First thing to do is to rearrange your data into a standard form. If you've got $n$ samples and $d$ features, that means you want
- An $n \times d$ input matrix $X$, in which each column has a mean of $0$ and variance of $1$. This ensures that LASSO's regularization effect treats each dimension "fairly" when deciding whether to shrink it to zero.
- A length-$n$ vector $y$ of outputs, which has a mean of $0$. This ensures the LASSO model doesn't need to use a constant term.
You'll probably want to encode time and location as dimensions (ie as extra columns in $X$), though without knowing the details of the problem I can't say for sure.
Anyway, if you feed $X$ and $y$ into a LASSO solver, you'll then get back a length-$d$ weights vector $w$, that you can then interpret in terms of time, location, and whatever other explanatory variables you have.