# Geospatial Assessment of the Territorial Road Network by Fractal Method
## 4 Discussion  
Using the created script, as an example of use, a map of the transport provision of Ukraine was constructed, shown in Figure 9.  
The area of the hexagonal cell of the fixed grid was chosen, equal to 1000 km\({}^{2}\). The resulting vector map, consisting of 710 polygon features, was classified by the TP field, which contained the calculated values of the transport provision indicator in accordance with Equation (11). Figure 8: Python script algorithm. Figure 9: Ukraine road network transport provision level. The script execution time was 44 m 54 s. For simulation, a PC with modest technical characteristics was used: an Intel Pentium Processor G4400 (3 M Cache, 3.30 GHz) with an Intel HD Graphics 510 integrated graphics processor, 4.00 Gb DDR4 RAM, and an Asus H110M-K motherboard. When using a PC with better performance, one should expect a reduction in script execution time. In the territory of Ukraine, low values of fractal dimensions are observed in the absence of settlements and an increase in its values in the vicinity of cities. At the same time, the relationship between the size of the population of the city and the growth of the area with a high fractal dimension index around it is clearly traced. In percentage terms, in 11% of the Ukraine territory, transport provision is less than 0.5 (very low value with a sparse road network). In 14%, the level of transport provision is from 0.5 to 0.625 (low value with a sparse road network; that is, there are single primary roads crossing the territory). In 63%, the value of the indicator is from 0.625 to 0.8 (average value; that is, there is a network of primary roads between settlements). Lastly, in 12%, a high level of transport provision appears in a range from 0.8 to 0.855. If we compare the transport provision of different countries around the world, we can see the relationship between the population density and the growth of the territory with a high fractal dimension index. In Figure 10, (a) shows the result of calculating the transport provision of Bolivia, located on the continent of South America, which has a low population density, and (b) shows Germany, a country in Europe with a high population density. The area of the hexagonal cell of the fixed grid was also chosen to be 1000 km\({}^{2}\). The simulation results are presented in Table 2. In 61% of Bolivia's territory, the indicator of transport provision is less than 0.5, including 47% of the territory that lacks a road network (i.e., the indicator is zero). This is due to the presence of large areas of Amazonian rainforests in this part of the country. The Bolivia territory is also crossed by the Andean mountains, which contributes to the scarcity of transportation lines. In the remaining 39% of the territory, various values of transport provision are observed, with a predominance of values in the range from 0.5 to 0.79. As for Germany, about 79% of its territory has a transport provision index above 0.625, including 34% above 0.75. Figure 10: The level of transport provision of the territory (**a**) for Bolivia and (**b**) for Germany. Table 2 shows the comparative characteristics of the calculation of the transport provision of a territory for different countries. We also presented these countries' transport network density values, calculated as the highway length ratio to the territory area, and the values of [PERSON]'s coefficient, in accordance with Equation (1). Although it is not possible to compare the indicators of transport development calculated using [PERSON]'s coefficient (generalized indicator) and the fractal method (geospatial indicator), the main differences are still visible. The transport development of the territory of Germany, based on the value of [PERSON]'s coefficient (8.3), is almost 1.7 times less than the transport development of the territory of Ukraine (14.3) and is practically equal to the transport development of Bolivia (8.8).