E negative weights because of the multiplicity of constraints [4]. In this case, a corner remedy [4], i.e., a answer prioritizing the right match of 1 constraint more than the other people or averaging the match of many constraints, is regarded as. In addition, the data processing applied to census totals introduces inconsistencies of totals involving distinctive geographic resolutions, making the right match of all the constraints even significantly less likely. Therefore, accounting for two resolutions could additional damage the good quality of the generated synthetic population. The choice from the RGR and whether to apply many geographic 7-Hydroxymethotrexate-d3 Drug Metabolite resolution controls or not, should really as a result be performed cautiously to attain the most effective compromise among the spatial precision on the synthetic population (representativeness from the genuine population’s spatial heterogeneity) and its CP-424174 Inhibitor Accuracy (representativeness in the sociodemographic qualities of your complete population). Accuracy and precision are completely defined in Section 3.six. 1.2. Contributions To improve the excellent with the synthetic population, its accuracy and precision should be optimized. Optimizing the accuracy amounts to minimizing fitting errors and optimizing precision to minimizing spatialization errors. Due to the fact a additional aggregate RGR would potentially lead to extra spatialization errors and also a significantly less aggregate RGR to a lot more fitting errors, the magnitudes of both forms of errors at various geographic resolutions needs to be assessed to identify the geographic resolution yielding the very best trade-off. The principle objective of this paper would be to assess the effect from the RGR around the high-quality with the synthetic population, thus suggesting suggests of minimizing fitting and spatialization errors. Specifically, fitting and spatialization errors are measured for many RGRs with a focus on the impact on the errors from (1) the aggregation with the RGR, (2) data inconsistencies between census geographic resolutions, and (3) many geographic resolution controls. The enhanced IPU algorithm is applied in this paper to generate many synthetic populations for the Census Metropolitan Areas (CMAs) of Montreal, Toronto, and Vancouver. Towards the ideal of our understanding, the two most recent population synthesizers handling various geographic resolutions will be the one particular introduced by Moreno and Moeckel [7] andISPRS Int. J. Geo-Inf. 2021, ten,five ofthe enhanced IPU [6]. Moreno and Moeckel’s algorithm [7] can manage 3 resolutions simultaneously. Nevertheless, as our will need is restricted to retrieving the best match at two geographic resolutions (i.e., by far the most along with the least aggregate geographic resolutions), an enhanced IPU-based algorithm is utilized. The remainder of this paper is organized as follows. In Section two, we talk about properties and variants of IPF and IPU-based population synthesis strategies as well as their positive aspects and limitations as exposed in the literature. Other multilevel and multiresolution population synthesis approaches are also briefly mentioned within this section. Section 3 is devoted to describing the methodology we have created to assess the effect of several RGRs on enhanced IPU-based synthetic populations [6]. The comparison of final results is then performed and discussed in Section 4. Section 5 concludes the paper and proposes some analysis perspectives. 2. Literature Assessment In this section, IPF, multilevel, and multiresolution synthesizers are briefly described primarily based around the literature. A special concentrate is provided for the evolution of population synthesis approaches from I.