Preserving Anonymity While Mapping Rural Survey Data in Wausau, Wisconsin

The Town of Wausau, Wisconsin, is a rural municipality in central Wisconsin with a 2010 Census population of 2,229 spread across 37.7 square miles. Its political boundary aligns precisely with one surveyed township under the U.S. Public Land Survey System (PLSS), divided into 36 one-square-mile sections. While most of the boundary is regular, the western edge reflects annexations by the neighboring City of Wausau, which has a population of 39,106.

In 2010, the Wausau Town Board sought to better understand residents’ priorities during a period of economic uncertainty. Leaders wanted to assess demand for public services, determine residents’ willingness to pay for those services, and evaluate broader community concerns. To accomplish this, the town partnered with the Wisconsin Institute of Public Policy and Service (WIPPS), a nonpartisan outreach unit affiliated with the University of Wisconsin Colleges and University of Wisconsin Extension. The collaboration provided cost-effective expertise in survey design, data analysis, GIS integration, and final reporting. As a University of Wisconsin faculty member, I served as co-principal investigator on the project.

The Challenge: Mapping Data Without Compromising Privacy

Wausau’s rural character posed a significant methodological challenge. With a population density of just 59 residents per square mile—compared to 2,082 per square mile in the adjacent city—protecting respondent anonymity was critical. Agricultural land comprises 48.5% of the town’s area, followed by woodlands (25.8%) and wetlands (13.4%). Residential development accounts for only 6.8% of land use, concentrated mainly near the city border. Commercial activity is minimal at 0.4%.

Given this sparse settlement pattern and slow population growth—only 141 additional residents between 1970 and 2010—displaying survey responses on a map risked revealing individual household identities. Community members voiced concerns about confidentiality, especially if results were geographically visualized.

The central question became: how can survey findings be spatially represented without exposing respondents?

The Solution: PLSS Sections as Aggregation Units

The project team resolved this issue by aggregating survey responses at the PLSS section level. Because the town boundary matches a single PLSS township, each of the 36 sections—approximately one square mile in size—served as a natural spatial reporting unit.

While PLSS sections are commonly used in geological, agricultural, and environmental research, their application to social survey mapping is rare. A thorough literature search revealed no comparable examples of PLSS-based mapping for rural social data.

Surveys were mailed to all residential addresses in October 2010, resulting in 505 completed returns from just over 900 mailings—a 56% response rate. The margin of error was ±3% at a 95% confidence level. The survey required approximately 10 minutes to complete and was entirely anonymous. Respondents were asked to identify their PLSS section using a reference map. In Wisconsin, property deeds and tax records are organized by PLSS descriptions, so residents were generally familiar with the system.

Data were analyzed using MYSTAT (version 12) and mapped using Caliper’s Maptitude (version 5) GIS software. Aggregating results by section ensured that no individual household responses could be isolated while still preserving meaningful geographic variation.

Survey Findings and Statistical Insights

Demographically, Wausau emerged as an older, middle-class community. The median age fell between 45 and 54 years. Household income most commonly ranged between $50,000 and $74,999. Most respondents had completed high school with some college education, and 95% were homeowners. The typical resident had lived in the community between 11 and 20 years.

Public service priorities were a major focus. Residents evaluated the importance of services such as fire protection, ambulance service, snow removal, road maintenance, garbage and recycling collection, asphalt paving, law enforcement, ordinance enforcement, equipment upgrades, and facility improvements. They also indicated their willingness to pay for each service.

To assess alignment between perceived need and willingness to fund services, a Spearman rank correlation coefficient was calculated. The result—r = 0.983 at the 95% confidence level—indicated an almost perfect correlation. In essence, residents who valued certain services were overwhelmingly willing to support their funding.

Other issues were examined using a modified Likert scale, allowing agreement or disagreement to be spatially summarized. Proportional pie charts were used to visualize section-level responses.

Regarding commercial development, most residents believed existing conditions were adequate and did not favor expansion. On aesthetic standards—specifically whether ordinances should regulate property appearance—opinion was nearly evenly divided.

Safety concerns were also addressed. A majority favored geographic restrictions on firearm use during deer hunting season in residential areas and near city borders. However, a minority opposed limitations on hunting private land. Questions about banning specific dog breeds revealed that most residents did not support breed restrictions within town limits.

Broader Implications

Using PLSS sections as aggregation units proved to be a practical and replicable approach for rural GIS-based survey reporting. Digital PLSS data are widely available across the United States and can be seamlessly integrated into GIS platforms. This method preserves anonymity while allowing spatial visualization of community perspectives.

Beyond methodological innovation, the project underscores the value of university-affiliated outreach programs like WIPPS. By combining academic expertise with public service, rural communities can access high-quality analytical resources at significantly reduced cost.

The Town of Wausau case demonstrates that careful spatial aggregation enables meaningful geographic insight without sacrificing privacy—an increasingly important balance in small, low-density communities.