Impact of the Geographic Resolution on Population Synthesis Quality

Abstract

Microsimulation-based models, increasingly used in the transportation domain, require richer datasets than traditional models. Precisely enumerated population data being usually unavailable, transportation researchers generate their statistical equivalent through population synthesis. While various synthesizers are proposed to optimize the accuracy of synthetic populations, no insight is given regarding the impact of the geographic resolution on population synthesis quality. In this paper, we synthesize populations for the Census Metropolitan Areas of Montreal, Toronto, and Vancouver at various geographic resolutions using the enhanced iterative proportional updating algorithm. We define accuracy (representativeness of the sociodemographic characteristics of the entire population) and precision (representativeness of the real population’s spatial heterogeneity) as metrics of synthetic populations’ quality and measure the impact of the reference resolution on them. Moreover, we assess census targets’ harmonization and double geographic resolution control as means of quality improvement. We find that with a less aggregate reference resolution, the gain in precision is higher than the loss in accuracy. The most disaggregate resolution is thus found to be the best choice. Harmonization proves to further optimize synthetic populations while double control harms their quality. Hence, synthesizing at the Dissemination Area resolution using harmonized census targets is found to yield optimal synthetic populations.

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

“…Indeed, it is not possible to apply choice models to areas as big as subdistricts while it is possible to do so when the degree of precision is within walking range. A more disaggregated synthetic population is found to hold the best precision while retaining good levels of accuracy (as proved in [35]). We report in Figure 5 the first version of the synthetic population resulting from applying the SimPop tool, where it may be observed that the two populations (real, i.e., from census, and synthetic) match almost perfectly in terms of household sizes and spatial distribution across Tallinn's subdistricts.…”