Creating a replicable, valid cross-platform buffering technique: the sausage network buffer for measuring food and...

Area of Science:

• Environmental Health
• Geographic Information Systems (GIS)
• Obesity Research

Background:

• Geographic Information Systems (GIS) are increasingly utilized by obesity researchers to assess exposure and access within neighborhood food and physical activity environments.
• Existing GIS buffering methods can yield inconsistent results due to proprietary software variations.
• A novel network buffering approach, the "sausage" buffer, is proposed to address these reproducibility issues.

Purpose of the Study:

• To introduce and describe the development of the "sausage" buffer approach for GIS.
• To evaluate the sausage buffer's performance against existing methods in terms of buffer characteristics and environmental measurements.
• To assess the association between environmental features measured by the sausage buffer and health-related behaviors.

Main Methods:

• The "sausage" buffer method was developed for cross-platform and cross-software consistency.
• Comparative analysis was conducted between the sausage buffer and traditional network buffers (e.g., ArcView 3.3).
• The approach was tested using data from the Eating and Activity in Teens (EAT 2010) study, involving 2,724 adolescents in the Minneapolis/St. Paul metropolitan area.

Main Results:

• The sausage buffer demonstrated comparable area measurements to classic network buffers, especially at larger buffer sizes.
• Similar environmental measurements and correlations between environmental features and health outcomes (physical activity, food purchases) were obtained using the sausage buffer compared to other techniques.
• The sausage buffer effectively measured variables related to food and physical activity environments.

Conclusions:

• The sausage buffer yields results comparable to existing buffering techniques.
• A key advantage of the sausage buffer is its replicability across different software programs and versions.
• This approach enhances research independence from specific software, promoting greater consistency in environmental exposure assessment for obesity research.