Aggregate RGR. This is anticipated considering that higher aggregation leads to a stronger spatial homogeneity assumption and as a result a less precise synthetic population. The spatial homogeneity assumption became stronger with additional aggregate RGRs because a big population more than a wide area is more likely to become spatially heterogeneous than a little population over a compact region. The three CMAs showed equivalent trends and values with all the maximum becoming about 1000 in the CMA resolution and the minimum about 300 in the DA resolution. Two observations are worth mentioning: 1st, the spatialization errors’ magnitude was larger than the fitting errors’ magnitude for the exact same synthesis region (the CMA). Second, the ratio with the highest error to the lowest error was greater than three for spatialization errors, when it remained reduced than 2 for fitting errors (1.2 if calculated for Montreal and Vancouver). This shows that a synthetic population is typically susceptible to much more spatialization errors than fitting errors. Hence, for the same synthesis location, perfect precision is additional tough to reach than ideal accuracy. Furthermore, it shows that the acquire in terms of precision when synthesizing in the least aggregate RGR is extra vital than the loss when it comes to Uniconazole custom synthesis accuracy and vice-versa. As we were thinking about optimizing each accuracy and precision, i.e., LAU159 Description minimizing ISPRS Int. J. Geo-Inf. 2021, 10, x FORboth fitting and spatialization errors, the variation from the total error ( ) according21 of 27 PEER Assessment for the RGR used was calculated as depicted in Figure 17.1400 1200800 600 400 200 0 CMA CSD ADA CT DAReference resolutionMTL TOR VANFigure 17. Variation of based on the RGR. Figure 17. Variation of according to the RGR.The synthetic populations in the DA resolution showed around 400 total errors per The synthetic populations in the DA resolution showed about 400 total errors per 1000 agents, although the CMA resolution about 1100 errors per 1000 1000 were were ob1000 agents, while at in the CMA resolution around 1100 errors per agentsagentsobserved. served. error was decreased by nearly 64 in the DA the DA resolution. Therefore, applying as the totalThe total error was lowered by practically 64 at resolution. Therefore, applying the DA the the RGR was shown to be theto becompromise among fitting and spatialization errors. In DA because the RGR was shown ideal the ideal compromise in between fitting and spatialization other words, utilizing the DA because the RGR because the RGRquality, i.e., the combination mixture errors. In other words, applying the DA allows the makes it possible for the top quality, i.e., the of accuracy and precision, of precision, on the synthetic population to become optimized. of accuracy along with the synthetic population to become optimized.4.2. How Does Vary according to In Other Words, How Will be the Precision Improved When four.two. How Does Differ In accordance with In Other Words, How Is the Precision Improved When Decreasing Accuracy, i.e., When Using a Significantly less Aggregate RGR, and Vice-Versa Decreasing Accuracy, i.e., When Making use of a Much less Aggregate RGR, and Vice-Versa was located to boost and to reduce when the RGR became significantly less aggregate. The was located to boost and to lower when the RGR became less aggregate. The variation of according to was then additional investigated inside the three CMAs (Figure 18). variation of in line with was then further investigated within the three CMAs (Figure 18). The relation among and may very well be fitted nicely by a decreasing linear trend as evidenced The relation amongst and could possibly be fitte.