Spatial association is a fundamental concept in geographic analysis describing how phenomena are similarly distributed across a space. This relationship occurs when the value of a variable in one location is related to the values of the same or another variable in nearby locations. Proximity matters, meaning objects or measurements close to one another tend to exhibit similarity or dissimilarity. Recognizing this tendency is necessary for properly interpreting geographic data and understanding the forces that drive the distribution of features in the real world.
Understanding Spatial Patterns
Spatial associations manifest in three primary patterns that can be visually interpreted on a map. The most common pattern is a positive spatial association, often described as clustering. Clustering occurs when high values of a feature are located near other high values, or when low values are grouped near other low values. This creates distinct “hot spots” and “cold spots” on the landscape.
The opposite pattern is a negative spatial association, characterized by dispersion or a checkerboard effect. Dissimilar values tend to be neighbors, meaning a high value is typically surrounded by low values, and vice versa. This dispersed pattern suggests a competitive or repulsive process is at work, forcing similar features to locate far apart.
The third possibility is a random spatial pattern, where no discernible relationship exists between the value at one location and the values of its neighbors. The location of a feature provides no predictive information about the values of surrounding areas. These conceptual patterns help understand the mechanisms that structure a geographic distribution, such as environmental conditions or human behavior.
Case Studies in Spatial Association
Clustering of disease outbreaks provides a clear example of a positive spatial association in public health and epidemiology. Studies have shown that waterborne infectious diseases, such as cholera and Hemorrhagic Fever with Renal Syndrome (HFRS), exhibit significant spatial clustering. The incidence of HFRS, for example, aggregates near specific water bodies or within certain radii. This suggests a localized exposure mechanism, such as a rodent reservoir or contaminated water source.
The distribution of high-value residential properties also demonstrates a distinct spatial association in real estate markets. Geospatial analysis of home values often reveals high-high clusters, where high-priced homes are concentrated in affluent neighborhoods, and low-low clusters in separate areas. This pattern reflects socioeconomic forces like segregation and amenity attraction, where similar property types group together to form distinct spatial markets. Conversely, a high-low spatial outlier might exist where a pocket of low-value homes is surrounded by a larger, high-value region.
In environmental science, a positive spatial association exists between industrial activity and specific environmental pollutants. Research on industrial centers, such as the Yellow River Basin, identifies a strong correlation between industrial agglomeration and the clustering of emissions like industrial wastewater and sulfur dioxide. This spatial relationship indicates that pollution is concentrated near its source, rather than being evenly spread. The concentration of these pollutants creates localized zones of environmental concern, emphasizing the need for geographically targeted regulatory measures.
Quantifying Spatial Relationships
Visual interpretation of patterns is an important first step, but quantitative analysis is necessary to confirm that an observed association is statistically significant and not due to chance. The most widely used global measure for quantifying the overall degree of clustering across an entire study area is Moran’s I. This index produces a single value summarizing the overall spatial association, indicating whether the pattern is clustered (positive value), dispersed (negative value), or random (near zero).
While a global measure confirms the presence of an overall pattern, it cannot identify where that clustering is strongest or weakest. To address this, analysts use Local Indicators of Spatial Association (LISA), such as Local Moran’s I. These local statistics break down the overall pattern to identify specific locations that contribute most to the clustering. LISA maps pinpoint “hot spots” (areas of high values surrounded by high values) and “cold spots” (low values surrounded by low values). Identifying these local clusters is crucial for policymakers targeting resource allocation, such as public health interventions or environmental enforcement.