Neighborsanalyse

Neighborsanalyse is a methodological approach used to examine how the characteristics and conditions of neighboring units influence a focal unit or outcome. It is based on the idea that proximity or network connections create contextual effects that can alter behavior, prices, health, or other phenomena. The concept is widely used in fields such as geography, urban planning, ecology, sociology, epidemiology, and data science. In practice, a neighborhood is defined by a rule about proximity or connectivity: a fixed radius around a location, a set of contiguously bordering units, or a subset of network neighbors. Analysts then collect data for these neighboring units and summarize their attributes, or directly model their influence on the focal unit through spatial weights or network features.

Common techniques include calculating neighborhood statistics (mean, median, variance) of neighboring attributes, and using spatial analysis methods such as spatial autocorrelation (Moran's I, Geary's C) or spatial econometric models with spatially lagged terms. Network-based approaches may use adjacency matrices, path lengths, or centrality measures to quantify neighbor influence.

Applications abound: in real estate, the value of a property is analyzed in the context of surrounding properties; in public health, local disease rates are studied alongside neighboring areas; in urban planning, neighbor conditions inform policy interventions; in ecology, interactions among neighboring individuals or habitats are assessed.

Limitations include sensitivity to the definition of the neighborhood, known as the modifiable areal unit problem, and data quality. The choice of radius, weights, and scale can substantially affect results.