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Hi,
A (unfortunately) common abbreviation for level I have come across is a simple L. For example : UNIT 900, L 9, 50 THINGO ST, HOOHAAVILLE, VIC 3000. I even tried adding L to lookups.py and delet…
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Like #370
> Overview
- Nationally, the Aug 2024 update of G-NAF shows an increase of 44,191 addresses overall (+0.28%). The total number of addresses in G-NAF now stands at 15,698,769 of which 14…
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To read in the extensive dataset into a Pandas dataframe that we can then Pickle, and stage for transformations and joining as required in Sprint 2. Also scope for changing to Polars and Lazy loading …
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While the [ABS tries to ensure mesh blocks don't have multiple zones](https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026), it's not re…
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# **Feature Summary**
Currently, converting GNAF data to a `GeoDataFrame` in `to_geo_dataframe` and `read_shapefile` takes a significant amount of time.
Instead of reading them one by one, modify …
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Create a basic `.yaml` configuration file called `configurations.yaml` and give it some settings for
- logging, see `config_logger()` in the `nhs.utils` folder for the parameters you can pass in. Cha…
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See: #3607
Maps to be upgraded (please expand list):
- [ ] NationalMap
- [ ] AREMI staging
- [ ] AREMI
- [ ] InvestorMap staging
- [ ] InvestorMap
- [ ] DroughtMap
- [ ] pamt…
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Posting this here for visibility. In short, we found a version of the Mexico INEGI address dataset with ~20x more address points - enough that this looks like the comprehensive coverage list. It seems…
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Eg LOC000038973474 "reasonCode":"FTTP_CT","altReasonCode":"FTTC_CT","techChangeStatus":"New Tech Connected"
vs LOC000058374654 "reasonCode":"FTTC_CT","altReasonCode":"FTTP_SA","techChangeStatus":"Eli…
trs80 updated
2 months ago
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Write a function that takes in two `LazyFrame`s, one for the census data with filtered down columns, and one for the GNAF data containing buildings we want to allocate for. The function will output a …