Methods
The analysis in this study used several computational and statistical methods. Geographic
Information System (GIS) was used to summarize large volumes of data for each NSA. Data for
each variable was gathered and geographically referenced to each Neighborhood Statistical
Areas (NSA). Next, the 26 analytical variables for each NSA were standardized by expressing
the data as standard deviation units above or below the mean (i.e., Z scores). Standardization
makes it possible for the different variables to be directly compared.
Prioritization Process for Analytical Variables
1. Once the data was standardized, the project team sorted the NSAs into those with the top 25
highest standardized scores, those in the middle, and those with the lowest 25 standardized
scores for each variable
A high standardized score, also called a “high priority score,” indicates
that the NSA may benefit from investments to improve quality of life as it relates to that variable.
For example, NSAs that received a high priority score for Percent of Persons Ages 5-19 may
benefit from investments in services that support children and families, whereas NSAs that
received a high priority score for Percent of Persons over Age 64 may benefit from investments
in services that support aging in place and other services for seniors. The results of this
classification for each NSA are provided in the Neighborhood Profiles in Appendix B.
2. Next, NSAs were also sorted into high, middle and low scores for each dimension (social,
crime, physical and economic). Dimensional scores were calculated by standardizing the sum of
the standardized individual variables. The results of this classification are provided in Figures 6,
8, 10 and 12 with maps illustrating High Priority NSAs for each dimension.
3. Finally, NSAs were grouped and assigned a Dimensional Priority Score based on whether they
met the high priority criteria for one, two, three or all four dimensions. Table 3 in the following
section lists the NSAs for each score. For example, a score of 4 was given for NSAs that met the
high priority criteria for all four dimensions. The NSAs were then mapped based on this
cumulative Dimensional Priority Score as shown in Figures 3 and 4 in the following section. The
results suggest that NSAs with a high score may be suffering across a number of quality of life
variables. The following section offers further discussion of these cumulative results.
Analysis of Profile Variables
In addition to the 26 analytical variables, this report also uses 10 additional profile variables to
provide a demographic and socioeconomic context for each NSA. These variables were not used
in the prioritization process described above; however, data for these variables has been provided
in the Neighborhood Profiles found in Appendix B.
2
In instances where NSAs had tied scores at the cutoff point (e.g., NSA 24, 25, 26, 27 had
identical scores), then all of the tied NSAs were counted.
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