Perceptual Scaling Map – Lab6Perceptual
Absolute Scaling Map – Lab6Absoulte
| rL | 8.0 | ||
| vL | 25 582000 | ||
| exponent | 0.50 | ||
| City name | City pop (vC) | Radius (rC) | Diameter |
| Ürümqi (Wulumqi) | 4 011 000 | 3.2 | 6.3 |
| Shantou | 4 174 000 | 3.2 | 6.5 |
| Kunming | 4 230 000 | 3.3 | 6.5 |
| Changchun | 4 241 000 | 3.3 | 6.5 |
| Changsha | 4 345 000 | 3.3 | 6.6 |
| Zhengzhou | 4 940 000 | 3.5 | 7.0 |
| Ji’nan, Shandong | 5 052 000 | 3.6 | 7.1 |
| Dalian | 5 300 000 | 3.6 | 7.3 |
| Qingdao | 5 381 000 | 3.7 | 7.3 |
| Haerbin | 6 115 000 | 3.9 | 7.8 |
| Suzhou, Jiangsu | 6 339 000 | 4.0 | 8.0 |
| Shenyang | 6 921 000 | 4.2 | 8.3 |
| Foshan | 7 196 000 | 4.2 | 8.5 |
| Hangzhou | 7 236 000 | 4.3 | 8.5 |
| Dongguan | 7 360 000 | 4.3 | 8.6 |
| Xi’an, Shaanxi | 7 444 000 | 4.3 | 8.6 |
| Wuhan | 8 176 000 | 4.5 | 9.0 |
| Nanjing, Jiangsu | 8 245 000 | 4.5 | 9.1 |
| Chengdu | 8 813 000 | 4.7 | 9.4 |
| Shenzhen | 11 908 000 | 5.5 | 10.9 |
| Guangzhou,Guangdong | 12 638 000 | 5.6 | 11.2 |
| Tianjin | 13 215 000 | 5.7 | 11.5 |
| Chongqing | 14 838 000 | 6.1 | 12.2 |
| Beijing | 19 618 000 | 7.0 | 14.0 |
| Shanghai | 25 582 000 | 8.0 | 16.0 |
| rL | 8.0 | ||
| vL | 25 582000 | ||
| exponent | 0.57 | ||
| City name | City pop (vC) | Radius (rC) | Diameter |
| Beijing | 19 618 000 | 6.9 | 13.8 |
| Changchun | 4 241 000 | 2.9 | 5.7 |
| Changsha | 4 345 000 | 2.9 | 5.8 |
| Chengdu | 8 813 000 | 4.4 | 8.7 |
| Chongqing | 14 838 000 | 5.9 | 11.7 |
| Dalian | 5 300 000 | 3.3 | 6.5 |
| Dongguan | 7 360 000 | 3.9 | 7.9 |
| Foshan | 7 196 000 | 3.9 | 7.8 |
| Guangzhou,Guangdong | 12 638 000 | 5.4 | 10.7 |
| Haerbin | 6 115 000 | 3.5 | 7.1 |
| Hangzhou | 7 236 000 | 3.9 | 7.8 |
| Ji’nan, Shandong | 5 052 000 | 3.2 | 6.3 |
| Kunming | 4 230 000 | 2.9 | 5.7 |
| Nanjing, Jiangsu | 8 245 000 | 4.2 | 8.4 |
| Qingdao | 5 381 000 | 3.3 | 6.6 |
| Shanghai | 25 582 000 | 8.0 | 16.0 |
| Shantou | 4 174 000 | 2.8 | 5.7 |
| Shenyang | 6 921 000 | 3.8 | 7.6 |
| Shenzhen | 11 908 000 | 5.2 | 10.3 |
| Suzhou, Jiangsu | 6 339 000 | 3.6 | 7.2 |
| Tianjin | 13 215 000 | 5.5 | 11.0 |
| Ürümqi (Wulumqi) | 4 011 000 | 2.8 | 5.6 |
| Wuhan | 8 176 000 | 4.2 | 8.4 |
| Xi’an, Shaanxi | 7 444 000 | 4.0 | 7.9 |
| Zhengzhou | 4 940 000 | 3.1 | 6.3 |
3. Lab6Absoulte
4. The difference between perceptual and absolute scaling is that absolute scaling has circles with larger diameters, while perceptual scaling has smaller diameters. This is because perceptual scaling takes into account that people tend to underestimate areas and volumes; therefore, making everything smaller except the larger diameters will increase the chance that people will not underestimate the area. There are also differences in the legend as you will have to display each scale a little differently, as one has smaller diameters and one with bigger diameters. The pros of absolute scaling are that it accurately represents the actual values in the dataset, maintains the quantitative relationship between data points, and is ideal when you need to make precise comparisons between data points. The cons of absolute scaling are that people underestimate areas, and without a proper legend, they can easily make errors in their judgment. Also, outliers in the data can also disproportionately affect the scaling, potentially causing difficulties in visualizing maps. Pros for perceptual scaling are that it can handle outliers by making areas smaller so that the outliers stand out more. It also allows for maps to be deciphered quickly as you are able to see which symbols are greater or smaller. Cons for perceptual scaling is that it doesn’t tell the truth about the size of the circles, meaning the data that is gathered is not used, but instead, the data is changed for human understanding, which masks the true values. Perceptual scaling is also based on an average subject, which ignores the fact that some people are able to correctly judge areas. The best way to communicate this data set would be the absolute scaling, in my opinion. This is because in this data set, there aren’t any outliers, and for perceptual scaling, you are just changing the circles to be smaller so the larger one can stand out. This dataset has too little info to be able to use perceptual scaling to its full benefit. The absolute scaling would be a better choice because the symbols are quite similar in shape, and there isn’t any way that you can underestimate areas of those symbols.
