Class 17

Geographic Fundamentals

AGENDA

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Today’s Class …

• Geographic variation
• Spatial patterns
• MAUP
• Ecological Fallacy

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During Class …

• In class exercise

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Next Class …

• Spatial Data Analysis

Fundamentals

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Geographic Variation

• Variation in some phenomenon across space or from place to place
• Observe in tables and view in maps
• Events (disease cases)
• Locations (hospitals)
• Values (average income)

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Geographic Variation

• Additional information not found in table
• Spatial relationship
• Useful for hypothesis generation
• Pattern to process
• Integrate other layers, measure distance to features

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Geographic Variation

• A basic concern for geography is variation in some phenomenon across space or from place to place
• We can observe this in tables, but view it in maps

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Spatial Patterns - Events

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Spatial Patterns - Attributes

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MAUP – Modifiable Areal Unit Problem

• Underlying data are aggregated to a single value
• Masks variation
• Location of boundaries is important
• Size of units and position
• Two main effects
• Zone and scale
• Affects statistical relationships 🡪 statistical bias

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Zone vs Scale Effect

• Zone Effect
• Reconfiguration of the boundaries of the study area
• Same number of zones in a different arrangement
• Scale Effect
• The result of rescaling due to a reconfiguration of the internal boundaries in the study area
• Fewer observations in the study area

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MAUP

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MAUP Zone Effect

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MAUP Zone Effect

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MAUP Zone Effect

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MAUP Scale Effect – Count Data

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MAUP Scale Effect - Rate Data

• Rate Data
• Aggerated units
• Soaks up some of the variability present in the original data
• Hidden within aggregated units

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Ecological Fallacy

• Cannot assume than an individual has the same attributes as the aggregate group to which it belongs
• Ecological “scale” of analysis
• Observation units are a group of people
• Limits our ability to assign causation among phenomena
• Correlation only!!

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Ecological Fallacy

• Central tendency leveling
• The more people in each observation unit, the less variation there will be among units
• Occurs when findings from one scale are assumed to be true for other scale

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Other Common Spatial Tasks - Spatial Interpolation

Techniques:

• Kriging

- Inverse Distance Weighting (IDW)

•Applications:

- Creating continuous surfaces from discrete data points

- Environmental monitoring (e.g., pollution levels)

- Meteorological data analysis (e.g., temperature, rainfall)

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Other Common Spatial Tasks- Remote Sensing Data Integration

Benefits:

• Enhances spatial analysis with satellite imagery
• Provides up-to-date and detailed information

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Applications:

• Land use and land cover mapping
• Disaster management and response
• Agricultural monitoring

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Big Data in GIS

Challenges:

• Data privacy
• Handling large spatial datasets
• Ensuring data quality and accuracy

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Tools and Techniques:

• Distributed computing (Hadoop, Research Clusters)
• Cloud-based GIS platforms (Google Earth Engine)

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Practical Applications of Spatial Analysis

• Disease outbreak tracking and modeling
• Site selection and zoning analysis
• Habitat suitability modeling
• Analyzing traffic patterns
• Mapping flood risk areas

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