We can now detect mouseclicks in layer[1] of the visualizationa (this corresponds to
adjusted_hws_threat) and SQL query based on the cartdb_id.  This gives us latlng, the value
the ST_AsGeoJSON(the_geom), meaning a JavaScript object notation form of the little grid
square where the user clicked. We will store these in our survey table. (Could be another
layer of the visualization.)
We have extracted the data values, but our new survey table really can be normalized.
We write only the cartodb_id from the adjusted_hws_threat to hws_survey, and include
in this table the survey_value, the latlng value the user picked (this will be different
from the grid square and depend on the user, whom we hope is accurate),
the canned tweet that we generate based on the users perception of quality, the user story and the photo URL.
Then we're done with the data model. We have to set markers, show an info window, give the user
the option to select bad, so-so, good and enter freeform text and a URL.
We also have some SQL to insert data into the new table, and we may want to generate
the new survey map from the inputed data...