The Spatial Concentration of Subsidized Housing - Poverty & Race ...

Visualizing ClustersOne of the challenges in a cluster analysis is that a lot of information iscombined which can make the result not as intuitive as pure descriptive statistics.Mapping the clusters could be a potentially useful tool to reduce the data and visualizethe complexity in a single MSA. Maps for the city of Philadelphia were createdincluding four maps for each type of subsidized housing; two maps of the indicators ofconcentration; and one map of the 9 clusters (see Figures 6.7 – 6.13).The individual maps of subsidized units are useful when discussing specificsubsidy types but are confusing if all types are to be considered at the same time. Thetwo indicator maps are comparable and either one is useful in visually identifyingareas of subsidized housing concentration but lack poverty rate information, which ifadded could be confusing.The cluster map combines three pieces of information including 1) subsidizedhousing concentration; 2) subsidy type; and 3) poverty rate. It is not necessarilyintuitive and requires attention to understand its meaning.The diversity of types of subsidized housing concentration is clearly visible inthe 9 colors used to map the 9 clusters. What was unexpected was that the overallspatial pattern was significantly scattered. Large swaths of similar cluster types do notform. From a policy perspective it would be easier if there were larger contiguousclusters for policy interventions rather than neighborhood or sub-neighborhood111