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. Author manuscript; available in PMC: 2017 Nov 1.
Published in final edited form as: Am J Prev Med. 2016 Aug 12;51(5):792–800. doi: 10.1016/j.amepre.2016.06.006

Table 1.

Potential Solutions to Unanswered Questions in Spatial Energetics

Question Potential solutions
Conceptual
  • Use spatial energetics data to isolate specific geographic contexts driving behaviors in certain individuals; algorithms can identify places individuals spend time and trips taken, use to generate hypotheses about their relative importance for energetics

  • Develop theory and experimental designs that utilize bouts of activity and sedentary behavior rather than day or week aggregations

  • Combine spatial energetics data with electronic mobility surveys to understand motivations for visiting certain locations, or use of specific routes/transit modes to account for selective daily mobility bias

  • Aggregate GPS data into known participant anchor points and examine behavior around these anchor points to account for selective daily mobility bias

Technical
  • Validate and update GIS layers to maximize accuracy

  • Identify factors related to compliance with GPS procedures to increase compliance in these populations or to statistically adjust for lack of compliance

  • Use software that automatically cleans large GPS datasets by imputing and reducing scatter, such as UCSD’s Personal Activity and Location Measurement System (PALMS)

  • Apply machine learning techniques derived from training data collected during free living to support classification of key behaviors e.g., driving, walking, and cycling

Analytical
  • Create tools for efficiently linking, processing, and analyzing diverse and complex data streams

  • Apply approaches to rapidly visualize GPS points, processed accelerometry data, and travel diaries

  • Develop cross-classified multilevel modeling approaches when analyzing data to account for correlations of measures within individuals

  • Borrow statistical and computing methods from other fields dealing with “big data,” such as genetics, economics, and machine learning

Ethical
  • Develop standardized protocols for data sharing and data security that ensure that the privacy of study participants is protected

  • Consider Certificates of Confidentiality to ensure participant data privacy

  • Remove or mask spatial data in sensitive locations and develop common standards for data masking to begin working towards open data

  • Communicate clearly with participants so that they have a full understanding of the data they are contributing and how those data will be used, as well as the ability to opt out of studies at any time

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