style="font-size:15px;"> By understanding the relative location of features, geographers can analyze how spatial relationships explain events. For instance, by knowing the relative location of countries in the Middle East and Europe, it is possible to understand migration flows out of war-torn Syria. Syrians will flee to nearby countries, such as Turkey, Lebanon, and Jordan, as well as to rich countries that are not too far away, such as Germany and Sweden. Many fewer migrants would be expected to go to farther away to Canada or the United States, which have a relative distance that is far from the Middle East.
As another example, relative location is useful in explaining real estate prices. Two identical houses, one adjacent to a golf course and one close to an industrial park, will have vastly different values, precisely because of their location relative to different land uses.
Closely related to location is the concept of distance. As with location, distance can be measured in absolute and relative terms. Absolute distance can be measured in traditional units, such as miles and feet or kilometers and meters. Relative distance looks at distance in terms of a surrogate value such as cost or difficulty.
Absolute distance is commonly measured by geographers in two ways (figure 1.22). Euclidean distance measures the distance between two points in a straight line. When people use the common vernacular “as the crow flies,” they are referring to Euclidean distance. Drawing a straight line from your house to school would give you the Euclidean distance. However, in peoples’ daily lives, they rarely travel in straight lines. For this reason, Manhattan distance, also called network distance, is also used in geographic analysis. Manhattan distance (named after the rectangular layout of Manhattan streets) is the distance between two places along a grid. When you travel from home to school, you probably don’t fly in a straight line. Most likely, you follow a street grid, which results in a longer total distance travelled.
Distance can also be measured in relative terms as cost distance. This can include cost in time or cost in difficulty of travel. For instance, cost distance can be calculated by measuring Euclidean or Manhattan distance and then weighting the distance value to account for the difficulty of travel. When walking from your house to the grocery store, you may have two options. Option one may be a flat route of 0.75 miles, while option two may be only 0.5 miles but include a steep hill. Because of the hill, you may add a cost value (either consciously or unconsciously) to give that distance a greater weight. If you decide that walking over the hill is twice as difficult as walking on the flat route, you can multiply the hill route by two (0.5 miles × 2 = 1.0 mile). Based on this calculation of cost distance, you would decide to take the flat 0.75-mile route.
Cost distance can also be measured in terms of time. People often say that they live “twenty minutes” from school rather than saying they live eight miles from school. Geographers use cost distance when calculating drive times. Different types of roads have different speed limits or are made of different materials. A vehicle travelling for twenty minutes will go much farther on a state highway than on a narrow dirt road. For this reason, different road types can be weighted differently for calculating travel time. Also, traffic conditions can vary by time of day, resulting in a cost distance that varies not only over space but also over time.
Figure 1.22.Measuring absolute distance. Euclidean distance in green (1.48 miles) follows a straight path between two points. Manhattan or network distance in red (1.93 miles) follows the street grid. The red line can also be measured as cost distance in terms of time. The cost in time will vary on the basis of traffic conditions, so that at midnight it may be 8.5 minutes, while at 5:30 p.m. it may be 12 minutes. Map by author. Data sources: City of Tuscaloosa, Esri, HERE, Garmin, INCREMENT P, NGA, USGS.
Go to ArcGIS Online to complete exercise 1.3: “Location and distance.”
Spatial patterns
Features on the earth’s surface arrange themselves in spatial patterns. Analyzing these patterns allows geographers to elucidate not only how human and physical features are arranged but also the processes behind their formation.
A commonly used description of spatial patterns is density. Density is the number of features per unit area, as in the number of people per square mile or number of trees per square kilometer. Density is useful for illustrating spatial patterns that would not be seen using raw numbers alone. For example, the population of California is about 39 million people, while the population of Singapore is only 5.5 million. With no additional information, one may get the impression that California is more crowded than Singapore. But when information on area is added, that impression quickly changes. California consists of 163,696 square miles, while Singapore is made up of just 278 square miles. So, in reality, Singapore has a much higher population density than California (figure 1.23).
Figure 1.23.Population density: Singapore. Singapore has one of the highest population densities in the world, with 5.5 million people living in just 278 square miles. Photo by joyfull. Stock photo ID: 138766448. Shutterstock.
Spatial patterns can also be viewed in terms of clustering, randomness, and dispersion (figure 1.24). As the name implies, clustered features are found grouped near each other. Clusters are often identified with hot spot analysis or with a heat map (figure 1.25). Randomly distributed features have no particular spatial pattern. Dispersed features are those that repel each other. They are certainly not clustered and are even farther from each other than if the distribution were random.
Figure 1.24.Spatial patterns can be seen as dispersed, random, or clustered. Image by author.
Analysis of these types of spatial patterns has many applications. For example, if home burglaries are found to be clustered in a specific neighborhood, police can increase patrols in that area, while detectives and community groups can focus on what the underlying causes of the crime cluster are. It may turn out that a prolific burglar lives nearby, or youth from a local high school may be committing crimes after school. If home burglaries are not clustered, but have a more random pattern, then other causes may be at play, such as burglaries being crimes of opportunity, where criminals take advantage of homes with open windows.
Diseases often cluster as well. If cancer rates are found to cluster in a neighborhood, then health researchers may search for environmental causes of the disease, such as a nearby toxic waste site. If cancer cases are randomly distributed around a city, then environmental factors are less likely to be the cause.
Dispersed features can include shopping malls or chain restaurants in an urban region. Mall owners may intentionally maintain a distance from competing malls to avoid competition, while owners of a restaurant chain may space their stores so that they do not cannibalize sales from each other.
Spatial patterns can also be analyzed by measuring the center of features. With a map of consumer purchasing behavior, a business may want to find a new store location that lies at the center of its specific market segment. Likewise, geographers can study shifts in population by mapping the center of US population over time.
Spatial relationships
Mapping the spatial relationships of two or more features can offer insight into why particular patterns exist. Whereas spatial distributions describe how features are clustered or dispersed, spatial relationships depict where features are located in relationship to other types of features. For instance, geographers study the distance between different types of features or whether different feature types overlap (figure 1.26). If there is a disease cluster, geographers can examine the distance between the cluster and factories
