The aspect ratio of a line chart heavily influences the perception of the underlying data. Different methods explore different criteria in choosing aspect ratios, but so far, it was still unclear how to select aspect ratios appropriately for any given data. This paper provides a guideline for the user to choose aspect ratios for any input 1D curves by conducting an in-depth analysis of aspect ratio selection methods both theoretically and experimentally. By formulating several existing methods as line integrals, we explain their parameterization invariance. Moreover, we derive a new and im-proved aspect ratio selection method, namely the L1-LOR (local orientation resolution), with a cer-tain degree of parameterization invariance. Furthermore, we connect different methods, including AL (arc length based method), the banking to 45° principle, RV (resultant vector) and AS (average absolute slope), as well as L1-LOR and AO (average absolute orientation). We verify these conne-ctions by a comparative evaluation involving various data sets, and show that the selections by RV and L1-LOR are complementary to each other for most data. Accordingly, we propose the dual-scalebanking technique that combines the strengths of RV and L1-LOR, and demonstrate its practicabilityusing multiple real-world data sets.