AP Human Geography Definition of Spatial Association Key Concepts in Spatial Association Applications of Spatial Association in Practice Conclusion Tables

Spatial association is a statistical measure that assesses the degree to which two or more geographic phenomena are related. It is a fundamental concept in human geography, as it allows geographers to identify patterns and relationships in the distribution of people, resources, and activities across space.

Types of Spatial Association

There are two main types of spatial association:

• Positive spatial association occurs when two or more phenomena are clustered together, meaning that they are more likely to occur in the same location.
• Negative spatial association occurs when two or more phenomena are dispersed, meaning that they are less likely to occur in the same location.

Measuring Spatial Association

Spatial association can be measured using a variety of statistical techniques, including:

• Moran’s I statistic measures the degree of clustering or dispersion of a single phenomenon.
• Geary’s C statistic measures the degree of spatial autocorrelation between two or more phenomena.
• Local Moran’s I statistic measures the degree of spatial association between a single phenomenon and its neighboring locations.

Applications of Spatial Association

Spatial association has a wide range of applications in human geography, including:

• Identifying patterns of disease Researchers can use spatial association to identify areas with high rates of disease, which can help them target prevention and treatment efforts.
• Planning land use Planners can use spatial association to identify areas that are suitable for different types of land use, such as residential, commercial, or industrial development.
• Modeling transportation networks Transportation planners can use spatial association to identify the optimal locations for transportation infrastructure, such as roads, railways, and airports.
• Predicting climate change impacts Climate scientists can use spatial association to predict the impacts of climate change on different regions, such as sea level rise and changes in temperature and precipitation.
• Spatial econometrics Spatial econometrics is a statistical technique that uses spatial association to model the relationship between economic variables.

Spatial association is a powerful tool that geographers use to understand the distribution of people, resources, and activities across space. It has a wide range of applications in both research and practice.

In addition to the basic concept of spatial association, there are several other key concepts that are important to understand:

• Spatial autocorrelation is the tendency for similar values to occur near one another. This can be caused by a variety of factors, such as the diffusion of ideas or the spread of disease.
• Spatial heterogeneity is the variation in the distribution of a phenomenon across space. This can be caused by a variety of factors, such as differences in climate, topography, or land use.
• Spatial scale is the level of detail at which a phenomenon is studied. The scale of a study can affect the results of spatial association analysis.
• Spatial dependency is the relationship between two or more phenomena that are located near one another. This can be caused by a variety of factors, such as the physical environment or social interactions.

Spatial association has a wide range of applications in practice, including:

• Public health Researchers can use spatial association to identify areas with high rates of disease and to target prevention and treatment efforts. For example, a study by the Centers for Disease Control and Prevention (CDC) found that people who live in areas with high levels of air pollution are more likely to develop heart disease and cancer.
• Land use planning Planners can use spatial association to identify areas that are suitable for different types of land use. For example, a study by the American Planning Association found that people who live in areas with a mix of land uses are more likely to be satisfied with their community.
• Transportation planning Transportation planners can use spatial association to identify the optimal locations for transportation infrastructure. For example, a study by the Federal Highway Administration found that people who live near highways are more likely to drive to work.
• Environmental planning Environmental planners can use spatial association to identify areas that are at risk for environmental hazards. For example, a study by the Environmental Protection Agency (EPA) found that people who live near toxic waste dumps are more likely to develop cancer.
• Economic development Economic developers can use spatial association to identify areas that are ripe for economic development. For example, a study by the World Bank found that people who live in areas with a high concentration of businesses are more likely to find jobs.

Spatial association is a powerful tool that geographers and other social scientists use to understand the distribution of people, resources, and activities across space. It has a wide range of applications in both research and practice.

Table 1. Types of Spatial Association

Table 2. Measures of Spatial Association

Table 3. Applications of Spatial Association

Table 4. Key Concepts in Spatial Association