Project snapshot
This project is a methodology case study. The central problem is geographic mismatch: Opportunity Zones are tract geography, while the strongest business-structure data is county geography. DaedalMap keeps the county business totals as observed truth, then allocates them downward using tract manufacturing employment density so the resulting tract estimates stay explicit about what is observed and what is modeled.
Data and methods
Sources used in the analysis:
- SUSB 2015-2022.
County business counts by NAICS and enterprise size inside the
usa_industrial_activitypack. Manufacturing SMEs are defined as firms with fewer than 500 employees inNAICS 31-33. - LODES 2022.
Tract workplace jobs inside the
usa_industrial_activitypack, used as the downscaling weight surface, withCNS05manufacturing jobs as the primary allocator. - USA Opportunity Zones. Tract-level OZ 1.0 and OZ 2.0 eligibility flags aligned to the same tract hierarchy.
Methodology follows a simple observed-plus-estimated rule. County SUSB totals remain the anchor truth. Within each county, manufacturing SMEs are distributed across tracts in proportion to tract manufacturing jobs. That creates tract-level estimated SME counts that can be intersected with OZ 1.0 and OZ 2.0 tracts without pretending the original business counts were directly observed at tract scale. The result is a tract-level industrial fit surface where business presence, manufacturing labor, and OZ geography are already aligned enough to justify deeper follow-up.
Methodology highlights
The same pattern works anywhere the data does not arrive at the same geographic level as the decision. A larger-area observed source can stay intact as the anchor truth, then be allocated downward with a smaller-area weighting surface so tract or neighborhood comparisons become possible without pretending the original source was more precise than it really was. In this project, county business counts and tract manufacturing jobs are enough to build a shortlist. In other settings, the same structure could be reused with buildings, land use, facilities, or other local weighting layers.
Current findings
The first pass is already useful as a shortlist tool. The tract-level estimates show clear separation between places with meaningful current manufacturing SME presence and places that only look promising at county scale. That is enough to rank OZ 1.0 growth patterns, surface stronger OZ 2.0 candidates, and focus deeper follow-up on a smaller set of tracts instead of treating every eligible zone as equally plausible.