Mapping Rural Public Opinion with PLSS-Based GIS in the Town of Wausau

The Town of Wausau, Wisconsin, is a small rural jurisdiction in central Wisconsin with a population of 2,229 residents according to the 2010 Census. Covering 37.7 square miles, the town’s political boundary aligns with a single surveyed township under the U.S. Public Land Survey System (PLSS), divided into 36 one-square-mile sections. While the township grid is regular, the western boundary is uneven due to annexations by the adjacent City of Wausau, which has a significantly higher population density.

Facing economic pressures and long-term planning challenges, the Town Board sought to assess residents’ priorities regarding public services, fiscal responsibility, and future development. In October 2010, the town partnered with the Wisconsin Institute of Public Policy and Service (WIPPS), affiliated with the University of Wisconsin Colleges and University of Wisconsin Extension. The project included survey design, statistical analysis, GIS integration, and final reporting. Collaboration with WIPPS provided the town with technical expertise at a substantially reduced cost. As a faculty member at the University of Wisconsin, I served as co-principal investigator.

The Challenge: Mapping Social Data in a Low-Density Community

The Town of Wausau’s population density is 59 people per square mile—dramatically lower than the neighboring city’s 2,082 people per square mile. Land use patterns reflect its rural character: agriculture accounts for nearly half of total land area (48.5%), with woodlands and wetlands comprising 25.8% and 13.4% respectively. Commercial development is minimal at 0.4%, and residential land use, concentrated in the western third near the city boundary, totals 6.8%. Population growth over four decades has been modest, increasing by just 141 residents between 1970 and 2010.

Conducting a social survey in such a sparsely populated area presents a particular complication: protecting respondent anonymity. When survey responses are spatially displayed in a GIS, small population clusters may inadvertently reveal individual household information. Several community members raised this concern during planning discussions, prompting careful deliberation between the Town Board and WIPPS. The central question was how to communicate geographically referenced survey results without exposing individual respondents.

The Solution: PLSS Sections as a Spatial Reporting Framework

The project addressed this challenge by aggregating survey responses at the level of PLSS sections. These one-square-mile units are widely used in geological, agricultural, and biological studies but are rarely applied to social survey mapping. After extensive research, no comparable rural projects using PLSS sections for social data presentation were identified.

More than 900 surveys were mailed to all residential addresses in October 2010, with 505 completed responses returned—a 56% response rate. Participants were asked to identify their PLSS section using a reference map included in the survey. Because Wisconsin land records and property deeds are commonly organized by PLSS section, residents were familiar with this geographic framework. Responses were analyzed using MYSTAT statistical software and spatially visualized in Caliper’s Maptitude GIS.

The survey achieved a 95% confidence level with a margin of error of ±3%. Demographic questions gathered information on median age, educational attainment, income, and length of residency. Most respondents were middle-aged (45–54 years), middle-income ($50,000–$74,999), long-term residents, and homeowners.

Public service preferences were measured using ranked responses, while broader policy questions employed a modified Likert scale to capture levels of agreement or disagreement.

Statistical Findings and Spatial Patterns

Residents evaluated the importance of services such as fire protection, ambulance coverage, snow removal, road maintenance, garbage and recycling, asphalt paving, law enforcement, ordinance enforcement, equipment upgrades, and building improvements. A Spearman rank correlation analysis assessed the relationship between desired services and willingness to pay for them. The result—r = 0.983 at a 95% confidence level—indicated an almost perfect correlation, demonstrating strong alignment between community priorities and fiscal commitment.

Likert-scale responses were mapped using proportional pie charts within each PLSS section, allowing policymakers to observe spatial variation in attitudes. On the topic of commercial development, most residents indicated satisfaction with existing conditions, suggesting limited appetite for expanded retail or office growth. Views on enforcing aesthetic property standards were more evenly divided, reflecting balanced perspectives across the township.

Safety-related concerns were also addressed. A majority supported geographic restrictions on firearm use during deer hunting season in residential areas and near subdivision boundaries. Conversely, most residents opposed banning specific dog breeds within town limits, preferring fewer regulatory constraints in that domain.

Conclusions: PLSS Sections as a Model for Rural GIS Survey Mapping

This project demonstrates that PLSS sections provide a practical and effective geographic unit for mapping rural survey data while preserving anonymity. Digital PLSS files are readily available nationwide and integrate seamlessly into GIS platforms. Aggregating responses at the section level protects privacy without sacrificing spatial clarity, enabling decision-makers to visualize trends and regional differences across the township.

The collaboration also highlights the value of university-affiliated public outreach organizations such as WIPPS in supporting rural governance. By combining statistical rigor, GIS expertise, and community engagement, the Town of Wausau was able to conduct a comprehensive survey project efficiently and economically.

Using PLSS-based spatial aggregation offers a replicable framework for other rural communities seeking to balance transparency, geographic analysis, and respondent confidentiality in social research.