Spatial Data types

⊕Rowe, F. Arribas-Bel, D. 2021. Spatial Modelling for Data Scientists.

Different classifications of spatial data types exist. Knowing the structure of the data at hand is important to think of appropriate analytical methods.

Area / Lattice data source: Önnerfors et al. (2019). Point data source: Tao et al. (2018). Flow data source: Rowe and Patias (2020). Trajectory data source: Kwan and Lee (2004).

Lattice/Areal Data

• Correspond to records of attribute values (e.g. population counts) for a fixed geographical area.
• Regular (e.g. grids or pixels) or irregular shapes (e.g. states, counties or travel-to-work areas).

Point Data

• Records of the geographic location of an discrete event.
  
• Number of occurrences of geographical process at a given location.

Flow Data

• Records of measurements for a pair of geographic point locations or pair of areas.
  
• Capture the linkage or spatial interaction between two locations.

Trajectory Data

• Records of moving objects at various points in time.
• Composed of a single string of data recording the geographic location of an object at various points in time.

Modifible Area Unit Problem (MAUP)

The MAUP represents a challenge that has troubled geographers for decades.

Two aspects of the MAUP are normally recognised in empirical analysis:

• Scale. The idea that a geographical area can be divided into geographies with differing numbers of spatial units.

• Zonation. The idea that a geographical area can be divided into the same number of units in a variety of ways.

Source: Loidl et al (2016)

⊕Loidl, M., Wallentin, G., Wendel, R. and Zagel, B., 2016. Mapping bicycle crash risk patterns on the local scale. Safety, 2(3), p.17.

MAUP can greatly impact our results and capacity to make inferences, leading to wrong conclusions

Ecological Fallacy

An error in the interpretation of statistical data based on aggregate information e.g.

• Robinson (1950): country of birth and literacy

⊕WS Robinson, Ecological Correlations and the Behavior of Individuals, International Journal of Epidemiology, Volume 38, Issue 2, April 2009, Pages 337–341.

Spatial Dependence

Refers to the spatial association of values for an indicator, esp. spatial proximity of more similar (or less similar) than expected for randomly associated pairs of observations.

Spatial Heterogeneity

Refers to the uneven distribution of a variable’s values across space.

Spatial nonstationarity

It refers to variations in the relationship between an outcome variable and a set of predictor variables across space.