ISSS608 2016-17 T1 Assign3 Franky Eddy

Abstract

DinoFun World

• How ?

• What ?

To answer these questions, data visualization is used to get insights:

• M
• R

Overview of Data

DinoFun World is a typical modest-sized amusement park, sitting on about 215 hectares and hosting thousands of visitors each day. It has a small town feel, but it is well known for its exciting rides and events.

One event last year was a weekend tribute to Scott Jones, internationally renowned football (“soccer,” in US terminology) star. Scott Jones is from a town nearby DinoFun World. He was a classic hometown hero, with thousands of fans who cheered his success as if he were a beloved family member. To celebrate his years of stardom in international play, DinoFun World declared “Scott Jones Weekend”, where Scott was scheduled to appear in two stage shows each on Friday, Saturday, and Sunday to talk about his life and career. In addition, a show of memorabilia related to his illustrious career would be displayed in the park’s Pavilion. However, the event did not go as planned. Scott’s weekend was marred by crime and mayhem perpetrated by a poor, misguided and disgruntled figure from Scott’s past.

While the crimes were rapidly solved, park officials and law enforcement figures are interested in understanding just what happened during that weekend to better prepare themselves for future events. They are interested in understanding how people move and communicate in the park, as well as how patterns changes and evolve over time, and what can be understood about motivations for changing patterns.

Objectives

The data is from the in-app communication data over the three days of the Scott Jones celebration. This includes communications between the paying park visitors, as well as communications between the visitors and park services. In addition, the data also contains records indicating if and when the user sent a text to an external party.

The objective is to use visual analytics to analyze the available data and develop responses to the questions below.

• Identify those IDs that stand out for their large volumes of communication.
  • Characterize the communication patterns you see.
  • Based on these patterns, what do you hypothesize about these IDs?
  • Describe up to 10 communications patterns in the data. Characterize who is communicating, with whom, when and where.
  • From this data, can you hypothesize when the vandalism was discovered? Describe your rationale.

Approaches

The step by step approaches done can be seen below.

• Identify those IDs that stand out for their large volumes of communication. For each of these IDs

In order to identify IDs that has large volume of communication, JMP Pro is used to check the communication data on the three days (Friday, Saturday, and Sunday). Some interesting observations can be seen below.

Based on the observation, there are three interesting IDs that has very large volume of communication compared to other IDs: 1278894, 839736, and external.

• Characterize the communication patterns you see

After identifying the IDs that stand out, the next step is characterizing the communication pattern.

1. 1278894
Characteristics of pattern:

• This ID makes the most communications in the three days of the event
• The location of this ID is only at Entry Corridor
• Communications of this ID is only at certain time with the message interval of 5 minutes between messages

Based on these communication patterns, this ID is most probably the Cindysaurus Trivia Game from the DinoFun World app.

2. 839736
Characteristics of pattern:

• This ID's location is also only at Entry Corridor

• Responds to message received in 5 minutes
• Same number of communications sent and received

Based on these communication patterns, this ID is most probably the Information Center.

3. External
Characteristics of pattern:

• Only receives message but never sends message
• Never communicates with the two IDs with highest number of communications (1278894 and 839736)

Based on these communication patterns, this ID is most probably external party.

• Communications Patterns in the Data

From the graph, it can be seen that the number of communications suddenly increased very significantly at about 11:45 AM to 12:00 PM on Sunday. There is also another high number of communiactions at about 2:40 PM to 3:00 PM. This cpuld be an indication that the vandalism was discovered at those timings.

Results

From this data, it can be hypothesized that the vandalism was discovered at about 11:45 AM to 12:00 PM on Sunday.

Visualisation Software

To perform the visual analysis, the following softwares are used:

• Tableau : to visualize the data
• JMP Pro : to prepare the data
• Gephi : to visualize the communication data