Friction of distance
From Wikipedia, the free encyclopedia
Friction of distance is a core principle of geography that states that movement incurs some form of cost, in the form of physical effort, energy, time, and/or the expenditure of other resources, and that these costs are proportional to the distance traveled. This cost is thus a resistance against movement, analogous (but not directly related) to the effect of friction against movement in classical mechanics.[1] The subsequent preference for minimizing distance and its cost underlies a vast array of geographic patterns from economic agglomeration to wildlife migration, as well as many of the theories and techniques of spatial analysis, such as Tobler's first law of geography, network routing, and cost distance analysis. To a large degree, friction of distance is the primary reason why geography is relevant to many aspects of the world, although its importance (and perhaps the importance of geography) has been decreasing with the development of transportation and communication technologies.[2][3]
It is not known who first coined the term "friction of distance," but the effect of distance-based costs on geographic activity and geographic patterns has been a core element of academic geography since its initial rise in the 19th Century. von Thünen's isolated state model of exurban land use (1826), possibly the earliest geographic theory, directly incorporated the cost of transportation of different agricultural products as one of the determinants for how far from a town each type of goods could be produced profitably.[4] The industrial location theory of Alfred Weber (1909) and the central place theory of Walter Christaller (1933)[5] were also basically optimizations of space to minimize travel costs.
By the 1920s, social scientists began to incorporate principles of physics (more precisely, some of its mathematical formalizations), such as gravity, specifically the inverse square law found in Newton's law of universal gravitation.[6] Geographers quickly identified a number of situations in which the interaction between places, whether migration between cities or the distribution of residences willing to patronize a shop, exhibited this distance decay due to the advantages of minimizing distance traveled. Gravity models and other Distance optimization models became widespread during the quantitative revolution of the 1950s and the subsequent rise of spatial analysis. Gerald Carrothers (1956) was one of the first to explicitly use the analogy of "friction" to conceptualize the effect of distance, suggesting that these distance optimizations needed to acknowledge that the effect varies according to localized factors.[6] Ian McHarg, as published in Design with Nature (1969), was among those who developed the multifaceted nature of distance costs, although he did not initially employ mathematical or computational methods to optimize them.[7]
In the era of geographic information systems, starting in the 1970s, many of the existing proximity models and new algorithms were automated as analysis tools, making them significantly easier to use by a wider set of professionals. These tools have tended to focus on problems that could be solved deterministically, such as buffers, Cost distance analysis, interpolation and network routing. Other problems that apply the friction of distance are much more difficult (i.e., NP-hard), such as the traveling salesman problem and cluster analysis, and automated tools to solve them (usually using heuristic algorithms such as k-means clustering) are less widely available, or only recently available, in GIS software.
Distance Costs
As an illustration, picture a hiker standing on the side of an isolated wooded mountain, who wishes to travel to the other side of the mountain. There are essentially an infinite number of paths she could take to get there. Traveling directly over the mountain peak is "expensive," in that every ten meters spent climbing requires significant effort. Traveling ten meters cross country through the woods requires significantly more time and effort than traveling ten meters along a developed trail or through open meadow. Taking a level route along a road going around the mountain has a much lower cost (in both effort and time) for every ten meters, but the total cost accumulates over a much longer distance. In each case, the amount of time and/or effort required to travel ten meters is a measurement of the friction of distance. Determining the optimal route requires balancing these costs, and can be solved using the technique of cost distance analysis.
In another, very common example, a person wants to drive from his home to the nearest hospital. Of the many (but finite) possible routes through the road network, the one with the shortest distance passes through residential neighborhoods with low speed limits and frequent stops. An alternative route follows a bypass highway around the neighborhoods, having a significantly longer distance, with much higher speed limits and infrequent stops. Thus, this alternative has a much lower unit friction of distance (in this case, time), but it accumulates over a greater distance, requiring calculations to determine the optimal (taking the least total travel time), perhaps using the network analysis algorithms commonly found in web maps such as Google Maps.
The costs that are proportional to distance can take a number of forms, each of which may or may not be relevant in a given geographic situation:
- Travel cost, the resources required to move through space. This is most commonly time, energy, or fuel consumption, but may also include more subjective costs such as nuisance.
- Traffic cost, the impedance resulting from the aggregate volume of travelers exceeding the optimum capacity of the space (usually a linear network in this case).
- Construction cost, the resources required to build the infrastructure that makes travel through the space possible, such as roads, pipes, and cables.
- Environmental impacts, the negative effects on the natural or human environment caused by the infrastructure or the travel along it. For example, one would want to minimize the length of residential neighborhood or wetland destroyed to build a highway.
Some of these costs are easily quantifiable and measurable, such as transit time, fuel consumption, and construction costs, thus naturally lending themselves to optimization algorithms. That said, there may be a significant amount of uncertainty in predicting them due to variability over time (e.g., travel time through a road network depending on changing traffic volume) or variability in individual situations (e.g., how fast a person wishes to drive). Other costs are much more difficult to measure due to their qualitative or subjective nature, such as political protest or ecological impact; these typically require the creation of "pseudo-measures" in the form of indices or scales to operationalize.[3]
All of these costs are fields in that they are spatially intensive (a "density" of cost per unit distance) and vary over space. The cost field (often called a cost surface) may be a continuous, smooth function or may have abrupt changes. This variability of cost occurs both in unconstrained (two- or three-dimensional) space, as well as in constrained networks, such as roads and cable telecommunications.