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People think of themselves as living in neighborhoods, or places that are sufficiently close to be experienced on a day-to-day basis. Very often neighborhood is the basis of spatial context, characterizing the nature of a person’s surroundings. Neighborhoods are often conceived as partitioning an urban space, such that every point lies in exactly one neighborhood, but this may conflict with individual perceptions of neighborhood, and by the expectation that neighborhood extends in all directions around every individual’s location. Figure 2‑9, below, shows three examples of possible neighborhood definitions.

Figure 2‑9 Three alternative ways of defining neighborhood, using simple GIS functions


In Figure 2‑9A the neighborhood is defined as a circle centered on the house, extending equally in all directions. In Figure 2‑9B neighborhood is equated with an existing zone, such as a census tract or precinct, reflecting the common strategy of using existing aggregated data to characterize a household’s surroundings. In Figure 2‑9C weights are applied to surroundings based on distance, allowing neighborhood to be defined as a convolution (see further, Section 2.2.10, Smoothing and sharpening) with weight decreasing as a simple function of distance.

In many geospatial analysis applications the term neighborhood is used to refer to the set of zones or cells that are immediately adjacent to a selected zone or cell. For example, a raster model is generally defined using a regular grid of square cells. The set of 8 cells that surround each cell (omitting the grid’s edge cells) is often referred to as the ‘Moore’ neighborhood  (see further, Section 8.2.1, Cellular automata (CA)) or ‘Queen’s move’ neighborhood (by analogy with the game of chess). Likewise the subset that consists of just the direct North, South, East and West neighbors is sometime called the ‘von Neumann’  or ‘Rook’s move’ neighborhood (see further, Section 8.2.1, Cellular automata (CA)). Note that whilst such neighborhoods apply to regular grids, irregular lattices (e.g. administrative areas)  also have neighborhood structures of that may be described in this or a similar manner.