building the ETL pipeline

the towardTreeEquity package is a flexible spatial and statistical scaffold that harmonizes foundational datasets and makes them queryable, so team members can build their own modular research modules on top of it. this is a research engine that can power critical social and environmental infrastructure queries, extending past just planning trees.

Goals:
Definitions
Prompting → Use these summaries to set up a conversation with your preferred coding agent.
Standardization
Project Roadmap

Layers may be joined/compared spatially, temporally, or demographically…

layer	contents	used for	data sources
`tree_canopy`	municipal trees, species, planting date, condition	urban forestry, biodiversity, planting strategy, % of land covered by trees	satellite imagery, LIDAR, iTree Canopy
`demographics`	ACS, EJ screeners, redlining overlays	social vulnerability, gentrification, disinvestment
`infrastructure`	zoning, parcel data, flood risk, heat surfaces	climate resilience, planning, infrastructure audits
`air_quality`	local monitors, EPA AQS, satellite NO₂/PM2.5	pollution mapping, asthma clusters, particulate matter	EPA, PurpleAir, field sensors
`econ_layers`	property values, tax assessments, green jobs, public climate resilience funding	economic burden of disaster resilience, displacement, exploring the “green economy”	census, tax records
`health`	hospitalization rates, chronic illness maps	public health inequities
`climate_metrics`	urban heat islands, canopy % by tract, urban heat exposure, surface temperature, average rainfall,	adaptation, heat resilience	NOAA, MODIS, local field data

We’ll need to create a variety of modules to export our package’s key functions…

function	purpose
`load_tree_canopy()`	pulls in raw municipal tree data and standardizes it
`load_demographics()`	imports and joins ACS / EJScreen data
`join_geography()`	creates consistent census geographies
`clean_canopy()`	fixes species names, conditions, and coordinates
`summarize_canopy_by_tract()`	aggregates tree metrics per geography
`join_air_quality()`	brings in air quality data from EPA or satellites
`make_data_dictionary()`	generates a dictionary for each dataset
`preview_dataset()`	lets users preview any processed table

These can become our exported APIs… (like towardTreeEquity::load_tree_canopy())

Internal Directory Structure for Data Mart Package

R/: where your functions will live (e.g. load_tree_canopy.R)
data-raw/: raw datasets used to create internal .rda files
man/: auto-generated documentation goes here
vignettes/: long-form usage examples
inst/extdata/: for sample CSVs or external resources
DESCRIPTION: will hold your package metadata
NAMESPACE: will list your exported functions