Quantitative Methods for Measuring Neighborhood Characteristics in Neighborhood Health Research

Research connecting neighborhoods and health has characterized neighborhood factors in multiple ways. This chapter discusses standard and emerging methods to measure and study neighborhood characteristics. In particular, this chapter provides an overview of neighborhood characteristic assessment methods, including self-report, systematic social observation, geographic information system (GIS) methods, Web-based geospatial methods, real-time geospatial methods, crowd-sourced geospatial methods, and information retrieval methods from online sources such as Instagram and Twitter. This chapter also discusses the strengths and limitations of each neighborhood characteristic assessment method (e.g., ease of administration, validity), and readers are provided with examples of each neighborhood assessment method applied in the epidemiology and population health literature.

The Impact of Neighborhood Characteristics on Housing Prices-An Application of Hierarchical Linear Modeling

Housing data are of a nested nature as houses are nested in a village, a town, or a county. This study thus applies HLM (hierarchical linear modelling) in an empirical study by adding neighborhood characteristic variables into the model for consideration. Using the housing data of 31 neighborhoods in the Taipei area as analysis samples and three HLM sub-models, this study discusses the impact of neighborhood characteristics on house prices. The empirical results indicate that the impact of various neighborhood characteristics on average housing prices is different and that the impact of house characteristics on house prices is also moderated by neighborhood characteristics.

The Neighborhood Characteristic Parameter for Graphs

Define the neighborhood characteristic of a graph to be $s_1 - s_2 + s_3 - \cdots$, where $s_i$ counts subsets of $i$ vertices that are all adjacent to some vertex outside the subset. This amounts to replacing cliques by neighborhoods in the traditional 'Euler characteristic' (the number of vertices, minus the number of edges, plus the number of triangles, etc.). The neighborhood characteristic can also be calculated by knowing, for all $i,j \ge 2$, how many $K_{i,j}$ subgraphs there are or, through an Euler-Poincaré-type theorem, by knowing how those subgraphs are arranged. Chordal bipartite graphs are precisely the graphs for which every nontrivial connected induced subgraph has neighborhood characteristic 2.