HBA Specto Incorporated

PECAS was developed by HBA Specto Incorporated, in Calgary, Alberta, Canada (J. Hunt & Abraham, 2005), as an offshoot of the “Martin Centre” (JD Hunt & DC Simmonds, 1993) approach, which had been embodied in the TRANUS and MEPLAN frameworks. This approach contained a ground-breaking combination of random utility theory and input-output modelling, which explained the importance of diversity in the modern economy and was an (unacknowledged) practical precursor to the New Economic Geography (Fujita et al., 1999) developments. However, these earlier frameworks used a sequential year-by-year long term equilibrium (with prices equal to costs), had a theoretical inconsistency (random utility optimizing behavior was not carried through into spatial choices nor into spatial performance metrics) (John E. Abraham, 1998), and did not have a discrete land representation of profit-driven developers. PECAS was designed to correct these shortcomings and provide an open-source software platform for further developments.

The Oregon Department of Transportation was the first user of the initial PECAS model and funded the early software development beginning in the late 1990s. Since then, the modelling framework has been improved by HBA Specto Incorporated working with various agencies. Major developments have included:

1. Parcel based microsimulation of space development, with zoning regulations as the relevant policy input (J. D. Hunt et al., 2007).
2. Reformulating the operational equations from first principles of random utility theory (Abraham, John E. & Hunt, J.D., 2005; John Abraham & Hunt, 2007).
3. Bayesian calibration procedures for space development (Hill, 2017).
4. Integration of population synthesis (using the HBA Specto Population Synthesizer) (John E. Abraham et al., 2012).
5. Deterministic microsimulation of parcel development, to completely eliminate variance between runs while maintaining the benefits of parcel-based microsimulation (John Douglas Hunt et al., 2019).
6. Bayesian hedonic price estimation of landlord rents, to address missing data, control for housing and building age, and account for detailed parcel-level characteristics (J. Hunt et al., 2019).
7. Calculation of logit model size terms based on an initial flat-price allocation of activities into space.
8. Constraint process for arbitrary disjoint groups of land use zones.
9. Calibration process to establish quantities of floorspace based on prices and demand functions, informed by any measured floorspace amounts.
10. Connection of construction quantities in SD to amounts of construction economic activity in AA, to both allocate construction employment and so that SD’s a priori myopic parcel-by-parcel view is enhanced with an overall representation of the constraints and capacity of the region’s construction industry.
11. Synthesis process to allocate zonal level amounts of floorspace onto detailed parcels based on a scoring algorithm, to address deficiencies and limitations in cadastral data (John Edward Abraham et al., 2010) (J. Abraham et al., 2005).
12. Non-linear treatment of travel model disutilities (“skims”) accounting for increasing payloads at long distances.
13. “MapIt”, a web-based data selection and crosstabbing tool with browser maps of scenario comparisons, that also serves as a Web Mapping Service (WMS) and Web Feature Service (WFS) server for GIS platforms. This supports large databases enabling comparison of hundreds of scenarios and hundreds of gigabytes of detailed model output data.
14. A plugin to the QGIS desktop GIS system that generates layers within MapIt and imports layers from MapIt.
15. A strict version control system <this is super important to protect agencies from legal troubles!!>
16. A web interface (Model Run System Graphical User Interface, “MrsGUI”) to start, stop, and monitor runs on multiple “run machines” from a central location, as well as a simplified front-end to the strict version control system.
17. A dump-and-restore of the parcel-based database that enables version control of spatial GIS inputs, bringing an entire PECAS model into version control.
18. A standardized run script, with well documented project-specific settings.
19. Standardized calibration scripts, with documentation of the sequence of calibration (e.g. for AA, iterating between flow lengths, technology option weights, floorspace quantities/prices).
20. A flow clustering tool, to visualize the origin-destination maps for trips or flows, in a web interface, with interactive user control of the degree of simplification.
21. A high-framerate visualization (“video game quality”) of the parcel development simulation results through time, using the CityPhi product from INRO.
22. Scaling of future technology as implied by differing growth rates of industry (e.g. high growth in the medical industry implies higher consumption of medical treatments in the future), so that, if desired, PECAS can respect arbitrary growth forecasts for population and industry categories without pre-specifying the implications on consumption rates.