integrated ch 6 Flashcards
spatial analysis
formalized approach to exploring relationships and patterns in geographic data, moving beyond visual observation of maps
types of spatial analysis
Point Pattern Analysis
Focuses on the spatial arrangement of locations within a single theme.
Key Question: How do the locations of objects/events in space vary in relationship to other objects/events?
Random Pattern: Locations distributed randomly.
Uniform Pattern: Locations are evenly scattered (e.g., fire stations).
Clustered Pattern: Locations are concentrated in specific areas (e.g., disease outbreaks).
Can be:
Global: Examines the entire study area.
Local: Examines specific subsets of the study area.
Autocorrelation Analysis
Considers both location and a single attribute to determine clustering or dispersion of similar values.
-Tobler’s First Law of Geography: “Everything is related to everything else, but near things are more related than distant things.”
Positive Autocorrelation: Similar values are clustered in space.
Negative Autocorrelation: Similar values are scattered.
No Autocorrelation: No discernible spatial pattern.
Proximity Analysis
Examines spatial relationships between two themes or types of data.
Euclidean Distance: Straight-line distance (“as the crow flies”).
Network Distance: Distance traveled via transportation networks.
Applications:
John Snow’s cholera map demonstrating proximity of cases to water pumps.
Accessibility analysis (e.g., reachability within 500 miles).
Correlation Analysis
Explores relationships between multiple attributes or themes.
Cautions:
Correlation Does Not Equal Causation: Spatial correlation does not imply causality.
Interoperability Issues: Data must be comparable across spatial, temporal, and attribute dimensions.
How does Tobler’s First Law of Geography relate to spatial autocorrelation?
Tobler’s First Law: “Everything is related to everything else, but near things are more related than distant things.”
Relation to Spatial Autocorrelation:
Spatial autocorrelation measures how similar or related nearby spatial data points are.
Positive autocorrelation aligns with Tobler’s Law (nearby areas show similar values).
Negative autocorrelation indicates dissimilarity between close areas, challenging the law.
Key Idea: Tobler’s Law provides the conceptual basis for understanding patterns of spatial dependence in autocorrelation.
practical applications of spatial analysis
Public Health
Mapping disease spread and identifying clusters to target interventions (e.g., cholera outbreaks).
Urban Planning
Analyzing accessibility of urban infrastructure such as parks or transit stations.
Environmental Studies
Studying the distribution of hazards (e.g., pollution) and their proximity to vulnerable populations.
Define point pattern analysis.
Answer: A method focusing on the spatial arrangement of locations within a single theme (e.g., clustering of disease cases)
What are the three types of patterns in point pattern analysis
Random: Locations distributed randomly.
Uniform: Locations evenly scattered.
Clustered: Locations concentrated in specific areas.
What is autocorrelation analysis?
Answer: Examines the clustering or dispersion of similar values considering both location and attributes.
What is Tobler’s First Law of Geography?
Answer: “Everything is related to everything else, but near things are more related than distant things.”
What is global point pattern analysis?
Answer: Examines spatial arrangements across an entire study area.
- What is local point pattern analysis?
Answer: Examines spatial arrangements within specific subsets of the study area.
Define positive spatial autocorrelation.
Answer: Similar values are clustered in space.
Define negative spatial autocorrelation.
Answer: Similar values are scattered.
What is Euclidean distance?
Answer: Straight-line distance between two points.
Give an example of proximity analysis in public health.
Answer: John Snow’s cholera map showing proximity of cases to water pumps.
Why is proximity analysis important in environmental studies?
Answer: It helps assess the spatial relationship between hazards (e.g., pollution) and vulnerable populations.
