RISEUP Geomarker Pipeline

This is a pipeline for appending place- and date- based geomarker data from multiple data sources at multiple geographic and temporal resolutions, with a specific focus on Hamilton County, Ohio:

%%{init: { "fontFamily": "arial" } }%%

graph TD

classDef id fill:#fff,stroke:#000,stroke-width:1px;
classDef input fill:#fff,stroke:#000,stroke-width:1px;
classDef data fill:#e8e8e8,stroke:#000,stroke-width:1px;

hosp[(hospitalizations)]:::input
hosp --> date(date related to address):::id
hosp -->  address(cleaned and parsed \naddress components):::input

daily("weather, air quality, pollen/mold, \nseasonality, instructional days"):::data

date --join by date --> daily

address --street range \ngeocoding--> geocode(geocoded \ncoordinates):::id
address --probabilistic \nmatching--> parcel(parcel \nidentifier):::id

pd(point-level \ndata resources, e.g.,\n greenspace, traffic,\n air pollution, \nhospital access):::data


tract_data(tract-level data resources, e.g.,\nChild Opportunity Index,\n Material Deprivation Index):::data
geocode --spatial intersection \nwith census tract --> tract_data
geocode -- geomarker \nassessment library --> pd

parcel_data(tax auditor databases,\nhousing code violations,\nhousing health pedigree):::data
parcel --join by parcel identifier--> parcel_data

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Data

See the metadata for information about specific data elements and sources.

Notes:

• Patient addresses are constructed by pasting together ADDRESS, CITY, STATE, ZIP fields
• hh_acs_measures has annual measures, but measures from 2019 are used here

Time Data Availability Notes

Data	Min Date	Max Date	Frequency	Data Availability Lag
AQI	2015-01-01	2022-10-01	daily	6 months
weather	2015-01-01	2022-09-30	daily	6 months
pollen and mold	2021-02-17	2022-12-09	random (skips weekends, some random days missing)	unknown
shotspotter (only for Avondale, E. Price Hill, and W. Price Hill)	2017-08-16	2023-01-03	daily	daily

Running & Developing

1. Clone github repository to destination; manually move input health data into place (DR1767_r2.csv)
2. Install pak (install.packages("pak", repos = sprintf("https://r-lib.github.io/p/pak/stable/%s/%s/%s", .Platform$pkgType, R.Version()$os, R.Version()$arch)))
3. Install all packages from DESCRIPTION file by running pak::pak() or remotes::install_deps() in the project root from R. (If you are on a linux machine, speed up installation to use binaries hosted by Posit, by setting options("repos" = c("CRAN" = "https://packagemanager.rstudio.com/all/__linux__/jammy/latest")), substituting jammy for your specific linux version.)
4. Install required python libraries. (Use reticulate::py_config() to check on available python environments):

reticulate::py_install("usaddress", pip = TRUE)
reticulate::py_install("dedupe", pip = TRUE)
reticulate::py_install("dedupe-variable-address", pip = TRUE)

1. Use make to create targets defined in Makefile or make tdr to create the final output as a tabular data resource. docker is required to run the geocode and geomark targets.

To specify the python executable to use for the pipeline without using R, set an environment variable in the shell being used to call make:

export RETICULATE_PYTHON=~/.virtualenvs/r-parcel/bin/python
make all

Notes:

• /data is for any output data associated with a participant and/or the raw hospital admissions file; any *.rds or *.csv files in this directory will always be git ignored
• /data-raw is for raw (e.g., violations spreadsheet) or intermediate data products (e.g., daily AQI) that are tracked using git
• Makefile defines the pipeline, see other high level targets there