I am looking to run a space-time Principal Component Analysis on Shotspotter data from Brockton, MA: http://justicetechlab.org/data/. Shotspotter sensors record the timing, location, and number of gunshots. There are gaps in the data when no shots were detected.
I would like to include the gaps in my PCA. At the temporal scale of a day, I have filled in the gaps and assigned a value of zero rounds to days when there were no gunshots detected (e.g. row six of the table below).
I am confused about how to handle the missing lat/lon data for these zero shots-fired days. When there are no shots detected, there are no shots detected everywhere, so assigning a lat/lon seems strange. But I would like to include the zero shots detected data points.
Statistically speaking, would the best solution be to? :
1. ignore the zero data completely 2. use the mean lat/lon for the observed data 3. randomly generate lat/lon data within the region of study and substitute these for missing lat/lon 4. use another statistical method well suited to my problem
I have found some papers on how to impute missing measurements, but have not found much explaining how to handle missing lat/lon data: