The Mirror Navigation use of triggers and indexes to automatise data flow and speedup analyses might imply processing times that are not sustainable when large data sets are imported at once. In this case, it might be preferable to update environmental attributes and calculate indexes in a later stage to speed up the import process. In this book, we assume that in the operational environment where the database is developed, the data flow is continuous, with large but still limited data sets imported at intervals. You can compare this processing time with what is generally required to achieve the same result in a classic GIS environment based on flat files . Do not forget to consider that you can use these minutes for a coffee break, while the database does the job for you, instead of clicking here and there in your favourite GIS application!
As the trigger function is run during GPS Rear View Mirror Camera data import, the function only works on the records that are imported after it was created, and not on data imported previously. To see the effects, you have to add new positions or delete and reload the GPS positions stored in gps_data. You can do this by saving the records in gps_sensors in an external .csv file, and then deleting the records from the table (which also deletes the records in gps_data in a cascade effect). When you reload them, the new function will be activated by the trigger that was just defined, and the new attributes will be calculated. This chapter looks into the spatiotemporal dimension of both car tracking data sets and the dynamic environmental data that can be associated with them. Typically, these geographic layers derive from remote sensing measurements, commonly those collected by sensors deployed on earth-orbiting satellites, which can be updated on a monthly, weekly or even daily basis. The modelling potential for integrating these two levels of ecological complexity (car movement and environmental variability) is huge and comes from the possibility to investigate processes as they build up, i.e. in a full dynamic framework.
Nowadays,GPS In Rear View Mirror satellite-based remote sensing can provide dynamic global coverage of medium-resolution images that can be used to compute a large number of environmental parameters very useful to car studies. Through remote sensing, it is possible to acquire spatial time series which can then be linked to animal locations, fully exploiting the spatiotemporal nature of car tracking data. Numerous satellites and other sensor networks can now provide information on resources on a monthly, weekly or even daily basis, which can be used as explanatory variables in statistical models or to parameterise Bayesian inferences or mechanistic models. One of the most commonly used satellite-derived environmental time series is the normalised difference vegetation index but other examples include data sets on ocean primary productivity, surface temperature or salinity, all available in equally fine spatial and temporal scales , and, in North America, snow depth data at daily scales , spatial temperature and precipitation at monthly scales , and meteorological data on wind and pressure.
More information at http://www.jimilab.com/