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## Question:

Trajectory Data Analysis

To dig into the context of the study, this chapter introduces some terms and concepts used in travel-generated knowledge.

A trajectory describes an object 's movements, so it can be described as a trace created by an object moving in geographical spaces, generally represented by a series of chronologically ordered points [15].

There has been a real evolution in recent years technology in the field of position acquisition which has generated large quantities of spatial data from which trajectories can be extracted relating to the mobility of objects, such as people , vehicles or animals. Thus, beginning with these results, it was decided to extract useful information to understand the movement of objects and their location, promoting a wide range of location-based applications within social networks [14].

This is the basis of data mining by trajectory. As already mentioned, a trajectory can be connected to different contexts, as defined by Zheng in [15]. Which can be classified as:

1.Mobility of transport vehicles: a large number of vehicles equipped with GPS (global positioning system) surround us on a daily basis, such as taxis, buses, ships and aircraft, which generate data consisting of at least the spatial reference point and time stamp (time stamp). Thus, by chronologically arranging the above, it would be possible to create trajectories that can be used for example: resource management, traffic analysis and development of the transport network.

2.Mobility of people: people 's activities are gathered in an active and passive manner for long periods. Knowledge is coming collected actively when, for example, the person wants to keep track and share his travels with his friends via GPS; or would like to gather information about a sporting activity. On the other hand, in the case of passive data collection, the consumer involuntarily produces spatial data by, for example, detecting it by repeaters or conducting transactions by ATM.

3.Animal mobility: memorising animal trajectories, such as tigers and birds, can help biologists track migration patterns, behaviours and living conditions.

4.Mobility of people: people 's activities are gathered in an active and passive manner for long periods. Knowledge is coming collected actively when, for example, the person wants to keep track and share his travels with his friends via GPS; or would like to gather information about a sporting activity. On the other hand, in the case of passive data collection, the consumer involuntarily produces spatial data by, for example, detecting it by repeaters or conducting transactions by ATM.

5.Natural phenomenon mobility: meteorologists, environmentalists, climate scientists and oceanographers collect trajectory data on natural phenomena such as hurricanes, tornadoes and ocean currents. This data tracks changes in the atmosphere and climate, helping scientists tackle natural disasters and designing the natural world we live in.

### CN7000 Artificial Intelligence

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## More CN7000 CN7000 Artificial Intelligence : Questions & Answers

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Assignment The T- Numbers are defined as follows: T0 = 0, T1 = 2, Tn = Tn - 1 + 3Tn - 2 + 1 The following algorithm Tnum is a recursive algorithm that computes the T- Numbers Algorithm Tnum (n) Input: A non-negative integer n Output: The T-number Tn if(n = 0) then return 0 else if (n = 1) th ...

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