Is There Really a “Wrong Side of the Tracks”in Urban Areas and Does It Matter for Spatial Analysis?

Sharp socioeconomic differences between adjacent neighborhoods run counter to Tobler's first law (TFL) of geography and call into question the blanket application of smoothing techniques designed to handle spatial autocorrelation. In a recent project, large socioeconomic differences between adjacent neighborhoods were observed coinciding with physical features at the neighborhood boundary such as rivers, parks, railroads, and highways. Literature on urban form suggests mechanisms by which these features might create or maintain socioeconomic differences. We therefore test whether the presence of physical features on neighborhood boundaries is associated with greater socioeconomic disparity between the neighborhoods and whether the types of features less easily crossed are more strongly associated. The study area was the city of Glasgow, Scotland. We used vector data to determine which of N = 1,914 neighborhood boundaries coincided with physical features, a well-validated measure of multiple deprivation to assess differences in socioeconomic character across these boundaries, and linear regression to assess associations. The presence of physical features was weakly associated with greater socioeconomic difference across neighborhood boundaries (B = 0.193, p = 0.006). Water (rivers/canals; B = 0.378, p = 0.005) and open spaces (B = 0.283, p = 0.016) were most strongly associated. The presence of physical features, however, was neither necessary nor sufficient for large interneighborhood differences in socioeconomic character. We thus confirm that TFL is not infallible and suggest that spatial analysts need to be concerned about the blanket application of spatial smoothing. Physical features do not hold influence of sufficient size or consistency to guide when and when not to smooth values in spatial analysis, however.

[1]  M. Kwan,et al.  Individual Accessibility Revisited: Implications for Geographical Analysis in the Twenty-first Century , 2003 .

[2]  David Manley,et al.  Constructing data zones for Scottish Neighbourhood Statistics , 2007, Comput. Environ. Urban Syst..

[3]  Stephen Tagg,et al.  The Social Severance Effects of Major Urban Roads , 1976 .

[4]  Robert J. Chaskin,et al.  Perspectives on Neighborhood and Community: A Review of the Literature , 1997, Social Service Review.

[5]  A. Morris,et al.  Locality deprivation and Type 2 diabetes incidence: a local test of relative inequalities. , 2007, Social science & medicine.

[6]  M. Petticrew,et al.  New roads and human health: a systematic review. , 2003, American journal of public health.

[7]  N. Dempsey,et al.  Defining the neighbourhood: Challenges for empirical research , 2007 .

[8]  W. Solecki,et al.  Urban parks: green spaces or green walls? , 1995 .

[9]  W. Tobler On the First Law of Geography: A Reply , 2004 .

[10]  L. Waller,et al.  Applied Spatial Statistics for Public Health Data: Waller/Applied Spatial Statistics , 2004 .

[11]  Reginald G. Golledge,et al.  Generalized Procedures for Evaluating Spatial Autocorrelation , 2010 .

[12]  Neighbourhoods and public service boundaries in the city: a geographical analysis , 1982 .

[13]  Duncan Lee,et al.  A comparison of conditional autoregressive models used in Bayesian disease mapping. , 2011, Spatial and spatio-temporal epidemiology.

[14]  Penny A. Cook,et al.  Area effects on health inequalities: the impact of neighbouring deprivation on mortality. , 2011, Health & place.

[15]  M. Kwan The Uncertain Geographic Context Problem , 2012 .

[16]  Terence Lee Urban Neighbourhood as a Socio-Spatial Schema , 1968 .

[17]  Jennifer S. Mindell,et al.  Community Severance and Health: What Do We Actually Know? , 2012, Journal of Urban Health.

[18]  Delbert S. Elliott,et al.  Subjective Constructions of Neighborhood Boundaries: Lessons from a Qualitative Study of Four Neighborhoods , 2009 .

[19]  S. Roitman Close but Divided: How Walls, Fences and Barriers Exacerbate Social Differences and Foster Urban Social Group Segregation , 2013 .

[20]  W. Tobler A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[21]  P. Gobster Urban parks as green walls or green magnets? Interracial relations in neighborhood boundary parks , 1998 .

[22]  T. Pratt Great American city: Chicago and the enduring neighborhood effect , 2013 .

[23]  G. Pryce,et al.  Measuring Deprivation in Scotland: Developing a Long-Term Strategy , 2003 .

[24]  R. G. Davies,et al.  Methods to account for spatial autocorrelation in the analysis of species distributional data : a review , 2007 .

[25]  Y. Kestens,et al.  Conceptualization and measurement of environmental exposure in epidemiology: accounting for activity space related to daily mobility. , 2013, Health & place.

[26]  R. Atkinson,et al.  Fortress UK? Gated communities, the spatial revolt of the elites and time–space trajectories of segregation , 2004 .

[27]  J. Wakefield,et al.  Spatial epidemiology: methods and applications. , 2000 .

[28]  Duncan Lee,et al.  Boundary detection in disease mapping studies. , 2011, Biostatistics.

[29]  Jon Wakefield,et al.  Disease mapping and spatial regression with count data. , 2007, Biostatistics.

[30]  P. Legendre Spatial Autocorrelation: Trouble or New Paradigm? , 1993 .

[31]  Sw. Banerjee,et al.  Hierarchical Modeling and Analysis for Spatial Data , 2003 .

[32]  (The Hidden City) Between the border and the vacuum: the impact of physical environment on aspects of social sustainability , 2008 .

[33]  Andrew Jones,et al.  Modifiable neighbourhood units, zone design and residents' perceptions. , 2007, Health & place.

[34]  Duncan Lee,et al.  Locally adaptive spatial smoothing using conditional auto‐regressive models , 2012, 1205.3641.

[35]  P. Diggle Applied Spatial Statistics for Public Health Data , 2005 .

[36]  Michael Parkinson,et al.  The Significance of Neighbourhood , 2001 .