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GSoC Proposal - Taylor Oshan- PySAL: Spatial Interaction Modeling
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GSoc2016App.md

Python Software Foundation 2016 Google Summer of Code Application

Sub-organization Information

PySAL: Python Spatial Analysis Library

Student Information

Code Samples

Link to a patch/code sample, preferably one you have submitted to your sub-org (*)

Project Information

Proposal Title:

PySAL: Integrating Poisson count models and spatial effects for spatial interaction modeling.

Proposal Abstract:

Spatial interaction modeling involves the analysis of flows from an origin to a destination either over physical space (i.e., migration) or through abstract space (i.e., telecommunication). While many models that have been developed and proposed, there is little to no software avaialble to carry out spatial interaction modeling and the analysis of flow data. This is especially true in the case of open source software and within the python ecosystem. Therefore, a comprehensive python package, which draws on existing PySAL infrastrucutre and extends it, would fill an important gap within the current set of avialable spatial analysis tools.

PySAL is intended to support the development of high level spatial analysis. As such, it currenty provides a rich set of tools for modeling spatial effects within a regression framework, which is typically applied to areal units. While it is possible to extend some of these models to the case of spatial interaction data, new spatial weight structures will be necessary to capture the unique spatial dependence that occurs between a data point that has both an origin and a destination, rather than a single areal unit. Furthermore, the existing spatial regression models are specifically designed for continuous data, whereas many spatial interaction phenomena are more properly modeled as counts (i.e., commuting, migration).

Finally, there are several paradigms for incorporating spatial effects into spatial interaction models (competing destinations, spatial autoregressive, eigenvector spatial filter). Therefore, the primary goals of this GSoC proposal are to:

  • Implement new structures and algorithms to capture dependence in flow data.
  • Develop Poisson count models that incorporate spatial effects.

A generalized linear model (GLM) approach will be adopted for modeling counts, so developing this framework would be the first outcome of this project, which could be used more widely throuhgout PySAL. While this functionality currently exists in another python library (statsmodels), the newly developed GLM framework would a) accommodate a sparse data structure often utilized in PySAL's spatial regression module and b) be light-weight so it would be simple to extend for various models with spatial effects. The other major outcome of this project would be to develop a comprehensive module focused on spatial interaction modeling, which would include exploratory statistics, data structures, and models, as well documentation and educational materials. This could be a module within the core of PySAL or as a contributor module, depending on which dependencies are necessary.

Proposal Description/Timeline

  • Generalized linear model (GLM) base class for modeling count data (Poisson model)1. (Week 1 & 2; ~ May 23rd - June 3rd)

  • Zero-inflated specifications for Poisson spatial interaction models2,3,4. (Week 3 & 4; ~ June 6th - June 17th)

  • Vector-based spatial autocorrelation statistics5. (Week 5; ~ June 20th - June 24th)

  • Flow-based spatial weight specifications. (Week 6; ~ June 27th - July 1st)

  • Origin-destination weights6

  • Network origin-destination weights7

  • Spatial autoregressive specifications6,8,9 (Week 7 & 8 & 9; ~ July 4th - July 22nd)

  • Competing destinations specifications14,15. (Week 10; ~ July 25th - July 29th)

  • Spatial eigenvector filter (SF) specifications7. (Week 11 & 12; ~ August 1st - August 12th)

  • Wrap up all code/double check all documentation/create educational materials (Week 13; ~ August 15th - August 23rd)

  • Additional goals if there is any extra time and project is ahead of schedule:

    • Non-parametric “universal” model varieties 10,11,12
    • Non-parametric Neural Network routines for calibrating spatial interaction models13

Citations

  1. Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized Linear Models. Journal of the Royal Statistical Society. Series A (General), 135(3), 370–384. http://doi.org/10.2307/2344614
  2. Santos Silva, J. M. C., & Tenreyro, S. (2006). The Log of Gravity. The Review of Econoomics and Statsitics, 88(4), 641–658.
  3. Burger, M., Van Oort, F., & Linders, G.-J. (2009). On the specification of the gravity model of trade: zeros, excess zeros and zero-inflated estimation. Spatial Economic Analysis, 4(2), 167–190.
  4. Philippidis, G., Resano-Ezcaray, H., & Sanjuán-López, A. I. (2013). Capturing zero-trade values in gravity equations of trade: an analysis of protectionism in agro-food sectors. Agricultural Economics, 44(2), 141–159. http://doi.org/10.1111/agec.12000
  5. Liu, Y., Tong, D., & Liu, X. (2014). Measuring Spatial Autocorrelation of Vectors: Measuring Spatial Autocorrelation of Vectors. Geographical Analysis, n/a–n/a. http://doi.org/10.1111/gean.12069
  6. LeSage, J. P., & Pace, R. K. (2008). Spatial econometric Modeling Of Origin-Destination Flows. Journal of Regional Science, 48(5), 941–967. http://doi.org/10.1111/j.1467-9787.2008.00573.x
  7. Chun, Y. (2008). Modeling network autocorrelation within migration flows by eigenvector spatial filtering. Journal of Geographical Systems, 10(4), 317–344. http://doi.org/http://dx.doi.org.ezproxy1.lib.asu.edu/10.1007/s10109-008-0068-2
  8. Lambert, D. M., Brown, J. P., & Florax, R. J. G. M. (2010). A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application. Regional Science and Urban Economics, 40(4), 241–252. http://doi.org/10.1016/j.regsciurbeco.2010.04.001
  9. Sellner, R., Fischer, M. M., & Koch, M. (2013). A Spatial Autoregressive Poisson Gravity Model: A SAR Poisson Gravity Model. Geographical Analysis, 45(2), 180–201. http://doi.org/10.1111/gean.12007
  10. Simini, F., González, M. C., Maritan, A., & Barabási, A.-L. (2012). A universal model for mobility and migration patterns. Nature, 484(7392), 96–100. http://doi.org/10.1038/nature10856
  11. Yan, X.-Y., Zhao, C., Fan, Y., Di, Z., & Wang, W.-X. (2013). Universal Predictability of Mobility Patterns in Cities. arXiv:1307.7502 [physics]. Retrieved from http://arxiv.org/abs/1307.7502
  12. Lenormand, M., Huet, S., Gargiulo, F., & Deffuant, G. (2012). A Universal Model of Commuting Networks. PLoS ONE, 7(10), e45985. http://doi.org/10.1371/journal.pone.0045985
  13. Fischer, M. M. (2006). Neural Networks: A General Framework for Non-Linear Function Approximation. Transactions in GIS, 10(4), 521–533. http://doi.org/10.1111/j.1467-9671.2006.01010.x
  14. Fotheringham, A. S. (1983). A new set of spatial-interaction models: the theory of competing destinations. Environment and Planning A, 15(1), 15–36.
  15. Fotheringham, A. S. (1985). Spatial competition and agglomeration in urban modelling. Environment and Planning A, 17(2), 213–230.

Other Commitments

###Other commitments during the main GSoC time period:

I currently have some academic papers submitted to journals so it could be possible that I may need to make some revisions.

Exams or classes that overlap with this period:

None

Other jobs or internships:

None

Other short term commitments:

Potentially attend the Scipy (Scientific Computing with Python) conference from July 11th-July 17th. This will include coding sprints, which could be used to work on GSoC project, in addition to attending workshps and presentations about python programming.

Other organizations:

None - I am only applying to PySAL during the 2016 cycle of GSoC.

Extra Information

Link to resume:

Resume

University Information:

  • University name: Arizona State University

  • Major: Geography

  • Current year: second year out of three

  • Expected graduation: August/2017

  • Degree: PhD

Other Contact Information:

  • Alternative email: toshan@asu.edu

  • Homepage: tayloroshan.github.io

  • Instant messaging: @TaylorOshan on Gitter

  • Twitter: @TaylorOshan

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