Some ACS download helpers and Research Software Papers

The blog has been a bit sparse recently, as moving has been kicking my butt (hanging up curtains and recycling 100 boxes today!). So just a few quick notes.

Downloading ACS Data

First, I have posted some helper functions to work with American Community Survey data (ACS) in python. For a quick overview, if you import/define those functions, here is a quick example of downloading the 2019 Texas micro level files (for census tracts and block groups) from the census FTP site. Can pipe in another year (if available) and and whatever state into the function.

# Python code to download American Community Survey data
base = r'??????' #put your path here where you want to download data
temp = os.path.join(base,'2019_5yr_Summary_FileTemplates')
data = os.path.join(base,'tables')

get_acs5yr(2019,'Texas',base)

Some locations have census tract data to download, I think the FTP site is the only place to download block group data though. And then based on those files you downloaded, you can then grab the variables you want, and here I show selecting out the block groups from those fields:

interest = ['B03001_001','B02001_005','B07001_017','B99072_001','B99072_007',
            'B11003_016','B11003_013','B14006_002','B01001_003','B23025_005',
            'B22010_002','B16002_004','GEOID','NAME']
labs, comp_tabs = merge_tabs(interest,temp,data)
bg = comp_tabs['NAME'].str.find('Block Group') == 0

Then based on that data, I have an additional helper function to calculate proportions given two lists of the numerators and denominators that you want:

top = ['B17010_002',['B11003_016','B11003_013'],'B08141_002']
bot = ['B17010_001',        'B11002_001'       ,'B08141_001']
nam = ['PovertyFamily','SingleHeadwithKids','NoCarWorkers']
prep_sdh = prop_prep(bg, top, bot, nam)

So here to do Single Headed Households with kids, you need to add in two fields for the numerator ['B11003_016','B11003_013']. I actually initially did this example with census tract data, so not sure if all of these fields are available at the block group level.

I have been doing some work on demographics looking at the social determinants of health (see SVI data download, definitions), hence the work with census data. I have posted my prior example fields I use from the census, but criminologists may just use the social-vulnerability-index from the CDC – it is essentially the same as how people typically define social disorganization.

Peer Review for Criminology Software

Second, jumping the gun a bit on this, but in the works is an overlay journal for CrimRxiv. Part of the contributions we will accept are software contributions, e.g. if you write an R package to do some type of analysis function common in criminology.

It is still in the works, but we have some details up currently and a template for submission (I need to work on a markdown template, currently just a word doc). High level I wanted something like the Journal of Statistical Software or the Journal of Open Source Software (I do not think the level of detail of JSS is necessary, but wanted an example use case, which JoSS does not have).

Just get in touch if you have questions whether your work is on topic. Aim is to be more open to contributions at first. Really excited about this, as publicly sharing code is currently a thankless prospect. Having a peer reviewed venue for such code contributions for criminologists fills a very important role that traditional journals do not.

Future Posts?

Hopefully can steal some time to continue writing posts here and there, but will definitely be busy getting the house in order in the next month. Hoping to do some work on mapping grids and KDE in python/geopandas, and writing about the relationship between healthcare data and police incident report data are two topics I hope to get some time to work on in the near future for the blog.

If folks have requests for particular topics on the blog though feel free to let me know in the comments or via email!

Buffers and hospital deserts with geopandas

Just a quick blog post today. As a bit of a side project at work I have been looking into medical service provider deserts. Most people simply use a geographic cutoff of say 1 mile (see Wissah et al., 2020 for example for Pharmacy deserts). Also for CJ folks, John Hipp has done some related work for parolees being nearby service providers (Hipp et al., 2009; 2011), measuring nearby as 2 miles.

So I wrote some code to calculate nice sequential buffer areas and dissolve them in geopandas. Files and code to showcase are here on GitHub. First, as an example dataset, I geocode (using the census geocoding API) CMS certified Home Healthcare facilities, so these are hospice facilities. To see a map of those facilities across the US, and you can click on the button to get info, go to here, CMS HOME FACILITY MAP. Below is a screenshot:

Next I then generate sequential buffers in kilometers of 2, 4, 8, 16, and then the leftover (just for Texas). So you can then zoom in and darker areas are at a higher risk of not having a hospice facility nearby. HOSPICE DESERT MAP

Plotting some of these in Folium were giving me fits, so I will need to familiarize myself with that more in the future. The buffers for the full US as well were giving me trouble (these just for Texas result in fairly large files, surprised Github doesn’t yell at me for them being too big).

Going forward, what I want to do is instead of relying on a fixed function of distance, is to fit a model to identify individuals probability of going to the doctor based on distance. So instead of just saying 1+ mile and you are at high risk, fit a function that defines that distance based on behavioral data (maybe using insurance claims). Also I think the distances matter quite a bit for urban/rural and car/no-car. So rural folks traveling a mile is not a big deal, since you need a car to really do anything in rural areas. But for folks in the city relying on public transportation going a mile or two is a bigger deal.

The model then would be similar to the work I did with Gio on gunshot death risk (Circo & Wheeler, 2020), although I imagine the model would spatially vary (so maybe geographically weighted regression may work out well).

References