This prototype shows recommended facility hub locations that could cover ARC properties within a selected one-way drive-time threshold.

Gold circles show the recommended hub coverage footprint. Blue circles and spokes show the selected hub and its assigned properties.

Choose a drive-time threshold to adjust the coverage radius.

Filter by division or region to focus the map and results on that operating area.

Select a recommended hub on the map or in the table to zoom to its radius circle and assigned properties.

Use Download hub summary for spreadsheet review or Print report / PDF for a clean executive summary.

real-estate-facilities-export-2026-05-29.csv: 909 bundled facilities; 872 model-ready facilities after basic coordinate/status validation.

The current exercise uses bundled facility data so the model opens directly without an upload step.

There are several routing options, each with pros and cons for cost, traffic, coverage, licensing, and repeatability.

ARC licenses may already support direct drive times and/or isochrones, so licensed Esri/ArcGIS routing should be checked before paying for a new provider.

In the interim, small calibration samples can use Geocodio or OpenStreetMap-based routing to compare straight-line estimates against road drive times.

When selecting a sample, use American Red Cross Corporate Real Estate Facilities metrics and start with the highest-intensity buildings, especially sites prioritized by ownership and size.

Save every road-routing result in a permanent route cache. Threshold changes should reuse cached drive times and rerun the solver, not call the provider again.

Check ArcGIS first because ARC may already have Esri routing privileges or credits. If credit costs are unfavorable, use a self-hosted OSRM or Valhalla server for predictable monthly infrastructure cost.

A good next sample is Southern California because same-mileage trips can produce very different drive times when congestion, freeway access, mountains, and coastal/urban bottlenecks are considered.

Run the current straight-line model for Southern California, then compare it with a traffic-aware provider for a limited set of hub-to-property pairs during peak and off-peak windows.

This sample can show stakeholders why straight-line routing is useful for inexpensive national screening, while real routing or traffic-aware provider data is needed before final decisions in congested metros.

Until true drive-time routing is connected, a regional travel efficiency factor can make the straight-line fallback more honest by shrinking or expanding effective coverage by geography.

Dense congestion areas such as Los Angeles may use factors like 0.60–0.75, while open-road areas such as West Texas may use 1.00–1.10.

Regions with water or mountain barriers, ferries, sparse crossings, or indirect roads should receive lower factors even when they are not dense metros.

This should eventually be editable by division or region with a reason field, so stakeholders can document why each adjustment exists.

Once real routing is available, replace the straight-line fallback correction with cached road drive times plus optional reliability buffers for traffic, parking, access, or weather.

A later version should allow compensation by region so each recommended person can be costed with the correct local pay assumption.

Future regional pay rates should be editable scenario inputs, separate from hub selection and drive-time coverage logic.

Use fully loaded cost, not salary alone. A loaded labor factor accounts for benefits, payroll taxes, insurance, retirement, HR/admin burden, and field support costs.

Because this model involves driving, convert annual loaded cost using productive service hours available, not just salary divided by 2,080 hours.

Factors to consider include driving time, admin time, parts runs, vendor coordination, meetings, leave, training, and equipment or vehicle costs.

For remote or low-hour sites, compare internal loaded labor plus travel against vendor cost so the model can flag outsource candidates instead of forcing inefficient internal coverage.

The report should show staffing cost by region and total modeled staffing cost, while still keeping recommended hubs separate from recommended people.

A later version should grade each facility by estimated service-tech workload hours per week or month instead of treating every property as equal.

Ownership Factor should make licensed/free-space locations low, leased sites moderate, owned sites high, and owned-building/leased-land sites highest because ARC carries more facility responsibility.

Site Type Factor should raise Biomed Direct, Shared Direct, and NHQ Direct sites above Indirect sites because operating intensity and facility expectations are higher.

Building Type Factor should keep parking lots, land, antennas, military-base space, and simple storage low while raising office, warehouse, industrial, hospital, and headquarters-style facilities.

Market Tier Factor and Strategy Factor should capture local complexity and real estate intent; Tier 1 markets and Analysis sites can be slightly higher, while Exit or N/A sites can be lower unless manually overridden.

B&O Intensity Factor should use FY26 B&O / SF where available, with higher operating cost per square foot increasing estimated workload.

Recommended people should eventually be calculated this way: divide total assigned weekly hours by max service hours per person per week, then round up to the next whole person; default: 32 productive service hours per person per week.

For outsource candidates, compare internal loaded cost plus travel against vendor cost, including monthly minimums, hourly rates, trip charges, and remote premiums.

GeoJSON gives GIS teams a map-ready file they can load into ArcGIS, QGIS, Mapbox, or other spatial tools with facility coordinates and model coverage attributes attached.

Batch only missing route pairs, show cache progress in the UI, and reuse cached drive times when only 2–3% of facilities change.

Max properties per person converts overloaded hub assignments into multiple recommended people at the same hub; it does not yet run a full workload-balanced optimization.

ARC Real Estate / Facilities Strategy contact