E damaging weights as a result of multiplicity of constraints [4]. Within this case, a corner option [4], i.e., a remedy prioritizing the ideal fit of 1 constraint more than the other folks or averaging the fit of many constraints, is considered. Additionally, the data processing applied to census totals introduces inconsistencies of totals among different geographic resolutions, producing the ideal match of all the constraints even significantly less likely. Therefore, accounting for two resolutions may further damage the high quality on the generated synthetic population. The decision of the RGR and whether to apply numerous geographic resolution controls or not, really should hence be done cautiously to reach the most beneficial compromise involving the spatial precision from the synthetic population (representativeness in the genuine population’s spatial heterogeneity) and its accuracy (representativeness of your sociodemographic qualities with the complete population). Accuracy and precision are completely defined in Section three.six. 1.two. Contributions To improve the excellent with the synthetic population, its accuracy and precision need to be optimized. Optimizing the accuracy amounts to minimizing fitting PSB 0474 medchemexpress errors and optimizing precision to minimizing spatialization errors. Due to the fact a extra aggregate RGR would potentially bring about more spatialization errors and also a less aggregate RGR to much more fitting errors, the magnitudes of each sorts of errors at various geographic resolutions need to be assessed to ascertain the geographic resolution yielding the best trade-off. The main objective of this paper should be to assess the effect on the RGR around the quality from the synthetic population, hence suggesting means of minimizing fitting and spatialization errors. Particularly, fitting and spatialization errors are measured for various RGRs having a concentrate on the impact around the errors from (1) the aggregation with the RGR, (two) data inconsistencies between census geographic resolutions, and (3) multiple geographic resolution controls. The enhanced IPU algorithm is employed in this paper to produce a variety of synthetic populations for the Census Metropolitan Regions (CMAs) of Montreal, Toronto, and Vancouver. For the greatest of our understanding, the two most current population synthesizers handling multiple geographic resolutions would be the one introduced by Moreno and Moeckel [7] andISPRS Int. J. Geo-Inf. 2021, ten,5 ofthe enhanced IPU [6]. Moreno and Moeckel’s algorithm [7] can handle 3 resolutions simultaneously. On the other hand, as our need to have is limited to retrieving the best fit at two geographic resolutions (i.e., the most and the least aggregate geographic resolutions), an enhanced Modafinil acid sulfone-d5 manufacturer IPU-based algorithm is made use of. The remainder of this paper is organized as follows. In Section two, we go over 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 in this section. Section three is devoted to describing the methodology we’ve developed to assess the influence of numerous RGRs on enhanced IPU-based synthetic populations [6]. The comparison of results is then performed and discussed in Section 4. Section 5 concludes the paper and proposes some research perspectives. 2. Literature Evaluation Within this section, IPF, multilevel, and multiresolution synthesizers are briefly described based on the literature. A specific concentrate is given towards the evolution of population synthesis approaches from I.