Category Archives: MEG-DM 2013 Winter

Module “Data Management”, winter term 2013.

MEG- DM- L11

# Author: Lars Fink # Version: “2013-10-23″

setwd (“C:/R”)

library(lattice) library(latticeExtra)

a<-read.table(“climadata.csv,header=T,sep=”,”) str(a)

b<-bwplot(~Ta_200|PlotId,data=a)

print(b)

 

 

 

 

# Author: Lars Fink # Version: “2013-10-23″

setwd (“C:/R”)

library(lattice) library(latticeExtra)

a<-read.table(“climadata.csv,header=T,sep=”,”) str(a)

xyplot(Ta_200~rH_200|PlotId,data=a)

MEG- DM- L10- 2

1. All user groups have in common, that everybody is a spacial- temporal analyst,  when they plan something in their daily schedule for example. So everybody is a potential user of visual analytics.

Spacial and temporal data have in common, that one can not exist without the other one, they are connected.

2.and 3. A major drawback of GIS-based visualization of spatio-temporal data there are not enough sensors to notice a local weather event. So data have to  be interpolated. The choice of spacial or temporal scale at visualizing influences the data. There are several kinds to depict time. If one of them is choosen, data will be influenced by that.

4.and 5. It was interesting to see, how people focus on different shown aspects, because of different kinds to visualize the same data. A big number of different kinds af data has to be prepared to make it possible for a big number of users to use them.