Digital Twin Readiness and Equity in Small Cities

Digital twins have transformed infrastructure management and urban planning in large, well-resourced cities — but their promise for small towns is far from guaranteed. Cities with robust data infrastructure, IT staffing, and governance capacity capture disproportionate benefits from smart city innovations, producing a growing “smart urban divide.” For small cities like Laramie and Afton, Wyoming, the question is not simply whether digital twins are technically feasible but whether they are equitable: who gets to use them, who benefits, and whether their adoption might reinforce rather than reduce existing inequalities in access and capability.

This thesis project addresses that question through two interconnected strands of inquiry. The first develops a conceptual model of digital twin inequality, identifying the mechanisms through which digital twins can reproduce inequalities in small cities — including biased or partial data, uneven sensing infrastructure, and vendor-driven models misaligned with local priorities — and the conditions under which they might reduce them, such as participatory governance, open data standards, and locally co-designed models. This theoretical work draws on a systematic critical literature review spanning 2015–2025.

The second strand conducts a comparative empirical assessment of digital twin readiness in Laramie and Afton. A GIS-based inventory catalogs the availability, accessibility, and quality of municipal and county datasets across domains including roads, utilities, hazards, land use, and demographics. Each dataset is scored for openness, metadata quality, update frequency, format interoperability, and spatial coverage. Spatial data analysis — including data density measures and Moran’s I clustering — identifies data deserts and quantifies gaps between communities. Multi-Criteria Decision Analysis (MCDA) ranks candidate digital twin use cases (winter road management, emergency response, water and utility monitoring, transportation) by feasibility and public value in each city context.

The governance dimension is equally central. Policy documents including data-sharing agreements, open-data policies, and privacy statements are analyzed alongside semi-structured interviews with elected officials and key informants, using thematic coding to surface how access control, ownership, and inter-agency collaboration shape what is and is not possible for digital twin development in each community.

The combined output is a replicable framework for assessing digital twin readiness and data equity in resource-constrained communities — one grounded in local governance realities rather than the assumptions of large-city smart city models. Md. Ismail Hossain leads this work as his MS thesis, with Jake advising.