Location Data for National Parks and Wildlife Management

According to Outside Magazine, U.S. national parks clocked 331 million visitors in 2024. Understanding how those visitors impact wildlife and various ecosystems is difficult, time intensive, and often requires years and years of data collection. But location data has emerged as a game-changing solution that offers insights beyond the traditional monitoring methods.

Why Location Data? 

Government agencies and researchers have long relied on tools like trail counters to measure foot traffic, camera traps to document wildlife behavior, and GPS tracking devices to understand animal movement patterns. These established methods provide crucial baseline data and detailed insights for specific locations and timeframes. 

The issue is they’re expensive, and for areas where hikers, hunters, and adventurers might not stay on trails, it's unlikely these methods will capture how humans are moving in the spaces. 

Human mobility data from GPS-enabled smartphone applications enriches these traditional monitoring approaches by filling critical data gaps because it offers more comprehensive spatial coverage. Ultimately, this creates a more complete picture of how humans interact with these environments.

Critical Use Cases for Location Data in National Parks

1. Wildlife Protection and Human-Wildlife Conflict Prevention

Human mobility data fundamentally transforms how park management protects wildlife by revealing spatiotemporal patterns of human-wildlife interactions that other monitoring methods might not capture. Recent research demonstrates that smartphone-derived location data can monitor compliance with wildlife protection zones, track seasonal activity patterns around sensitive habitats, and identify which species might be most prone to human conflict.

What’s most interesting about this approach is its ability to detect both expected and surprising patterns. While mandatory closure areas show excellent compliance rates, voluntary restrictions reveal more complex boundary effects and dramatic increases in human activity immediately after restrictions end. Perhaps most significantly, conflict analysis reveals that the vast majority of potentially dangerous interactions involve only a small subset of individual animals, suggesting that targeted management of specific high-risk animals may be more effective than broad population-level interventions.

This granular understanding enables park managers to refine closure timing based on actual rather than estimated activity patterns, optimize boundary placement using real human movement data, and implement preventive measures before dangerous situations develop. The approach shifts wildlife management from reactive to predictive, fundamentally improving both conservation outcomes and visitor safety.

2. Urban Wildlife Management and Disease Risk Assessment

The integration of mobility data with wildlife tracking reveals complex temporal and spatial patterns that have critical implications for public health and wildlife management in urban park environments. This approach demonstrates that humans and wildlife often share the same spaces but at different times, creating a dynamic landscape of interaction risk that varies predictably throughout the day and season.

Research integrating human mobility data with wildlife tracking shows that animals like white-tailed deer adapt their behavior to human activity patterns, avoiding high-traffic areas during peak human hours but utilizing those same spaces during low-activity periods. These findings are particularly significant for zoonotic disease management, as they identify specific temporal windows and geographic locations where human-wildlife contact is most likely to occur.

The temporal separation patterns observed in urban environments suggest that targeted interventions during high-risk periods, such as enhanced surveillance during early morning and evening hours in commercial areas, may be more effective than broad spatial restrictions. This approach enables park managers to balance wildlife habitat access with public health protection through precise timing of management actions, rather than blanket exclusions.

3. Dynamic Human Footprint Assessment for Adaptive Conservation Planning 

Location data enables researchers to develop human footprint assessments that capture the time-varying nature of human activities across landscapes, providing conservation planners with tools that respond to real-world patterns rather than static assumptions. This represents a shift from traditional approaches that treat human impact as fixed or static to dynamic frameworks that acknowledge the temporal variability inherent in human activity.

The dynamic footprint concept recognizes that the same landscape features, whether roads, trails, or buildings, can have dramatically different impacts on wildlife depending on current human activity levels. A trail system may function as a significant barrier during peak weekend use but serve as a viable wildlife corridor during weekday periods (a common experience for bear encounters). This temporal dimension is critical for species that move across landscapes at different time scales, from daily foraging to seasonal migration.

By incorporating human activity data, conservation planning can identify temporal windows of opportunity for wildlife movement, optimize the timing of management interventions, and predict how changes in human behavior might affect ecosystem connectivity. This approach enables adaptive management strategies that respond to both predictable patterns and unexpected changes in human activity, such as those observed during the COVID-19 pandemic when reduced human mobility led to measurable changes in wildlife behavior and habitat use.

The Future of Conservation Informed by Location Data

Location data provides a transformative tool for wildlife management by offering scalable, dynamic insights into human activity that traditional methods cannot fully capture. It enables managers to prioritize intervention areas, improve compliance with management zones, mitigate conflict risks, and enhance connectivity for sensitive species to support effective conservation strategies.

The integration of location data with other observation data and tools provides park managers and researchers with unprecedented insights into:

• Environmental monitoring across vast landscapes
• Predictive modeling for climate change adaptation strategies
• Evidence-based policy development supporting both conservation and recreation goals
• Cost-effective monitoring replacing expensive traditional survey methods
  
  

Implement Considerations for Location Data

While location data offers tremendous potential, successful implementation requires addressing several considerations:

Data Quality and Bias: While location data excels in capturing relative patterns of human space use, variability in smartphone user sampling over time complicates the estimation of raw abundance patterns through time without high-quality validation data.

Technical Expertise: Working with mobility data requires expertise in handling large datasets to derive meaning. It’s imperative to work with a team that takes the burden of mobility data processing off your team’s plate.

Integration with Traditional Methods: Location data works best when combined with, rather than replacing, established monitoring approaches. There is no out-of-the-box solution to correlating these datasets. 

Conclusion

From understanding visitor patterns to protecting sensitive wildlife and adapting to climate change, location data provides a glimpse into the dynamic movements of humans inside of national parks and wilderness areas. As human pressures on protected areas continue to intensify, the parks that embrace these technological tools will be best positioned to fulfill their dual mandate of preservation and public access. 

For park managers and researchers looking to implement location-based monitoring, starting with pilot projects in high-priority areas can demonstrate value while building internal expertise. The investment in these capabilities today will pay dividends in more effective, efficient, and adaptive park management. 

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