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  • Worked with Car GPS Tracker position data

     
    The integration of inside the database also opens the door to the automation of real-time analysis performed routinely on massive sets of data. For instance, this gapless framework could be used to set up early warning systems that detect behaviours of the animals that can be potentially dangerous or of particular importance for researchers. In this chapter, you will be introduced to the use of Pl/R in the context of PostGIS. You will start by exercises involving simple calculations in (logarithms, median and quantiles) to understand how Pl/R works. More elaborated exercises designed to compute the daylight times of a given location at a given date or to compute complex home range methods will then give you a basic overview of the potential of Pl/R for the study of GPS Made In China locations.
     
    You have exclusively worked with Personal GPS Tracker position data. We showed how to organise these data in databases, how to link them to environmental data and how to connect them to R for further analysis. In this chapter, we introduce an example of data recorded by another type of sensor: acceleration data, which can be measured by many tags where they are associated with the GPS sensors and are widely used to interpret the behaviour of tagged cars. The general structure of these data and an overview of possibilities for analysis are given. In the exercise for this chapter, you will learn how to integrate an acceleration data set into the database created in the previous chapters and link it with other information from the database. At the end, the database is extended with acceleration data and with an automated procedure to intersect these layers with GPS positions.
     
     
     
    you learned how to correlate GPS Cell Phone Tracking Device positions with other spatiotemporal information such as NDVI values and DEMs. However, many kinds of bio-logging sensors are available to record a large set of information related to cars. In fact, we are quickly moving from cars monitored by one single sensor, usually a GPS receiver, to cars monitored by multiple, integrated sensors that register spatial (i.e. GPS positions) and non-spatial measurements such as acceleration, temperature or GSM signal quality. In recent years, commercial solutions have emerged to deploy a camera on cars, or even internal sensors in the cars’ body to register heartbeat and body temperature. These sensors are usually integrated on a unique physical support (the collar or tag). Data from all these different sensors can be related to the spatial position of the car and to each other on the basis of the acquisition time of the recorded information, thus giving a complete picture of the car at a certain time. This integrated set of information can be used to fully decipher the cars’ behaviour in space and time. The opportunity to answer new biological questions through the information derived from these multi-sensor monitoring platforms implies a number of further challenges in terms of data management. To be able to explore the multifaceted aspects of cars’ behaviour, researchers must deal with even bigger and more diverse sets of data that require a more complex database data model and data acquisition procedures.
     
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