1. Why are Spatial Data Special?
Spatial Data Models
2/19/2019
Why are Spatial Data Special?
The Pitfalls and Potential of Spatial Data
Why does adding a spatial location to some attribute data change these data in fundamental ways??
© J.M. Piwowar
Why are Spatial Data Special?
© J.M. Piwowar

2. Why are Spatial Data Special?
1-Introduction to GIS
2/19/2019
Why are Spatial Data Special?
Why does adding a spatial location to some attribute data change these data in fundamental ways??
© J.M. Piwowar
Why are Spatial Data Special?
© J.M. Piwowar

3. The Pitfalls of Spatial Data
Spatial Data Models
2/19/2019
The Pitfalls of Spatial Data
Spatial Autocorrelation
Tobler’s 1st Law of Geography
All features are related and near features are more related than distant ones.
Without spatial autocorrelation, Geography would be irrelevant.
One of the most basic of assumptions of conventional statistical analyses is that the data are random.
Spatial data almost always violate this fundamental requirement.
© J.M. Piwowar
Why are Spatial Data Special?
© J.M. Piwowar

4. The Pitfalls of Spatial Data
Spatial Data Models
2/19/2019
The Pitfalls of Spatial Data
Spatial Autocorrelation
One of the most basic of assumptions of conventional statistical analyses is that the data are random.
Spatial autocorrelation means that there is redundancy in the data.
This can affect the calculation of confidence intervals, etc.
There is a strong case for assessing the degree of spatial autocorrelation in a data set before doing any conventional statistics at all.
Joins count statistic, Moran’s I, Geary’s C, (semi-)variograms
One of the most basic of assumptions of conventional statistical analyses is that the data are random.
Spatial data almost always violate this fundamental requirement.
© J.M. Piwowar
Why are Spatial Data Special?
© J.M. Piwowar

5. The Pitfalls of Spatial Data
Spatial Data Models
2/19/2019
The Pitfalls of Spatial Data
Spatial Autocorrelation
Describing and modelling patterns of variation across a study area – i.e. measuring the autocorrelation present – is of primary importance in spatial analysis.
© J.M. Piwowar
Why are Spatial Data Special?
© J.M. Piwowar

6. The Pitfalls of Spatial Data
Spatial Data Models
2/19/2019
The Pitfalls of Spatial Data
Modifiable Areal Unit Problem (MAUP)
The definition of a boundary is arbitrary with respect to the feature being mapped.
If you change the boundary, the reported values for the region may change too.
© J.M. Piwowar
Why are Spatial Data Special?
© J.M. Piwowar

7. The Pitfalls of Spatial Data
Spatial Data Models
2/19/2019
The Pitfalls of Spatial Data
Modifiable Areal Unit Problem (MAUP)
© J.M. Piwowar
Why are Spatial Data Special?
© J.M. Piwowar

8. The Pitfalls of Spatial Data
Spatial Data Models
2/19/2019
The Pitfalls of Spatial Data
Ecological Fallacy
Occurs when it is inferred that data for areas under study can be applied to individuals in that area.
Do all the people living in a census tract have the mean income?
Are all the locations in an elevation zone have the mean elevation?
© J.M. Piwowar
Why are Spatial Data Special?
© J.M. Piwowar

9. The Pitfalls of Spatial Data
Spatial Data Models
2/19/2019
The Pitfalls of Spatial Data
Scale
The scale at which we work affects the representations we use and the spatial analyses we apply.
The correct (or appropriate) scale for a study is usually impossible to determine beforehand.
© J.M. Piwowar
Why are Spatial Data Special?
© J.M. Piwowar

10. The Pitfalls of Spatial Data
Spatial Data Models
2/19/2019
The Pitfalls of Spatial Data
Nonuniformity of Space
Arises because features are not evenly distributed everywhere.
By simply plotting break & enter locations we may see clusters in the data that have little to do with crime, e.g. in high density residential neighbourhoods. We may also see gaps in the data in parks and over lakes.
© J.M. Piwowar
Why are Spatial Data Special?
© J.M. Piwowar

11. The Pitfalls of Spatial Data
Spatial Data Models
2/19/2019
The Pitfalls of Spatial Data
Edge Effects
Arise along artificial boundaries.
If we are trying to identify neighbours of a particular feature, those features along the edges of the study area will have far fewer neighbours than those in the central area.
© J.M. Piwowar
Why are Spatial Data Special?
© J.M. Piwowar

12. The Potential of Spatial Data
Spatial Data Models
2/19/2019
The Potential of Spatial Data
Distance
Euclidean distance
Perceptual distance
What are some of the potentials of spatial data for added insight provided by examination of the locational attributes of data.
When we analyze spatial data we are not only interested in the distribution of observational data values (e.g. through classical statistics), but also in the distribution of the associated entities in space.
© J.M. Piwowar
Why are Spatial Data Special?
© J.M. Piwowar

13. The Potential of Spatial Data
Adjacency
Whether 2 objects are situated next to each other.
How do you decide?
Within a fixed distance?
Nearest neighbour?
Connectivity?
© J.M. Piwowar
Why are Spatial Data Special?

14. The Potential of Spatial Data
Interaction
A combination of distance and adjacency.
A mathematical formulation of Tobler’s Law.
Generally represented as a number between 0 (no interaction) and 1 (tightly coupled interaction)
© J.M. Piwowar
Why are Spatial Data Special?

15. The Potential of Spatial Data
Neighbourhood
The set of all features adjacent to another feature.
The set of all features within a given distance of another feature.
© J.M. Piwowar
Why are Spatial Data Special?