Chronologies and timelines
Chronologies are simple but useful tools that help order events sequentially; display information graphically; and identify possible gaps, anomalies, and correlations. The technique pulls the analyst out of the evidentiary weeds to view a data set from a more strategic vantage point. A Chronology places events or actions in the order in which they occurred. A Timeline is a visual depictions of those events showing both the time of events and the time between events. Chronologies can be paired with a Timeline and mapping software to create geospatial products that display multiple layers of information such as time, location, and multiple parallel events. The geographic scope and many details of this case make a Chronology, Timeline, and Map particularly useful in understanding how the case unfolded both temporally and spatially.
Deliverable 1:
Create a Chronology of the anthrax attacks and investigation
Step 1: Identify the relevant information from the case narrative with the date and order in which it occurred.
Step 2: Review the Chronology by asking the following questions:
What does the timing of the appearance of symptoms tell me about when the letters were mailed?
Could there be any other letters than the four in the government’s possession?
What additional information should we seek?
Are there any anomalies in the timing of events?
Deliverable 2:
Create a Timeline of the attacks based on geographic location.
Step 1: Identify the relevant information about the victims from the Chronology with the date and order in which the events occurred. Consider how best to arrange the data along the Timeline. Can any of the information be categorized?
Step 2: Review the Timeline by asking the following questions:
Do any of the events appear to occur too rapidly or too slowly to have reasonably occurred in the order or timing suggested by the data? (e.g. the letters and their postmarks).
Are there any underlying assumptions about the evidence that merit attention?
Does the case study contain any anomalous data or information that could be viewed as an outlier? What should be done about it?
