A primer for working with the Spatial Interaction modeling (SpInt) module in the python spatial analysis library (PySAL)


  • Taylor M Oshan Arizona State University




This primer provides a practical guide to get started with spatial interaction modeling using the SpInt module in the python spatial analysis library (PySAL).


Akaike, H. (1974). A new look at the statistical model identification. Automatic Control, IEEE Transactions on, 19(6):716–723.

Cameron, A. C. and Trivedi, P. K. (2013). Regression Analysis of Count Data. Cambridge University Press, New York.

Chun, Y. (2008). Modeling network autocorrelation within migration flows by eigenvector spatial filtering. Journal of Geographical Systems, 10(4):317–344.

Dennett, A. (2012). Estimating flows between geographical locations:‘get me started in’spatial interaction modelling. Working Paper 184, Citeseer, UCL.

Flowerdew, R. and Aitkin, M. (1982). A Method of Fitting the Gravity Model Based on the Poisson Distribution. Journal of Regional Science, 22(2):191–202.

Flowerdew, R. and Lovett, A. (1988). Fitting Constrained Poisson Regression Models to Interurban Migration Flows. Geographical Analysis, 20(4):297–307.

Fotheringham, A. S. (1983). A new set of spatial-interaction models: the theory of competing destinations. Environment and Planning A, 15(1):15–36.

Fotheringham, A. S. and Brunsdon, C. (1999). Local Forms of Spatial Analysis. Geographical Anal- ysis, 31(4):340–358.

Fotheringham, A. S. and O’Kelly, M. E. (1989). Spatial Interaction Models:Formulations and Applica- tions. Kluwer Academic Publishers, London.

Knudsen, D. and Fotheringham, A. (1986). Matrix comparison, Goodness-of-fit, and spatial inter- action modeling. International Regional Science Review, 10:127–147.

Kordi, M., Kaiser, C., and Fotheringham, A. S. (2012). A possible solution for the centroid-to- centroid and intra-zonal trip length problems. In International Conference on Geographic Informa- tion Science, Avignon.

Lenormand, M., Huet, S., Gargiulo, F., and Deffuant, G. (2012). A Universal Model of Commuting Networks. PLoS ONE, 7(10):e45985.

LeSage, J. P. and Pace, R. K. (2008). Spatial Econometric Modeling Of Origin-Destination Flows. Journal of Regional Science, 48(5):941–967.

Masucci, A. P., Serras, J., Johansson, A., and Batty, M. (2012). Gravity vs radiation model: on the importance of scale and heterogeneity in commuting flows. arXiv:1206.5735 [physics]. arXiv: 1206.5735.

McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In Frontiers in Econometrics, pages 105–142. Academic Press, New York.

Nelder, J. A. and Wedderburn, R. W. M. (1972). Generalized Linear Models. Journal of the Royal Statistical Society. Series A (General), 135(3):370–384.

Tiefelsdorf, M. and Boots, B. (1995). The specification of constrained interaction models using the SPSS loglinear procedure. Geographical Systems, 2:21–38.

Tsutsumi, M. and Tamesue, K. (2011). Intraregional Flow Problem in Spatial Econometric Model for Origin-destination Flows. Procedia - Social and Behavioral Sciences, 21:184–192.

Wedderburn, R. W. M. (1974). Quasi-Likelihood Functions, Generalized Linear Models, and the Gauss-Newton Method. Biometrika, 61(3):439–447.

Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Envi- ronment and Planning A, 3:1–32.

Yan, X.-Y., Zhao, C., Fan, Y., Di, Z., and Wang, W.-X. (2013). Universal Predictability of Mobility Patterns in Cities. arXiv:1307.7502 [physics]. arXiv: 1307.7502.



How to Cite

Oshan, T. M. (2016) “A primer for working with the Spatial Interaction modeling (SpInt) module in the python spatial analysis library (PySAL)”, REGION, 3(2), pp. R11-R23. doi: 10.18335/region.v3i2.175.