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Chapter 2: The Pitfalls and Potential of Spatial Data

Chapter 2: The Pitfalls and Potential of Spatial Data. (O’Sullivan and Unwin, 2003). Bad News First:. Spatial Autocorrelation Data from locations near one another in space are more likely to be similar than data from locations remote from one another (positive, negative, zero)

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Chapter 2: The Pitfalls and Potential of Spatial Data

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  1. Chapter 2: The Pitfalls and Potential of Spatial Data (O’Sullivan and Unwin, 2003)

  2. Bad News First: • Spatial Autocorrelation • Data from locations near one another in space are more likely to be similar than data from locations remote from one another (positive, negative, zero) • Modifiable Areal Unit Problem (MAUP) • Ecological Fallacy • Scale • how an object type is represented is scale dependent • Nonuniformity • space is not uniform • spatial distribution of people is bound to have clusters and gaps (doesn’t conform to regular statistical methods)

  3. Modifiable Areal Unit Problem • This is a problem of data aggregation and the impact that the level of aggregation has on the patterns and relationships observed • Usually, regression relationships are strengthened by aggregation

  4. High Correlation between crime and low-income areas Commit EF if we conclude that low-income people commit crimes Perhaps, low-income households don’t have good alarm systems; Or drug-addicts (regardless of income) live in these areas and commit crimes Ecological Fallacy

  5. Now for the Good News: • Spatial Analysis focuses on the distribution of data in space; spatial relationships are described using the following: • Distance (dist. between spatial entities) • Adjacency (spatial relationship between entities: adjacent or not adjacent) • Interaction (I ~ 1/D) – (the nearer things are, the more related they are) • Neighborhood- region of space within a certain distance of a particular entity

  6. Proximity Polygons The proximity polygon of an entity is that region of space that is closer to that entity than it is to any other.

  7. Variogram Cloud Helps explore the spatial dependence between attributes of objects; plot the differences in attribute values for pairs of entities against the difference in their location (dist. between entities). A variogram cloud is a tool used to study spatial dependencies between attributes of objects.

  8. Questions • What are the 5 major problems in the analysis of geographic information??? • How are proximity polygons constructed for point objects??

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