Distance measure for ternary feature

I have a data set consisting of 100 features each of which are ternary: values of -1 if it exists in one category, 0 if it doesn't exist, and 1 if it exists in the second category. For example

F1 F2 F3 ... F90 F91 F92 ... F99 F100
0  0  0  ... 1   -1  0   ... 0   -1
0  -1 0  ... -1   0  1   ... 0   0


The data is very sparse, ~20 of the 100 features have values of -1 or 1 for each row of data. I want to find similar rows of data through a heatmap visualization and dendrogram but I was confused on whether to use Euclidean distance or Cityblock distance. I'm quite new to data mining and while reading the scipy pages, I found many distance measures which I have no idea means what. Is there a good distance measure for my type of data set?

Since apparently each feature is encoding something about two different categories, I would suggest that you should replace that with two features. Your two features would be $(x,y)$ where $x$ is 0 or 1 according to whether it exists in the first category, and $y$ is 0 or 1 according to whether it exists in the second category. In other words, instead of -1, 0, and 1, you would use $(1,0)$, $(0,0)$, and $(0,1)$, respectively. I think that is closer to the true data and might give better results. It might also make your results easier to interpret.