Cost distance mapping and spatial analysis often involves far more than simply calculating distance and elevation. Cost distance can also be a variety of variables, ranging from economic, social, cultural, environmental, or other barriers that can create a type of ‘cost’ in travel or movement. It can also be conceptual, where cost is a type of social barrier or prohibitive aspect. Regardless what is used to calculate cost, those values are then typically used to create a cost surface and different set of analyses. This includes cost path, or the path with the least cost, and cost distance, that is the least cumulative cost travel distance in a surface.
Applications of Cost Distance Modeling in GIS
Typical applications of cost distance modeling in GIS include applications in ecology, where understanding species migration and movement are a major concern for conservation and animal behavior methods. For instance, the Australian robin has been studied as a bird that has preferential flight paths. It was found that such birds often avoid open grasslands or pasture, where the bird was more vulnerable. This type of modeling can be used to determine where pasture should be allowed so that areas for robin movement do not become patchy and there are corridors they can migrate through without being excessively vulnerable. In another case from ecology, to understand susceptibility of plants, such as pine trees, to diseases, cost distance modeling can be used to determine areas where trees are more likely to be vulnerable. This allows ecologists then to better target preventative measures. For insects that cause disease, this can be challenging as many factors can affect their dispersal, including climatic and human factors.
Other applications include determining optimal search and rescue in maritime contexts, where wave conditions, search capabilities, and spatial accessibility can affect response times. Having cost distances that account for these variable circumstances allow for more efficient and lest time costly search to be conducted, particularly if the data are dynamic and can be applied to real-time conditions.
For airline travel, cost distance is also not a simple problem of distance. Traffic to major cities, weather conditions, and even political factors can affect travel to cities for airlines. Creating cost distance maps can help better determine current conditions for a city that airlines can use to better determine travel times and efficient air travel pathways to save fuel costs.
Archaeologists have also attempted to better understand ancient foraging and hunting behavior from the Pleistocene for hunter-gatherer societies. In this case, researchers reconstructed the paleolandscape’s vegetation and geographic barriers to better understand regions where hunters and gatherers would have preferred. Foraging and hunting ranges and choice of where to settle were affected by potential. The choice of where to settle encampments and focus activities for ancient populations could imply foraging or hunting preferences. This also likely affected strategies for groups to separate or fuse together.
In the United States, biofuels have become one potential fuel source that could expand in the near future. This has created the problem on where to put fuel production facilities so that they reach the most people and meet the greatest fuel needs. Optimization using cost distancing is one way this problem could be approached, where transport costs, production potential, and markets can be calculated together to determine overall costs and areas of best benefits for fuel provisioning.
Overall, cost distance modeling continues to be an important optimization approach and used as part of simulations that attempt to understand changing cost surfaces and movement corridors. The applicability of the approach to the natural and social sciences indicates it broad relevance.
 For basic information on cost distance, see: Zhu, X. (2016) GIS for environmental applications: a practical approach. London ; New York, NY, Routledge, pg. 155.
 For more on an example of cost distance modeling in ecology, see: Richard, Y. & Armstrong, D.P. (2010) Cost distance modelling of landscape connectivity and gap-crossing ability using radio-tracking data. Journal of Applied Ecology. [Online] 47 (3), 603–610.
 For more on cost distance modeling for plant diseases spread by insects, see an example at: Roversi, P.F., Sciarretta, A., Marziali, L., Marianelli, L. & Bagnoli, M. (2013) A GIS-Based Cost Distance Approach to Analyse the Spread of Matsucoccus feytaudi in Tuscany, Italy (Coccoidea Matsucoccidae). Redia, 46, 9–16.
 For more on maritime rescue modeling using cost distance measures, see: Siljander, M., Venäläinen, E., Goerlandt, F. & Pellikka, P. (2015) GIS-based cost distance modelling to support strategic maritime search and rescue planning: A feasibility study. Applied Geography. [Online] 57, 54–70.
 For more on an example of airline travel using cost distance, see: Dudás, G., Boros, L., Pál, V. & Pernyész, P. (2016) Mapping cost distance using air traffic data. Journal of Maps. [Online] 12 (4), 695–700.
 For more on using cost distance modeling to understand ancient human foraging and hunting behavior, see: Byrd, B.F., Garrard, A.N. & Brandy, P. (2016) Modeling foraging ranges and spatial organization of Late Pleistocene hunter–gatherers in the southern Levant – A least-cost GIS approach. Quaternary International. [Online] 396, 62–78.
 For more on using cost distance for fuel transport, see: Venteris, E.R., McBride, R.C., Coleman, A.M., Skaggs, R.L., et al. (2014) Siting Algae Cultivation Facilities for Biofuel Production in the United States: Trade-Offs between Growth Rate, Site Constructability, Water Availability, and Infrastructure. Environmental Science & Technology. [Online] 48 (6), 3559–3566.