---
title: "Area Identification"
description: "Score every area in a market on explicit criteria (activity, mix, momentum, seasonality, timing, audience, presence) and combine them into a ranked shortlist."
slug: "/docs/guides/area-identification"
last_updated: "2026-08-31"
status: "published"
industry: ["retail", "real-estate", "municipalities"]
location_types: ["area"]
tags: ["area-identification", "screening", "scoring", "expansion"]
is_case_study: false
locked: false
related_metrics: ["foot-traffic-vwr", "visitor-demographics-area", "visitor-profile-area"]
related_datasets: []
example_count: 1
example_tools: ["python"]
---


# Area Identification

Score every area in a market on explicit criteria (activity, mix, momentum, seasonality, timing, audience, presence) and combine them into a ranked shortlist.

## Metadata

- Industry: retail, real-estate, municipalities
- Location types: area
- Tags: area-identification, screening, scoring, expansion

Our area metrics support screening a whole market: score every area on explicit criteria and rank them into a shortlist. A scored screen covers the market on equal terms, including the areas that would not come up from familiarity alone. The same method (indicators, normalization, a weighted composite) serves expansion targeting, media planning, and development prioritization.

---

## 1. Define the candidate universe

Fix the area system and the market bounds before touching indicators: which areas compete for a place on the shortlist? A metro's neighborhoods, a region's grid cells, every area in a state. Two rules:

- **Score within the universe.** Percentiles and comparisons only mean something against the set a candidate actually competes with.
- **Match the grain to the decision.** Neighborhood grain for siting and local marketing; coarser grains for market entry. Finer grain means more candidates and more small-area noise (see step 5).

## 2. Choose scoring indicators

Indicators fall into seven families; a workable shortlist usually draws on one or two from each:

| Family | Example indicators | What it captures |
| --- | --- | --- |
| **Activity volume** | total visits; top-percentile membership | How much happens there at all |
| **Activity mix** | destination share (visitors who neither live nor work there) | Whether people choose to come, beyond resident and worker activity |
| **Momentum** | year-over-year change on identical months; recent vs. prior trend | Where activity is heading, not just where it is |
| **Seasonality** | summer index (season average ÷ annual average); event spikes | When the area lives, critical for seasonal concepts |
| **Timing** | activity concentrated in target hours (evenings, weekends) | Whether the area is alive when your business needs it |
| **Audience** | share of a target demographic or persona among visitors | Whether the right people are there |
| **Presence** | number of places in the target category; visits those places receive | What already operates in the area: competition or co-tenancy, depending on the question |

One trap in the audience family: shares rank areas by composition, which favors small areas with a lean mix over big areas with a big audience. If you care about absolute audience size, multiply the share by visitor volume before scoring. "Many of the right people" and "mostly the right people" are different indicators; pick one deliberately.

## 3. Normalize

Raw indicators live on incompatible scales. Convert each to a percentile rank within the candidate universe before combining. Percentiles are robust to the heavy skew of visitation data, where a mean-based z-score is dragged by a few giant areas.

Some criteria work better as gates than as scores: "at least N monthly visitors" or "destination share above X" filters the universe before scoring, instead of letting a strong score elsewhere compensate for a disqualifying weakness.

## 4. Composite score and weights

The composite is a weighted sum of normalized indicators:

```
score(area) = Σ  wᵢ · pctl(xᵢ)        with  Σ wᵢ = 1
```

The weights are the strategy, stated as numbers: a late-night concept weights timing, an expansion screen weights momentum and audience, a development agency weights mix and trend. What matters is that the weighting is explicit and defensible. Two disciplines:

- **Sensitivity-check the shortlist.** Move the weights (±10 points between the top factors) and re-rank. Areas that stay in the top set under any reasonable weighting are robust picks; areas that appear under one exact weighting are artifacts of it.
- **Same window for every indicator.** Compute all indicators over the same period (or an explicitly chosen one, like a momentum lookback) so the score compares areas, not periods.

## 5. Sanity-check the shortlist

Put the shortlist on a map: spatial clusters and lone outliers both carry information the table hides. Check the smallest areas for small-sample noise; low-activity areas swing more and get more privacy suppression, and a suppressed value is missing, not zero. Read the top areas' raw indicator values, not just the composite. A top score built from four mediocre percentiles is a different finding than one built from two exceptional ones.

From the shortlist, the next step is a [store profile](/docs/guides/store-profiling) of what already operates in an area, or a [void analysis](/docs/guides/void-analysis) of what it lacks.

## Examples

### Finding Denver's up-and-coming neighborhoods _(Python · USA)_

Score every Denver block group on 2025 activity, destination share, momentum (Jul–Nov 2025 vs. Jul–Nov 2024), summer index and audience, combine them with explicit weights, and stress-test the shortlist, in Python.

Full worked example: [/docs/guides/area-identification/us-cbg-scoring.md](/docs/guides/area-identification/us-cbg-scoring.md)

## Related metrics

- Foot Traffic w/ Visitor, Worker & Resident (area)
- Visitor Demographics (area)
- Visitor Profile (area)