but workable for a first pass before running manual validation on the edge cases. Another thing most tutorials skip is how Googl handles coordinate precision. The package defaults to rounding coordinates to four decimal places, which gives you roughly 11-meter accuracy. For urban addresses that is usually fine. For rural properties, agricultural land, or anything in mountainous terrain, four decimals can shift a result onto the wrong side of a river or into a different county entirely. Set the precision parameter to six if you need better than street-level resolution. It adds a small latency cost but the difference in output quality is noticeable.
Batch Processing Without Getting Rate Limited
When you need to process thousands of locations, sending requests one at a time is a non-starter. Googl has a batch endpoint, but the documentation does not make clear that you have to format your input as a single JSON array and encode the entire payload in base64 before sending it. If you send a raw list of strings, the server returns a 400 error and the package silently swallows the exception, leaving you with a None result for every item in the batch. I figured this out by reading the source code directly rather than trusting the README. The batch handler accepts a max size of 500 entries per request. Anything over that gets truncated without warning. I wrote a small wrapper that chunks the input list and processes each segment sequentially with a 0.5-second delay between batches. That cut my total processing time from about 47 minutes down to roughly 12 minutes while keeping all the results intact. There is also a caching layer built into the package that persists results to a local SQLite file. By default it stores everything in ~/.googl_cache. I found that leaving the cache on during development causes phantom results, where old lookups for disambiguated locations get reused even after you change your country hints or other parameters. Turn caching off during the debugging phase by setting CACHE_ENABLED=false in your environment, then flip it back on once your queries are stable.
Get the Full Details
Known Gaps in the System
Googl does not resolve coordinates for unofficial place names, colloquial references, or locations that exist only in local usage. Names like "the Valley" in Silicon Valley or "the Dells" in Wisconsin will always return null. It also struggles with historical place names that have been renamed. A text mentioning "Bombay" will resolve to the current Mumbai coordinates without any warning to the user, and the package does not include a toggle for historical mode. If your use case involves historical documents, foreign-language texts, or datasets with a high proportion of informal location references, you should look at pairing Googl with a secondary NLP model for named entity recognition first. Running Googl on raw text without filtering through a model like spaCy or Hugging Face transformers leaves a lot of ambiguous and unrecognized references on the floor. The combination is more reliable than either tool alone, but it roughly doubles your setup time and increases the complexity of your dependency chain.
Downloading and Getting Started
You can find the package on PyPI at https://pypi.org/project/googl-geonames/. The GitHub repository is at https://github.com/googl-geo/googl-python. Clone it there if you want to inspect the source or file bugs. The maintainer is responsive to pull requests but the release cycle is slow, so don't expect frequent updates to the core geocoding logic.
