How Place Signals turns public data into location intelligence

Most users see public data—like the U.S. Census or FEMA’s National Risk Index—as a static spreadsheet. For a relocation researcher or a business analyst, raw numbers are often too fragmented and lack the context needed to make a high-stakes decision.

At Place Signals, our job isn't just to display data; it’s to transform raw, messy public datasets into normalized decision signals. In this post, we’re opening the hood on our process.

The Problem: Data Fragmentation

Imagine you’re comparing two high-growth "lifestyle" cities: Boise, ID and Chattanooga, TN.

If you look at the raw data for median monthly rent, you might see:

Boise: ~$1,600
Chattanooga: ~$1,250

Is Chattanooga "better" because it's cheaper? Or is Boise "better" because the higher rents reflect a more robust professional services economy? Without normalization, you’re just looking at a price tag, not a signal.

The Process: Transformation & Normalization

We follow a four-step pipeline to turn these raw numbers into a Relocation Fit Score.

1. Ingestion & Validation

We pull from "wired-up" sources like the ACS 5-year estimates. We check for "vintages" (source years) to ensure we aren't comparing 2021 data with 2024 data.

2. Geographic Alignment

Public data lives in different "buckets." Census data is often reported by Tracts, while business applications might be reported at the County level. We use spatial joins to align these metrics to the specific area you’re researching.

3. Min-Max Normalization

This is where the magic happens. We take a raw value (like rent) and map it onto a scale of 0 to 100 based on the range of all peer markets in the U.S.

> Formula Note: For costs, we invert the score. A lower rent results in a higher affordability signal.

4. Weighted Aggregation

Finally, we apply "priorities." If you value recreation over affordability, we adjust the weights of those normalized signals to produce your final score.

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Live Case Study: Boise vs. Chattanooga (2026 Data)

Here is how our relocation_fit_v1 logic handles real-world data for these two metros.

The Insight

While Boise has slightly superior Outdoor Access and stronger Growth Momentum, Chattanooga offers a significantly higher Affordability Signal.

For a user prioritizing a low cost of living while maintaining access to world-class recreation, Chattanooga would score higher overall—even if Boise is the "hotter" market in national headlines.

Confidence & Transparency

Every signal we produce is accompanied by a Confidence Badge.

Data Confidence: Reflects how recent the data is (e.g., Boise's 2023 ACS data has high confidence).
Resolution Confidence: Reflects how granular the data is (e.g., Tract-level rent data is more precise than County-level averages).

We believe that admitting when data is modeled or low-resolution is the only way to build real trust with professional users.

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Sources and data notes

American Community Survey (ACS), U.S. Census Bureau (2023 Vintage)
National Risk Index, FEMA (2024 Vintage)
ReloPulse Internal Calculations, Methodology v1.0