LocPick

locpick is a Python library for estimating discrete choice models of location decisions — where individuals, households, or firms choose among spatially-defined alternatives (neighborhoods, jobs, housing units, transit stops). These methods have broad applicability but are common in regional land-use and housing market modeling. The package is designed for:

• Large-scale urban models: 100K+ choosers, 1K+ alternatives
• Sampling-based estimation: Most alternatives are irrelevant; only a sampled subset is evaluated per chooser
• Spatial dependence: Nearby alternatives can affect each other or share unobserved attributes (via graph= on any model)
• Heterogeneous preferences: Mixed logit for random taste variation
• Nested structure: Nested logit for hierarchical choice (e.g., county → tract → block)
• JAX-native computation: JIT-compiled kernels, GPU acceleration, automatic differentiation

The package is not a general-purpose ML library. It is specifically for structural econometric models of choice where the likelihood has a closed form (or simulated approximation) and parameters have behavioral interpretations. For more transportation-oriented problems, see larch

Features

LocPick can automate the creation of choice tables for estimation or simulation, using census choice sets, uniform or weighted random sampling of alternatives, generated interaction terms, and cartesian merges. A unique feature is the implementation of spatial choice models, which take one of two forms.

• The Bhat et al approach is similar to a spatial error model, assuming that nearby alternatives are more similar (closer substitutes).
• The SAR style approach assumes that the structural utility of each alternative $V$ has a simultaneous autoregressive structure, and is estimated with either PML or GMM

It also provides tools for Monte Carlo simulation of choices given probability distributions from fitted models, with fast algorithms for independent or capacity-constrained choices.

LocPick includes estimators for classic, spatially-correlated, and simultaneous autoregressive:

• Multinomial Logit
• Nested Logit
• Mixed Logit and
• Mixed/Nested

models, and an internal data pipeline designed around pandas inputs, xarray-backed alignment, and NumPy/JAX-ready arrays.