Combining parameters for Douglas-Peucker Simplification

NOTE : I'm not sure if this is the right forum for this question. if not, please advice.

Context : I am collecting a huge amount of data using an android app that is placed on a vehicle. I collect the data at ~1second intervals for about 2 hours, which gives almost 7200 data sets. These are the parameters :

1. Timestamp (milliseconds)
2. Latitude
3. Longitude
4. Speed
5. Acceleration

Now I was looking at ways to simplify this data, as processing and rendering these many date points, especially on mobile devices is not a good idea.

EDIT : as answer to Spacedman's comment : I want to simplify the data because most of it is redundant. The data is collected every 1 second regardless of whether the values are constant or changing. Accuracy is not crucial as its only used for displaying visual graphs on a website, drawing a polyline on a map. etc. So I want to keep only the minimum necessary data points required to reproduce the graph/line.

While searching, I came across the Ramer–Douglas–Peucker algorithm and also found a library that implements this.

The issue : I am a bit confused as to how to simplify this data. I could :

1. Somehow simply the entire thing by considering each data as a 5D point , or
2. Generate three sets of arrays, namely : [Lat, Long, time], [Speed, time] and [acceleration, time].

So my question is :

Are there any advantages/disadvantages of using either of the two approaches?

I was thinking that - as each metric will have different patterns of variations, will combining them reduce the efficiency of the whole simplification process?

Or is it best to keep the metrics separate, so that each will be simplified to its maximum efficiency?

I am a Java/Obj-C developer, normally active on SO, and I am not an expert on these things, So I would like to know what you guys think.

Thanks in advance