Boundary problem (spatial analysis)

For example, biodiversity such as the density of species of plants and animals is high near the surface, so if the identically divided height or depth is used as a spatial unit, it is more likely to find fewer number of the plant and animal species as the height or depth increases.

By drawing a boundary around a study area, two types of problems in measurement and analysis takes place.

A typical example is a cross-boundary influence such as cross-border jobs, services and other resources located in a neighbouring municipality.

[17][18][19] For example, the shape can affect the measurement of origin-destination flows since these are often recorded when they cross an artificial boundary.

[21] From the same perspective, Theobald (2001; retrieved from[5]) argued that measures of urban sprawl should consider interdependences and interactions with nearby rural areas.

The problem is noted when the average degree of a variable and its unequal distribution over space are measured.

Its main shortcoming is that empirical phenomena occur within a finite area, so an infinite and homogeneous surface is unrealistic.

[15] The remaining five approaches are similar in that they attempted to produce unbiased parameter estimation, that is, to provide a medium by which the edge effects are removed.

[28] For example, the solution according to the generalized least squares theory utilizes time-series modeling that needs an arbitrary transformation matrix to fit the multidirectional dependencies and multiple boundary units found in geographical data.

[14] Martin also argued that some of the underlying assumptions of the statistical techniques are unrealistic or unreasonably strict.

[30] As particularly applicable using GIS technologies,[31][32] a possible solution for addressing both edge and shape effects is to an re-estimation of the spatial or process under repeated random realizations of the boundary.

Accordingly, such a sensitivity analysis allows the evaluation of the reliability and robustness of place-based measures that defined within artificial boundaries.