Powell City approves AI surveillance of psychographics for economic planning 

Powell City Council members unanimously approved a two-year contract authorizing the use of an AI-powered tool that analyzes anonymized cellphone location data to track foot traffic and consumer behavior patterns in the growing suburb north of Columbus.

The contract with Placer Labs Inc., approved during a Jan. 20 meeting, is for $15,000 in the first year, rising to $16,000 in the second. The deal includes an opt-out clause.

According to a report, the city plans to access the company’s platform for economic development insights. Officials say the data will help identify shopping “leakage” — where residents travel elsewhere to spend — and pitch retailers on new sites, while spotting areas of declining or surging visitation to guide marketing or infrastructure fixes before businesses falter.

Economic Development Administrator Sean Hughes told council members the insights will support business growth without direct surveillance — highlighting the tool’s privacy safeguards, including aggregated and anonymized data drawn from opt-in app partnerships.

Critics, however, warn of free market risks if centralized planners gain unchecked control over such granular intelligence.

Placer.ai builds foot traffic patterns using psychographics — detailed profiles of consumer lifestyles, values and belief structures, interests and attitudes — often layered from third-party datasets on behaviors such as eco-conscious shopping, fitness preferences, or luxury spending. This allows planners to segment populations beyond basic demographics, targeting “best-fit” businesses that align with affluent or high-spending visitor profiles.

The city also reports that it intends to use the data to spot when a business district is declining and intervene with improvements. However, studies find that such capabilities — especially when utilized to determine zoning, land use, or government interventions — enable government planners to pick winners and losers more deliberately.

The marketplace is fluid, warns a CATO Institute report. Using past aggregated data to predict future trends is rigid, risking homogenized commercial districts that widen economic gaps between favored corridors and overlooked neighborhoods.

By prioritizing developments in high-traffic zones or for retailers that match preferred psychographics, planners might funnel incentives, zoning changes, or public investments toward chain stores or upscale brands, sidelining local independents or diverse enterprises that don’t have comparable metrics.

Economic viability also affects census mapping, potentially extending to future elections through redistricting. While Powell’s council races are at-large and not ward-based, broader Ohio redistricting for state and congressional seats relies on population data.

Location analytics could also have far-reaching consequences, indirectly influencing where growth concentrates. Concentrated development could lead to demographics and electoral maps favorable to incumbents or specific parties during decennial redistricting.

While no direct link exists in Powell’s adoption, the tool’s role in long-term planning raises questions about how data-driven decisions could entrench political advantages.