Forest Gregg

this is working beautifully: ```python from contextlib import contextmanager @contextmanager def abstract(base_model): previous_abstract_state = base_model._meta.abstract base_model._meta.abstract = True try: yield base_model finally: base_model._meta.abstract = previous_abstract_state with abstract(opencivicdata.core.models.Person) as AbstractPerson: class...

this looks good: https://adamj.eu/tech/2022/03/30/how-to-make-django-error-for-undefined-template-variables/#with-a-logging-filter-that-raises-exceptions

Might be easiest to just add these through the django admin http://ocd.datamade.us/admin/

i think handling this with the scraper would be great. something like https://github.com/opencivicdata/scrapers-us-municipal/blob/chicago_comprehensive/chicago/rule_forty_five.py if you could make a file with a python dictionary with the keys being the alders names...

Sounds great! I think I would start by following the Census's guidance on aggregating statistics https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018_ch08.pdf It would be very, very nice to make use of the variance data that...

1. could you you tell me a little bit more about what you mean by "necessary to properly downscale the statistics for the partial edge geometries"? 2. I think it's...

Do you mean that the desired shape can cut across census geographies, and you'll need to figure out what data to apportion?

Okay, finding the intersections is a fairly expensive operation. When we do it here: https://github.com/datamade/census_area/blob/5e62f7d114efd6076916ed6ecffcb7ff76bf4dd6/census_area/core.py#L62-L63 It would be probably be a good idea to go ahead and return the proportion...

weights are going to be important as, for example, sometimes you'll want to know size of the associated population. Anyway, i think you have enough to move forward.

@dmarulli, any updates on your project?