There are a few considerations to make when growing populations/cities organically.
- Initial populations of villages are set to $P_0 = 150$. The growth equation at time $t$ then becomes \[P(t) = {K \over {1 + {K - P_0 \over P_0} e^{-r t}}}\] In the future, I'll actually progressively modify the population number, but for now I just evaluate this equation using the age of the city.
- The carrying capacity $K$ is a function of the desirability $D$ of the location. \[K_0 = 5000 \exp\left(10 \left({D \over \textrm{max}(D)} - {1 \over 2}\right)\right)\]
- $K$ is then modified according to Central Place Theory (CPT). One of the tenets of CPT is that cities are spaced according to the product of their populations and inversely to the distance between them. My method for doing this is as follows: the city in question is assigned a level $\ell = \log_{10} K_0 - 1$. $K_0$ is then reduced by a factor of 10 if $2 ^ i > \delta_c$ for all $i < \ell$, where $\delta_c$ is the distance to the closest city of the same $\ell$. So a city of 9500 people cannot be closer than 4 hexes to another similarly sized city. If it is, then $K_0$ is reduced by a factor of 10. This will hopefully ensure an exponential distribution of city sizes, and prevent a concentration of very large settlements.
- $r$ will be a random number with a max of $2\% \cdot D \over \textrm{max}(D)$. This sets the maximum growth rate at 2%, but allows for a lot of variation in between. Maybe the different races should have different growth rates?
- Any given hex which meets all other requirements has only a 5% chance of generating a city. I'll give this a little boost for particularly desirable places.
- I won't force them to obey Zipf's Law, but I'm wondering how the distribution will shake out. Zipf's Law has been demonstrated to hold most true for "small cities" over 100,000 - for a fantasy low-tech world, that might be a "huge city" instead!
- Another thing I need is a mechanism to destroy or stagnate a town - many things can affect a city's growth or longevity. Not every city built in 1000 BC still exists. In fact, most don't. Plague, famine, war, resource depletion, etc can all wipe out even large settlements.
- A dynamic $K$ value might be cool also. That way a settlement might have a bright future, but overcrowding could really hurt long-term. As groups compete for resources, which larger cities need more of, smaller settlements tend to be crowded out. I need to capture that, and that might help my goal of CPT-arranged cities.
Some observations:
- Since dwarfs (blue cities) are not "constrained" to the coast, their cities are placed much faster. This isn't necessarily a problem, but I want to consider how heavily the coast is weighted vs the inland. As a result, non-dwarfen cities are isolated, and have more difficulty coalescing into an entity.
- The desirability map takes a dozen seconds or so to populate. This adds up and makes the year-by-year simulation very slow.
- Similarly, it's expensive to figure out where roads should be placed so that they make sense. I've added some timeout functions to the road placement algorithm to make sure that A* doesn't get stuck somewhere it can't get out, but the constraints need some work. As shown in these images, it's made slower by the fact that eventually, every city gets a full 6 roads coming in and out.
- Some of the orc cities (purple) behave well with roads, but there are no special road considerations for different races.
- River crossings are a problem. They would be easier to implement if I used edges instead of tiles, but that would be a big pain to implement with the drainage code.
After 550 years of simulation, the total city populations are as follows (not including rural population, which is expected to be about 5x urban):
| elf | 13,552,065 |
| dwarf | 5,570,650 |
| halfling | 11,144,894 |
| orc | 28,884,984 |
| human | 14,688,643 |
Orc growth is much faster because their cities form at great distances from the other, allowing for rapid growth and large carrying capacities from CPT. The opposite is true of the dwarfs: they have many cities, but densely packed, which reduces the maximum carrying capacity.
I could do more analysis of the cities, but I'd like to tackle the road problem first, since it feeds back into the city placement algorithm, and is the current bottleneck for any kind of long-term simulation.
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